Antibodies to Commonly Circulating Viral Pathogens Modulate Serological Response to Severe Acute Respiratory Syndrome Coronavirus 2 Infection
Bibliographic record
Abstract
The purpose of this study was to determine the seropositivity of circulating viral pathogens and their association with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seropositivity. In a cross-sectional design, inhabitants (aged 10–60 years) of the slum and surrounding non-slum areas of Dhaka and Chattogram Metropolitan cities in Bangladesh were enrolled from October 2020 to February 2021. Antibodies to SARS-CoV-2, influenza B, parainfluenza, respiratory syncytial virus (RSV), human coronavirus HKU1 (HCoV-HKU1), dengue and chikungunya viruses were determined in plasma. The association of SARS-CoV-2 seropositivity with seropositivity to other viruses was assessed using the multi-variate logistic regression model. Seroprevalence of SARS-CoV-2, influenza B, RSV, dengue, chikungunya, HCoV-HKU1 and the parainfluenza virus were 68.3%, 98%, 50.0%, 16.5%, 15.5%, 3.36% and 0.0%, respectively. Individuals seropositive for RSV had lower odds (OR = 0.60; 95% CI= 0.49, 0.73) of SARS-CoV-2 seropositivity compared to RSV-seronegative individuals. Conversely, higher odds of SARS-CoV-2 seropositivity were observed in participants seropositive for dengue (OR= 1.73; 95% CI = 1.14, 2.66, only in slum) or chikungunya (OR = 1.48; 95% CI = 1.11, 1.95) compared to their seronegative counterparts. The study findings indicated that exposure to vector-borne virus dengue or chikungunya enhance, while antibodies to respiratory virus RSV decrease, the serological response to SARS-CoV-2.
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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) | 0.001 | 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".