Injury from spike protein, whether induced by COVID-19 infection or vaccination, constitutes a significant health concern for numerous individuals. Considerable heterogeneity exists in individual responses to both COVID-19 infection and vaccination, despite the latter being principally more controlled and consistent than the wide variety of infection circumstances. This review explores the possible mechanisms by which the spike protein contributes to cellular and systemic damage, highlighting the importance of understanding these processes for developing effective diagnostics and treatments.

1. Introduction

The issue of differential vulnerability to spike protein–related diseases is a vital question for understanding the basis of injury and developing general and individualized treatments for individuals. Addressing vaccine injury and long COVID, both of which are at least partially mediated by the spike protein, is crucial for future treatment strategies. Individual susceptibility to these conditions can inform the development of personalized therapeutic approaches. Adverse events after vaccination have been far more common with COVID-19 vaccines than with any other licensed vaccine [1]. Due to the pathological mechanisms of the vaccine-encoded spike protein, the potential for damage can exist at low levels for long periods of time, and those having received a vaccine can be in a “sword of Damocles” situation for years or even decades. Long-term impacts of COVID-19 vaccines were not studied in depth at the time of approval and cannot be definitively ruled out [2].

The extent of subclinical danger, as well as the increase in sudden and unexplained deaths, motivates the diagnosis of vaccine injury through biomarkers. Given its role as a major pathological agent driving vaccine injury, long COVID, and acute COVID-19 infection [3], the presence and concentration of the spike protein or its subunits in plasma serve as valuable biomarkers for assessing related diseases.

The present study is organized as follows: Section 2 provides an overview of the possible sources of vaccination response heterogeneity. The response to vaccines varies significantly due to individual genetic differences, administration factors, and the quality of the vaccine batches. Section 3 explores the pharmacogenomics of spike protein interactions and their effect on individual susceptibility and response. System-specific pathologies are described in more detail, providing an overview of how genetic factors affect individual vulnerability to spike protein–induced diseases. Lastly, Section 4 gives a brief overview of diagnostic biomarkers and assays that could be useful to distinguish postvaccination syndromes from viral infection and inform the development of targeted therapeutic strategies.

2. Sources of Variation

Individual variation in vaccine response can be due to multiple factors, both owing to the recipient’s genetic makeup and health status, as well as factors surrounding the administration itself. Some differences manifest quite obviously, such as the recipient having an anaphylactic reaction to polyethylene glycol (PEG), where the causality is well understood and the clinical presentation immediately apparent [4]. Other sources of variation in vaccine response are more investigational, having less accumulated evidence, and causality may be more difficult to show.

In addition to those individual health factors, there is some variation between vaccine administrations. While, in principle, the product should be consistent, there are challenges to delivering a consistent product to billions of people [5]. Furthermore, the AstraZeneca COVID-19 vaccine demonstrated process and product-related impurities [6]. The main factors implicated in the variability of vaccination responses are described below.

2.1. Factors Affecting Individual Susceptibility

2.1.1. Administration

Differences in vaccine dosing can have a substantial impact on vaccine response. Significant variations were noted in the occurrence of adverse events across different vaccine batches [7]. Though there have not been large-scale studies on differences in administration, the practice of aspiration may possibly contribute to different vaccine responses [8–10].

A higher cumulative dose of mRNA is more likely to be associated with AEs, and the relationship is evident in both brand-to-brand comparisons (where the Moderna COVID-19 vaccine delivers a higher mRNA dose than the Pfizer BNT162b2) [11]. Additionally, one’s risk of a vaccine adverse event will be expected to increase with the number of doses received.

2.1.2. Vaccine Type

In the United States, several COVID-19 vaccines were authorized for emergency use or received full approval by the U.S. Food and Drug Administration (FDA) [12]. The most widely used vaccine was the Pfizer-BioNTech (Comirnaty) mRNA vaccine. Initially granted Emergency Use Authorization (EUA) in December 2020, it received full approval in August 2021 for individuals aged 16 and older and was eventually approved for ages as young as 6 months [12]. The vaccine required two doses, spaced 3 weeks apart, with booster doses available later. Pfizer-BioNTech became the most administered vaccine, accounting for approximately 60% of vaccinations in the United States [13] due to early distribution agreements [14] and its suitability for younger populations compared to the alternatives [15].

The Moderna (Spikevax) vaccine, also an mRNA-based vaccine, was another prominent option. It received its EUA in December 2020, around the same time as Pfizer [12], and full approval in January 2022 for those aged 18 and older [16]. Moderna’s vaccine was approved for children 6 months and up, with a two-dose regimen spaced 4 weeks apart. Booster doses were also made available. Moderna vaccines accounted for about 37% of vaccinations.

Johnson & Johnson (Janssen) offered a viral vector vaccine that was initially popular due to its single-dose regimen. It received EUA in February 2021 for adults aged 18 and older [12]. However, concerns emerged over rare but serious side effects, such as blood clots, leading to reduced usage over time. Early on, J&J was a larger proportion of total doses but declined to 3% [12] as the J&J vaccine was discontinued owing to safety concerns [17].

Later in the vaccination campaign, the Novavax vaccine was introduced. It used a protein subunit technology and received EUA in July 2022 for adults aged 18 and older [18]. Novavax required two doses administered 3 weeks apart. Its uptake was relatively low, particularly since it was introduced after the majority of the population had already received other vaccines. Novavax accounted for less than 1% of the total vaccinations [13].

As the pandemic evolved, mRNA bivalent boosters from Pfizer and Moderna were introduced in 2022. These updated vaccines targeted both the original strain of the virus and the Omicron subvariants. By late 2022 and into 2023, these bivalent boosters became dominant in booster shot administration, as they were adapted for variants.

There are some structural differences between the spike depending on if one received an updated booster or one of the original vaccines.

2.1.3. Contents and Purity

Concentrations of genetic material could vary, stemming from discrepancies in manufacturing quality and potential heterogeneity within each vial [19]. There are variances in the levels of adverse reactions, depending on the lot number of the vaccine [7, 20].

Another factor is the presence of adulterants, including DNA plasmids in the mRNA products [21]. It is known that Pfizer used a different manufacturing process for vaccine production than was used in the clinical trials, and the technique used in mass production may be more vulnerable to adulteration [22, 23].

2.2. Patient Characteristics

Viewing the series of events sequentially, the RNA must enter the cell and be read by the ribosome to express spike protein, after which it is degraded (in principle). Lacking an intact degradation pathway, the modified RNA will linger around and keep producing spike protein in principle. Minimal information is known about the biodegradation of modified mRNA in humans [2].

Vaccine response may depend on other factors as well. For example, time of day [24, 25], level of recent sleep [26–29], and prior infection [30] affect the immune response to COVID-19 vaccines. Several interventions have undergone clinical trials to determine their impact on the vaccine immune response, including iron supplementation (NCT04915820), mushroom mixtures (NCT04951336), immunomodulators (NCT04877496 [31] and NCT05060991), the ketogenic diet (NCT05163743), metformin (NCT03996538), probiotics (NCT05195151) and osteopathy (NCT04928456 and NCT05069636). Of these trials, only the trial on the immune impact of anti-CD20 therapy had results, which were not significantly different from controls [31].

2.2.1. Variation in Cell Uptake and Biodistribution

The most crucial factor here is whether the injection was performed into the deltoid muscle, as intended, or some escaped into the bloodstream, which is largely set by the conditions of the injection itself (if aspiration was standard procedure). Individuals vary in their cellular affinity to take up extracellular RNA via endocytosis [32–34]. While it is unclear of their relevance in this case, there are genetic variations in endocytic pathways [35, 36].

Limited pharmacodynamic data existed on the novel use of LNP particles containing modified mRNA. The adenovirus and protein subunit vaccines had a more predictable pharmacodynamic profile, owing to previous data, which was limited for mRNA vaccines [2]. The biodistribution studies which did exist for mRNA-LNP products demonstrated distribution throughout the body [37], and later studies have found vaccine contents or artifacts in bodily fluids, including the breast milk of vaccinated mothers [38–40].

2.2.2. Variation in Spike Protein and RNA Persistence

The existing literature on genetic determinants of COVID-19 vaccine adverse events is very limited [41]; therefore, we provide a mechanistic understanding. After endocytosis, the modified mRNA is present in the cytosol to be expressed, which can be highly variable depending on host genetics [42, 43]. From this point on, the major factors regulating the amount of pathogenic spike protein produced are the clearance and degradation mechanisms acting on the chemically modified mRNA. While pseudouridinylated RNA is far more stable than RNA [44], the degradation curve of N1-methyl pseudouridinylated RNA is less known, though N1-methyl-pseudouridine in mRNA has been observed to enhance translation [45]. Individual genetic variation influences the degradation of exogenous RNA [46].

Furthermore, other wildcards include the potential for reverse transcription into the host genome [47]. Another point of concern is the observed contamination of mRNA vaccine vials with the DNA plasmid vector [48], which may possibly be a factor in the observed persistence of spike protein in vaccine-injured patients [49]. This could be explained by transfection of bacteria in the human gut [50–52]. Were this a significant factor, we would expect differential vaccine injury susceptibility based on gut microbiota composition. Long COVID susceptibility varies according to gut barrier health [53], and acute SARS-CoV-2 infection severity is negatively associated with gut microbial diversity [54]. Long COVID is associated with higher levels of Ruminococcus gnavus and Bacteroides vulgatus and lower levels of Faecalibacterium prausnitzii [55]. Still, it is unknown to what degree this “gut reservoir” hypothesis for spike protein persistence is significant.

The primary factors are the variations in the initial dose of spike protein–encoding mRNA, which can vary due to storage, dilution, and administration. Once the mRNA is in the body, the level of spike results from the competition between mRNA degradation and protein expression from the mRNA, as shown in Figure 1.

Details are in the caption following the image — **Figure 1**
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A model for long-term spike protein persistence in vaccine-injured individuals. This figure is an original figure created by the authors. A version of this figure appears in a preprint authored by the first author [56].

We propose a third alternative mechanism to degradation and expression: conversion to a reservoir, as reverse transcription into the genome is possible [47]. Additionally, DNA contamination was discovered in a broad swathe of mRNA vaccine vials [48], potentially opening the possibility of gut microbiota transfection through the mechanism of horizontal gene transfer [57].

While the half-life of RNA is well known, and endogenous mRNA has a half-life of approximately 10 h [58], it is known that pseudouridinylated RNA is far more persistent [44, 59], and less is known about the degradation of the N1-methyl-psuedouridnylated RNA used in the mRNA vaccines [2]. The persistence of spike protein appears to be the differentiating factor between the presence or absence of postvaccination myocarditis [49]. Attempts have been made to quantify the lifetime of spike protein and mRNA in the human body [60], but it is unknown why some people appear to harbor the spike protein for longer durations than expected [61].

Tissue variation exists in the cellular uptake of modRNA containing LNPs, as factors such as LNP production processes [62] and individual genetic variation can impact delivery of modRNA to cells. Host factors impacting biodistribution and pharmacokinetics have been investigated for other drugs [63, 64], but the topic is not well understood for novel LNP-modRNA products. The closest examples are liposomal drugs, which are delivered with a liposome, often PEGylated [65], similar to the delivery vehicle of modRNA vaccines [66]. Individual variation in pharmacokinetic profile is more pronounced for liposomally encapsulated drugs than their nonencapsulated counterparts [65, 67].

Younger people tend to clear LNP-based drugs faster [65, 67, 68], but their rates of protein synthesis are also higher [69, 70]. Women also generally clear liposomal drugs slower than men [71], and this effect is possibly due to differences in body composition or organ differences in the liver or spleen [72].

External factors such as heat, light, or ultrasound may also play a role in LNP drug kinetics [66].

In principle, LNPs could be engineered to deliver to specific tissues [73, 74], but the Pfizer and Moderna modRNA vaccines showed high promiscuity, and entering into multiple tissue and organ types [37].

Differential capacity for protein synthesis exists between individuals due to many factors [75], including genetic [76]. These factors could affect the levels of spike protein produced following exposure to RNA vaccines or viral infections, potentially influencing the magnitude of tissue-specific responses. Although direct evidence linking tissue-specific protein synthesis efficiency to spike protein–induced adverse effects is currently lacking, this area warrants further investigation. Research focusing on the systemic biodistribution and tissue-specific localization of RNA and the resulting spike protein is crucial to better understand the interplay between protein synthesis and adverse outcomes following exposure.

In addition to RNA persistence and genomic considerations, SARS-CoV-2 spike protein mutations have implications for vaccine efficacy and disease severity. Notable mutations such as D614G, N501Y, and E484K are prevalent among variants of concern and significantly affect viral infectivity, transmissibility, and antibody evasion. For instance, D614G enhances infectivity by improving spike protein stability and ACE2 receptor affinity [77, 78], whereas N501Y similarly strengthens receptor binding, thereby increasing transmissibility [79]. The E484K mutation allows escape from neutralizing antibodies elicited by previous infection or vaccination, potentially reducing vaccine effectiveness [80]. These mutations highlight the necessity for ongoing surveillance and adaptive vaccine strategies.

2.2.3. Pharmacogenomics of Spike Protein Interaction

Differential infection effects have been observed for different variants of SARS-CoV-2. Generally, later strains are milder than earlier strains, following a well-described evolutionary path for respiratory viruses [81]. Additionally, tissue tropism tends to move from the lower to the upper airways [81].

Each host has unique genetic makeup and unique environment, which mediate their interaction with (unique) viruses. The genetic factors that influence COVID-19 infection have been investigated through genome-wide association studies (GWAS), associating markers of disease severity with an individual’s genetic makeup. One such study found that gene polymorphisms of proteins involved in coagulation were associated with acute COVID-19 prognosis [82]. These include MBL2 [83], ADAMTS13 [84], F8 [85], and PDGFRL [85], which are involved in clotting and have also been observed to be associated with acute COVID-19 severity. Our search reveals limited GWAS studies of long COVID [86, 87] or COVID-19 vaccine adverse events [41], though GWAS studies of SARS-CoV-2 vaccine antibody responses have been performed [88, 89] and several mechanistic factors behind the differential vaccine response are identified [90]. Genetic correlates of vaccine adverse reactions have been studied in the context of Hepatitis B, measles, rubella, and other vaccines [91].

Pharmacogenomics is another approach for determining the mechanistic basis of individual host susceptibility. While GWAS is observational, a pharmacogenetic approach can generate hypotheses for gene loci associated with differential vulnerability and their impact. For each gene that spike protein directly interacts with, changes in both spike and host receptor conformation will impact the interaction. While interactions are complex phenomena, broadly speaking, the energetic stability of the interaction is generally positively associated with the impact. This relationship holds for drug impacts on receptors [92].

In this analogy, the spike protein becomes the “drug”, and its impact will depend on the sequence and subsequent conformation of the human receptors that interact with it. Furthermore, as the spike protein interacts with multiple proteins, the interaction with host genetics is more complex and multifactorial.

Several recent reviews have provided comprehensive pictures of the pathological mechanisms of spike protein [93–97]. The pathological impacts on different organ systems are described in Figure 2 and Table 1 [95].

Table 1. An overview of pathological mechanisms of spike protein.

Interacting protein	Interaction	Physiological impact	Clinical evidence
ACE-2	Binding of spike receptor binding domain (RBD) to ACE2 [98, 99]	Cell entry of SARS-CoV-2 (in the case of acute infection) [100], thrombosis [93], myocarditis/pericarditis [93], and vasculitis/endotheliitis [93]	Myocarditis [101], thrombosis/thrombocytopenia [102], vasculitis [103], and endotheliitis [104]
CD-147	Inhibition of CD-147 [105]	Myocarditis/pericarditits [106], microvascular disease [106], and hemolytic anemia [107]	Myocarditis [101], microvascular disease [108], and hemolytic anemia [109, 110]
Toll-like receptors	Binding/activation TLR4 [111], activation of TLR2 [112]	Cytokine release and inflammation [112–115]	Inflammatory disorders [116–118]
High-affinity estrogen receptor (ERα)	Binding/modulation to ERα [119]	Menstrual irregularities [120]	Menstrual irregularities [120, 121]
P53 BP1, BRCA1	Interaction with spike S2 subunit observed in silico [122]	Potential inhibition of tumor suppression mechanisms [123]	Non-Hodgkin lymphoma [124], myeloproliferative disorders [125]
Spike proteins or other potentially misfolding proteins	Prion-like propagation of spike aggregates [126]. Interaction with human prion protein and amyloid beta peptide [127]	Potential neurodegeneration [128] and blood microclots [126]	Dementia [129, 130]
Functional α7 nicotinic acetylcholine receptor (α7nAChR)	A helical motif in the neck of spike protein downregulates cell surface α7nAChR [131]	Neuropsychiatric symptoms [132]	Chronic fatigue [133], cognitive deficits [134]
Blood–brain barrier (BBB)	Degradation of barrier function [135, 136]. Relative permeability of BBB to spike [137, 138]	Possible contributor to neurologic manifestations [139]	Neurologic complications [140, 141]
Molecular mimicry	Several spike protein epitopes bear similarity to human proteins [142–144]	Varied symptomatology	Several disorders
Mitochondrial damage	Mechanism unknown, but relationship is established	Fatigue, brain fog	Chronic fatigue [133], cognitive deficits [134]

Note: Reproduced from [95] under a CC Attribution 4.0 international license (CC BY 4.0 DEED, https://creativecommons.org/licenses/by/4.0/).

The following section covers system-specific pathologies. Although some interaction pairs contribute to pathologies across multiple systems, we will discuss the first instance of the pharmacogenetic basis of variation.

3. System-Specific Pathologies

3.1. Cardiovascular Disorders: ACE2, CD-147, and Anti-PF4 Antibodies

Cardiovascular disorders were the first vaccine-related safety concern to be observed [145] and acted upon [146]. Several other countries discontinued the AstraZeneca vaccine in the wake of cases of myocarditis, especially in young males [146]. Later, Moderna vaccines were restricted in young people in some European countries [147].

While controversy exists over the rates of cardiovascular disease following COVID-19 vaccination, a survey of Thai boys aged 13–18 receiving a Pfizer/BioNTech BNT162b2 revealed that 2.3% of the boys had at least one elevated cardiac biomarker or positive lab assessment, and 29% had at least one cardiac manifestation, such as tachycardia, palpitation, or myopericarditis [148]. Markers of inflammation as well as clotting were elevated in groups receiving either mRNA or AstraZeneca vaccines compared to controls [118]. Spike protein demonstrably damages human cardiac pericytes independent of infection [106]. It should be noted that variation in disease susceptibility is not restricted to the below mechanisms, for example, coagulopathies are implicated in some cases of vaccine injury [149] and long COVID [150]; therefore, genetic factors underlying coagulability may be associated with the prognosis of either condition.

3.1.1. ACE-2

ACE-2 is the most prominent interaction partner for spike protein, as it is the primary mechanism by which cell entry is mediated [151]. Variations in ACE2 conformation owing to genetic variation may result in differential susceptibility to COVID-19 infection and variation in disease course and severity [152].

Interactions with ACE-2 can have metabolic consequences [153], as well as consequences for hypertension [151]. Long-term stimulation of the receptors can result in ACE-2 downregulation, which can lead to metabolic abnormalities [153].

Several compounds may disrupt the binding interface between spike protein and ACE2, notably ivermectin [154], quercetin [155], and N-acetyl cysteine [156].

The pharmacogenomics of the ACE2 receptor is the best-studied interaction out of any human receptor interaction with spike protein. Several sites of human host variation are documented to be associated with COVID-19 disease outcomes. These include I21V, E23K, K26R, T27A, N64K, T92I, Q102P, and H378R [157], Arg514Gly [158], rs2106806 AND rs2106807 in combination [159], rs6629110 [159], K31R and E37K (lowered susceptibility), K26R and T92I (raised susceptibility) [160, 161], S19P, I21V, E23K, K26R, T27A, N64K, T92I, Q102P, and H378R (raised susceptibility), K31R, N33I, H34R, E35K, E37K, D38V, Y50F, N51S, M62V, K68E, F72V, Y83H, G326E, G352V, D355N, Q388L, and D509Y (lowered susceptibility) [161], rs35803318/Val749Val and rs4646179/Asn690Asn [162], K26R and S331F [163], Arg652 (interaction with TMPRSS2) [163], rs41303171 [164], rs35803318, rs41303171, rs774469453, rs773676270, and rs2285666 [165], rs4646120 and rs2285666 [166], and rs2074192 [167]. Figure 3 shows the sites of these variations in the spike receptor binding domain (RBD)–ACE2 complex.

A model of the spike protein–ACE2 interaction is shown below [168] (Figure 3).

3.1.2. CD-147

Transmembrane glycoprotein CD-147 is an alternate mechanism by which the SARS-CoV-2 virus can enter cells [105]. Activation of CD-147 by spike protein has been implicated in some of the cardiovascular symptoms observed in long COVID and postvaccination syndrome patients [106, 169, 170]. While most therapeutic modalities ignore this interface, azithromycin is a potential downregulator of CD-147 [171].

The interaction of spike protein and CD-147 is characterized structurally [172] interaction, and several associations have been observed [173]. CD-147 is an alternative entry route for the SARS-CoV-2 virus [168]. Models exist of the interaction between the two proteins (Spike and CD-147). Structurally, the RBD of spike protein interacts with several residues of the CD-147 (alternate name TMPRSS2) protein, as shown in Figure 4. SNPs of interest for interactions with spike protein include Rs8259T > A [174], rs371073966 [175], rs104894669 (E208K), rs2283574, rs6757, rs8637, rs4919862, rs6758, rs8259, rs4919859, and rs28915400 [176], and rs2283574, rs6757, rs8637, rs4919862, rs6758, rs8259, rs4919859, and rs28915400 [176]. An additional study identified differences in the noncoding region of CD-147 between vaccinated controls and infected individuals, albeit using a small sample size (n = 12) [177].

3.2. Anti-PF4 Antibodies

While it is controversial if the development of anti-PF4 antibodies is due to the spike protein, or another vaccine product, these have been observed in those experiencing vaccine-induced thrombosis and thrombocytopenia (VITT) [178, 179]. The development of anti-PF4 antibodies is thought to be related to the polyanion content of the adenovirus vector vaccines, both AstraZeneca and Johnson and Johnson, and not the spike protein.

