Pre-pandemic cross-reactive T-cell responses are associated with subsequent protection against SARS-CoV-2 infection, but not against symptomatic illness 2345
Bibliographic record
Abstract
Abstract Description There was a relatively low burden of COVID-19 disease in sub-Saharan Africa (SSA) during the first pandemic wave. We hypothesized that pre-existing immune responses to SARS-CoV-2 induced by prior seasonal coronavirus infections would protect against infection and/or symptomatic disease. Female Sex Workers (FSWs) from Nairobi, Kenya had blood collected mid-2019 (pre-pandemic) and approximately 1 year later. SARS-CoV-2 infection was defined by the presence of antibodies against ≥2/3 SARS-CoV-2 antigens. ELISPOT was used to define pre-pandemic T-cell responses against comprehensive peptide pools covering SARS-CoV-2 structural proteins and defined non-structural epitopes. Pre-pandemic serology for the 4 seasonal coronaviruses was also performed using a multiplex immunoassay. We matched 100 cases who were infected during the first wave with 100 controls, based on age (±5 years) and sample date (±2 weeks). Our primary endpoints and analysis approach were predefined, and all assays were run blind. We found that both the presence and frequency of pre-pandemic IFNy-secreting T-cell responses against structural (but not non-structural) proteins were associated with significant protection against SARS-CoV-2 infection, but not with reduced symptoms among cases. IgG antibodies recognizing seasonal coronaviruses were ubiquitous. These results suggest that cross-reactive cellular responses induced by prior seasonal coronavirus infections may protect against SARS-CoV-2 infection Funding Sources CIHR MMI-174919 Topic Categories Viral Immunology (VIR)
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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".