Growth-promoting effects of pepsin- and trypsin-treated caseinomacropeptide from bovine milk on probiotics
Bibliographic record
Abstract
Probiotic Lactobacillus and Bifidobacterium species are generally fastidious bacteria and require rich media for propagation. In milk-based media, they grow poorly, and nitrogen supplementation is required to produce high bacterial biomass levels. It has been reported that caseinomacropeptide (CMP), a 7-kDa peptide released from κ-casein during renneting or gastric digestion, exhibits some growth-promoting activity for lactobacilli and bifidobacteria. During the digestive process, peptides derived from CMP are detected in the intestinal lumen The aim of this study was to evaluate the effects of peptic and tryptic digests of CMP on probiotic lactic acid bacteria growth in de Man, Rogosa and Sharpe broth (MRS) and in milk during fermentation at 37 °C under anaerobic conditions. The study showed that pepsin-treated CMP used as supplements at 0.5 g/l can promote the growth of probiotics even in peptone-rich environments such as MRS. The effect was strain-dependent and evident for the strains that grow poorly in MRS, with an improvement of >1.5 times (P<0.05) by addition of pepsin-treated CMP. Trypsin-treated CMP was much less efficient as growth promoter. Moreover, pepsin-treated CMP was effective in promoting the growth in milk of all probiotic lactic acid bacteria tested, with biomass levels being improved significantly, by 1.7 to 2.6 times (P<0.05), depending on the strain. Thus, supplementation of MRS and of milk with pepsin-treated CMP would be advantageous for the production of high biomass levels for Bifidobacteria and Lactobacilli.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".