3.3. Neurological Disorders: ACE2, Nicotinic Adrenergic Receptor (ADR), Protein Misfolding, Barrier Function, and Others

Neurological disorders have been observed after vaccination and also as part of the symptoms of long COVID. A review of case series and case reports found the most common serious CNS manifestations to be cerebral venous sinus thrombosis (CVST), mostly occurring after the first AstraZeneca dose (93% of total reports) [180]. Other frequently reported symptoms were CNS demyelinating disorders and encephalopathy/encephalitis. The most common manifestations in the peripheral nervous system were Guillain–Barre syndrome (GBS) and Bell’s palsy (BP).

Other complications impacting the brain include intracerebral hemorrhages, strokes, akathisia (inability to remain still), delirium, seizures, and epilepsy [181]. The disorders transverse myelitis (TM), multiple sclerosis (MS), and neuromyelitis optica (NMO), an autoimmune disorder attacking the nervous tissue of the eyes and spinal cord, have also been documented after vaccination [181]. Other nervous system disorders include optic neuritis, anosmia (inability or reduced ability to smell), and tinnitus (unabating ringing sensation in ears). COVID-19 vaccination can, in some cases, reactivate Herpes Zoster, which can impair peripheral nervous sensation. Parsonage-Turner syndrome, characterized by shoulder weakness, was also reported after COVID-19 vaccination [181]. COVID-19 vaccines, as well as viral infection, can also result in postural orthostatic tachycardia syndrome (POTS) [182].

Less serious and more transient symptoms are very common after vaccination. In a survey of vaccine recipients experiencing neurological symptoms, 46% experienced fatigue, 36% experienced cognitive impairment, 30% experienced a headache, 10% experienced tinnitus, and 16% experienced vertigo [183]. In total, 32% of those with some neurological symptoms experienced some central nervous system manifestation and 40% experienced some peripheral nervous system manifestation. A survey of vaccinated individuals may provide more accurate insights into occurrence rates. 36% of vaccinees reported some long-term issue, 20% experienced myalgia, 14% experienced fatigue, 1% experienced paresthesia, 1% experienced ageusia, 2% experienced sadness or irritability, and 3% experienced lack of concentration or excessive worry [184, 185].

Cognitive impairment characterizes a subset of vaccine recipients [130, 134, 186–188]. Probable mechanisms include modulation of the ACE2 and nicotinic ADRs, protein misfolding, damage to blood–brain barrier (BBB) function, and molecular mimicry [95, 189]. Spike protein reduces burst activities in neurons, which may contribute to neurological symptoms [190].

It bears mentioning that the metabolic and cardiovascular effects of the spike protein also significantly impact nervous system disorders [95], and symptoms are not neatly separable by body system.

3.3.1. ACE2

ACE2 interactions with spike protein may also contribute to neurological disorders [191, 192]. ACE2 is expressed in human neurons, and SARS-CoV-2 shows neurovirulence [193]. Activation of the Renin Angiotensin system, of which ACE2 belongs to, may promote inflammation, and are associated with cell death and cognitive impairment in the nervous system [194].

Spike protein is expressed during both COVID-19 infection and COVID-19 vaccination, and ACE2-mediated symptoms may be common to both [195].

3.3.2. Protein Misfolding

Misfolding of SARS-CoV-2 spike protein and its translation fragments has been hypothesized as a mechanism contributing to neurodegenerative diseases [196]. Furthermore, laboratory data has observed aggregates formed when spike protein is seeded [196], and spike protein may potentiate the aggregation of other proteins [197]. Fragments of spike protein may also contribute to prion-like propagation of aggregates [198].

Generally, prion-like diseases are progressive [199]. However, aggrephagy, an autophagic process that consumes aggregates, is a possible therapeutic mechanism for the removal of aggregates [200].

The aggregation proneness of spike protein is not the only concern, as the use of N1-methyl-pseudouridine as a replacement for vaccine mRNA can also contribute to unintended ribosomal frameshifting [201], which may increase the aggregation proneness of translated fragments. Differences in translation were at the threshold of statistical significance in the initial experiments testing the translation fidelity of N1-methyl-pseudouridine [202].

3.3.3. Functional α7 Nicotinic Acetylcholine Receptor (α7nAChR)

The proposed interaction of spike protein with the α7nAChR, shown in Figure 5, is a potential mechanisms behind the cognitive and neuropsychiatric symptoms observed in individuals with long COVID or postvaccine syndrome [205]. α7nAChRs downregulate the production of proinflammatory cytokines [206], so interference can dysregulate inflammation. α7nAChRs play important roles in memory and appear in decreased numbers in conditions of memory disorders or neuroinflammation [207]. Prior studies show that electrical vagal nerve stimulation inhibits tumor necrosis factor (TNF) synthesis, a proinflammatory agent, and that this is mediated through α7nAChRs [206].

Spike protein was shown to dampen the electronic activation of other nicotinic cholinergic receptors, but did not affect α7nAChR [208].

In an acid-induced acute lung injury mouse model (unrelated to COVID-19), α7nAChR agonists, including nicotine, decrease inflammatory manifestations [209]. Agonists may be a potential therapeutic for the treatment of COVID-19 and long COVID [208], and the approach may possibly be broadened to other respiratory diseases [210].

The endogenous metabolite of nicotine, 1-methylnicotinamide (1-MNA), is shown to reduce postexercise fatigue in patients recovering from SARS-CoV-2 [211].

Beyond the direct interaction of spike protein with the α7nAChR receptor, α7nAChR also interacts with ACE2, and there may be a role for anti-idiotypic antibodies in long-term cognitive dysfunction following long COVID or SARS-CoV-2 vaccination [205].

3.3.4. BBB Permeability

Spike protein has been observed not only to pass through the BBB [137] but also to induce permeability of the BBB to other proteins and to degrade barrier function [212]. Lack of BBB integrity is associated with cognitive issues [213, 214] and is proposed as a contributing factor to some neurological conditions [213]. COVID-19 demonstrates neurovirulence [215]. Vaccinal lipid nanoparticles may enter brain tissue [216], as spike protein of possible vaccine origin has been observed in brain tissue from postvaccine autopsies [217].

Several investigational therapeutics [218, 219] and nutraceuticals [220] have been explored for the restoration of BBB function, and may be of therapeutic benefit for neurological symptoms after vaccination [140] or COVID-19 infection [221].

3.4. Systemic Inflammation: Toll-Like Receptors (TLRs) and Autoantibodies

Systemic inflammation has been an observed adverse event of COVID-19 vaccination [118, 222], including immune thrombocytopenia [118], Still’s disease [223], rheumatic diseases [224], and multisystem inflammatory syndromes (MIS) [222, 225–228].

Mast cell activation syndrome (MCAS) is a potential contributor to long COVID-like symptoms, as mast cells (MCs) are activated by SARS-CoV-2 [229]. People experiencing long COVID show a similar symptom profile to MCAS patients [229–231]. MCAS patients can present with general inflammation patterns in any system, including neuroinflammation.

Recent research has shown that SARS-CoV-2, including its spike protein, can modulate host immune pathways and may have the capacity to disrupt normal epigenetic regulation. For example, viral proteins can mimic histone structures, interfering with chromatin organization and gene transcription [232]. Additionally, infection-induced inflammatory signaling, particularly via TLR activation, can alter the activity of epigenetic enzymes, leading to changes in DNA methylation and histone modifications that affect immune gene expression and may contribute to persistent immune dysregulation [233]. Recent epigenetic immune monitoring studies suggest that such changes are relevant not only to the acute immune response but also to the disease course and prognosis in COVID-19 [234]. These epigenetic changes have been implicated in long-term health consequences and may partially explain individual variation in responses to infection or vaccination.

3.4.1. TLR Activation

Stimulation of TLRs by spike protein has been observed in cell culture experiments and can contribute to an inflammatory cascade [235]. TLR activation may contribute to MCAS [236]. Much of COVID-19’s hyperinflammation is concordant with manners of inflammation which MC activation can drive [237].

Antagonists of TLR4 may lower the inflammation associated with COVID-19, including sparstolonin B, which was studied prior to COVID-19 [238].

TLRs are a class of several proteins that are important for immune response, especially in response to foreign RNA. The spike protein is predicted to interact with several of the TLR proteins, and 3D models of the interaction exist for TLR1, TLR4, and TLR6 [239], as shown in Figure 6.

Several TLR polymorphisms are associated with COVID-19 outcomes, including in TLR1 (rs5743551 [243]), TLR2 (rs5743708 [244] and rs3804100 [243]), TLR3 (rs3775290 [245]), TLR4 (rs4986791 [244] and rs4986790 [246]), and TLR7 (rs179008 [245]).

3.4.2. Molecular Mimicry

Molecular mimicry is a possible explanation for the anti-PF4 cascade observed in VITT patients. The production of autoantibodies is more often linked to specific rather than systemic inflammation. Several epitopes of the spike protein share conformational similarities to many human protein motifs, and autoantibodies may form, potentially triggering autoimmune attacks.

The spike protein possesses a TQLPP motif similar to thrombopoietin and an ELDKY, similar to the human protein PRKG1, involved in platelet activation [144].

Autoantibodies for ADR B1 and B2 and the CNS and vasoregulation-associated muscarinic acetylcholine receptor (CHR) M3 and M4 were associated with long COVID severity [247, 248]. Other relevant autoantibodies for ACE2, MDA5, CD255, SS-B/La, and PM/Scl-75 [249] exist, as well as autoantibodies for immune proteins [250]. Computational alignment to evaluate molecular mimicry of these antigens suggests plausibility. These proteins are highly variable in humans, and this may play a role in differential disease outcomes [251].

Other proteins with potential cross-immunity with spike protein are CHL1, ENTPD1, MEAF6, SLC35G2, and ZFHX2 [252].

3.5. Reproductive System: Estrogen Receptor Alpha

The spike protein may interact with high affinity with estrogen receptor alpha, which can result in menstrual abnormalities, as the delicate coordination of estrogen and other hormones regulates menstruation. Beyond menstrual cycle abnormalities [121, 253–269], other reproductive issues have been observed in vaccinated patients, including heavy menstrual bleeding [270, 271].

Studies on changes in the menstrual cycle document changes in roughly 40% of menstruating women receiving the first dose of a COVID-19 vaccine [121, 253–269]. Heavy menstrual bleeding occurred at higher rates in reproductive-aged women receiving a COVID-19 vaccine compared to this same group measured prior to vaccination, affecting 34.9% of vaccinated women of childbearing age within 28 days of their first dose, as opposed to 20.6% in an unvaccinated control group [272].

Thorp et al. using data from a US pharmacovigilance database known as the vaccine adverse event reporting system (VAERS), compared miscarriage rates of COVID-19 vaccines with those of other vaccines, finding significantly higher rates of miscarriage from COVID-19 vaccines compared to influenza vaccines [273].

While the precise mechanism for COVID-19 vaccine-induced menstrual changes has not been confirmed, one plausible explanation is that the spike protein interacts with the estrogen receptor alpha [119]. Structurally, the nuclear receptor coregulator (NRC) LXD-like motif on the S2 subunit [119] of the spike protein interacts with Helix 11 of the estrogen receptor, as shown in Figure 7.

The region of the spike protein interacting with ERα does not show a significant difference between SARS-CoV-2 variants (Table 2). Human host variation does exist in this region, but no studies have identified any correlation between genetic variants and menstrual outcomes (Table 2). The genetic variability in this region may be a possible explanation for differential reproductive system responses to COVID-19 vaccination.

Table 2. An overview of the spike protein-interacting proteins in the human body and their genetic variants, presenting a possible basis for variation in response to spike protein.

Interaction partner	Spike protein domain	Spike protein residues	Spike site variation	Interaction partner residues	SNPs	Potential interface inhibitors
ACE 2	RBD	Y449, Y453, L455, F456, Y473, F486, N487, Y489, Q493, G496, T500, N501, G502, Y505	F456L, F486V/P, Q493R, G496S, N501Y, Y505H	Q24, T27, D30, K31, H34, D38, Q42, L79, M82, Y83, E35, Y41, D355, K353, G354, R357, E374	I21V, E23K, K26R, T27A, N64K, T92I, Q102P, and H378R [157] R514G [158] rs2106806 and rs2106807 in combination [159] rs6629110 [159] K31R and E37K (lowered susceptibility) K26R and T92I (raised susceptibility) [160, 161] S19P, I21V, E23K, K26R, T27A, N64K, T92I, Q102P, and H378R (raised susceptibility) K31R, N33I, H34R, E35K, E37K, D38V, Y50F, N51S, M62V, K68E, F72V, Y83H, G326E, G352V, D355N, Q388L, and D509Y (lowered susceptibility) [161] V749V (rs35803318) and N690N (rs4646179) [162] K26R and S331F [163] R652 (interaction with TMPRSS2) [163] N720D (rs41303171) [164, 165] V749V (rs35803318), rs774469453, rs773676270, and rs2285666 [165] rs4646120 and rs2285666 [166] rs2074192 [167]	Nicotine (reduces ACE2 levels) [274] Nicotine [275] Investigational compounds [276]
CD147	RBD [172]	R403, K417, G446, Y449, Y453, L455, F456, P479, C480, N481, G482, V483, E484, G485, F486, C488, Y489, F490, L492, Q493, S494, Y495, G496, F497, Q498, P499, T500, N501, G502, V503, Y505, Q506	K417N/T, E484K/A, N501Y, Q493R, G496S, Q498R, Y505H, G446S, F456L, F486P, F490S, Q498R, N501Y, Y505H, F486V	54, 56, 57, 58, 59, 60, 61, 62, 74, 82, 83, 102, 106, 129, 130, 131, 132, 133, 135, 164, 165, 188, 190, 191, 192 Surrounding 37, 38, 63, 64, 65, 73, 75, 77, 78, 79, 80, 81, 84, 86, 92, 100, 103, 104, 105, 107, 108, 127, 128, 136, 137, 138, 162, 163, 166, 186, 189, 193, 194	rs8259 [174, 176] rs371073966 [175] E208K (rs104894669) rs2283574, rs6757, rs8637, rs4919862, rs6758, rs8259, rs4919859, and rs28915400 [176] Others [177]	Azithromycin (downregulator of CD-147) [171]. αCD147 (antibody) [277]
TLR1		ASN87, THR51, TYR204, SER305, THR761, THR240, and ASN30 ILE105, VAL36, PHE58, PHE220, and LEU117	No variation	SER399, HIS370, THR372, CYS368, and GLN402 MET397, ALA391, PHE350, and LEU377	rs5743551 [243]	Curcumin [278] Rationally designed compounds [279] Phloretin [280]
TLR2					R753Q (rs5743708) [244] rs3804100 [243]	Phloretin [280] Ortho-vanillin [281] N-Acetyl cysteine [282]
TLR4	Spike S1 [239]	S221, N280, T588, T208, N657, Y204, F562, L226, P289, I584	No variation in residues	N409, N333, S386, S352, H431, N361, L385, V411, F342, F408	rs4986790, rs4986791 [243, 244] D299G (rs4986790) [283] (n.s.) and T399I (rs4986791) [246] Candidate SNPs N409S, S386N, N361D/T/S/K, L385F, V411I, F342Y	N-Acetyl Cysteine [282] Reviewed in [284]
TLR6	S1	N536, T581, Q563, S221, Q564, and Y38 F559, L582, L552, F565, P561, and I587	No variation	H350, N423, N438, N387, H345, and T302 P349, L382, M335, I338, L304, and I430	S249P (rs5743810) [243] V327M (rs3796508) [285]	Baicalin [286]
ER-alpha	(NRC) LXD-like motif on the S2 subunit [119]	S816-L822, T859-L865	No variation	Helix 11. L404, E405, G406	None investigated in COVID-19 outcomes.	Investigational compounds [287], estrogen
P53	S2 [122]	D1165, L1166, N1173	No variation	T281, R270, R277, H175	No validated polymorphisms in proximity to interaction site	Investigational in silico [288]
BRCA1	S2	D1165, G1171, N1173	No variation	K1648, R1649, H1672	S1613G [289], M1652I [290],	Investigational peptides against S2 subunit of spike [291] Taxodione, Withaferin D [292].
BRCA2		D1165, L1166, S1175	No variation	E916, L938, R1117	Not a significant site of variation.	Taxodione, Camptothecin, Glaucarubinone [292]
α7nAChR	Helical domain in spike neck [131]	Y674-R685 [293]	N679K, P681H/R	W171, Y210, Y217, Y115 [294]	rs16969968 [295] Candidate SNPS: M1I, W9L, L60P, Y30C, R101H, C164R, L87S, D104E, I112V, S20F, A373P, Y233C, N381D, C241W, A434V, G424R, R363H, R446C, V395M, R448H	Nicotine, acetylcholine [296, 297]
TMPRSS2		Cleavage site R685, S686, R815, S816	No variation	H296, S441, S460	V160M (rs12329760) [158] 331G > A, c.23G > T, and c.589G > A [298] G385G (rs61735794) G290G (rs2298659) I256I (rs17854725) Y180Y (rs61735789) V160M (rs12329760) T75T (rs3787950) P63P (rs61735792) [162] rs75603675, rs12329760, and rs200291871 [164] V160M (rs12329760) [299] rs12329760, rs2070788, rs383510 [300] V197M (rs12329760) [301]	Reviewed in [302] Genistein, Quercetin [303]

Note: Variations in the interaction interface are shown for both the spike and human proteins, especially if clinically significant for COVID-19 infection. Highlighted residues are either variable or near a variable residue.

Little is known about treating spike-related menstrual disturbances specifically. However, one word of caution should be noted: nattokinase [304] and bromelain should not be used in pregnancy and breastfeeding mothers because of the potential risk of bleeding complications in the preborn, newborn, and breastfed infants until there are more reassuring data available [305].

3.6. Cancer: P53, BRCA1/2, and Possible Genomic Instability

Much of the evidence for a carcinogenic role of COVID-19 vaccines in cancer remains anecdotal [306]. Limited case reports exist for the formation of neoplasms after vaccination, both malignant [307] and benign [308, 309]. During clinical trials, two cases of emergent malignancies occurred soon after vaccination, which may well be coincidental given the size of the trials [310].

Cases of cancer recurrence possibly attributable to COVID-19 vaccination have been reviewed recently [311]. Disease recurrence has been observed in select case reports [312, 313], as well as suspicious but ultimately benign recurrences [314–316]. In rare cases, vaccination can increase the risk of metastasis from lymph nodes [317].

3.6.1. P53 BP1

The spike protein is computationally predicted to interact with the P53 and BRCA1 tumor suppressor proteins, which could be a possible contributor to carcinogenesis [122]. In addition, spike protein DNA inhibits the p53-mediated activation of proteins p21, TRAIL death receptor and MDM2, which increases cancer cell viability after chemotherapy [318].

3.6.2. BRCA1/2

The spike protein is predicted to interact with BRCA1 and 2 (Figure 8), which have tumor suppressor roles [122].

There are several possible sites for pharmacogenomic variation in patient response to vaccination; these are summarized in Table 2.

4. Diagnostics

Given that disease etiology is often attributed to the spike protein, the presence and concentration of the spike protein are suitable biomarkers for spike protein diseases. Additional biomarkers can probe other issues related to the spike protein, including inflammatory markers, markers of heart damage, and markers for blood coagulability.

Still, most diagnoses are made based on patient history and clinical presentation. While several spike protein assays exist, they are still mostly restricted to the research settings [320–322] and are not available as diagnostic tools. In the future, ideally, spike protein concentration assays will guide treatment, allowing for tracking progress as well as exploring other non-spike-related disease etiologies. If the pathology is spike protein–related, developing a diagnostic test may be a helpful step in assessing treatment progress.

The World Health Organization (WHO) guidelines for Adverse Events Following Immunization (AEFI) are widely used for causality assessment to evaluate the likelihood of a causal relationship between a vaccine and an adverse event. However, as highlighted by previously published critiques, the WHO AEFI guidelines have limitations that may hinder their ability to effectively identify causal relationships in the context of COVID-19 vaccines [323, 324].

The WHO AEFI causality assessment framework is designed primarily to identify clear, well-defined adverse events that follow vaccination. It requires that the type of adverse event be identified in large-scale epidemiological studies beforehand, placing a significant burden on when assigning causality [323]. As a result, rare, delayed, or complex adverse events—particularly those that may manifest differently across individuals due to genetic, age-related, or tissue-specific factors—are often excluded from causal determination.

Moreover, the guidelines are generally stringent about excluding coincidental events or those lacking strong immediate evidence of causality. Under the current guidelines, for causality to be demonstrated, a rechallenge with the agent must be performed to see if the symptoms manifest. This is not possible in the cases where the subject has died, and is not advisable in cases when the adverse event is life threatening. A paradox exists where the end result is, under the current criteria, it becomes virtually impossible for a vaccine ever to be considered a cause of death under the WHO AEFI criteria [323]. Deaths observed after vaccination are not considered as causally related to the vaccine unless there was a statistically significant increase in deaths during the trials with few participants that preceded approval. Such a vaccine would not be approved, which means that for vaccines and death, there is not functioning postmarketing surveillance [323].

4.1. General Diagnostics

In guiding treatment, there are multiple biomarkers that one can test to gain insight into the progression of the injury sustained from the vaccine. General biomarkers for cardiovascular risk, nonspecific for vaccine injury, include troponin, D-dimer, and C-reactive protein [49]. Table 3 summarizes the biomarkers for general diagnostics. It must be noted that these biomarkers are specific to cardiac injury and cannot be used to determine disease etiology.

Table 3. Biomarkers that can be altered in the case of vaccine injury.

Biomarker	Upper limit of normal
Peak cardiac troponin (T)	14 ng/L [49]
Brain natriuretic Peptide (BNP)	35 ng/L [325]
N-terminal prohormone of brain natriuretic peptide (NT-proBNP)	125 ng/L (under 75 years of age) [325] 450 ng/L (over 75 years of age) [325]
C-reactive protein (CRP)	8 mg/L [49]
D-dimer	(patient’s age in years × 10 mcg/L)^∗ [326]

^∗For patients 50 years or older.

