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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".