Biodistribution of mRNA COVID-19 vaccines in human breast milk
Bibliographic record
Abstract
BACKGROUND: COVID-19 mRNA vaccines play a vital role in the fight against SARS-CoV-2 infection. However, lactating women have been largely excluded from most vaccine clinical trials. As a result, limited research has been conducted on the systemic distribution of vaccine mRNA during lactation and whether it is excreted in human breast milk (BM). Here, we evaluated if COVID-19 vaccine mRNA is detectable in BM after maternal vaccination and determined its potential translational activity. METHODS: We collected BM samples from 13 lactating, healthy, post-partum women before and after COVID-19 mRNA vaccination. Vaccine mRNA in whole BM and BM extracellular vesicles (EVs) was assayed using quantitative Droplet Digital PCR, and its integrity and translational activity were evaluated. FINDINGS: Of 13 lactating women receiving the vaccine (20 exposures), trace mRNA amounts were detected in 10 exposures up to 45 h post-vaccination. The mRNA was concentrated in the BM EVs; however, these EVs neither expressed SARS-COV-2 spike protein nor induced its expression in the HT-29 cell line. Linkage analysis suggests vaccine mRNA integrity was reduced to 12-25% in BM. INTERPRETATION: Our findings demonstrate that the COVID-19 vaccine mRNA is not confined to the injection site but spreads systemically and is packaged into BM EVs. However, as only trace quantities are present and a clear translational activity is absent, we believe breastfeeding post-vaccination is safe, especially 48 h after vaccination. Nevertheless, since the minimum mRNA vaccine dose to elicit an immune reaction in infants <6 months is unknown, a dialogue between a breastfeeding mother and her healthcare provider should address the benefit/risk considerations of breastfeeding in the first two days after maternal vaccination. FUNDING: This study was supported by the Department of Pediatrics, NYU-Grossman Long Island School of Medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".