Differential pre-pandemic breast milk IgA reactivity against SARS-CoV-2 and circulating human coronaviruses in Ugandan and American mothers
Bibliographic record
Abstract
OBJECTIVE: Uganda has registered fewer coronavirus disease 2019 (COVID-19) cases and deaths per capita than Western countries. The lower numbers of cases and deaths might be due to pre-existing cross-immunity induced by circulating common cold human coronaviruses (HCoVs) before the COVID-19 pandemic. To investigate pre-existing mucosal antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, a comparison was performed of IgA reactivity to SARS-CoV-2 and HCoVs in milk from mothers collected in 2018. METHODS: Ugandan and United States milk samples were run on an ELISA to measure specific IgA to SARS-CoV-2 and HCoVs NL63, OC43, HKU1, and 229E spike proteins. Pooled plasma from United States SARS-CoV-2-positive and negative cases were positive and negative controls, respectively. RESULTS: One Ugandan mother had high milk IgA reactivity against all HCoVs and SARS-CoV-2 spike proteins. Ugandan mothers had significantly higher IgA reactivity against the betacoronavirus HCoV-OC43 than United States mothers (P = 0.018). By contrast, United States mothers had significantly higher IgA reactivity against the alphacoronaviruses HCoV-229E and HCoV-NL63 than Ugandan mothers (P < 0.0001 and P = 0.035, respectively). CONCLUSION: Some Ugandan mothers have pre-existing HCoV-induced IgA antibodies against SARS-CoV-2, which may be passed to infants via breastfeeding.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".