Troponin is a general biomarker associated with diagnosis of acute coronary syndromes [327, 328], as troponins are released into the blood following damage to cardiac muscle [329]. D-dimer is a biomarker associated with the breakdown of fibrin clots by the fibrinolytic system [330]. As the test measures the breakdown of clots, a high measure can indicate a high level of clot burden and a high degree of breakdown [331], and this must be taken into consideration by the clinician.

C-reactive protein is an inflammatory biomarker, and higher values are associated with increased cardiovascular risk [49, 325, 326, 332].

4.2. Specific Diagnostics

A recent paper by Yonker et al. surveyed the biomarkers of vaccinated individuals, both with and without postvaccination myocarditis. The main differentiator between the group with myocarditis and those without was the persistence of full-length spike protein, unbound by antibodies [49]. Given that this is the sole gene encoded by most vaccines and has multiple documented pathological mechanisms [3], it is a likely etiological factor in postvaccination syndrome. Differentiation between spike protein of viral or vaccine origin can in principle be accomplished through several ways (Table 4).

Table 4. Basis of diagnostic difference between vaccinal and viral SARS-CoV-2 spike protein.

Vaccine spike	Viral spike
No N protein present [217]	N protein present [333]
Sequence identical to vaccine sequence [334]	Sequence much less constrained, reflects currently circulating variants [335]
Locked into prefusion conformation [336, 337]	Conformationally flexible [338]

Cases of blood thrombosis after vaccination typically occur within 1 month of receiving the injection [339, 340]. A test for spike protein contains two important quantities: 1) the concentration of spike protein and 2) the time since vaccination. Most often, the spike protein concentration drops off quickly within 1 week of the injection [341]. However, the persistence of high levels of spike protein for months after injection has been documented in a subset of vaccinated individuals [342]. While the specific factors affecting long-term spike protein levels are still unclear, we proposed a model for the long-term persistence of spike protein (detailed in Section 2.2.2).

In interpreting the causes of interpersonal variation in vaccine response, we restricted our analysis to disease etiologies tracing to the spike protein in the vaccines. While there may be other contributing factors, they are outside the scope of this review.

5. Conclusions

Addressing vaccine injury and long COVID, which are at least partially mediated by the spike protein, is crucial for future treatment strategies. Understanding the mechanisms of individual susceptibility to these conditions can guide the development of personalized therapeutic strategies.

We reviewed the possible sources of vaccination response heterogeneity, including administration and batch quality of the vaccines and individual genetic differences in the population. We provided a detailed exploration of the pharmacogenomics of spike protein interactions, giving a mechanistic explanation of how genetic factors affect individual vulnerability to spike protein–induced, system-specific diseases. We reviewed diagnostic biomarkers and assays that could be useful to track cardiovascular damage, distinguish postvaccination syndromes from viral infection, and inform the development of targeted therapeutic strategies. While current diagnostic methods primarily rely on patient history and clinical presentation, the advancement of spike protein assays holds promise for more precise tracking and treatment of these conditions.

Although our review primarily focuses on mechanistic and clinical aspects, we recognize the increasing value of in silico toxicity prediction models for evaluating the potential adverse effects of spike protein variants and related vaccine components. Computational approaches such as molecular docking and molecular dynamics simulations have been used to assess the cytotoxicity and safety profiles of viral proteins, including the SARS-CoV-2 spike protein [343, 344]. Expanding and integrating these methods into future research could enhance our understanding of spike-related toxicities and support the rational design of safer vaccines and therapeutics.

Further research is needed to understand the factors influencing long-term spike protein persistence and to develop effective diagnostics and treatments tailored to individual patient profiles.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

This research was supported by Frontline COVID-19 Critical Care Alliance.

Acknowledgments

We thank Mikolaj Raszek for his suggestions.

Supporting Information

Graphical Abstract. The factors affecting individual susceptibility to COVID-19 vaccine adverse effects. These are separated into factors related to the vaccine product itself, factors surrounding administration, host metabolic factors, host pharmacogenetics, and the pharmacogenomics of the spike protein.

