Freeze-drying donor human milk allows compositional stability for 12 months at ambient temperatures
Bibliographic record
Abstract
Breastfeeding, which is recognised as the optimum nutrition for infants, offers numerous benefits. However, circumstances can arise when infants are unable to be breastfed from birth. In such cases, the World Health Organisation (WHO) recommends donor human milk (DHM) as the safest alternative. Current practices freeze DHM and transport it under a cold supply chain, which can create logistical challenges. Here, we investigated the efficacy of freeze-drying as a method for determining the compositional stability of DHM. The samples were freeze-dried and stored at −20°C, 4°C and ambient temperature, with sampling at 1, 3, 6, 9, and 12 months. The macronutrient composition was assessed before and after freeze-drying, protein and lipid profiles were studied using MALDI-TOF MS, and the metabolite profile was analysed through LA-REIMS. The findings revealed that freeze-drying did not significantly alter the macronutrient composition and that microbiological safety was preserved. Lipid, protein, and metabolite fingerprints remained consistent across storage conditions over 12 months. This work provides a broad insight into the compositional stability of DHM after freeze-drying. It suggests the applicability of freeze-drying for long-term preservation without a cold supply chain. The use of freeze-dried DHM may broaden its use in emergency situations and resource-limited settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".