Anti‐inflammatory, anti‐oxidant, anti‐microbial properties of human milk derived peptides
Bibliographic record
Abstract
Human milk (HM) digested and derived peptides (100 ug/mL) and tryptophan (Trp 50 ug/mL) were evaluated using MQ cells for NO assay, cell proliferation and cytotoxicity; cell viability and cytokines in MQ and Caco 2 cells and antioxidant properties with ORAC using standard protocols. Peptides ORAC Values (X±SD) TNFα (pg/ml) IL‐6 (pg/ml) NO (μM) Medium ‐ 781 976 35 Medium+LPS ‐ 6156 1136 90 23‐1 114±10 7684* 114* 60 23‐2 ‐ 5059 1068 150* 23‐3 ‐ 3952 1085 50 23‐4 3940±623 4188 1885 80 23‐5 3462±560 5259 1098 60 23‐6 3380±592 5590 1127 120* 23‐7 8205±552* 4933 1121 45 23‐8 6372±354* 4407 1055 55 Trp 50μg/ml 23563±317* 4869 1137 70 Trp showed the highest ORAC value. Peptide 23‐7 (YGYTGA) and peptide 23‐8 (ISELGW) with a single Trp gave higher ORAC values. All peptides excluding peptide 23‐1 significantly inhibited the production of TNFα in LPS stimulated mouse macrophage cells. The profile of IL‐1 β was similar to that of TNF α. Peptides 23‐2 and 23‐6 enhanced the production of NO. Peptides 23‐7 and peptide 23‐8 produced relatively low quantities of vasodilator molecule NO. These peptides appear to be anti‐oxidant and anti‐inflammatory due to their unique structure. The MTT assay showed all peptides did not affect the viability of mouse macrophage cells but induced a moderate degree of growth proliferation in Caco 2 cells. 23‐8 (ISELGW) also exhibited strong anti‐microbial properties. This specific peptide may be suitable as additives to infant formulas to reduce inflammation and oxidation in vulnerable infants. Supported by AFMNet and MICH.
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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".