Open Research

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Supporting Information

References

1 Kim K. Q., Burgute B. D., Tzeng S.-C. et al., N1-Methylpseudouridine Found Within COVID-19 mRNA Vaccines Produces Faithful Protein Products, Cell Reports. (2022) 40, no. 9, https://doi.org/10.1016/j.celrep.2022.111300.
10.1016/j.celrep.2022.111300
Web of Science® Google Scholar
2 Halma M. T. J., Rose J., and Lawrie T., The Novelty of mRNA Viral Vaccines and Potential Harms: A Scoping Review, D-J Series. (2023) 6, no. 2, 220–235, https://doi.org/10.3390/j6020017.
10.3390/j6020017
CAS Google Scholar
3 Theoharides T. C. and Conti P., Be Aware of SARS-CoV-2 Spike Protein: There is More Than Meets the Eye, Journal of Biological Regulators & Homeostatic Agents. (2021) 35, no. 3, 833–838, https://doi.org/10.23812/THEO_EDIT_3_21.
10.23812/THEO_EDIT_3_21
CAS PubMed Web of Science® Google Scholar
4 Cox F., Khalib K., and Conlon N., PEG That Reaction: A Case Series of Allergy to Polyethylene Glycol, The Journal of Clinical Pharmacology. (2021) 61, no. 6, 832–835, https://doi.org/10.1002/jcph.1824.
10.1002/jcph.1824
CAS PubMed Web of Science® Google Scholar
5 Uddin M. N. and Roni M. A., Challenges of Storage and Stability of mRNA-Based COVID-19 Vaccines, Vaccines. (2021) 9, https://doi.org/10.3390/vaccines9091033.
10.3390/vaccines9091033
Web of Science® Google Scholar
6 Krutzke L., Rösler R., Allmendinger E., Engler T., Wiese S., and Kochanek S., Process- and Product-Related Impurities in the ChAdOx1 nCov-19 Vaccine, eLife. (2022) 11, https://doi.org/10.7554/eLife.78513.
10.7554/eLife.78513
Web of Science® Google Scholar
7 Schmeling M., Manniche V., and Hansen P. R., Batch-Dependent Safety of the BNT162b2 mRNA COVID-19 Vaccine, European Journal of Clinical Investigation. (2023) 53, no. 8, https://doi.org/10.1111/eci.13998.
10.1111/eci.13998
Web of Science® Google Scholar
8 Kajan J., Sablić M., and Heffer M., Aspiration During Vaccination: Evidence for SARS-CoV-2 Vaccination, Southeastern European medical journal. (2022) 6, no. 1, 121–128, https://doi.org/10.26332/seemedj.v6i1.237.
10.26332/seemedj.v6i1.237
Google Scholar
9 Rzymski P. and Fal A., To Aspirate or Not to Aspirate? Considerations for the COVID-19 Vaccines, Pharmacological Reports. (2022) 74, no. 6, 1223–1227, https://doi.org/10.1007/s43440-022-00361-4.
10.1007/s43440-022-00361-4
CAS PubMed Google Scholar
10 Vaithinathan A. G., Khonji L., and Narayanan G., Necessitating Evidence-Based Practice in COVID-19 Intramuscular Vaccination Techniques (To Aspirate or Not) in the Context of Scientific/Evidence Uncertainty Amidst the Pandemic, Journal of Evidence-Based Medicine. (2023) 16, no. 2, 126–129, https://doi.org/10.1111/jebm.12537.
10.1111/jebm.12537
PubMed Web of Science® Google Scholar
11 Meo S. A., Bukhari I. A., Akram J., Meo A. S., and Klonoff D. C., COVID-19 Vaccines: Comparison of Biological, Pharmacological Characteristics and Adverse Effects of Pfizer/BioNTech and Moderna Vaccines, European Review for Medical and Pharmacological Sciences. (2021) 25, no. 3, 1663–1669, https://doi.org/10.26355/eurrev_202102_24877.
10.26355/eurrev_202102_24877
CAS PubMed Web of Science® Google Scholar
12 Tran A. and Witek T. J., The Emergency Use Authorization of Pharmaceuticals: History and Utility During the COVID-19 Pandemic, Pharmaceutical Medicine. (2021) 35, no. 4, 203–213, https://doi.org/10.1007/s40290-021-00397-6.
10.1007/s40290-021-00397-6
CAS PubMed Web of Science® Google Scholar
13 Halma M. and Varon J., DARE-SAFE: Denominator-Adjusted Rate Estimates of Substance Adverse Events Frequency Evaluation in Pharmaceuticals and Vaccines, Pharmacoepidemiology. (2025) 4, no. 2, https://doi.org/10.3390/pharma4020007.
10.3390/pharma4020007
Google Scholar
14 Pfizer, Pfizer and BioNTech Announce an Agreement With U.S. Government for up to 600 Million Doses of mRNA-Based Vaccine Candidate against SARS-CoV-2, 2020, Pfizer, https://www.pfizer.com/news/press-release/press-release-detail/pfizer-and-biontech-announce-agreement-us-government-600.
Google Scholar
15 Lee C. W., Sa S., Hong M., Kim J., Shim S. R., and Han H. W., Adverse Events and Safety Profile of the COVID-19 Vaccines in Adolescents: Safety Monitoring for Adverse Events Using Real-World Data, Vaccines (Basel). (2022) 10, no. 5, https://doi.org/10.3390/vaccines10050744.
10.3390/vaccines10050744
Web of Science® Google Scholar
16 U.S. Food and Drug Administration, BLA Approval Letter: COVID-19 Vaccine, mRNA (SPIKEVAX), 2022, U.S. Food and Drug Administration.
Google Scholar
17 US Centers for Disease Control and Prevention, Administration Overview for Johnson & Johnson’s Janssen COVID-19 Vaccine, 2023, CDC, https://archive.cdc.gov/www_cdc_gov/vaccines/covid-19/info-by-product/janssen/index.html.
Google Scholar
18 Novavax, U.S. FDA Grants Emergency Use Authorization for Novavax COVID-19 Vaccine, Adjuvanted for Individuals Aged 18 and Over—Jul 13, 2022, 2022, https://ir.novavax.com/press-releases/2022-07-13-U-S-FDA-Grants-Emergency-Use-Authorization-for-Novavax-COVID-19-Vaccine%252C-Adjuvanted-for-Individuals-Aged-18-and-Over.
Google Scholar
19 Bruce Yu Y., Taraban M. B., and Briggs K. T., All Vials are not the Same: Potential Role of Vaccine Quality in Vaccine Adverse Reactions, Vaccine. (2021) 39, no. 45, 6565–6569, https://doi.org/10.1016/j.vaccine.2021.09.065.
10.1016/j.vaccine.2021.09.065
CAS PubMed Google Scholar
20 Rosenblum H. G., Gee J., Liu R. et al., Safety of mRNA Vaccines Administered During the Initial 6 Months of the US COVID-19 Vaccination Programme: An Observational Study of Reports to the Vaccine Adverse Event Reporting System and V-Safe, The Lancet Infectious Diseases. (2022) 22, 802–812, https://doi.org/10.1016/S1473-3099(22)00054-8.
10.1016/S1473-3099(22)00054-8
CAS PubMed Web of Science® Google Scholar
21 König B. and Kirchner J. O., Methodological Considerations Regarding the Quantification of DNA Impurities in the COVID-19 mRNA Vaccine Comirnaty, Methods and Protocols. (2024) 7, no. 3, https://doi.org/10.3390/mps7030041.
10.3390/mps7030041
PubMed Web of Science® Google Scholar
22 European Medicines Agency, CHMP Assessment Report on Group of an Extension of Marketing Authorisation and Variations: COMIRNATY, 2021, European Medicines Agency.
Google Scholar
23 Block J. and Levi R., Covid-19: Researchers Face Wait for Patient Level Data From Pfizer and Moderna Vaccine Trials, BMJ. (2022) 378, https://doi.org/10.1136/bmj.o1731.
10.1136/bmj.o1731
Google Scholar
24 Filippatos F., Tatsi E.-B., Efthymiou V., Syriopoulou V., and Michos A., Time of Day of BNT162b2 COVID-19 Immunization Affects Total SARS-CoV-2 Antibody Levels but not Neutralizing Activity, Journal of Biological Rhythms. (2022) 37, no. 5, 562–566, https://doi.org/10.1177/07487304221100951.
10.1177/07487304221100951
CAS PubMed Web of Science® Google Scholar
25 Zhang H., Liu Y., Liu D. et al., Time of Day Influences Immune Response to an Inactivated Vaccine Against SARS-CoV-2, Cell Research. (2021) 31, no. 11, 1215–1217, https://doi.org/10.1038/s41422-021-00541-6.
10.1038/s41422-021-00541-6
CAS PubMed Web of Science® Google Scholar
26 Lammers-van der Holst H. M., Lammers G. J., van der Horst G. T. J. et al., Understanding the Association Between Sleep, Shift Work and COVID-19 Vaccine Immune Response Efficacy: Protocol of the S-CORE Study, Journal of Sleep Research. (2022) 31, no. 2, https://doi.org/10.1111/jsr.13496.
10.1111/jsr.13496
PubMed Web of Science® Google Scholar
27 Mason A. E., Kasl P., Hartogensis W. et al., Metrics From Wearable Devices as Candidate Predictors of Antibody Response Following Vaccination against COVID-19: Data From the Second TemPredict Study, Vaccines. (2022) 10, no. 2, https://doi.org/10.3390/vaccines10020264.
10.3390/vaccines10020264
Web of Science® Google Scholar
28 Schmitz N. C. M., van der Werf Y. D., and Lammers-van der Holst H. M., The Importance of Sleep and Circadian Rhythms for Vaccination Success and Susceptibility to Viral Infections, Clocks & Sleep. (2022) 4, no. 1, 66–79, https://doi.org/10.3390/clockssleep4010008.
10.3390/clockssleep4010008
PubMed Web of Science® Google Scholar
29 Wagenhäuser I., Reusch J., Gabel A. et al., The Relationship Between Mental Health, Sleep Quality and the Immunogenicity of COVID-19 Vaccinations, Journal of Sleep Research. (2024) 33, no. 3, https://doi.org/10.1111/jsr.13929.
10.1111/jsr.13929
PubMed Web of Science® Google Scholar
30 Bongiovanni M., Liuzzi G., Schiavon L., Gianturco L., and Giuliani G., Evaluation of the Immune Response to COVID-19 Vaccine mRNA BNT162b2 and Correlation With Previous COVID-19 Infection, Journal of Clinical Virology. (2021) 143, https://doi.org/10.1016/j.jcv.2021.104962.
10.1016/j.jcv.2021.104962
PubMed Web of Science® Google Scholar
31 Sidler D., Born A., Schietzel S. et al., Trajectories of Humoral and Cellular Immunity and Responses to a Third Dose of mRNA Vaccines Against SARS-CoV-2 in Patients With a History of Anti-CD20 Therapy, RMD Open. (2022) 8, no. 1, https://doi.org/10.1136/rmdopen-2021-002166.
10.1136/rmdopen-2021-002166
Web of Science® Google Scholar
32 Doherty G. J. and McMahon H. T., Mechanisms of Endocytosis, Annual Review of Biochemistry. (2009) 78, no. 1, 857–902, https://doi.org/10.1146/annurev.biochem.78.081307.110540, 2-s2.0-67650711138.
10.1146/annurev.biochem.78.081307.110540
CAS PubMed Web of Science® Google Scholar
33 Fang E., Liu X., Li M. et al., Advances in COVID-19 mRNA Vaccine Development, Signal Transduction and Targeted Therapy. (2022) 7, 94–31, https://doi.org/10.1038/s41392-022-00950-y.
10.1038/s41392-022-00950-y
CAS PubMed Web of Science® Google Scholar
34 Patel S., Kim J., Herrera M., Mukherjee A., Kabanov A. V., and Sahay G., Brief Update on Endocytosis of Nanomedicines, Advanced Drug Delivery Reviews. (2019) 144, 90–111, https://doi.org/10.1016/j.addr.2019.08.004, 2-s2.0-85070803136.
10.1016/j.addr.2019.08.004
CAS PubMed Web of Science® Google Scholar
35 Beyer A., Koch T., Schröder H., Schulz S., and Höllt V., Effect of the A118G Polymorphism on Binding Affinity, Potency and Agonist-Mediated Endocytosis, Desensitization, and Resensitization of the Human Mu-Opioid Receptor, Journal of Neurochemistry. (2004) 89, no. 3, 553–560, https://doi.org/10.1111/j.1471-4159.2004.02340.x, 2-s2.0-2042527666.
10.1111/j.1471-4159.2004.02340.x
CAS PubMed Web of Science® Google Scholar
36 Zhan L., Li J., Jew B., and Sul J. H., Rare Variants in the Endocytic Pathway are Associated With Alzheimer’s Disease, its Related Phenotypes, and Functional Consequences, PLoS Genetics. (2021) 17, no. 9, https://doi.org/10.1371/journal.pgen.1009772.
10.1371/journal.pgen.1009772
Web of Science® Google Scholar
37 Pateev I., Seregina K., Ivanov R., and Reshetnikov V., Biodistribution of RNA Vaccines and of Their Products: Evidence From Human and Animal Studies, Biomedicines. (2023) 12, no. 1, https://doi.org/10.3390/biomedicines12010059.
10.3390/biomedicines12010059
PubMed Google Scholar
38 Hanna N., De Mejia C. M., Heffes-Doon A. et al., Biodistribution of mRNA COVID-19 Vaccines in Human Breast Milk, EBioMedicine. (2023) 96, https://doi.org/10.1016/j.ebiom.2023.104800.
10.1016/j.ebiom.2023.104800
PubMed Web of Science® Google Scholar
39 Hanna N., Heffes-Doon A., Lin X. et al., Detection of Messenger RNA COVID-19 Vaccines in Human Breast Milk, JAMA Pediatrics. (2022) 176, no. 12, https://doi.org/10.1001/jamapediatrics.2022.3581.
10.1001/jamapediatrics.2022.3581
PubMed Web of Science® Google Scholar
40 Shook L. L. and Edlow A. G., Safety and Efficacy of Coronavirus Disease 2019 (COVID-19) mRNA Vaccines During Lactation, Obstetrics & Gynecology. (2023) 141, no. 3, 483–491, https://doi.org/10.1097/AOG.0000000000005093.
10.1097/AOG.0000000000005093
CAS PubMed Web of Science® Google Scholar
41 Nogawa S., Kanamori H., Tokuda K. et al., Identification of Susceptibility Loci for Adverse Events Following COVID-19 Vaccination in the Japanese Population: A Web-Based Genome-Wide Association Study, medRxiv 2021.11.30.21267043. (2021) https://doi.org/10.1101/2021.11.30.21267043.
10.1101/2021.11.30.21267043
Google Scholar
42 Rockman M. V. and Kruglyak L., Genetics of Global Gene Expression, Nature Reviews Genetics. (2006) 7, no. 11, 862–872, https://doi.org/10.1038/nrg1964, 2-s2.0-33750204290.
10.1038/nrg1964
CAS PubMed Web of Science® Google Scholar
43 Zhang W., Duan S., Kistner E. O. et al., Evaluation of Genetic Variation Contributing to Differences in Gene Expression Between Populations, The American Journal of Human Genetics. (2008) 82, no. 3, 631–640, https://doi.org/10.1016/j.ajhg.2007.12.015, 2-s2.0-41149161113.
10.1016/j.ajhg.2007.12.015
CAS PubMed Web of Science® Google Scholar
44 Karikó K., Muramatsu H., Welsh F. A. et al., Incorporation of Pseudouridine Into mRNA Yields Superior Nonimmunogenic Vector With Increased Translational Capacity and Biological Stability, Molecular Therapy. (2008) 16, no. 11, 1833–1840, https://doi.org/10.1038/mt.2008.200, 2-s2.0-54949112814.
10.1038/mt.2008.200
CAS PubMed Web of Science® Google Scholar
45 Svitkin Y. V., Cheng Y. M., Chakraborty T., Presnyak V., John M., and Sonenberg N., N1-Methyl-Pseudouridine in mRNA Enhances Translation Through eIF2α-Dependent and Independent Mechanisms by Increasing Ribosome Density, Nucleic Acids Research. (2017) 45, no. 10, 6023–6036, https://doi.org/10.1093/nar/gkx135, 2-s2.0-85021287824.
10.1093/nar/gkx135
CAS PubMed Google Scholar
46 Lim J. K., Lisco A., McDermott D. H. et al., Genetic Variation in OAS1 is a Risk Factor for Initial Infection With West Nile Virus in Man, PLoS Pathogens. (2009) 5, no. 2, https://doi.org/10.1371/journal.ppat.1000321, 2-s2.0-61449157813.
10.1371/journal.ppat.1000321
Web of Science® Google Scholar
47 Aldén M., Olofsson Falla F., Yang D. et al., Intracellular Reverse Transcription of Pfizer BioNTech COVID-19 mRNA Vaccine BNT162b2 In Vitro in Human Liver Cell Line, Current Issues in Molecular Biology. (2022) 44, no. 3, 1115–1126, https://doi.org/10.3390/cimb44030073.
10.3390/cimb44030073
CAS PubMed Web of Science® Google Scholar
48 McKernan K., Helbert Y., Kane L. T., and McLaughlin S., Sequencing of Bivalent Moderna and Pfizer mRNA Vaccines Reveals Nanogram to Microgram Quantities of Expression Vector dsDNA Per Dose, OSF Preprints. (2023) .
Google Scholar
49 Yonker L. M., Swank Z., Bartsch Y. C. et al., Circulating Spike Protein Detected in Post–COVID-19 mRNA Vaccine Myocarditis, Circulation. (2023) 147, no. 11, 867–876, https://doi.org/10.1161/CIRCULATIONAHA.122.061025.
10.1161/CIRCULATIONAHA.122.061025
CAS PubMed Web of Science® Google Scholar
50 Groussin M., Poyet M., Sistiaga A. et al., Elevated Rates of Horizontal Gene Transfer in the Industrialized Human Microbiome, Cell. (2021) 184, no. 8, 2053.e18–2067.e18, https://doi.org/10.1016/j.cell.2021.02.052.
10.1016/j.cell.2021.02.052
Web of Science® Google Scholar
51 Liu L., Chen X., Skogerbø G. et al., The Human Microbiome: A Hot Spot of Microbial Horizontal Gene Transfer, Genomics. (2012) 100, no. 5, 265–270, https://doi.org/10.1016/j.ygeno.2012.07.012, 2-s2.0-84867918293.
10.1016/j.ygeno.2012.07.012
CAS PubMed Web of Science® Google Scholar
52 van Reenen C. A. and Dicks L. M. T., Horizontal Gene Transfer Amongst Probiotic Lactic Acid Bacteria and Other Intestinal Microbiota: What are the Possibilities? A Review, Archives of Microbiology. (2011) 193, no. 3, 157–168, https://doi.org/10.1007/s00203-010-0668-3, 2-s2.0-79954575645.
10.1007/s00203-010-0668-3
CAS PubMed Web of Science® Google Scholar
53 Sfera A., Osorio C., Hazan S. et al., Long COVID and the Neuroendocrinology of Microbial Translocation Outside the GI Tract: Some Treatment Strategies, Endocrine. (2022) 3, no. 4, 703–725, https://doi.org/10.3390/endocrines3040058.
10.3390/endocrines3040058
CAS Google Scholar
54 Hazan S., Stollman N., Bozkurt H. S. et al., Lost Microbes of COVID-19: Bifidobacterium, Faecalibacterium Depletion and Decreased Microbiome Diversity Associated With SARS-CoV-2 Infection Severity, BMJ Open Gastroenterol. (2022) 9, no. 1, https://doi.org/10.1136/bmjgast-2022-000871.
10.1136/bmjgast-2022-000871
PubMed Web of Science® Google Scholar
55 Liu Q., Mak J. W. Y., Su Q. et al., Gut Microbiota Dynamics in a Prospective Cohort of Patients With Post-Acute COVID-19 Syndrome, Gut. (2022) 71, no. 3, 544–552, https://doi.org/10.1136/gutjnl-2021-325989.
10.1136/gutjnl-2021-325989
CAS Web of Science® Google Scholar
56 Halma M., Disarming the Sword of Damocles: Biomarkers and Susceptibilities to Spike Protein, Diseases. (2023) https://doi.org/10.20944/preprints202307.0840.v2.
10.20944/preprints202307.0840.v2
Google Scholar
57 Brito I. L., Examining Horizontal Gene Transfer in Microbial Communities, Nature Reviews Microbiology. (2021) 19, no. 7, 442–453, https://doi.org/10.1038/s41579-021-00534-7.
10.1038/s41579-021-00534-7
CAS PubMed Web of Science® Google Scholar
58 Lugowski A., Nicholson B., and Rissland O. S., Determining mRNA Half-Lives on a Transcriptome-Wide Scale, Methods. (2018) 137, 90–98, https://doi.org/10.1016/j.ymeth.2017.12.006, 2-s2.0-85039437430.
10.1016/j.ymeth.2017.12.006
CAS PubMed Web of Science® Google Scholar
59 Leppek K., Byeon G. W., Kladwang W. et al., Combinatorial Optimization of mRNA Structure, Stability, and Translation for RNA-Based Therapeutics, Nature Communications. (2022) 13, no. 1, https://doi.org/10.1038/s41467-022-28776-w.
10.1038/s41467-022-28776-w
PubMed Web of Science® Google Scholar
60 Röltgen K., Nielsen S. C. A., Silva O. et al., Immune Imprinting, Breadth of Variant Recognition, and Germinal Center Response in Human SARS-CoV-2 Infection and Vaccination, Cell. (2022) 185, no. 6, 1025.e14–1040.e14, https://doi.org/10.1016/j.cell.2022.01.018.
10.1016/j.cell.2022.01.018
Web of Science® Google Scholar
61 Cosentino M. and Marino F., The Spike Hypothesis in Vaccine-Induced Adverse Effects: Questions and Answers, Trends in Molecular Medicine. (2022) 28, no. 10, 797–799, https://doi.org/10.1016/j.molmed.2022.07.009.
10.1016/j.molmed.2022.07.009
CAS PubMed Web of Science® Google Scholar
62 Strelkova Petersen D. M., Chaudhary N., Arral M. L., Weiss R. M., and Whitehead K. A., The Mixing Method Used to Formulate Lipid Nanoparticles Affects mRNA Delivery Efficacy and Organ Tropism, European Journal of Pharmaceutics and Biopharmaceutics. (2023) 192, 126–135, https://doi.org/10.1016/j.ejpb.2023.10.006.
10.1016/j.ejpb.2023.10.006
CAS PubMed Google Scholar
63 Johannessen Landmark C., Johannessen S. I., and Tomson T., Host Factors Affecting Antiepileptic Drug Delivery—Pharmacokinetic Variability, Advanced Drug Delivery Reviews. (2012) 64, no. 10, 896–910, https://doi.org/10.1016/j.addr.2011.10.003, 2-s2.0-84862835377.
10.1016/j.addr.2011.10.003
CAS PubMed Google Scholar
64 Zimmermann M., Zimmermann-Kogadeeva M., Wegmann R., and Goodman A. L., Separating Host and Microbiome Contributions to Drug Pharmacokinetics and Toxicity, Science. (2019) 363, no. 6427, https://doi.org/10.1126/science.aat9931, 2-s2.0-85061193510.
10.1126/science.aat9931
Web of Science® Google Scholar
65 Song G., Wu H., Yoshino K., and Zamboni W. C., Factors Affecting the Pharmacokinetics and Pharmacodynamics of Liposomal Drugs, Journal of Liposome Research. (2012) 22, no. 3, 177–192, https://doi.org/10.3109/08982104.2012.655285, 2-s2.0-84865232192.
10.3109/08982104.2012.655285
CAS PubMed Web of Science® Google Scholar
66 Hou X., Zaks T., Langer R., and Dong Y., Lipid Nanoparticles for mRNA Delivery, Nature Reviews Materials. (2021) 6, no. 12, 1078–1094, https://doi.org/10.1038/s41578-021-00358-0.
10.1038/s41578-021-00358-0
CAS PubMed Web of Science® Google Scholar
67 Zamboni W. C., Ramalingam S., Friedland D. M. et al., Phase I and Pharmacokinetic Study of Pegylated Liposomal CKD-602 in Patients With Advanced Malignancies, Clinical Cancer Research. (2009) 15, no. 4, 1466–1472, https://doi.org/10.1158/1078-0432.CCR-08-1405, 2-s2.0-63149086093.
10.1158/1078-0432.CCR-08-1405
CAS PubMed Web of Science® Google Scholar
68 Zamboni W. C., Strychor S., Maruca L. et al., Pharmacokinetic Study of Pegylated Liposomal CKD-602 (S-Ckd602) in Patients With Advanced Malignancies, Clinical Pharmacology & Therapeutics. (2009) 86, no. 5, 519–526, https://doi.org/10.1038/clpt.2009.141, 2-s2.0-70350231916.
10.1038/clpt.2009.141
CAS PubMed Web of Science® Google Scholar
69 Geary S. and Florini J. R., Effect of Age on Rate of Protein Synthesis in Isolated Perfused Mouse Hearts, Journal of Gerontology. (1972) 27, no. 3, 325–332, https://doi.org/10.1093/geronj/27.3.325, 2-s2.0-0015368257.
10.1093/geronj/27.3.325
CAS PubMed Google Scholar
70 Rooyackers O. E., Adey D. B., Ades P. A., and Nair K. ., Effect of Age on In Vivo Rates of Mitochondrial Protein Synthesis in Human Skeletal Muscle, Proceedings of the National Academy of Sciences. (1996) 93, no. 26, 15364–15369, https://doi.org/10.1073/pnas.93.26.15364, 2-s2.0-0030451773.
10.1073/pnas.93.26.15364
CAS PubMed Web of Science® Google Scholar
71 La-Beck N., Wu H., Infante J. et al., The Evaluation of Gender on the Pharmacokinetics (PK) of Pegylated Liposomal Anticancer Agents, Journal of Clinical Oncology. (2010) 28, no. 15, https://doi.org/10.1200/jco.2010.28.15_suppl.e13003.
10.1200/jco.2010.28.15_suppl.e13003
Web of Science® Google Scholar
72 La-Beck N. M., Zamboni B. A., Gabizon A. et al., Factors Affecting the Pharmacokinetics of Pegylated Liposomal Doxorubicin in Patients, Cancer Chemotherapy and Pharmacology. (2012) 69, no. 1, 43–50, https://doi.org/10.1007/s00280-011-1664-2, 2-s2.0-84856759606.
10.1007/s00280-011-1664-2
CAS PubMed Web of Science® Google Scholar
73 Kimura S. and Harashima H., On the Mechanism of Tissue-Selective Gene Delivery by Lipid Nanoparticles, Journal of Controlled Release. (2023) 362, 797–811, https://doi.org/10.1016/j.jconrel.2023.03.052.
10.1016/j.jconrel.2023.03.052
CAS PubMed Web of Science® Google Scholar
74 Meyer R. A., Neshat S. Y., Green J. J., Santos J. L., and Tuesca A. D., Targeting Strategies for mRNA Delivery, Materials Today Advances. (2022) 14, https://doi.org/10.1016/j.mtadv.2022.100240.
10.1016/j.mtadv.2022.100240
Web of Science® Google Scholar
75 Curtis E., Litwic A., Cooper C., and Dennison E., Determinants of Muscle and Bone Aging, Journal of Cellular Physiology. (2015) 230, no. 11, 2618–2625, https://doi.org/10.1002/jcp.25001, 2-s2.0-84937886227.
10.1002/jcp.25001
CAS PubMed Web of Science® Google Scholar
76 Stewart C. E. H. and Rittweger J., Adaptive Processes in Skeletal Muscle: Molecular Regulators and Genetic Influences, Journal of Musculoskeletal and Neuronal Interactions. (2006) 6, no. 1, 73–86.
CAS PubMed Google Scholar
77 Groves D. C., Rowland-Jones S. L., and Angyal A., The D614G Mutations in the SARS-CoV-2 Spike Protein: Implications for Viral Infectivity, Disease Severity and Vaccine Design, Biochemical and Biophysical Research Communications. (2021) 538, 104–107, https://doi.org/10.1016/j.bbrc.2020.10.109.
10.1016/j.bbrc.2020.10.109
CAS PubMed Web of Science® Google Scholar
78 Plante J. A., Liu Y., Liu J. et al., Spike Mutation D614G Alters SARS-CoV-2 Fitness, Nature. (2021) 592, no. 7852, 116–121, https://doi.org/10.1038/s41586-020-2895-3.
10.1038/s41586-020-2895-3
CAS PubMed Web of Science® Google Scholar
79 Liu Y., Liu J., Plante K. S. et al., The N501Y Spike Substitution Enhances SARS-CoV-2 Infection and Transmission, Nature. (2022) 602, no. 7896, 294–299, https://doi.org/10.1038/s41586-021-04245-0.
10.1038/s41586-021-04245-0
CAS PubMed Web of Science® Google Scholar
80 Harvey W. T., Carabelli A. M., Jackson B. et al., SARS-CoV-2 Variants, Spike Mutations and Immune Escape, Nature Reviews Microbiology. (2021) 19, no. 7, 409–424, https://doi.org/10.1038/s41579-021-00573-0.
10.1038/s41579-021-00573-0
CAS PubMed Web of Science® Google Scholar
81 Metz J. A. J. and Boldin B., Immunity-Driven Evolution of Virulence and Diversity in Respiratory Diseases, Evolution. (2023) 77, no. 11, 2392–2408, https://doi.org/10.1093/evolut/qpad145.
10.1093/evolut/qpad145
PubMed Web of Science® Google Scholar
82 Pairo-Castineira E., Rawlik K., Bretherick A. D. et al., GWAS and Meta-Analysis Identifies 49 Genetic Variants Underlying Critical COVID-19, Nature. (2023) 617, no. 7962, 764–768, https://doi.org/10.1038/s41586-023-06034-3.
10.1038/s41586-023-06034-3
CAS PubMed Web of Science® Google Scholar
83 Hultström M., Frithiof R., Grip J. et al., Genetic Determinants of Mannose-Binding Lectin Activity Predispose to Thromboembolic Complications in Critical COVID-19, Nature Immunology. (2022) 23, no. 6, 861–864, https://doi.org/10.1038/s41590-022-01227-w.
10.1038/s41590-022-01227-w
PubMed Web of Science® Google Scholar
84 Mancini I., Baronciani L., Artoni A. et al., The ADAMTS13-Von Willebrand Factor Axis in COVID-19 Patients, Journal of Thrombosis and Haemostasis. (2021) 19, no. 2, 513–521, https://doi.org/10.1111/jth.15191.
10.1111/jth.15191
CAS PubMed Web of Science® Google Scholar
85 Bordon Y., A New Set of Genes Linked to Critical COVID-19, Nature Reviews Immunology. (2022) 22, no. 4, https://doi.org/10.1038/s41577-022-00709-0.
10.1038/s41577-022-00709-0
Web of Science® Google Scholar
86 Lammi V., Nakanishi T., Jones S. E. et al., Genome-Wide Association Study of Long COVID, Nature Genetics. (2025) 57, no. 6, 1402–1417, https://doi.org/10.1038/s41588-025-02100-w.
10.1038/s41588-025-02100-w
CAS PubMed Web of Science® Google Scholar
87 Ledford H., Gene Linked to Long COVID Found in Analysis of Thousands of Patients, Nature. (2023) 619, no. 7970, https://doi.org/10.1038/d41586-023-02269-2.
10.1038/d41586-023-02269-2
