PASC (Post Acute Sequelae of COVID-19) is associated with decreased neutralizing antibody titers and increased inflammatory cytokines
Bibliographic record
Abstract
Abstract Post-acute sequelae of COVID-19 (PASC) or the continuation of COVID-19 (Coronavirus disease 2019) symptoms past 12 weeks may affect as many as 30% of people recovering from a SARS-CoV-2 (severe acute respiratory coronavirus 2) infection. The mechanisms regulating the development of PASC are currently not known; however, hypotheses including poor antibody responses have been suggested. Due to the importance of virus neutralizing antibodies during COVID-19 recovery and protection from reinfection, we designed a cross-sectional study to investigate systemic antibody and cytokine responses during COVID-19 recovery and PASC. In total, 195 participants were recruited in one of five groups: 1.) those who had PASC (PASC); 2.) those who recovered from COVID-19 (Recovered); 3.) those in acute recovery (Acute Recovery); 4.) those experiencing acute COVID-19 (Acute COVID-19); and 5.) those who never had COVID-19 (No COVID). Participants completed a questionnaire detailing their demographics, as well as COVID-19 experiences. Serum samples were evaluated for virus binding and neutralizing antibodies as well as serum cytokine levels. We found that participants with PASC reported more pre-existing conditions (such as hypertension), and PASC symptoms (ie., shortness of breath) following COVID-19 than Recovered individuals. PASC individuals also had significantly decreased levels of neutralizing antibodies toward both SARS-CoV-2 and the Omicron BA.1 variant. Sex analysis indicated that female PASC study participants had sustained antibody levels as well as inflammatory cytokines (GM-CSF) over time following COVID-19 while males had decreasing concentrations. Our study reports for the first time that people experiencing PASC have lower levels of virus neutralizing antibodies and females experiencing PASC have sustained levels of antibodies and inflammatory markers. With lower levels of virus neutralizing antibodies, this data suggests that PASC individuals not only have had a suboptimal antibody response during acute SARS-CoV-2 infection but may also have increased susceptibility to subsequent infections which may exacerbate or prolong current PASC illnesses. The work may be applied directly to developing PASC diagnostic screening tools, treatments, as well as public health policies.
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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 | 0.001 |
| 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".