SARS-CoV-2 spike antigen-specific B cell and antibody responses in pre-vaccination period COVID-19 convalescent males and females with or without post-covid condition
Bibliographic record
Abstract
Background: Following SARS-CoV-2 infection a significant proportion of convalescent individuals develop the post-COVID condition (PCC) that is characterized by wide spectrum of symptoms encompassing various organs. Even though the underlying pathophysiology of PCC is not known, detection of viral transcripts and antigens in tissues other than lungs raise the possibility that PCC may be a consequence of aberrant immune response to the viral antigens. To test this hypothesis, we evaluated B cell and antibody responses to the SARS-CoV-2 antigens in PCC patients who experienced mild COVID-19 disease during the pre-vaccination period of COVID-19 pandemic. Methods: The study subjects included unvaccinated male and female subjects who developed PCC or not (No-PCC) after clearing RT-PCR confirmed mild COVID-19 infection. SARS-CoV-2 D614G and omicron RBD specific B cell subsets in peripheral circulation were assessed by flow cytometry. IgG, IgG3 and IgA antibody titers toward RBD, spike and nucleocapsid antigens in the plasma were evaluated by ELISA. Results: ) B cells showed significant correlation with the number of symptoms at acute of infection. Anti-spike antibody responses were also higher at 3 months post-infection in females who developed PCC, but not in the male PCC group. On the other hand, the male PCC group also showed consistently high anti-RBD IgG responses compared to all other groups. Conclusions: The antibody responses to the spike protein, but not the anti-RBD B cell responses diverge between convalescent males and females who develop PCC. Our findings also suggest that sex-related factors may also be involved in the development of PCC via modulating antibody responses to the SARS-CoV-2 antigens.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".