Evidence map›Paper›PMID 42350987›Full record

ArticleBMC infectious diseases2026

Expression levels of SARS-CoV-2 IgM, IgG, and neutralizing antibodies in a Chinese cohort and detection of peptide-specific antibodies in COVID-19 patients.

Suli Yang, Xiaosha Wen, Yi Lin, Ranrong Zhang, Dixian Luo

Abstract read
In one paragraph

Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Suli Yang *Shenzhen Nanshan People's Hospital, ShenZhen, Guangdong, China.
Xiaosha Wen *Shenzhen Luohu People's Hospital, The Third Affiliated Hospital (The Affiliated Luohu Hospital; Shenzhen Luohu Hospital Group) of Shenzhen University, Shenzhen University, Shen Zhen, Guangdong, China.
Yi LinShenzhen Nanshan People's Hospital, ShenZhen, Guangdong, China.
Ranrong ZhangShenzhen Nanshan People's Hospital, ShenZhen, Guangdong, China.
Dixian LuoShenzhen Luohu People's Hospital, The Third Affiliated Hospital (The Affiliated Luohu Hospital; Shenzhen Luohu Hospital Group) of Shenzhen University, Shenzhen University, Shen Zhen, Guangdong, China. luodixian_2@163.com.

Funding

General Projects of the National Natural Science Foundation of China 82273236National Key R&D Program of China 2023YFA0915602Newly introduced discipline leader fund project in Nanshan District of Shenzhen City NSZD2023020Shenzhen Natural Science Foundation Project JCYJ20240813114502004
6 · The paper itself

Abstract

backgroundLong COVID is a complex multisystem disorder affecting approximately 65 million individuals worldwide, with autoimmune mechanisms implicated in its pathogenesis. This study aimed to elucidate the role of SARS-CoV-2-induced autoantibodies and immune dysregulation in the development of Long COVID, focusing specifically on autoimmunity and aberrant immune activation. The investigation sought to explore the underlying mechanisms through serological and peptide-based analyses.

methodsSerum samples from 315 healthcare workers, serving as a control cohort, were analyzed for the presence of SARS-CoV-2 IgM, IgG, and Neutralizing Antibodies (NAbs), with stratification based on age, gender, vaccination timing, and symptom clusters. Bioinformatic approaches were employed to identify SARS-CoV-2 epitopes with homology to human proteins, screening for epitopes of the SARS-CoV-2 Spike (S) and Nucleocapsid (N) proteins and their human homologous peptides through analyses of hydrophilicity, antigenicity, and homology prediction. An ELISA-based method was utilized to detect antibody responses to these peptides in both COVID-19 infected groups (categorized as mild, moderate, or severe) and an uninfected group.

resultsThe primary findings indicated a high seropositivity rate for IgG (97%) and Neutralizing Antibodies (94%) among the control group, with notable age-related trends: IgM levels increased with age, whereas IgG and Neutralizing Antibodies showed a decline. Female participants demonstrated higher IgG levels compared to their male counterparts. Antibody levels did not exhibit significant differences across acute or persistent symptom clusters (≥ 6 months post-infection). However, neurological symptoms, whether acute or chronic, were associated with elevated IgG and Neutralizing Antibody titers, suggesting that neurotropic infections may elicit a more robust humoral immune response. Bioinformatic analyses identified 29 potential epitopes within the S protein and 10 within the N protein, with 91 and 24 human homologous peptides, respectively. The detection of peptide-specific antibodies in the serum of the COVID-19 infected group revealed that antibodies against eight peptides displayed a greater than 2.5-fold difference between the COVID-19-infected and uninfected groups.

conclusionThese findings underscore age- and gender-related variations in antibody responses, identify immunodominant peptides with potential implications for COVID-19 symptoms, and suggest that cross-reactive antibodies targeting viral-human homologous peptides (e.g., S68-CHL1) may play a role in autoimmune mechanisms associated with COVID-19.

trial registrationClinical trial number: Not applicable.

Indexed as

Antibodies, NeutralizingAntibodies, ViralCOVID-19Immunoglobulin GImmunoglobulin MSARS-CoV-2AdultAgedChinaCohort StudiesCoronavirus Nucleocapsid ProteinsEpitopesFemaleHumansMaleMiddle AgedAntibodies, NeutralizingAntibodies, ViralCoronavirus Nucleocapsid ProteinsEpitopesImmunoglobulin GImmunoglobulin MPeptidesSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2AutoantibodiesImmune dysregulationLong COVIDMolecular mimicrySARS-CoV-2

Identifiers

PMID42350987
PMCPMC13563895

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.