Web of Science® Google Scholar
88 Delshad M., Sanaei M.-J., Pourbagheri-Sigaroodi A., and Bashash D., Host Genetic Diversity and Genetic Variations of SARS-CoV-2 in COVID-19 Pathogenesis and the Effectiveness of Vaccination, International Immunopharmacology. (2022) 111, https://doi.org/10.1016/j.intimp.2022.109128.
10.1016/j.intimp.2022.109128
PubMed Web of Science® Google Scholar
89 Li J., Wang Y., Liu Y. et al., Polymorphisms and Mutations of ACE2 and TMPRSS2 Genes are Associated With COVID-19: A Systematic Review, European Journal of Medical Research. (2022) 27, no. 1, https://doi.org/10.1186/s40001-022-00647-6.
10.1186/s40001-022-00647-6
Web of Science® Google Scholar
90 Falahi S. and Kenarkoohi A., Host Factors and Vaccine Efficacy: Implications for COVID-19 Vaccines, Journal of Medical Virology. (2022) 94, no. 4, 1330–1335, https://doi.org/10.1002/jmv.27485.
10.1002/jmv.27485
CAS PubMed Web of Science® Google Scholar
91 Dudley M. Z., Gerber J. E., Budigan Ni H. et al., Vaccinomics: A Scoping Review, Vaccine. (2023) 41, no. 14, 2357–2367, https://doi.org/10.1016/j.vaccine.2023.02.009.
10.1016/j.vaccine.2023.02.009
CAS PubMed Web of Science® Google Scholar
92 Tesseromatis C. and Alevizou A., The Role of the Protein-Binding on the Mode of Drug Action as Well the Interactions With Other Drugs, European Journal of Drug Metabolism & Pharmacokinetics. (2008) 33, no. 4, 225–230, https://doi.org/10.1007/BF03190876, 2-s2.0-59949085146.
10.1007/BF03190876
CAS PubMed Web of Science® Google Scholar
93 Bellavite P., Ferraresi A., and Isidoro C., Immune Response and Molecular Mechanisms of Cardiovascular Adverse Effects of Spike Proteins From SARS-CoV-2 and mRNA Vaccines, Biomedicines. (2023) 11, no. 2, https://doi.org/10.3390/biomedicines11020451.
10.3390/biomedicines11020451
Web of Science® Google Scholar
94 Cosentino M. and Marino F., Understanding the Pharmacology of COVID-19 mRNA Vaccines: Playing Dice With the Spike?, International Journal of Molecular Sciences. (2022) 23, no. 18, https://doi.org/10.3390/ijms231810881.
10.3390/ijms231810881
Google Scholar
95 Halma M. T. J., Marik P. E., and Saleeby Y. M., Exploring Autophagy in Treating SARS-CoV-2 Spike Protein-Related Pathology, Endocrine and Metabolic Science. (2024) 14, https://doi.org/10.1016/j.endmts.2024.100163.
10.1016/j.endmts.2024.100163
PubMed Google Scholar
96 Lin Z., More Than a Key—The Pathological Roles of SARS-CoV-2 Spike Protein in COVID-19 Related Cardiac Injury, Sports Medicine and Health Science. (2024) 6, no. 3, 209–220, https://doi.org/10.1016/j.smhs.2023.03.004.
10.1016/j.smhs.2023.03.004
CAS Web of Science® Google Scholar
97 Parry P. I., Lefringhausen A., Turni C. et al., Spikeopathy: COVID-19 Spike Protein is Pathogenic, From Both Virus and Vaccine mRNA, Biomedicines. (2023) 11, no. 8, https://doi.org/10.3390/biomedicines11082287.
10.3390/biomedicines11082287
PubMed Web of Science® Google Scholar
98 Ozono S., Zhang Y., Ode H. et al., SARS-CoV-2 D614G Spike Mutation Increases Entry Efficiency With Enhanced ACE2-Binding Affinity, Nature Communications. (2021) 12, no. 1, https://doi.org/10.1038/s41467-021-21118-2.
10.1038/s41467-021-21118-2
PubMed Web of Science® Google Scholar
99 Yang J., Petitjean S. J. L., Koehler M. et al., Molecular Interaction and Inhibition of SARS-CoV-2 Binding to the ACE2 Receptor, Nature Communications. (2020) 11, no. 1, https://doi.org/10.1038/s41467-020-18319-6.
10.1038/s41467-020-18319-6
Web of Science® Google Scholar
100 Jackson C. B., Farzan M., Chen B., and Choe H., Mechanisms of SARS-CoV-2 Entry Into Cells, Nature Reviews Molecular Cell Biology. (2022) 23, no. 1, 3–20, https://doi.org/10.1038/s41580-021-00418-x.
10.1038/s41580-021-00418-x
CAS PubMed Web of Science® Google Scholar
101 Buoninfante A., Andeweg A., Genov G., and Cavaleri M., Myocarditis Associated With COVID-19 Vaccination, NPJ Vaccines. (2024) 9, 122–128, https://doi.org/10.1038/s41541-024-00893-1.
10.1038/s41541-024-00893-1
PubMed Web of Science® Google Scholar
102 Buoninfante A., Andeweg A., Baker A. T. et al., Understanding Thrombosis With Thrombocytopenia Syndrome After COVID-19 Vaccination, NPJ Vaccines. (2022) 7, no. 1, https://doi.org/10.1038/s41541-022-00569-8.
10.1038/s41541-022-00569-8
PubMed Web of Science® Google Scholar
103 Fiorillo G., Pancetti S., Cortese A. et al., Leukocytoclastic Vasculitis (Cutaneous Small-Vessel Vasculitis) After COVID-19 Vaccination, Journal of Autoimmunity. (2022) 127, https://doi.org/10.1016/j.jaut.2021.102783.
10.1016/j.jaut.2021.102783
PubMed Web of Science® Google Scholar
104 Terentes-Printzios D., Gardikioti V., Solomou E. et al., The Effect of an mRNA Vaccine Against COVID-19 on Endothelial Function and Arterial Stiffness, Hypertension Research. (2022) 45, no. 5, 846–855, https://doi.org/10.1038/s41440-022-00876-6.
10.1038/s41440-022-00876-6
CAS PubMed Web of Science® Google Scholar
105 Wang Q., Zhang Y., Wu L. et al., Structural and Functional Basis of SARS-CoV-2 Entry by Using Human ACE2, Cell. (2020) 181, no. 4, 894–904.e9, https://doi.org/10.1016/j.cell.2020.03.045.
10.1016/j.cell.2020.03.045
CAS PubMed Web of Science® Google Scholar
106 Avolio E., Carrabba M., Milligan R. et al., The SARS-CoV-2 Spike Protein Disrupts Human Cardiac Pericytes Function Through CD147 Receptor-Mediated Signalling: A Potential Non-Infective Mechanism of COVID-19 Microvascular Disease, Clinical Science. (2021) 135, no. 24, 2667–2689, https://doi.org/10.1042/CS20210735.
10.1042/CS20210735
CAS Web of Science® Google Scholar
107 Al-kuraishy H. M., Al-Gareeb A. I., Kaushik A., Kujawska M., and Batiha G. E.-S., Hemolytic Anemia in COVID-19, Annals of Hematology. (2022) 101, no. 9, 1887–1895, https://doi.org/10.1007/s00277-022-04907-7.
10.1007/s00277-022-04907-7
CAS PubMed Web of Science® Google Scholar
108 De Michele M., Kahan J., Berto I. et al., Cerebrovascular Complications of COVID-19 and COVID-19 Vaccination, Circulation Research. (2022) 130, no. 8, 1187–1203, https://doi.org/10.1161/CIRCRESAHA.122.319954.
10.1161/CIRCRESAHA.122.319954
CAS PubMed Web of Science® Google Scholar
109 De Bruyne S., Van Landeghem S., Schauwvlieghe A., and Noens L., Life-Threatening Autoimmune Hemolytic Anemia Following mRNA COVID-19 Vaccination: Don’t Be Too Prudent With the Red Gold, Clinical Chemistry and Laboratory Medicine. (2022) 60, no. 6, e125–e128, https://doi.org/10.1515/cclm-2022-0118.
10.1515/cclm-2022-0118
CAS PubMed Web of Science® Google Scholar
110 Jafarzadeh A., Jafarzadeh S., Pardehshenas M., Nemati M., and Mortazavi S. M. J., Development and Exacerbation of Autoimmune Hemolytic Anemia Following COVID-19 Vaccination: A Systematic Review, The International Journal of Literary Humanities. (2023) 45, no. 2, 145–155, https://doi.org/10.1111/ijlh.13978.
10.1111/ijlh.13978
Google Scholar
111 Zhao Y., Liu S., Zhou Y., Zhang M. et al., Structural Basis of Human α7 Nicotinic Acetylcholine Receptor Activation, Cell Research. (2021) 31, no. 6, 713–716, https://doi.org/10.1038/s41422-021-00509-6.
10.1038/s41422-021-00509-6
CAS PubMed Google Scholar
112 Khan S., Shafiei M. S., Longoria C., Schoggins J. W., Savani R. C., and Zaki H., SARS-CoV-2 Spike Protein Induces Inflammation via TLR2-dependent Activation of the NF-Κb Pathway, eLife. (2021) 10, https://doi.org/10.7554/eLife.68563.
10.7554/eLife.68563
Google Scholar
113 Alturaiki W., Alkadi H., Alamri S. et al., Association Between the Expression of Toll-Like Receptors, Cytokines, and Homeostatic Chemokines in SARS-CoV-2 Infection and COVID-19 Severity, Heliyon. (2023) 9, no. 1, https://doi.org/10.1016/j.heliyon.2022.e12653.
10.1016/j.heliyon.2022.e12653
PubMed Web of Science® Google Scholar
114 Halajian E. A., LeBlanc E. V., Gee K., and Colpitts C. C., Activation of TLR4 by Viral Glycoproteins: A Double-Edged Sword?, Frontiers in Microbiology. (2022) 13, https://doi.org/10.3389/fmicb.2022.1007081.
10.3389/fmicb.2022.1007081
PubMed Google Scholar
115 Shirato K. and Kizaki T., SARS-CoV-2 Spike Protein S1 Subunit Induces Pro-Inflammatory Responses via Toll-Like Receptor 4 Signaling in Murine and Human Macrophages, Heliyon. (2021) 7, no. 2, https://doi.org/10.1016/j.heliyon.2021.e06187.
10.1016/j.heliyon.2021.e06187
PubMed Web of Science® Google Scholar
116 Chen C.-C. and Chen C.-J., New-Onset Inflammatory Arthritis After COVID-19 Vaccination: A Systematic Review, International Journal of Rheumatic Diseases. (2023) 26, no. 2, 267–277, https://doi.org/10.1111/1756-185X.14482.
10.1111/1756-185X.14482
CAS PubMed Web of Science® Google Scholar
117 Khayat-Khoei M., Bhattacharyya S., Katz J. et al., COVID-19 mRNA Vaccination Leading to CNS Inflammation: A Case Series, Journal of Neurology. (2022) 269, no. 3, 1093–1106, https://doi.org/10.1007/s00415-021-10780-7.
10.1007/s00415-021-10780-7
CAS PubMed Web of Science® Google Scholar
118 Ostrowski S. R., Søgaard O. S., Tolstrup M. et al., Inflammation and Platelet Activation After COVID-19 Vaccines—Possible Mechanisms Behind Vaccine-Induced Immune Thrombocytopenia and Thrombosis, Frontiers in Immunology. (2021) 12, https://doi.org/10.3389/fimmu.2021.779453.
10.3389/fimmu.2021.779453
Web of Science® Google Scholar
119 Solis O., Beccari A. R., Iaconis D. et al., The SARS-CoV-2 Spike Protein Binds and Modulates Estrogen Receptors, Science Advances. (2022) 8, no. 48, https://doi.org/10.1126/sciadv.add4150.
10.1126/sciadv.add4150
Web of Science® Google Scholar
120 Nazir M., Asghar S., Rathore M. A. et al., Menstrual Abnormalities After COVID-19 Vaccines: A Systematic Review, Vacunas. (2022) 23, S77–S87, https://doi.org/10.1016/j.vacune.2022.10.019.
10.1016/j.vacun.2022.07.001
PubMed Google Scholar
121 Laganà A. S., Veronesi G., Ghezzi F. et al., Evaluation of Menstrual Irregularities After COVID-19 Vaccination: Results of the MECOVAC Survey, Open Medicine. (2022) 17, no. 1, 475–484, https://doi.org/10.1515/med-2022-0452.
10.1515/med-2022-0452
CAS PubMed Web of Science® Google Scholar
122 Singh N. and Bharara Singh A., S2 Subunit of SARS-nCoV-2 Interacts With Tumor Suppressor Protein P53 and BRCA: An In Silico Study, Translational Oncology. (2020) 13, no. 10, https://doi.org/10.1016/j.tranon.2020.100814.
10.1016/j.tranon.2020.100814
Web of Science® Google Scholar
123 Zong Z., Wei Y., Ren J., Zhang L., and Zhou F., The Intersection of COVID-19 and Cancer: Signaling Pathways and Treatment Implications, Molecular Cancer. (2021) 20, no. 1, https://doi.org/10.1186/s12943-021-01363-1.
10.1186/s12943-021-01363-1
PubMed Web of Science® Google Scholar
124 Cavanna L., Grassi S. O., Ruffini L. et al., Non-Hodgkin Lymphoma Developed Shortly After mRNA COVID-19 Vaccination: Report of a Case and Review of the Literature, Medicina. (2023) 59, no. 1, https://doi.org/10.3390/medicina59010157.
10.3390/medicina59010157
Google Scholar
125 Gentilini P., Lindsay J., Konishi N., Fukushima M., and Polykretis P., A Case Report of Acute Lymphoblastic Leukaemia (ALL)/Lymphoblastic Lymphoma (LBL) Following the Second Dose of Comirnaty: An Analysis of the Potential Pathogenic Mechanism Based on of the Existing Literature, Preprint. (2024) https://doi.org/10.20944/preprints202403.1661.v1.
10.20944/preprints202403.1661.v1
Google Scholar
126 Grobbelaar L. M., Venter C., Vlok M. et al., SARS-CoV-2 Spike Protein S1 Induces Fibrin (Ogen) Resistant to Fibrinolysis: Implications for Microclot Formation in COVID-19, Bioscience Reports. (2021) 41, no. 8, https://doi.org/10.1042/BSR20210611.
10.1042/BSR20210611
PubMed Web of Science® Google Scholar
127 Larsson J., Hellstrand E., Hammarström P., and Nyström S., SARS-CoV-2 Spike Amyloid Fibrils Specifically and Selectively Accelerates Amyloid Fibril Formation of Human Prion Protein and the Amyloid β Peptide, BioRxiv (PREPRINT). (2023) https://doi.org/10.1101/2023.09.01.555834.
10.1101/2023.09.01.555834
Google Scholar
128 Zhao Y., Jaber V. R., and Lukiw W. J., SARS-CoV-2, Long COVID, Prion Disease and Neurodegeneration, Frontiers in Neuroscience. (2022) 16, https://doi.org/10.3389/fnins.2022.1002770.
10.3389/fnins.2022.1002770
Web of Science® Google Scholar
129 Chakrabarti S. S., Tiwari A., Jaiswal S. et al., Rapidly Progressive Dementia With Asymmetric Rigidity Following ChAdOx1 nCoV-19 Vaccination, Aging and Disease. (2022) 13, no. 3, 633–636, https://doi.org/10.14336/AD.2021.1102.
10.14336/AD.2021.1102
PubMed Web of Science® Google Scholar
130 Jung H.-S., Hong Y. J., Kim S. H. et al., Post COVID-19 Vaccination Encephalitis as a Cause of Subacute Progressive Dementia: A Case Report and Literature Review, Dement Neurocogn Disord. (2023) 22, no. 2, 81–83, https://doi.org/10.12779/dnd.2023.22.2.81.
10.12779/dnd.2023.22.2.81
PubMed Google Scholar
131 Tillman T. S., Chen Q., Bondarenko V., Coleman J. A., Xu Y., and Tang P., SARS-CoV-2 Spike Protein Downregulates Cell Surface α7nAChR through a Helical Motif in the Spike Neck, ACS Chemical Neuroscience. (2023) 14, no. 4, 689–698, https://doi.org/10.1021/acschemneuro.2c00610.
10.1021/acschemneuro.2c00610
CAS PubMed Google Scholar
132 Changeux J.-P., Amoura Z., Rey F. A., and Miyara M., A Nicotinic Hypothesis for Covid-19 With Preventive and Therapeutic Implications, Comptes Rendus Biologies. (2020) 343, no. 1, 33–39, https://doi.org/10.5802/crbiol.8.
10.5802/crbiol.8
PubMed Web of Science® Google Scholar
133 Semmler A., Mundorf A. K., Kuechler A. S. et al., Chronic Fatigue and Dysautonomia Following COVID-19 Vaccination is Distinguished From Normal Vaccination Response by Altered Blood Markers, Vaccines. (2023) 11, https://doi.org/10.3390/vaccines11111642.
10.3390/vaccines11111642
Web of Science® Google Scholar
134 Chaurasia B., Chavda V., Lu B., Garg K., and Montemurro N., Cognitive Deficits and Memory Impairments After COVID-19 (Covishield) Vaccination, Brain, Behavior, & Immunity—Health. (2022) 22, https://doi.org/10.1016/j.bbih.2022.100463.
10.1016/j.bbih.2022.100463
PubMed Web of Science® Google Scholar
135 Buzhdygan T. P., DeOre B. J., Baldwin-Leclair A. et al., The SARS-CoV-2 Spike Protein Alters Barrier Function in 2D Static and 3D Microfluidic In-Vitro Models of the Human Blood-Brain Barrier, Neurobiology of Disease. (2020) 146, https://doi.org/10.1016/j.nbd.2020.105131.
10.1016/j.nbd.2020.105131
PubMed Web of Science® Google Scholar
136 DeOre B. J., Tran K. A., Andrews A. M., Ramirez S. H., and Galie P. A., SARS-CoV-2 Spike Protein Disrupts Blood–Brain Barrier Integrity via RhoA Activation, Journal of Neuroimmune Pharmacology. (2021) 16, no. 4, 722–728, https://doi.org/10.1007/s11481-021-10029-0.
10.1007/s11481-021-10029-0
PubMed Web of Science® Google Scholar
137 Petrovszki D., Walter F. R., Vigh J. P. et al., Penetration of the SARS-CoV-2 Spike Protein Across the Blood-Brain Barrier, as Revealed by a Combination of a Human Cell Culture Model System and Optical Biosensing, Biomedicines. (2022) 10, no. 1, https://doi.org/10.3390/biomedicines10010188.
10.3390/biomedicines10010188
PubMed Web of Science® Google Scholar
138 Rhea E. M., Logsdon A. F., Hansen K. M. et al., The S1 Protein of SARS-CoV-2 Crosses the Blood–Brain Barrier in Mice, Nature Neuroscience. (2021) 24, no. 3, 368–378, https://doi.org/10.1038/s41593-020-00771-8.
10.1038/s41593-020-00771-8
CAS PubMed Web of Science® Google Scholar
139 Mao L., Jin H., Wang M. et al., Neurologic Manifestations of Hospitalized Patients With Coronavirus Disease 2019 in Wuhan, China, JAMA Neurology. (2020) 77, no. 6, 683–689, https://doi.org/10.1001/jamaneurol.2020.1127.
10.1001/jamaneurol.2020.1127
PubMed Web of Science® Google Scholar
140 Assiri S. A., Althaqafi R. M. M., Alswat K. et al., Post COVID-19 Vaccination-Associated Neurological Complications, Neuropsychiatric Disease and Treatment. (2022) 18, 137–154, https://doi.org/10.2147/NDT.S343438.
10.2147/NDT.S343438
CAS PubMed Web of Science® Google Scholar
141 Chatterjee A. and Chakravarty A., Neurological Complications Following COVID-19 Vaccination, Current Neurology and Neuroscience Reports. (2023) 23, 1–14, https://doi.org/10.1007/s11910-022-01247-x.
10.1007/s11910-022-01247-x
PubMed Web of Science® Google Scholar
142 Felipe Cuspoca A., Isaac Estrada P., and Velez-van-Meerbeke A., Molecular Mimicry of SARS-CoV-2 Spike Protein in the Nervous System: A Bioinformatics Approach, Computational and Structural Biotechnology Journal. (2022) 20, 6041–6054, https://doi.org/10.1016/j.csbj.2022.10.022.
10.1016/j.csbj.2022.10.022
CAS PubMed Google Scholar
143 Kanduc D. and Shoenfeld Y., Molecular Mimicry Between SARS-CoV-2 Spike Glycoprotein and Mammalian Proteomes: Implications for the Vaccine, Immunologic Research. (2020) 68, no. 5, 310–313, https://doi.org/10.1007/s12026-020-09152-6.
10.1007/s12026-020-09152-6
CAS PubMed Web of Science® Google Scholar
144 Nunez-Castilla J., Stebliankin V., Baral P. et al., Potential Autoimmunity Resulting From Molecular Mimicry Between SARS-CoV-2 Spike and Human Proteins, Viruses. (2022) 14, no. 7, https://doi.org/10.3390/v14071415.
10.3390/v14071415
PubMed Google Scholar
145 Rosner C. M., Genovese L., Tehrani B. N. et al., Myocarditis Temporally Associated With COVID-19 Vaccination, Circulation. (2021) 144, no. 6, 502–505, https://doi.org/10.1161/CIRCULATIONAHA.121.055891.
10.1161/CIRCULATIONAHA.121.055891
CAS PubMed Web of Science® Google Scholar
146 Wise J., Covid-19: European Countries Suspend Use of Oxford-AstraZeneca Vaccine After Reports of Blood Clots, BMJ. (2021) 372, https://doi.org/10.1136/bmj.n699.
10.1136/bmj.n699
Google Scholar
147 Paterlini M., Covid-19: Sweden, Norway, and Finland Suspend Use of Moderna Vaccine in Young People as a Precaution, BMJ. (2021) 375, https://doi.org/10.1136/bmj.n2477.
10.1136/bmj.n2477
Google Scholar
148 Mansanguan S., Charunwatthana P., Piyaphanee W., Dechkhajorn W., Poolcharoen A., and Mansanguan C., Cardiovascular Manifestation of the BNT162b2 mRNA COVID-19 Vaccine in Adolescents, Tropical Medicine and Infectious Disease. (2022) 7, no. 8, https://doi.org/10.3390/tropicalmed7080196.
10.3390/tropicalmed7080196
PubMed Web of Science® Google Scholar
149 Aladdin Y., Algahtani H., and Shirah B., Vaccine-Induced Immune Thrombotic Thrombocytopenia With Disseminated Intravascular Coagulation and Death Following the ChAdOx1 nCoV-19 Vaccine, Journal of Stroke and Cerebrovascular Diseases. (2021) 30, no. 9, https://doi.org/10.1016/j.jstrokecerebrovasdis.2021.105938.
10.1016/j.jstrokecerebrovasdis.2021.105938
PubMed Web of Science® Google Scholar
150 Turner S., Khan M. A., Putrino D., Woodcock A., Kell D. B., and Pretorius E., Long COVID: Pathophysiological Factors and Abnormalities of Coagulation, Trends in Endocrinology and Metabolism. (2023) 34, no. 6, 321–344, https://doi.org/10.1016/j.tem.2023.03.002.
10.1016/j.tem.2023.03.002
CAS PubMed Web of Science® Google Scholar
151 Scialo F., Daniele A., Amato F. et al., ACE2: The Major Cell Entry Receptor for SARS-CoV-2, Lung. (2020) 198, no. 6, 867–877, https://doi.org/10.1007/s00408-020-00408-4.
10.1007/s00408-020-00408-4
CAS PubMed Web of Science® Google Scholar
152 Cao Y., Li L., Feng Z. et al., Comparative Genetic Analysis of the Novel Coronavirus (2019-nCoV/SARS-CoV-2) Receptor ACE2 in Different Populations, Cell Discov. (2020) 6, no. 1, https://doi.org/10.1038/s41421-020-0147-1.
10.1038/s41421-020-0147-1
Web of Science® Google Scholar
153 Campos M. F., Constant L. E. C., Teixeira D. E. et al., SARS-CoV-2 Spike Protein Increases Angiotensin Converting Enzyme-2 Expression and Promotes an Increase in Glucose Uptake in Endothelial Cells, Acta Virologica. (2024) 68, https://doi.org/10.3389/av.2024.12136.
10.3389/av.2024.12136
Web of Science® Google Scholar
154 Saha J. K. and Raihan M. J., The Binding Mechanism of Ivermectin and Levosalbutamol With Spike Protein of SARS-CoV-2, Structural Chemistry. (2021) 32, no. 5, 1985–1992, https://doi.org/10.1007/s11224-021-01776-0.
10.1007/s11224-021-01776-0
CAS PubMed Web of Science® Google Scholar
155 Alavi M., Mozafari M. R., Ghaemi S., Ashengroph M., Hasanzadeh Davarani F., and Mohammadabadi M., Interaction of Epigallocatechin Gallate and Quercetin With Spike Glycoprotein (S-Glycoprotein) of SARS-CoV-2: In Silico Study, Biomedicines. (2022) 10, no. 12, https://doi.org/10.3390/biomedicines10123074.
10.3390/biomedicines10123074
PubMed Web of Science® Google Scholar
156 Debnath U., Mitra A., Dewaker V. et al., Conformational Perturbation of SARS-CoV-2 Spike Protein Using N-Acetyl Cysteine: An Exploration of Probable Mechanism of Action to Combat COVID-19, Journal of Biomolecular Structure and Dynamics. (2023) 42, no. 10, 5042–5052, https://doi.org/10.1080/07391102.2023.2234031.
10.1080/07391102.2023.2234031
PubMed Web of Science® Google Scholar
157 Kaltoum A. B. O., Mutations and Polymorphisms in Genes Involved in the Infections by Covid 19: A Review, Gene Reports. (2021) 23, https://doi.org/10.1016/j.genrep.2021.101062.
10.1016/j.genrep.2021.101062
PubMed Web of Science® Google Scholar
158 Hou Y., Zhao J., Martin W. et al., New Insights into Genetic Susceptibility of COVID-19: An ACE2 and TMPRSS2 Polymorphism Analysis, BMC Medicine. (2020) 18, no. 1, https://doi.org/10.1186/s12916-020-01673-z.
10.1186/s12916-020-01673-z
Web of Science® Google Scholar
159 Martínez-Sanz J., Jiménez D., Martínez-Campelo L. et al., Role of ACE2 Genetic Polymorphisms in Susceptibility to SARS-CoV-2 Among Highly Exposed but Non Infected Healthcare Workers, Emerging Microbes & Infections. (2021) 10, no. 1, 493–496, https://doi.org/10.1080/22221751.2021.1902755.
10.1080/22221751.2021.1902755
CAS PubMed Web of Science® Google Scholar
160 Li P., Shi D., Shen W. et al., Pilot Genome-Wide Association Study of Antibody Response to Inactivated SARS-CoV-2 Vaccines, Frontiers in Immunology. (2022) 13, https://doi.org/10.3389/fimmu.2022.1054147.
10.3389/fimmu.2022.1054147
Web of Science® Google Scholar
161 Suryamohan K., Diwanji D., Stawiski E. W. et al., Human ACE2 Receptor Polymorphisms and Altered Susceptibility to SARS-CoV-2, Communications Biology. (2021) 4, no. 1, https://doi.org/10.1038/s42003-021-02030-3.
10.1038/s42003-021-02030-3
PubMed Web of Science® Google Scholar
162 Vargas-Alarcón G., Posadas-Sánchez R., and Ramírez-Bello J., Variability in Genes Related to SARS-CoV-2 Entry into Host Cells (ACE2, TMPRSS2, TMPRSS11A, ELANE, and CTSL) and its Potential Use in Association Studies, Life Sciences. (2020) 260, https://doi.org/10.1016/j.lfs.2020.118313.
10.1016/j.lfs.2020.118313
PubMed Web of Science® Google Scholar
163 Lanjanian H., Moazzam-Jazi M., Hedayati M. et al., SARS-CoV-2 Infection Susceptibility Influenced by ACE2 Genetic Polymorphisms: Insights From Tehran Cardio-Metabolic Genetic Study, Scientific Reports. (2021) 11, no. 1, https://doi.org/10.1038/s41598-020-80325-x.
10.1038/s41598-020-80325-x
PubMed Web of Science® Google Scholar
164 Torre-Fuentes L., Matías-Guiu J., Hernández-Lorenzo L. et al., ACE2, TMPRSS2, and Furin Variants and SARS-CoV-2 Infection in Madrid, Spain, Journal of Medical Virology. (2021) 93, no. 2, 863–869, https://doi.org/10.1002/jmv.26319.
10.1002/jmv.26319
CAS PubMed Web of Science® Google Scholar
165 Strafella C., Caputo V., Termine A. et al., Analysis of ACE2 Genetic Variability Among Populations Highlights a Possible Link With COVID-19-Related Neurological Complications, Genes. (2020) 11, no. 7, https://doi.org/10.3390/genes11070741.
10.3390/genes11070741
PubMed Web of Science® Google Scholar
166 Srivastava A., Pandey R. K., Singh P. P. et al., Most Frequent South Asian Haplotypes of ACE2 Share Identity by Descent With East Eurasian Populations, PLoS One. (2020) 15, no. 9, https://doi.org/10.1371/journal.pone.0238255.
10.1371/journal.pone.0238255
Web of Science® Google Scholar
167 Cafiero C., Rosapepe F., Palmirotta R. et al., Angiotensin System Polymorphisms’ in SARS-CoV-2 Positive Patients: Assessment Between Symptomatic and Asymptomatic Patients: A Pilot Study, PGPM. (2021) 14, 621–629, https://doi.org/10.2147/PGPM.S303666.
10.2147/PGPM.S303666
Google Scholar
168 Wang K., Chen W., Zhang Z. et al., CD147-Spike Protein is a Novel Route for SARS-CoV-2 Infection to Host Cells, Signal Transduction and Targeted Therapy. (2020) 5, https://doi.org/10.1038/s41392-020-00426-x.
10.1038/s41392-020-00426-x
Web of Science® Google Scholar
169 Imig J. D., SARS-CoV-2 Spike Protein Causes Cardiovascular Disease Independent of Viral Infection, Clinical Science. (2022) 136, no. 6, 431–434, https://doi.org/10.1042/CS20220028.
10.1042/CS20220028
CAS Web of Science® Google Scholar
170 Maugeri N., De Lorenzo R., Clementi N. et al., Unconventional CD147-Dependent Platelet Activation Elicited by SARS-CoV-2 in COVID-19, Journal of Thrombosis and Haemostasis. (2022) 20, no. 2, 434–448, https://doi.org/10.1111/jth.15575.
10.1111/jth.15575
CAS PubMed Web of Science® Google Scholar
171 Ulrich H. and Pillat M. M., CD147 as a Target for COVID-19 Treatment: Suggested Effects of Azithromycin and Stem Cell Engagement, Stem Cell Rev and Rep. (2020) 16, no. 3, 434–440, https://doi.org/10.1007/s12015-020-09976-7.
10.1007/s12015-020-09976-7
CAS PubMed Web of Science® Google Scholar
172 Helal M. A., Shouman S., Abdelwaly A. et al., Molecular Basis of the Potential Interaction of SARS-CoV-2 Spike Protein to CD147 in COVID-19 Associated-Lymphopenia, Journal of Biomolecular Structure and Dynamics. (2022) 40, no. 3, 1109–1119, https://doi.org/10.1080/07391102.2020.1822208.
10.1080/07391102.2020.1822208
CAS PubMed Web of Science® Google Scholar
173 Kaidashev I., Izmailova O., Shlykova O. et al., Polymorphism of Tmprss2 (Rs12329760) but Not Ace2 (Rs4240157), Tmprss11a (Rs353163) and Cd147 (Rs8259) is Associated With the Severity of COVID-19 in the Ukrainian Population, Acta BioMedica. (2023) 94, no. 1, https://doi.org/10.23750/abm.v94i1.13543.
