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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".