Characterization of immune-active peptides obtained from milk fermented by<i>Lactobacillus helveticus</i>
Bibliographic record
Abstract
The objectives of this research were to confirm the effect of compounds derived from milk fermented by Lactobacillus helveticus (LH-2) on the nonspecific host defence system, and isolate and characterize the active peptides that mediate the immune response. The cell-free supernatant obtained from the fermented milk and its fractions were tested in vitro for immuno-modulating activity using murine macrophages (RAW 264.7 cell line). Cytokine production (Interleukin-6 (IL-6), Tumor Necrosis Factor-alpha (TNF-alpha), and Interleukin-1beta (IL1-beta)), nitric oxide (NO) production and phagocytosis were used as biomarkers. Macrophages stimulated with cell-free supernatant of fermented milk showed higher production of cytokines and NO compared with macrophages stimulated with LPS (Lipopolysaccharide) and a commercial immunomodulator derived from beta-casein (f54-59). Phagocytosis was observed by macrophages stimulated with the supernatant. Two of nine fractions collected from the supernatant using size exclusion chromatography produced the highest response when used to stimulate macrophages. The results of the dose-response study of the effect of the fraction with the highest stimulation effect on the production of TNF-alpha showed a direct correlation between protein concentration and TNF-alpha release. The fraction contained four novel peptides, three derived from the hydrolysis of beta-casein and one from the hydrolysis of alpha-lactalbumin. These results confirm that fermentation of milk by Lactobacillus helveticus (LH-2) results in the production of specific peptides capable of modulating macrophage activity.
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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) | 0.000 | 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".