10.23750/abm.v94i1.13543
Google Scholar
174 Amezcua-Guerra L. M., Guzmán-Martín C. A., Montúfar-Robles I. et al., CD147 rs8259T > A Variant Confers Susceptibility to COVID-19 Infection Within the Mexican Population, Microorganisms. (2023) 11, no. 8, https://doi.org/10.3390/microorganisms11081919.
10.3390/microorganisms11081919
Web of Science® Google Scholar
175 Ilikci Sagkan R. and Akin-Bali D. F., Structural Variations and Expression Profiles of the SARS-CoV-2 Host Invasion Genes in Lung Cancer, Journal of Medical Virology. (2020) 92, no. 11, 2637–2647, https://doi.org/10.1002/jmv.26107.
10.1002/jmv.26107
CAS PubMed Web of Science® Google Scholar
176 Hashemi S. M. A., Thijssen M., Hosseini S. Y., Tabarraei A., Pourkarim M. R., and Sarvari J., Human Gene Polymorphisms and Their Possible Impact on the Clinical Outcome of SARS-CoV-2 Infection, Archives of Virology. (2021) 166, no. 8, 2089–2108, https://doi.org/10.1007/s00705-021-05070-6.
10.1007/s00705-021-05070-6
CAS PubMed Web of Science® Google Scholar
177 Al-Murshidy M. A. H. and Shareef H. K., Molecular Analysis of CD147 Gene Among Sars-Cov2 Patients and Vaccinated People in Babylon Province, HIV Nursing. (2022) 22, 1473–1477.
Google Scholar
178 Chong K.-M., Yang C.-Y., Lin C.-C., and Lien W.-C., Severe Immune Thrombocytopenia Following COVID-19 Vaccination (Moderna) and Immune Checkpoint Inhibitor, The American Journal of Emergency Medicine. (2022) 56, 395.e1–395.e3, https://doi.org/10.1016/j.ajem.2022.03.030.
10.1016/j.ajem.2022.03.030
PubMed Web of Science® Google Scholar
179 Ling V. W. T., Fan B. E., Lau S. L., Lee X. H., Tan C. W., and Lee S. Y., Severe Thrombocytopenia, Thrombosis and Anti-PF4 Antibody After Pfizer-BioNTech COVID-19 mRNA Vaccine Booster—Is it Vaccine-Induced Immune Thrombotic Thrombocytopenia?, Vaccines. (2022) 10, no. 12, https://doi.org/10.3390/vaccines10122023.
10.3390/vaccines10122023
Google Scholar
180 Sriwastava S., Sharma K., Khalid S. H. et al., COVID-19 Vaccination and Neurological Manifestations: A Review of Case Reports and Case Series, Brain Sciences. (2022) 12, no. 3, https://doi.org/10.3390/brainsci12030407.
10.3390/brainsci12030407
PubMed Web of Science® Google Scholar
181 Hosseini R. and Askari N., A Review of Neurological Side Effects of COVID-19 Vaccination, European Journal of Medical Research. (2023) 28, no. 1, https://doi.org/10.1186/s40001-023-00992-0.
10.1186/s40001-023-00992-0
Web of Science® Google Scholar
182 Kwan A. and Cheng S., POTS Association With COVID-19 Vaccination and COVID-19 Infection, Nature Cardiovascular Research. (2022) 1, 1132–1133, https://doi.org/10.1038/s44161-022-00194-7.
10.1038/s44161-022-00194-7
PubMed Web of Science® Google Scholar
183 Gerhard A., Raeder V., Pernice H. F. et al., Neurological Symptoms After COVID-19 Vaccination: A Report on the Clinical Presentation of the First 50 Patients, Journal of Neurology. (2023) 270, no. 10, 4673–4677, https://doi.org/10.1007/s00415-023-11895-9.
10.1007/s00415-023-11895-9
PubMed Web of Science® Google Scholar
184 Shrestha Y. and Venkataraman R., The Prevalence of Post-COVID-19 Vaccination Syndrome and Quality of Life Among COVID-19-Vaccinated Individuals, Vacunas. (2024) 25, no. 1, 7–18, https://doi.org/10.1016/j.vacun.2023.10.002.
10.1016/j.vacun.2023.10.002
Google Scholar
185 Shrestha Y. and Venkataraman R., The Prevalence of Inverse Health Consequences of COVID-19 Vaccines: A Post-vaccination Study, Vacunas. (2022) 23, S67–S76, https://doi.org/10.1016/j.vacun.2022.03.002.
10.1016/j.vacun.2022.03.002
CAS PubMed Google Scholar
186 Dutta S., Kaur R., Charan J. et al., Analysis of Neurological Adverse Events Reported in VigiBase From COVID-19 Vaccines, Cureus. (2022) 14, no. 1, https://doi.org/10.7759/cureus.21376.
10.7759/cureus.21376
Google Scholar
187 Finsterer J., A Case Report: Long Post-COVID Vaccination Syndrome During the Eleven Months After the Third Moderna Dose, Cureus. (2022) 14, no. 12, https://doi.org/10.7759/cureus.32433.
10.7759/cureus.32433
Google Scholar
188 Frontera J. A., Tamborska A. A., Doheim M. F. et al., Neurological Events Reported After COVID-19 Vaccines: An Analysis of Vaccine Adverse Event Reporting System, Annals of Neurology. (2022) 91, no. 6, 756–771, https://doi.org/10.1002/ana.26339.
10.1002/ana.26339
CAS PubMed Web of Science® Google Scholar
189 Posa A., Spike Protein-Related Proteinopathies: A Focus on the Neurological Side of Spikeopathies, Annals of Anatomy—Anatomischer Anzeiger. (2025) 260, https://doi.org/10.1016/j.aanat.2025.152662.
10.1016/j.aanat.2025.152662
PubMed Web of Science® Google Scholar
190 Salvador M., Tseng N., Park C. et al., SARS-CoV-2 Spike Protein Reduces Burst Activities in Neurons Measured by Micro-Electrode Arrays, Annals of Medicine and Surgery. (2023) 85, no. 7, 3469–3476, https://doi.org/10.1097/MS9.0000000000000950.
10.1097/MS9.0000000000000950
PubMed Web of Science® Google Scholar
191 Angelopoulou E., Karlafti E., Georgakopoulou V. E. et al., Exploring the Role of ACE2 as a Connecting Link Between COVID-19 and Parkinson’s Disease, Life. (2023) 13, no. 2, https://doi.org/10.3390/life13020536.
10.3390/life13020536
PubMed Google Scholar
192 Hernández V. S., Zetter M. A., Guerra E. C. et al., ACE2 Expression in Rat Brain: Implications for COVID-19 Associated Neurological Manifestations, Experimental Neurology. (2021) 345, https://doi.org/10.1016/j.expneurol.2021.113837.
10.1016/j.expneurol.2021.113837
PubMed Web of Science® Google Scholar
193 Xu J. and Lazartigues E., Expression of ACE2 in Human Neurons Supports the Neuro-Invasive Potential of COVID-19 Virus, Cellular and Molecular Neurobiology. (2022) 42, no. 1, 305–309, https://doi.org/10.1007/s10571-020-00915-1.
10.1007/s10571-020-00915-1
CAS PubMed Web of Science® Google Scholar
194 Méndez-García L. A., Escobedo G., Minguer-Uribe A. G. et al., Role of the Renin-Angiotensin System in the Development of COVID-19-Associated Neurological Manifestations, Frontiers in Cellular Neuroscience. (2022) 16, https://doi.org/10.3389/fncel.2022.977039.
10.3389/fncel.2022.977039
PubMed Web of Science® Google Scholar
195 Halma M. T. J., Plothe C., Marik P., and Lawrie T. A., Strategies for the Management of Spike Protein-Related Pathology, Microorganisms. (2023) 11, no. 5, https://doi.org/10.3390/microorganisms11051308.
10.3390/microorganisms11051308
PubMed Web of Science® Google Scholar
196 Nyström S. and Hammarström P., Amyloidogenesis of SARS-CoV-2 Spike Protein, Journal of the American Chemical Society. (2022) 144, no. 20, 8945–8950, https://doi.org/10.1021/jacs.2c03925.
10.1021/jacs.2c03925
PubMed Web of Science® Google Scholar
197 Idrees D. and Kumar V., SARS-CoV-2 Spike Protein Interactions With Amyloidogenic Proteins: Potential Clues to Neurodegeneration, Biochemical and Biophysical Research Communications. (2021) 554, 94–98, https://doi.org/10.1016/j.bbrc.2021.03.100.
10.1016/j.bbrc.2021.03.100
CAS PubMed Web of Science® Google Scholar
198 Cao S., Song Z., Rong J. et al., Spike Protein Fragments Promote Alzheimer’s Amyloidogenesis, ACS Applied Materials and Interfaces. (2023) 15, no. 34, 40317–40329, https://doi.org/10.1021/acsami.3c09815.
10.1021/acsami.3c09815
CAS PubMed Web of Science® Google Scholar
199 Soto C. and Satani N., The Intricate Mechanisms of Neurodegeneration in Prion Diseases, Trends in Molecular Medicine. (2011) 17, no. 1, 14–24, https://doi.org/10.1016/j.molmed.2010.09.001, 2-s2.0-78650994378.
10.1016/j.molmed.2010.09.001
CAS PubMed Web of Science® Google Scholar
200 Lamark T. and Johansen T., Aggrephagy: Selective Disposal of Protein Aggregates by Macroautophagy, International Journal of Cell Biology. (2012) 2012, 1–21, https://doi.org/10.1155/2012/736905, 2-s2.0-84859736977.
10.1155/2012/736905
Google Scholar
201 Mulroney T. E., Pöyry T., Yam-Puc J. C. et al., N1-Methylpseudouridylation of mRNA Causes +1 Ribosomal Frameshifting, Nature. (2024) 625, no. 7993, 189–194, https://doi.org/10.1038/s41586-023-06800-3.
10.1038/s41586-023-06800-3
CAS PubMed Web of Science® Google Scholar
202 Kim M. S., Jung S. Y., Ahn J. G. et al., Comparative Safety of mRNA COVID-19 Vaccines to Influenza Vaccines: A Pharmacovigilance Analysis Using WHO International Database, Journal of Medical Virology. (2022) 94, no. 3, 1085–1095, https://doi.org/10.1002/jmv.27424.
10.1002/jmv.27424
CAS PubMed Web of Science® Google Scholar
203 Huo J., Mikolajek H., Le Bas A. et al., A Potent SARS-CoV-2 Neutralising Nanobody Shows Therapeutic Efficacy in the Syrian Golden Hamster Model of COVID-19, Nature Communications. (2021) 12, no. 1, https://doi.org/10.1038/s41467-021-25480-z.
10.1038/s41467-021-25480-z
Web of Science® Google Scholar
204 Zhao Y., Kuang M., Li J., Zhu L. et al., SARS-CoV-2 Spike Protein Interacts With and Activates TLR41, Cell Research. (2021) 31, no. 7, 818–820, https://doi.org/10.1038/s41422-021-00495-9.
10.1038/s41422-021-00495-9
PubMed Web of Science® Google Scholar
205 Skok M., The Role of α7 Nicotinic Acetylcholine Receptors in Post-Acute Sequelae of Covid-19, The International Journal of Biochemistry & Cell Biology. (2024) 168, https://doi.org/10.1016/j.biocel.2024.106519.
10.1016/j.biocel.2024.106519
PubMed Google Scholar
206 Wang H., Ulloa L., Ochani M., Amella C. A. et al., Nicotinic Acetylcholine Receptor α7 Subunit is an Essential Regulator of Inflammation, Nature. (2003) 421, no. 6921, 384–388, https://doi.org/10.1038/nature01339, 2-s2.0-0037461768.
10.1038/nature01339
CAS PubMed Web of Science® Google Scholar
207 Lykhmus O., Voytenko L., Koval L. et al., α7 Nicotinic Acetylcholine Receptor-Specific Antibody Induces Inflammation and Amyloid β42 Accumulation in the Mouse Brain to Impair Memory, PLoS One. (2015) 10, no. 3, https://doi.org/10.1371/journal.pone.0122706, 2-s2.0-84928791760.
10.1371/journal.pone.0122706
PubMed Google Scholar
208 Carlson E. C., Macsai M., Bertrand S., Bertrand D., and Nau J., The SARS-CoV-2 Virus and the Cholinergic System: Spike Protein Interaction With Human Nicotinic Acetylcholine Receptors and the Nicotinic Agonist Varenicline, International Journal of Molecular Sciences. (2023) 24, no. 6, https://doi.org/10.3390/ijms24065597.
10.3390/ijms24065597
Web of Science® Google Scholar
209 Su X., Lee J. W., Matthay Z. A. et al., Activation of the α7 nAChR Reduces Acid-Induced Acute Lung Injury in Mice and Rats, American Journal of Respiratory Cell and Molecular Biology. (2007) 37, no. 2, 186–192, https://doi.org/10.1165/rcmb.2006-0240OC, 2-s2.0-34547867437.
10.1165/rcmb.2006-0240OC
CAS PubMed Web of Science® Google Scholar
210 Yamada M. and Ichinose M., The Cholinergic Anti-inflammatory Pathway: An Innovative Treatment Strategy for Respiratory Diseases and Their Comorbidities, Current Opinion in Pharmacology. (2018) 40, 18–25, https://doi.org/10.1016/j.coph.2017.12.003, 2-s2.0-85040326822.
10.1016/j.coph.2017.12.003
CAS PubMed Web of Science® Google Scholar
211 Chudzik M., Burzyńska M., and Kapusta J., Use of 1-MNA to Improve Exercise Tolerance and Fatigue in Patients After COVID-19, Nutrients. (2022) 14, no. 15, https://doi.org/10.3390/nu14153004.
10.3390/nu14153004
PubMed Web of Science® Google Scholar
212 Zhang L., Zhou L., Bao L. et al., SARS-CoV-2 Crosses the Blood–Brain Barrier Accompanied With Basement Membrane Disruption Without Tight Junctions Alteration, Signal Transduction and Targeted Therapy. (2021) 6, 337–12, https://doi.org/10.1038/s41392-021-00719-9.
10.1038/s41392-021-00719-9
CAS PubMed Web of Science® Google Scholar
213 Barisano G., Montagne A., Kisler K., Schneider J. A., Wardlaw J. M., and Zlokovic B. V., Blood–Brain Barrier Link to Human Cognitive Impairment and Alzheimer’s Disease, Nature Cardiovascular Research. (2022) 1, no. 2, 108–115, https://doi.org/10.1038/s44161-021-00014-4.
10.1038/s44161-021-00014-4
PubMed Web of Science® Google Scholar
214 Nation D. A., Sweeney M. D., Montagne A. et al., Blood–Brain Barrier Breakdown is an Early Biomarker of Human Cognitive Dysfunction, Nature Medicine. (2019) 25, no. 2, 270–276, https://doi.org/10.1038/s41591-018-0297-y, 2-s2.0-85060113946.
10.1038/s41591-018-0297-y
CAS PubMed Web of Science® Google Scholar
215 Bauer L., Laksono B. M., de Vrij F. M. S., Kushner S. A., Harschnitz O., and van Riel D., The Neuroinvasiveness, Neurotropism, and Neurovirulence of SARS-CoV-2, Trends in Neurosciences. (2022) 45, no. 5, 358–368, https://doi.org/10.1016/j.tins.2022.02.006.
10.1016/j.tins.2022.02.006
CAS PubMed Web of Science® Google Scholar
216 Kirshina A. S., Kazakova A. A., Коlosova E. S. et al., Effects of Various mRNA-LNP Vaccine Doses on Neuroinflammation in BALB/c Mice, Bulletin of Russian State Medical University. (2022) 119–125.
Web of Science® Google Scholar
217 Mörz M., A Case Report: Multifocal Necrotizing Encephalitis and Myocarditis After BNT162b2 mRNA Vaccination Against COVID-19, Vaccines. (2022) 10, https://doi.org/10.3390/vaccines10101651.
10.3390/vaccines10101651
Web of Science® Google Scholar
218 Shen J., Xu G., Zhu R. et al., PDGFR-Β Restores Blood-Brain Barrier Functions in a Mouse Model of Focal Cerebral Ischemia, Journal of Cerebral Blood Flow and Metabolism. (2019) 39, no. 8, 1501–1515, https://doi.org/10.1177/0271678X18769515, 2-s2.0-85045151408.
10.1177/0271678X18769515
PubMed Google Scholar
219 Sun M., Shinoda Y., and Fukunaga K., KY-226 Protects Blood–Brain Barrier Function Through the Akt/FoxO1 Signaling Pathway in Brain Ischemia, Neuroscience. (2019) 399, 89–102, https://doi.org/10.1016/j.neuroscience.2018.12.024, 2-s2.0-85059468926.
10.1016/j.neuroscience.2018.12.024
CAS PubMed Web of Science® Google Scholar
220 Kaddoumi A., Denney T. S., Deshpande G. et al., Extra-Virgin Olive Oil Enhances the Blood–Brain Barrier Function in Mild Cognitive Impairment: A Randomized Controlled Trial, Nutrients. (2022) 14, no. 23, https://doi.org/10.3390/nu14235102.
10.3390/nu14235102
PubMed Web of Science® Google Scholar
221 Ahmed M., Roy S., Iktidar M. A. et al., Post-COVID-19 Memory Complaints: Prevalence and Associated Factors, Neurologia. (2024) 39, no. 8, 651–657, https://doi.org/10.1016/j.nrl.2022.03.007.
10.1016/j.nrl.2022.03.007
CAS PubMed Web of Science® Google Scholar
222 Park J. W., Yu S. N., Chang S. H., Ahn Y. H., and Jeon M. H., Multisystem Inflammatory Syndrome in an Adult After COVID-19 Vaccination: A Case Report and Literature Review, Journal of Korean Medical Science. (2021) 36, no. 45, https://doi.org/10.3346/jkms.2021.36.e312.
10.3346/jkms.2021.36.e312
Web of Science® Google Scholar
223 Magliulo D., Narayan S., Ue F., Boulougoura A., and Badlissi F., Adult-Onset Still’s Disease After mRNA COVID-19 Vaccine, The Lancet Rheumatology. (2021) 3, no. 10, e680–e682, https://doi.org/10.1016/S2665-9913(21)00219-8.
10.1016/S2665-9913(21)00219-8
CAS PubMed Google Scholar
224 Safary A., Esalatmanesh K., Eftekharsadat A. T., Jafari Nakjavani M.-R., and Khabbazi A., Autoimmune Inflammatory Rheumatic Diseases Post-Covid-19 Vaccination, International Immunopharmacology. (2022) 110, https://doi.org/10.1016/j.intimp.2022.109061.
10.1016/j.intimp.2022.109061
PubMed Web of Science® Google Scholar
225 Ahmed S. H., Shaikh T. G., Waseem S., Qadir N. A., Yousaf Z., and Ullah I., Vaccine-Induced Thrombotic Thrombocytopenia Following Coronavirus Vaccine: A Narrative Review, Annals of medicine and surgery (2012). (2022) 73, https://doi.org/10.1016/j.amsu.2021.102988.
10.1016/j.amsu.2021.102988
Web of Science® Google Scholar
226 Belay E. D., Godfred Cato S., Rao A. K. et al., Multisystem Inflammatory Syndrome in Adults After Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection and Coronavirus Disease 2019 (COVID-19) Vaccination, Clinical Infectious Diseases. (2022) 75, no. 1, e741–e748, https://doi.org/10.1093/cid/ciab936.
10.1093/cid/ciab936
PubMed Web of Science® Google Scholar
227 Choi Y. K., Moon J. Y., Kim J. et al., Postvaccination Multisystem Inflammatory Syndrome in Adult With No Evidence of Prior SARS-CoV-2 Infection, Emerging Infectious Diseases. (2022) 28, no. 2, 411–414, https://doi.org/10.3201/eid2802.211938.
10.3201/eid2802.211938
CAS PubMed Web of Science® Google Scholar
228 Ouldali N., Bagheri H., Salvo F. et al., Hyper Inflammatory Syndrome Following COVID-19 mRNA Vaccine in Children: A National Post-Authorization Pharmacovigilance Study, The Lancet Regional Health–Europe. (2022) 17, https://doi.org/10.1016/j.lanepe.2022.100393.
10.1016/j.lanepe.2022.100393
Google Scholar
229 Afrin L. B., Weinstock L. B., and Molderings G. J., Covid-19 Hyperinflammation and Post-Covid-19 Illness May Be Rooted in Mast Cell Activation Syndrome, International Journal of Infectious Diseases. (2020) 100, 327–332, https://doi.org/10.1016/j.ijid.2020.09.016.
10.1016/j.ijid.2020.09.016
CAS PubMed Web of Science® Google Scholar
230 Sumantri S. and Rengganis I., Immunological Dysfunction and Mast Cell Activation Syndrome in Long COVID, Asia Pacific Allergy. (2023) 13, no. 1, 50–53, https://doi.org/10.5415/apallergy.0000000000000022.
10.5415/apallergy.0000000000000022
PubMed Web of Science® Google Scholar
231 Weinstock L. B., Brook J. B., Walters A. S., Goris A., Afrin L. B., and Molderings G. J., Mast Cell Activation Symptoms are Prevalent in Long-COVID, International Journal of Infectious Diseases. (2021) 112, 217–226, https://doi.org/10.1016/j.ijid.2021.09.043.
10.1016/j.ijid.2021.09.043
CAS PubMed Web of Science® Google Scholar
232 Kee J., Thudium S., Renner D. M. et al., SARS-CoV-2 Disrupts Host Epigenetic Regulation via Histone Mimicry, Nature. (2022) 610, no. 7931, 381–388, https://doi.org/10.1038/s41586-022-05282-z.
10.1038/s41586-022-05282-z
CAS PubMed Web of Science® Google Scholar
233 Bhat S., Rishi P., and Chadha V. D., Understanding the Epigenetic Mechanisms in SARS CoV-2 Infection and Potential Therapeutic Approaches, Virus Research. (2022) 318, https://doi.org/10.1016/j.virusres.2022.198853.
10.1016/j.virusres.2022.198853
PubMed Web of Science® Google Scholar
234 Samans B., Rosselló Chornet M., Rosselló Chornet A. et al., Epigenetic Immune Monitoring for COVID-19 Disease Course Prognosis, Frontiers in Immunology. (2023) 14, https://doi.org/10.3389/fimmu.2023.1107900.
10.3389/fimmu.2023.1107900
PubMed Web of Science® Google Scholar
235 Sohn K. M., Lee S.-G., Kim H. J. et al., COVID-19 Patients Upregulate Toll-Like Receptor 4-Mediated Inflammatory Signaling that Mimics Bacterial Sepsis, Journal of Korean Medical Science. (2020) 35, no. 38, https://doi.org/10.3346/jkms.2020.35.e343.
10.3346/jkms.2020.35.e343
Web of Science® Google Scholar
236 Akin C., Valent P., and Metcalfe D. D., Mast Cell Activation Syndrome: Proposed Diagnostic Criteria, Journal of Allergy and Clinical Immunology. (2010) 126, no. 6, 1099–104.e4, https://doi.org/10.1016/j.jaci.2010.08.035.
10.1016/j.jaci.2010.08.035
CAS PubMed Web of Science® Google Scholar
237 Afrin L. B., Pöhlau D., Raithel M. et al., Mast Cell Activation Disease: An Underappreciated Cause of Neurologic and Psychiatric Symptoms and Diseases, Brain, Behavior, and Immunity. (2015) 50, 314–321, https://doi.org/10.1016/j.bbi.2015.07.002.
10.1016/j.bbi.2015.07.002
CAS PubMed Web of Science® Google Scholar
238 Liang Q., Wu Q., Jiang J. et al., Characterization of Sparstolonin B, a Chinese Herb-Derived Compound, as a Selective Toll-like Receptor Antagonist with Potent Anti-inflammatory Properties, Journal of Biological Chemistry. (2011) 286, no. 30, 26470–26479, https://doi.org/10.1074/jbc.M111.227934.
10.1074/jbc.M111.227934
CAS Web of Science® Google Scholar
239 Choudhury A. and Mukherjee S., In Silico Studies on the Comparative Characterization of the Interactions of SARS-CoV-2 Spike Glycoprotein With ACE-2 Receptor Homologs and Human TLRs, Journal of Medical Virology. (2020) 92, no. 10, 2105–2113, https://doi.org/10.1002/jmv.25987.
10.1002/jmv.25987
CAS PubMed Web of Science® Google Scholar
240 Su L., Wang Y., Wang J. et al., Structural Basis of TLR2/TLR1 Activation by the Synthetic Agonist Diprovocim, Journal of Medicinal Chemistry. (2019) 62, no. 6, 2938–2949, https://doi.org/10.1021/acs.jmedchem.8b01583.
10.1021/acs.jmedchem.8b01583
CAS PubMed Web of Science® Google Scholar
241 Park B. S., Song D. H., Kim H. M., Choi B.-S., Lee H., and Lee J.-O., The Structural Basis of Lipopolysaccharide Recognition by the TLR4–MD-2 Complex, Nature. (2009) 458, no. 7242, 1191–1195, https://doi.org/10.1038/nature07830.
10.1038/nature07830
CAS PubMed Web of Science® Google Scholar
242 Kang J. Y., Nan X., Jin M. S. et al., Recognition of Lipopeptide Patterns by Toll-Like Receptor 2-Toll-Like Receptor 6 Heterodimer, Immunity. (2009) 31, 873–884, https://doi.org/10.1016/j.immuni.2009.09.018.
10.1016/j.immuni.2009.09.018
CAS PubMed Web of Science® Google Scholar
243 Salamaikina S., Karnaushkina M., Korchagin V., Litvinova M., Mironov K., and Akimkin V., TLRs Gene Polymorphisms Associated With Pneumonia before and During COVID-19 Pandemic, Diagnostics. (2022) 13, no. 1, https://doi.org/10.3390/diagnostics13010121.
10.3390/diagnostics13010121
PubMed Google Scholar
244 Bakaros E., Voulgaridi I., Paliatsa V. et al., Innate Immune Gene Polymorphisms and COVID-19 Prognosis, Viruses. (2023) 15, no. 9, https://doi.org/10.3390/v15091784.
10.3390/v15091784
PubMed Google Scholar
245 Vakil M. K., Mansoori Y., Al-Awsi G. R. L. et al., Individual Genetic Variability Mainly of Proinflammatory Cytokines, Cytokine Receptors, and Toll-Like Receptors Dictates Pathophysiology of Covid-19 Disease, Journal of Medical Virology. (2022) 94, no. 9, 4088–4096, https://doi.org/10.1002/jmv.27849.
10.1002/jmv.27849
CAS PubMed Web of Science® Google Scholar
246 Taha S. I., Shata A. K., Baioumy S. A. et al., Toll-Like Receptor 4 Polymorphisms (896A/G and 1196C/T) as an Indicator of COVID-19 Severity in a Convenience Sample of Egyptian Patients, Journal of Inflammation Research. (2021) 14, 6293–6303, https://doi.org/10.2147/JIR.S343246.
10.2147/JIR.S343246
CAS PubMed Web of Science® Google Scholar
247 Achleitner M., Steenblock C., Dänhardt J. et al., Clinical Improvement of Long-COVID is Associated With Reduction in Autoantibodies, Lipids, and Inflammation Following Therapeutic Apheresis, Molecular Psychiatry. (2023) 28, no. 7, 2872–2877, https://doi.org/10.1038/s41380-023-02084-1.
10.1038/s41380-023-02084-1
CAS PubMed Web of Science® Google Scholar
248 Seibert F. S., Stervbo U., Wiemers L. et al., Severity of Neurological Long-COVID Symptoms Correlates With Increased Level of Autoantibodies Targeting Vasoregulatory and Autonomic Nervous System Receptors, Autoimmunity Reviews. (2023) 22, no. 11, https://doi.org/10.1016/j.autrev.2023.103445.
10.1016/j.autrev.2023.103445
PubMed Web of Science® Google Scholar
249 Son K., Jamil R., Chowdhury A. et al., Circulating Anti-Nuclear Autoantibodies in COVID-19 Survivors Predict Long COVID Symptoms, European Respiratory Journal. (2023) 61, https://doi.org/10.1183/13993003.00970-2022.
10.1183/13993003.00970-2022
PubMed Web of Science® Google Scholar
250 Rojas M., Rodríguez Y., Acosta-Ampudia Y. et al., Autoimmunity is a Hallmark of Post-COVID Syndrome, Journal of Translational Medicine. (2022) 20, no. 1, https://doi.org/10.1186/s12967-022-03328-4.
10.1186/s12967-022-03328-4
Web of Science® Google Scholar
251 Gemmati D., Longo G., Gallo I. et al., Host Genetics Impact on SARS-CoV-2 Vaccine-Induced Immunoglobulin Levels and Dynamics: The Role of TP53, ABO, APOE, ACE2, HLA-A, and CRP Genes, Frontiers in Genetics. (2022) 13, https://doi.org/10.3389/fgene.2022.1028081.
10.3389/fgene.2022.1028081
PubMed Web of Science® Google Scholar
252 Talotta R., Molecular Mimicry and HLA Polymorphisms May Drive Autoimmunity in Recipients of the BNT-162b2 mRNA Vaccine: A Computational Analysis, Microorganisms. (2023) 11, no. 7, https://doi.org/10.3390/microorganisms11071686.
10.3390/microorganisms11071686
PubMed Web of Science® Google Scholar
253 M M Al-Mehaisen L., A MahfouzI.III, Khamaiseh K., N Al-Beitawe S., and Al-Kuran O. A., Short Term Effect of Corona Virus Diseases Vaccine on the Menstrual Cycles, International Journal of Women’s Health. (2022) 14, 1385–1394, https://doi.org/10.2147/IJWH.S376950.
10.2147/IJWH.S376950
PubMed Google Scholar
254 Alvergne A., Boniface E., Darney B. et al., Associations Among Menstrual Cycle Length, Coronavirus Disease 2019 (COVID-19), and Vaccination, Obstetrics & Gynecology. (2024) 143, no. 1, 83–91, https://doi.org/10.1097/AOG.0000000000005343.
10.1097/AOG.0000000000005343
CAS PubMed Web of Science® Google Scholar
255 Alvergne A., Kountourides G., Argentieri M. A. et al., A Retrospective Case-Control Study on Menstrual Cycle Changes Following COVID-19 Vaccination and Disease, iScience. (2023) 26, no. 4, https://doi.org/10.1016/j.isci.2023.106401.
10.1016/j.isci.2023.106401
PubMed Google Scholar
256 Alvergne A., Woon E. V., and Male V., Effect of COVID-19 Vaccination on the Timing and Flow of Menstrual Periods in Two Cohorts, Frontiers in Reproductive Health. (2022) 4, https://doi.org/10.3389/frph.2022.952976.
10.3389/frph.2022.952976
PubMed Web of Science® Google Scholar
257 Amer A. A., Amer S. A., Alrufaidi K. M. et al., Menstrual Changes after COVID-19 Vaccination And/or SARS-CoV-2 Infection and Their Demographic, Mood, and Lifestyle Determinants in Arab Women of Childbearing Age, 2021, Frontiers in Reproductive Health. (2022) 4, https://doi.org/10.3389/frph.2022.927211.
10.3389/frph.2022.927211
PubMed Web of Science® Google Scholar
