SARS-CoV-2 and HCoV IgG Antibodies in the Breast Milk of a Postpartum SARS-CoV-2 Patient Following Bamlanivimab Administration: A Case Report
Bibliographic record
Abstract
Breast milk can provide passive immunity to infants, serving as a valuable source of maternal antibodies while remaining a non-invasive sample for investigating maternal immune responses. To date, no studies have evaluated SARS-CoV-2 and potentially cross-reactive HCoV antibodies in breast milk following bamlanivimab administration. A 36-year-old postpartum female was PCR-positive for SARS-CoV-2 four days post-delivery. Bamlanivimab was administered intravenously two days later. Breast milk was collected before bamlanivimab infusion, daily for two weeks post-infusion, then weekly until 102 days post-infusion. Mother and infant sera were collected only at 102 days post-infusion. All milk and serum samples were tested for IgG antibodies against SARS-CoV-2 and HCoV. We observed two distinct SARS-CoV-2 antibody peaks at days 3 and 29 post-infusion, likely representing bamlanivimab transfer and the post-infection antibody response. Beta-HCoV antibodies showed two peaks at days 6 and 29, potentially representing backboosted beta-HCoV responses and/or antibody cross-reactivity with SARS-CoV-2. Infant seropositivity for SARS-CoV-2 102 days post-infusion may represent antibodies from passive transfer via breastfeeding or a subclinical infection. This case highlights the value of breast milk as a non-invasive and repeatable sample to help understand maternal immune responses post-infection, exogenous antibody infusion, and passive antibody transfer during breastfeeding, which can provide insights into maternal–infant health research.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".