Differential pre-pandemic IgA reactivity against SARS-CoV-2 and circulating human coronaviruses measured in milk collected in Uganda and the USA
Bibliographic record
Abstract
Abstract Objective Uganda, like other African countries, has registered fewer COVID-19 cases and deaths per capita than non-African countries. The lower numbers of cases and deaths in Uganda might be due to pre-existing cross-immunity induced by zoonotic coronaviruses or circulating common cold human coronaviruses (HCoVs) before the COVID-19 pandemic. In order to test this premise, we compared IgA reactivity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and HCoVs in breast milk of US and rural Ugandan mothers collected in 2018 before the COVID-19 epidemic. Ugandan and US pre-pandemic breast milk samples were run in duplicate on enzyme-linked immunoadsorbent assay (ELISA) to measure specific IgA antibody reactivity to the spike proteins of SARS-CoV-2, human coronaviruses (HCoV) NL63, OC43, HKU1, and 229E. Pooled plasma from US COVID-19 positive and negative cases were employed as positive and negative controls, respectively. One Ugandan pre-pandemic milk sample had remarkably high reactivity against all HCoVs and SARS-CoV-2 spike proteins. There was higher IgA reactivity against the betacoronavirus HCoV-OC43 in Ugandan pre-pandemic milk samples by comparison with US pre-pandemic milk samples (p = 0.018). By contrast, there was significantly higher IgA reactivity against the alphacoronaviruses HCoV-229E and HCoV-NL63 in US pre-pandemic milk samples by comparison with Ugandan pre-pandemic milk samples (p < 0.0001 and 0.035, respectively). Conclusion Some Ugandan mothers may have robust pre-existing immunity against SARS-CoV-2 due to cross-immunity induced by HCoVs which may be passed on to their infants via breastfeeding. The differential pre-pandemic reactivity of US mothers to HCoV 229E and HCoV NL63 may have contributed to suboptimal antibody responses 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 | 0.001 | 0.001 |
| 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".