258 Barabás K., Makkai B., Farkas N. et al., Influence of COVID-19 Pandemic and Vaccination on the Menstrual Cycle: A Retrospective Study in Hungary, Frontiers in Endocrinology. (2022) 13, https://doi.org/10.3389/fendo.2022.974788.
10.3389/fendo.2022.974788
Web of Science® Google Scholar
259 Chao M. J., Menon C., and Elgendi M., Effect of COVID-19 Vaccination on the Menstrual Cycle, Frontiers of Medicine. (2022) 9, https://doi.org/10.3389/fmed.2022.1065421.
10.3389/fmed.2022.1065421
Google Scholar
260 Edelman A., Boniface E. R., Benhar E. et al., Association Between Menstrual Cycle Length and Coronavirus Disease 2019 (COVID-19) Vaccination, Obstetrics & Gynecology. (2022) 139, no. 4, 481–489, https://doi.org/10.1097/AOG.0000000000004695.
10.1097/AOG.0000000000004695
CAS PubMed Web of Science® Google Scholar
261 Farland L. V., Khan S. M., Shilen A. et al., COVID-19 Vaccination and Changes in the Menstrual Cycle Among Vaccinated Persons, Fertility and Sterility. (2023) 119, no. 3, 392–400, https://doi.org/10.1016/j.fertnstert.2022.12.023.
10.1016/j.fertnstert.2022.12.023
CAS PubMed Web of Science® Google Scholar
262 Gibson E. A., Li H., Fruh V. et al., Covid-19 Vaccination and Menstrual Cycle Length in the Apple Women’s Health Study, NPJ Digital Medicine. (2022) 5, 165–168, https://doi.org/10.1038/s41746-022-00711-9.
10.1038/s41746-022-00711-9
PubMed Web of Science® Google Scholar
263 Gopaul C. D., Bassaw B., Ventour D., and Thomas D., Effects of COVID-19 Vaccines on the Menstrual Cycle: A Cross-Sectional Study, The Open Public Health Journal. (2023) 16, https://doi.org/10.2174/18749445-v16-e230619-2022-203.
10.2174/18749445-v16-e230619-2022-203
PubMed Google Scholar
264 Granese R., Incognito G. G., Gulino F. A. et al., Effects of SARS-CoV-2 Vaccination on Menstrual Cycle: An Italian Survey-Based Study, Journal of Clinical Medicine. (2023) 12, no. 24, https://doi.org/10.3390/jcm12247699.
10.3390/jcm12247699
Web of Science® Google Scholar
265 Lukac S., Hancke K., Janni W. et al., Disturbances of Menstrual Cycle After Immunization Against SARS-CoV-2 and Their Risk Factors: Cross-Sectional Clinical Study, International Journal of Gynecology & Obstetrics. (2023) 163, no. 2, 445–452, https://doi.org/10.1002/ijgo.15060.
10.1002/ijgo.15060
CAS PubMed Web of Science® Google Scholar
266 Marcelino A. C., Fim A. B., da Cunha Pereira P., Monteiro I., Darney B. G., and Bahamondes L., Association Between COVID-19 and Vaccination on Menstrual Cycle, International Journal of Gynecology & Obstetrics. (2023) 164, no. 2, 571–577, https://doi.org/10.1002/ijgo.15200.
10.1002/ijgo.15200
PubMed Web of Science® Google Scholar
267 Rodríguez Quejada L., Toro Wills M. F., Martínez-Ávila M. C., and Patiño-Aldana A. F., Menstrual Cycle Disturbances After COVID-19 Vaccination, Women’s Health. (2022) 18, https://doi.org/10.1177/17455057221109375.
10.1177/17455057221109375
Web of Science® Google Scholar
268 Wang S., Mortazavi J., Hart J. E. et al., A Prospective Study of the Association Between SARS-CoV-2 Infection and COVID-19 Vaccination With Changes in Usual Menstrual Cycle Characteristics, American Journal of Obstetrics and Gynecology. (2022) 227, no. 5, 739.e1–739.e11, https://doi.org/10.1016/j.ajog.2022.07.003.
10.1016/j.ajog.2022.07.003
CAS PubMed Web of Science® Google Scholar
269 Wesselink A. K., Lovett S. M., Weinberg J. et al., COVID-19 Vaccination and Menstrual Cycle Characteristics: A Prospective Cohort Study, Vaccine. (2023) 41, no. 29, 4327–4334, https://doi.org/10.1016/j.vaccine.2023.06.012.
10.1016/j.vaccine.2023.06.012
PubMed Web of Science® Google Scholar
270 Lee K. M. N., Junkins E. J., Luo C., Fatima U. A., Cox M. L., and Clancy K. B. H., Investigating Trends in Those Who Experience Menstrual Bleeding Changes After SARS-CoV-2 Vaccination, Science Advances. (2022) 8, no. 28, https://doi.org/10.1126/sciadv.abm7201.
10.1126/sciadv.abm7201
Web of Science® Google Scholar
271 Wong K. K., Heilig C. M., Hause A. et al., Menstrual Irregularities and Vaginal Bleeding After COVID-19 Vaccination Reported to V-Safe Active Surveillance, USA in December, 2020–January, 2022: An Observational Cohort Study, The Lancet Digital Health. (2022) 4, no. 9, e667–e675, https://doi.org/10.1016/S2589-7500(22)00125-X.
10.1016/S2589-7500(22)00125-X
CAS PubMed Google Scholar
272 Blix K., Laake I., Juvet L. et al., Unexpected Vaginal Bleeding and COVID-19 Vaccination in Nonmenstruating Women, Science Advances. (2023) 9, no. 38, https://doi.org/10.1126/sciadv.adg1391.
10.1126/sciadv.adg1391
PubMed Web of Science® Google Scholar
273 Thorp J. A., Claire Rogers M., Deskevich C. M. P. et al., COVID-19 Vaccines: The Impact on Pregnancy Outcomes and Menstrual Function, Journal of American Physicians and Surgeons. (2023) 28.
Google Scholar
274 Tindle H. A., Newhouse P. A., and Freiberg M. S., Beyond Smoking Cessation: Investigating Medicinal Nicotine to Prevent and Treat COVID-19, Nicotine & Tobacco Research. (2020) 22, no. 9, 1669–1670, https://doi.org/10.1093/ntr/ntaa077.
10.1093/ntr/ntaa077
CAS PubMed Web of Science® Google Scholar
275 Mohammadi S., Heidarizadeh M., Entesari M. et al., In Silico Investigation on the Inhibiting Role of Nicotine/Caffeine by Blocking the S Protein of SARS-CoV-2 Versus ACE2 Receptor, Microorganisms. (2020) 8, no. 10, https://doi.org/10.3390/microorganisms8101600.
10.3390/microorganisms8101600
PubMed Web of Science® Google Scholar
276 Kouznetsova V. L., Zhang A., Miller M. A., Tatineni M., Greenberg J. P., and Tsigelny I. F., Potential SARS-CoV-2 Spike Protein-ACE2 Interface Inhibitors: Repurposing FDA-Approved Drugs, Journal of Exploratory Research in Pharmacology. (2022) 7, no. 1, 17–29, https://doi.org/10.14218/JERP.2021.00050.
10.14218/JERP.2021.00050
Google Scholar
277 Jin R., Xiao A. Y., Chen R., Granger D. N., and Li G., Inhibition of CD147 (Cluster of Differentiation 147) Ameliorates Acute Ischemic Stroke in Mice by Reducing Thromboinflammation, Stroke. (2017) 48, no. 12, 3356–3365, https://doi.org/10.1161/STROKEAHA.117.018839.
10.1161/STROKEAHA.117.018839
CAS PubMed Web of Science® Google Scholar
278 Chen D., Wang H., and Cai X., Curcumin Interferes With Sepsis-Induced Cardiomyocyte Apoptosis via TLR1 Inhibition, Revista Portuguesa de Cardiologia. (2023) 42, no. 3, 209–221, https://doi.org/10.1016/j.repc.2023.01.013.
10.1016/j.repc.2023.01.013
PubMed Web of Science® Google Scholar
279 Cheng K., Wang X., Zhang S., and Yin H., Discovery of Small-Molecule Inhibitors of the TLR1/TLR2 Complex, Angewandte Chemie International Edition. (2012) 51, no. 49, 12246–12249, https://doi.org/10.1002/anie.201204910.
10.1002/anie.201204910
CAS PubMed Web of Science® Google Scholar
280 Kim J., Durai P., Jeon D. et al., Phloretin as a Potent Natural TLR2/1 Inhibitor Suppresses TLR2-Induced Inflammation, Nutrients. (2018) 10, no. 7, https://doi.org/10.3390/nu10070868.
10.3390/nu10070868
Web of Science® Google Scholar
281 Mistry P., Laird M. H. W., Schwarz R. S. et al., Inhibition of TLR2 Signaling by Small Molecule Inhibitors Targeting a Pocket the TLR2 TIR Domain, Proceedings of the National Academy of Sciences. (2015) 112, no. 17, 5455–5460, https://doi.org/10.1073/pnas.1422576112.
10.1073/pnas.1422576112
CAS PubMed Google Scholar
282 Jin X., Wang L., Wu H.-S. et al., N-Acetylcysteine Inhibits Activation of Toll-Like Receptor 2 and 4 Gene Expression in the Liver and Lung After Partial Hepatic Ischemia-Reperfusion Injury in Mice, Hepatobiliary and Pancreatic Diseases International. (2007) 6, no. 3, 284–289.
CAS PubMed Web of Science® Google Scholar
283 Fadil Jabber M. and Mohammed Khudhair A., Association Between SNP Rs4986790 and COVID-19 Infection Severity Among Baghdad Patients, Biomedicine. (2023) 43, no. 4, 1188–1192, https://doi.org/10.51248/.v43i4.3108.
10.51248/.v43i4.3108
Google Scholar
284 Zhang Y., Liang X., Bao X., Xiao W., and Chen G., Toll-Like Receptor 4 (TLR4) Inhibitors: Current Research and Prospective, European Journal of Medicinal Chemistry. (2022) 235, https://doi.org/10.1016/j.ejmech.2022.114291.
10.1016/j.ejmech.2022.114291
Web of Science® Google Scholar
285 Semlali A., Almutairi M., Rouabhia M. et al., Novel Sequence Variants in the TLR6 Gene Associated With Advanced Breast Cancer Risk in the Saudi Arabian Population, PLoS One. (2018) 13, no. 11, https://doi.org/10.1371/journal.pone.0203376.
10.1371/journal.pone.0203376
PubMed Web of Science® Google Scholar
286 Zou M., Yang L., Niu L. et al., Baicalin Ameliorates Mycoplasma Gallisepticum-Induced Lung Inflammation in Chicken by Inhibiting TLR6-Mediated NF-Κb Signalling, British Poultry Science. (2021) 62, no. 2, 199–210, https://doi.org/10.1080/00071668.2020.1847251.
10.1080/00071668.2020.1847251
CAS Google Scholar
287 Min J., Nwachukwu J. C., Min C. K. et al., Dual-Mechanism Estrogen Receptor Inhibitors, Proceedings of the National Academy of Sciences of the United States of America. (2021) 118, no. 35, https://doi.org/10.1073/pnas.2101657118.
10.1073/pnas.2101657118
Web of Science® Google Scholar
288 Das T. and Mukhopadhyay C., Computational Studies Suggest Compounds Restoring Function of P53 Cancer Mutants Can Bind SARS-CoV-2 Spike Protein, Journal of Biomolecular Structure and Dynamics. (2023) 41, no. 8, 3368–3381, https://doi.org/10.1080/07391102.2022.2048081.
10.1080/07391102.2022.2048081
CAS PubMed Web of Science® Google Scholar
289 Dunning A. M., Chiano M., Smith N. R. et al., Common BRCA1 Variants and Susceptibility to Breast and Ovarian Cancer in the General Population, Human Molecular Genetics. (1997) 6, no. 2, 285–289, https://doi.org/10.1093/hmg/6.2.285.
10.1093/hmg/6.2.285
CAS PubMed Web of Science® Google Scholar
290 Deffenbaugh A. M., Frank T. S., Hoffman M., Cannon-Albright L., and Neuhausen S. L., Characterization of Common BRCA1 and BRCA2 Variants, Genetic Testing. (2002) 6, no. 2, 119–121, https://doi.org/10.1089/10906570260199375.
10.1089/10906570260199375
CAS PubMed Web of Science® Google Scholar
291 Kandeel M., Yamamoto M., Tani H. et al., Discovery of New Fusion Inhibitor Peptides Against SARS-CoV-2 by Targeting the Spike S2 Subunit, Biomolecules & Therapeutics. (2021) 29, no. 3, 282–289, https://doi.org/10.4062/biomolther.2020.201.
10.4062/biomolther.2020.201
CAS PubMed Google Scholar
292 Prabhavathi H., Dasegowda K. R., Renukananda K. H., Lingaraju K., and Naika H. R., Exploration and Evaluation of Bioactive Phytocompounds Against BRCA Proteins by In Silico Approach, Journal of Biomolecular Structure and Dynamics. (2021) 39, no. 15, 5471–5485, https://doi.org/10.1080/07391102.2020.1790424.
10.1080/07391102.2020.1790424
CAS PubMed Google Scholar
293 Chrestia J. F., Oliveira A. S., Mulholland A. J., Gallagher T., Bermúdez I., and Bouzat C., A Functional Interaction Between Y674-R685 Region of the SARS-CoV-2 Spike Protein and the Human α7 Nicotinic Receptor, Molecular Neurobiology. (2022) 59, no. 10, 6076–6090, https://doi.org/10.1007/s12035-022-02947-8.
10.1007/s12035-022-02947-8
CAS PubMed Google Scholar
294 Oliveira A. S. F., Ibarra A. A., Bermudez I. et al., A Potential Interaction Between the SARS-CoV-2 Spike Protein and Nicotinic Acetylcholine Receptors, Biophysical Journal. (2021) 120, no. 6, 983–993, https://doi.org/10.1016/j.bpj.2021.01.037.
10.1016/j.bpj.2021.01.037
CAS PubMed Web of Science® Google Scholar
295 Valencia-Pérez Rea D., Falfán-Valencia R., Fricke-Galindo I. et al., The Rs16969968 Tobacco Smoking-Related Single-Nucleotide Variant is Associated With Clinical Markers in Patients With Severe COVID-19, International Journal of Molecular Sciences. (2023) 24, no. 12, https://doi.org/10.3390/ijms24129811.
10.3390/ijms24129811
Google Scholar
296 Alexandris N., Lagoumintzis G., Chasapis C. T. et al., Nicotinic Cholinergic System and COVID-19: In Silico Evaluation of Nicotinic Acetylcholine Receptor Agonists as Potential Therapeutic Interventions, Toxicology Reports. (2021) 8, 73–83, https://doi.org/10.1016/j.toxrep.2020.12.013.
10.1016/j.toxrep.2020.12.013
CAS PubMed Web of Science® Google Scholar
297 Xu Z.-Q., Zhang W.-J., Su D.-F., Zhang G.-Q., and Miao C.-Y., Cellular Responses and Functions of α7 Nicotinic Acetylcholine Receptor Activation in the Brain: A Narrative Review, Annals of Translational Medicine. (2021) 9, no. 6, https://doi.org/10.21037/atm-21-273.
10.21037/atm-21-273
Google Scholar
298 Latini A., Agolini E., Novelli A. et al., COVID-19 and Genetic Variants of Protein Involved in the SARS-CoV-2 Entry into the Host Cells, Genes. (2020) 11, no. 9, https://doi.org/10.3390/genes11091010.
10.3390/genes11091010
PubMed Web of Science® Google Scholar
299 Zarubin A., Stepanov V., Markov A. et al., Structural Variability, Expression Profile, and Pharmacogenetic Properties of TMPRSS2 Gene as a Potential Target for COVID-19 Therapy, Genes. (2020) 12, no. 1, https://doi.org/10.3390/genes12010019.
10.3390/genes12010019
PubMed Google Scholar
300 Schönfelder K., Breuckmann K., Elsner C. et al., Transmembrane Serine Protease 2 Polymorphisms and Susceptibility to Severe Acute Respiratory Syndrome Coronavirus Type 2 Infection: A German Case-Control Study, Frontiers in Genetics. (2021) 12, https://doi.org/10.3389/fgene.2021.667231.
10.3389/fgene.2021.667231
PubMed Google Scholar
301 Monticelli M., Hay Mele B., Benetti E. et al., Protective Role of a TMPRSS2 Variant on Severe COVID-19 Outcome in Young Males and Elderly Women, Genes. (2021) 12, no. 4, https://doi.org/10.3390/genes12040596.
10.3390/genes12040596
PubMed Web of Science® Google Scholar
302 Mantzourani C., Vasilakaki S., Gerogianni V.-E., and Kokotos G., The Discovery and Development of Transmembrane Serine Protease 2 (TMPRSS2) Inhibitors as Candidate Drugs for the Treatment of COVID-19, Expert Opinion on Drug Discovery. (2022) 17, no. 3, 231–246, https://doi.org/10.1080/17460441.2022.2029843.
10.1080/17460441.2022.2029843
CAS PubMed Web of Science® Google Scholar
303 Manjunathan R., Periyaswami V., Mitra K. et al., Molecular Docking Analysis Reveals the Functional Inhibitory Effect of Genistein and Quercetin on TMPRSS2: SARS-COV-2 Cell Entry Facilitator Spike Protein, BMC Bioinformatics. (2022) 23, no. 1, https://doi.org/10.1186/s12859-022-04724-9.
10.1186/s12859-022-04724-9
PubMed Web of Science® Google Scholar
304 Geda Y. E., Topazian H. M., Lewis R. A. et al., Engaging in Cognitive Activities, Aging, and Mild Cognitive Impairment: A Population-Based Study, Journal of Neuroparasitology. (2011) 23, no. 2, 149–154, https://doi.org/10.1176/jnp.23.2.jnp149.
10.1176/jnp.23.2.jnp149
Google Scholar
305 Chakraborty A. J., Mitra S., Tallei T. E. et al., Bromelain a Potential Bioactive Compound: A Comprehensive Overview From a Pharmacological Perspective, Life. (2021) 11, no. 4, https://doi.org/10.3390/life11040317.
10.3390/life11040317
Google Scholar
306 Chambers P., The CD147 Epitope on SARS CoV2 and the Spike in Cancer, Autoimmunity and Organ Fibrosis, Qeios. (2023) https://doi.org/10.32388/S86J75.
10.32388/S86J75
Google Scholar
307 Bae E., Bae S., Vaysblat M., Abdelwahed M., Sarkar K., Bae S., and Bae S., Development of High-Grade Sarcoma After Second Dose of Moderna Vaccine, Cureus. (2023) 15, no. 4, https://doi.org/10.7759/cureus.37612.
10.7759/cureus.37612
Google Scholar
308 Quintero D., Patel N., Harris G. et al., COVID-19 Vaccine-Associated Ganulomatous Mass Mimicking a Sarcoma: A Case Report, Radiology Case Reports. (2022) 17, no. 8, 2775–2778, https://doi.org/10.1016/j.radcr.2022.05.035.
10.1016/j.radcr.2022.05.035
PubMed Google Scholar
309 Yang Z., Song F., Winters L., and Zhong J., Development of Pilomatrixoma at the Vaccination Site: A Rare Complication of COVID-19 Vaccination—A Case Report, American Journal of Case Reports. (2023) 24, e942280-1–e942280-4, https://doi.org/10.12659/AJCR.942280.
10.12659/AJCR.942280
Web of Science® Google Scholar
310 Zamfir M.-A., Moraru L., Dobrea C. et al., Hematologic Malignancies Diagnosed in the Context of the mRNA COVID-19 Vaccination Campaign: A Report of Two Cases, Medicina. (2022) 58, no. 7, https://doi.org/10.3390/medicina58070874.
10.3390/medicina58070874
Google Scholar
311 Soleimani P., Yadollahifarsani S., Motieian M. et al., Cancer Recurrence or Aggravation Following COVID-19 Vaccination, Journal of Nephropharmacology. (2023) 12, no. 2, https://doi.org/10.34172/npj.2023.10593.
10.34172/npj.2023.10593
Google Scholar
312 Gambichler T., Boms S., Hessam S. et al., Primary Cutaneous Anaplastic Large-Cell Lymphoma With Marked Spontaneous Regression of Organ Manifestation After SARS-CoV-2 Vaccination, British Journal of Dermatology. (2021) 185, no. 6, 1259–1262, https://doi.org/10.1111/bjd.20630.
10.1111/bjd.20630
CAS PubMed Web of Science® Google Scholar
313 Panou E., Nikolaou V., Marinos L. et al., Recurrence of Cutaneous T-Cell Lymphoma Post Viral Vector COVID-19 Vaccination, Journal of the European Academy of Dermatology and Venereology: JEADV. (2022) 36, no. 2, e91–e93, https://doi.org/10.1111/jdv.17736.
10.1111/jdv.17736
CAS PubMed Web of Science® Google Scholar
314 Andresciani F., Ricci M., Grasso R. F., Zobel B. B., and Quattrocchi C. C., COVID-19 Vaccination Simulating Lymph Node Progression in a Patient With Prostate Cancer, Radiology Case Reports. (2022) 17, no. 9, 2996–2999, https://doi.org/10.1016/j.radcr.2022.05.072.
10.1016/j.radcr.2022.05.072
PubMed Google Scholar
315 Hl C., Gj W., Jwt L., Sun J., Lp M., and Ht L. P., Ultrasound Features to Differentiate COVID-19 Vaccine-Induced Benign Adenopathy From Breast Cancer Related Malignant Adenopathy, Academic Radiology. (2022) 29, no. 7, 1004–1012, https://doi.org/10.1016/j.acra.2022.02.015.
10.1016/j.acra.2022.02.015
PubMed Google Scholar
316 Soeder E., Toro-Pape F. W., and Lampen-Sachar K., Isolated Breast Parenchymal Changes Following COVID-19 Vaccine Booster, Radiology Case Reports. (2022) 17, no. 12, 4556–4560, https://doi.org/10.1016/j.radcr.2022.08.094.
10.1016/j.radcr.2022.08.094
PubMed Google Scholar
317 Skawran S., Gennari A. G., Dittli M. et al., [18F]FDG Uptake of Axillary Lymph Nodes After COVID-19 Vaccination in Oncological PET/CT: Frequency, Intensity, and Potential Clinical Impact, European Radiology. (2022) 32, no. 1, 508–516, https://doi.org/10.1007/s00330-021-08122-2.
10.1007/s00330-021-08122-2
CAS PubMed Web of Science® Google Scholar
318 Zhang S. and El-Deiry W. S., Transfected SARS-CoV-2 Spike DNA for Mammalian Cell Expression Inhibits P53 Activation of p21(WAF1), TRAIL Death Receptor DR5 and MDM2 Proteins in Cancer Cells and Increases Cancer Cell Viability After Chemotherapy Exposure, Oncotarget. (2024) 15, no. 1, 275–284, https://doi.org/10.18632/oncotarget.28582.
10.18632/oncotarget.28582
PubMed Google Scholar
319 Coquelle N., Green R., and Glover J. N. M., Impact of BRCA1 BRCT Domain Missense Substitutions on Phosphopeptide Recognition, Biochemistry. (2011) 50, no. 21, 4579–4589, https://doi.org/10.1021/bi2003795.
10.1021/bi2003795
CAS PubMed Web of Science® Google Scholar
320 Abbexa, SARS-CoV-2 Spike Protein S1 RBD ELISA Kit, 2025.
Google Scholar
321 Elabscience, Elabscience SARS-CoV-2 Spike Protein S1 RBD ELISA Kit the Manual, 2025, https://www.elabscience.com.
Google Scholar
322 Invitrogen, Human SARS-CoV-2 RBD ELISA Kit Product Information Sheet, 2021, https://www.thermofisher.com/elisa/product/Human-SARS-CoV-2-RBD-ELISA-Kit/EH492RB.
Google Scholar
323 Puliyel J. and Naik P., Revised World Health Organization (WHO)’s Causality Assessment of Adverse Events Following Immunization-A Critique, F1000Res. (2018) 7, https://doi.org/10.12688/f1000research.13694.2.
10.12688/f1000research.13694.1
PubMed Google Scholar
324 Saxena K. B., Banerji D., Qadeer I. et al., Antivaccine Lobby Replies to the BMJ Replies to the BMJ, BMJ. (2010) 341, https://doi.org/10.1136/bmj.c4001.
10.1136/bmj.c4001
PubMed Google Scholar
325 Sandoval Y., Apple F. S., Saenger A. K., Collinson P. O., Wu A. H. B., and Jaffe A. S., 99th Percentile Upper-Reference Limit of Cardiac Troponin and the Diagnosis of Acute Myocardial Infarction, Clinical Chemistry. (2020) 66, no. 9, 1167–1180, https://doi.org/10.1093/clinchem/hvaa158.
10.1093/clinchem/hvaa158
PubMed Web of Science® Google Scholar
326 Urban K., Kirley K., and Stevermer J. J., PURLs: It’s Time to Use an Age-Based Approach to D-Dimer, Journal of Family Practice. (2014) 63, no. 3, 155–158.
PubMed Google Scholar
327 Mahajan V. S. and Jarolim P., How to Interpret Elevated Cardiac Troponin Levels, Circulation. (2011) 124, no. 21, 2350–2354, https://doi.org/10.1161/CIRCULATIONAHA.111.023697.
10.1161/CIRCULATIONAHA.111.023697
PubMed Web of Science® Google Scholar
328 Melanson S. E. F., Morrow D. A., and Jarolim P., Earlier Detection of Myocardial Injury in a Preliminary Evaluation Using a New Troponin I Assay With Improved Sensitivity, American Journal of Clinical Pathology. (2007) 128, no. 2, 282–286, https://doi.org/10.1309/Q9W5HJTT24GQCXXX.
10.1309/Q9W5HJTT24GQCXXX
CAS PubMed Web of Science® Google Scholar
329 Apple F. S., Sandoval Y., Jaffe A. S., and Ordonez-Llanos J., Cardiac Troponin Assays: Guide to Understanding Analytical Characteristics and Their Impact on Clinical Care, Clinical Chemistry. (2017) 63, no. 1, 73–81, https://doi.org/10.1373/clinchem.2016.255109.
10.1373/clinchem.2016.255109
CAS PubMed Web of Science® Google Scholar
330 Thachil J., Lippi G., and Favaloro E. J., E. J. Favaloro and G. Lippi, D-dimer Testing: Laboratory Aspects and Current Issues, Hemostasis and Thrombosis: Methods and Protocols, Methods in Molecular Biology, 2017, Springer, New York, NY, USA, 91–104, https://doi.org/10.1007/978-1-4939-7196-1_7.
10.1007/978-1-4939-7196-1_7
Google Scholar
331 Linkins L. and Takach Lapner S., Review of D-Dimer Testing: Good, Bad, and Ugly, The International Journal of Literary Humanities. (2017) 39, no. S1, 98–103, https://doi.org/10.1111/ijlh.12665.
10.1111/ijlh.12665
Google Scholar
332 Ridker P. M., A Test in Context, Journal of the American College of Cardiology. (2016) 67, no. 6, 712–723, https://doi.org/10.1016/j.jacc.2015.11.037.
10.1016/j.jacc.2015.11.037
PubMed Web of Science® Google Scholar
333 Gao T., Gao Y., Liu X. et al., Identification and Functional Analysis of the SARS-COV-2 Nucleocapsid Protein, BMC Microbiology. (2021) 21, no. 1, https://doi.org/10.1186/s12866-021-02107-3.
10.1186/s12866-021-02107-3
Web of Science® Google Scholar
334 Brogna C., Cristoni S., Marino G. et al., Detection of Recombinant Spike Protein in the Blood of Individuals Vaccinated Against SARS-CoV-2: Possible Molecular Mechanisms, Proteomics—Clinical Applications. (2023) 17, no. 6, https://doi.org/10.1002/prca.202300048.
10.1002/prca.202300048
PubMed Web of Science® Google Scholar
335 Chakraborty C., Sharma A. R., Bhattacharya M., Agoramoorthy G., and Lee S.-S., Evolution, Mode of Transmission, and Mutational Landscape of Newly Emerging SARS-CoV-2 Variants, mBio. (2021) 12, no. 4, https://doi.org/10.1128/mbio.01140-21.
10.1128/mbio.01140-21
PubMed Web of Science® Google Scholar
336 Baden L. R., El Sahly H. M., Essink B. et al., Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine, New England Journal of Medicine. (2021) 384, no. 5, 403–416, https://doi.org/10.1056/NEJMoa2035389.
10.1056/NEJMoa2035389
CAS PubMed Web of Science® Google Scholar
337 Polack F. P., Thomas S. J., Kitchin N. et al., Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine, New England Journal of Medicine. (2020) 383, no. 27, 2603–2615, https://doi.org/10.1056/NEJMoa2034577.
10.1056/NEJMoa2034577
CAS PubMed Web of Science® Google Scholar
338 Pramanick I., Sengupta N., Mishra S. et al., Conformational Flexibility and Structural Variability of SARS-CoV2 S Protein, Structure. (2021) 29, no. 8, 834–845.e5, https://doi.org/10.1016/j.str.2021.04.006.
10.1016/j.str.2021.04.006
CAS PubMed Web of Science® Google Scholar
339 Bilotta C., Perrone G., Adelfio V. et al., COVID-19 Vaccine-Related Thrombosis: A Systematic Review and Exploratory Analysis, Frontiers in Immunology. (2021) 12, https://doi.org/10.3389/fimmu.2021.729251.
10.3389/fimmu.2021.729251
PubMed Web of Science® Google Scholar
340 Mani A. and Ojha V., Thromboembolism After COVID-19 Vaccination: A Systematic Review of Such Events in 286 Patients, Annals of Vascular Surgery. (2022) 84, 12–20.e1, https://doi.org/10.1016/j.avsg.2022.05.001.
10.1016/j.avsg.2022.05.001
PubMed Web of Science® Google Scholar
341 Ogata A. F., Cheng C.-A., Desjardins M. et al., Circulating Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Vaccine Antigen Detected in the Plasma of mRNA-1273 Vaccine Recipients, Clinical Infectious Diseases. (2022) 74, no. 4, 715–718, https://doi.org/10.1093/cid/ciab465.
10.1093/cid/ciab465
CAS PubMed Web of Science® Google Scholar
342 Bansal S., Perincheri S., Fleming T. et al., Cutting Edge: Circulating Exosomes With COVID Spike Protein are Induced by BNT162b2 (Pfizer–BioNTech) Vaccination Prior to Development of Antibodies: A Novel Mechanism for Immune Activation by mRNA Vaccines, The Journal of Immunology. (2021) 207, no. 10, 2405–2410, https://doi.org/10.4049/jimmunol.2100637.
10.4049/jimmunol.2100637
CAS Web of Science® Google Scholar
343 Garg P., Vanamamalai V. K., Jali I., and Sharma S., In Silico Prediction of the Animal Susceptibility and Virtual Screening of Natural Compounds against SARS-CoV-2: Molecular Dynamics Simulation Based Analysis, Frontiers in Genetics. (2022) 13, https://doi.org/10.3389/fgene.2022.906955.
10.3389/fgene.2022.906955
Web of Science® Google Scholar
344 Haseeb M., Amir A., and Ikram A., In Silico Analysis of SARS-CoV-2 Spike Proteins of Different Field Variants, Vaccines. (2023) 11, no. 4, https://doi.org/10.3390/vaccines11040736.
10.3390/vaccines11040736
Web of Science® Google Scholar

