The human milk endocannabinoidome is preserved by high hydrostatic pressure processing but altered by holder pasteurization
Bibliographic record
Abstract
Introduction: The microbiological safety of donor milk (DM) is commonly ensured by holder pasteurization (HoP, 62.5°C for 30 min) in human milk banks despite its detrimental effects on several bioactive factors. We compared the concentration of twelve endocannabinoid (eCB)-like mediators in raw DM and in DM after holder pasteurization or high hydrostatic pressure processing (HHP, 350 MPa at 38°C), a non-thermal substitute for DM pasteurization. Methods: We measured five N-acyl-ethanolamines (NAEs) and seven 2-mono-acyl-glycerols (2-MAGs) in raw-DM, HHP-DM and HoP-DM using high-performance liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Results: HoP-DM and HHP-DM demonstrated comparable concentrations compared with raw DM of 2-MAGs as well as for N-docosapentaenoyl-ethanolamine (DHEA, an NAE). However, four other NAEs, including N-arachidonoyl-ethanolamine (AEA), N-palmitoyl-(PEA), N-oleoyl -ethanolamine (OEA) and N-linoleoyl-ethanolamine (LEA) were significantly increased by HoP. Conclusion: Our study suggests that HHP-DM may more suitable than HoP-DM to improve the development of preterm infants through the preservation of milk eCB mediators at level close to their initial levels in raw DM.
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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 | 0.000 |
| 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".