Effect of bovine colostrum, cheese whey, and spray-dried porcine plasma on the in vitro growth of probiotic bacteria and <i>Escherichia coli</i>
Bibliographic record
Abstract
The aim of this study is to evaluate the effects of defatted colostrum (Col), defatted decaseinated colostrum whey, cheese whey, and spray-dried porcine plasma (SDPP) as supplements of a growth medium (de Man - Rogosa - Sharpe (MRS) broth) on the multiplication of lactic acid bacteria, probiotic bacteria, and potentially pathogenic Escherichia coli. Using automated spectrophotometry (in vitro system), we evaluated the effect of the 4 supplements on maximum growth rate (μ(max)), lag time (LagT), and biomass (OD(max)) of 12 lactic acid bacteria and probiotic bacteria and of an E. coli culture. Enrichment of MRS broth with a Col concentration of 10 g/L increased the μ(max) of 5 of the 12 strains by up to 55%. Negative effects of Col or SDPP on growth rates were also observed with 3 probiotic strains; in one instance μ(max) was reduced by 40%. The most effective inhibitor of E. coli growth was SDPP, and this effect was not linked to its lysozyme content. The positive effect of enrichment with the dairy-based ingredient might be linked to enrichment in sugars and increased buffering power of the medium. These in vitro data suggest that both Col and SDPP could be considered as supplements to animal feeds to improve intestinal health because of their potential to promote growth of probiotic bacteria and to inhibit growth of pathogenic bacteria such as E. coli.
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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".