All articles

The Possible Mechanistic Basis of Individual Susceptibility to Spike Protein Injury

Abstract

1. Introduction

2. Sources of Variation

2.1. Factors Affecting Individual Susceptibility

2.1.1. Administration

2.1.2. Vaccine Type

2.1.3. Contents and Purity

2.2. Patient Characteristics

2.2.1. Variation in Cell Uptake and Biodistribution

2.2.2. Variation in Spike Protein and RNA Persistence

2.2.3. Pharmacogenomics of Spike Protein Interaction

3. System-Specific Pathologies

3.1. Cardiovascular Disorders: ACE2, CD-147, and Anti-PF4 Antibodies

3.1.1. ACE-2

3.1.2. CD-147

3.2. Anti-PF4 Antibodies

3.3. Neurological Disorders: ACE2, Nicotinic Adrenergic Receptor (ADR), Protein Misfolding, Barrier Function, and Others

3.3.1. ACE2

3.3.2. Protein Misfolding

3.3.3. Functional α7 Nicotinic Acetylcholine Receptor (α7nAChR)

3.3.4. BBB Permeability

3.4. Systemic Inflammation: Toll-Like Receptors (TLRs) and Autoantibodies

3.4.1. TLR Activation

3.4.2. Molecular Mimicry

3.5. Reproductive System: Estrogen Receptor Alpha

3.6. Cancer: P53, BRCA1/2, and Possible Genomic Instability

3.6.1. P53 BP1

3.6.2. BRCA1/2

4. Diagnostics

4.1. General Diagnostics

4.2. Specific Diagnostics

5. Conclusions

Conflicts of Interest

Funding

Acknowledgments

Supporting Information

Open Research

Data Availability Statement

Supporting Information

References

Figures

References

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