Immunomodulating Effects of Peptidic Fractions Issued from Milk Fermented with Lactobacillus helveticus
Bibliographic record
Abstract
The effect of peptides released during the fermentation of milk on the humoral immune system and on fibrosarcoma growth was studied. Lactobacillus helveticus was able to release peptidic compounds during milk fermentation due to its high proteolytic activity, as was shown by the degree of proteolysis and size-exclusion HPLC elution profiles. Three fractions of these compounds were separated and fed to mice during different periods (2, 5, and 7 d). The humoral immune response was assessed by following the number of IgA-secreting cells, and the antitumor activity was monitored by studying the regression of subcutaneously implanted fibrosarcomas. Feeding during 2 and 7 d with the medium-sized fraction (Fraction II) significantly increased the IgA-producing cells in the intestines, whereas feeding with the large compound fraction (Fraction I) during 5 d and the small compound fraction (Fraction III) during all three feeding periods provided similar increases. A double dose of Fraction II showed the highest IgA-producing cell count. The increase by Fraction III was shown to be caused by the presence of L-Tryptophan. Fraction II significantly decreased the size of fibrosarcoma when previously fed during 7 d, and feeding with Fraction I during 5 d decreased significantly its size after 35 d of growth. Although the mechanisms by which lactic acid bacteria enhance the immune system are not clear, this study clearly shows that bioactive compounds released in fermented milks contribute to the immunoenhancing and antitumor properties of these products. The release of bioactive peptides by lactic acid bacteria can have important implications on the modulation of the cellular immune response.
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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".