Malaria and other infections induce polyreactive antibodies that impact SARS‐CoV‐2 seropositivity estimations in endemic settings
Bibliographic record
Abstract
Anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence is used to estimate the proportion of individuals within a population previously infected, to track viral transmission, and to monitor naturally and vaccine-induced immune protection. However, in sub-Saharan African settings, antibodies induced by higher exposure to pathogens may increase unspecific seroreactivity to SARS-CoV-2 antigens, resulting in false positive responses. To investigate the level and type of unspecific seroreactivitiy to SARS-CoV-2 in Africa, we measured immunoglobulin G (IgG), IgA, and IgM to a broad panel of antigens from different pathogens by Luminex in 602 plasma samples from African and European subjects differing in coronavirus disease 2019, malaria, and other exposures. Seroreactivity to SARS-CoV-2 antigens was higher in prepandemic African than in European samples and positively correlated with antibodies against human coronaviruses, helminths, protozoa, and especially Plasmodium falciparum. African subjects presented higher levels of autoantibodies, a surrogate of polyreactivity, which correlated with P. falciparum and SARS-CoV-2 antibodies. Finally, we found an improved sensitivity in the IgG assay in African samples when using urea as a chaotropic agent. In conclusion, our data suggest that polyreactive antibodies induced mostly by malaria are important mediators of the unspecific anti-SARS-CoV-2 responses, and that the use of dissociating agents in immunoassays could be useful for more accurate estimates of SARS-CoV-2 seroprevalence in African settings.
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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 | 0.001 | 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".