Microbiological analysis of donor human milk over seven years from the Hearts Milk Bank (United Kingdom)
Bibliographic record
Abstract
When maternal milk is unavailable, donor human milk (DHM) from human milk banks (HMBs) is the optimal alternative, as recommended by the World Health Organisation. The microbiota of DHM could contain opportunistic pathogens, which means rigorous microbiological screening for DHM, prior to pasteurisation, is recommended to safeguard recipients. Here, an analysis of 6863 DHM samples from 1419 donors at the Hearts Milk Bank between 2017 and 2023 showed approximately 70.1% of samples exhibited a total viable count (TVC) between 10³-10⁵ CFU/mL, while 18.3% yielded no growth; 11.5% of samples exceeded the 10⁵ CFU/mL threshold. Staphylococcus was the most prevalent genus, with S. epidermidis found in 61.5% of samples. A significant ( p < 0.05) negative co-occurrence was observed between S. epidermidis and Gram-negative opportunistic pathogens. Overall, 16.8% of DHM samples failed to meet UK microbiological screening criteria, with 68.3% of these failures due to exceeding TVC thresholds. S. epidermidis accounted for approximately 10.2% of the total failed samples. The majority of DHM samples met the current microbiological criteria specified in the National Institute for Health and Care Excellence (NICE) clinical guidance (CG93), “Donor milk banks: service operation”. The core species in DHM reflects microorganisms typically found on the skin. These findings highlight that the current UK thresholds and criteria could potentially be modified to increase the available supply of DHM without increasing microbiological risk. • Microbiological screening is conducted on raw and pasteurized donor human milk. • Of 6863 milk samples evaluated, 18.3% showed no microbial growth. • 17% of samples failed UK criteria - 68.3% as above total viable count threshold. • S. epidermidis dominated and negatively associated with Gram-negative pathogens.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".