The effect of drinking milk containing conjugated linoleic acid on fecal microbiological profile, enzymatic activity, and fecal characteristics in humans
Bibliographic record
Abstract
BACKGROUND: The primary objective was to determine whether consumption of conjugated linoleic acids (CLAs) affected the fecal microbiota composition, fecal enzyme activity or fecal composition. METHODS: Human subjects consumed (1 L/day) cows' milk (4% fat) containing (5 mg/g fat) cis-9, trans-11 CLA (CONT), (32 mg/g fat) cis-9, trans-11 CLA (NAT) and (32 mg/g fat) trans-10, cis-12 CLA and cis-9, trans-11 CLA (SYN) for 8 weeks, in addition to their normal diet. Milk feeding periods were separated by 4 week washout periods. Fecal samples were obtained at the beginning (day 0) and the end (day 56) of each milk feeding period. Fecal samples were analysed for microbiological profile, enzyme activity, pH and short chain fatty acid content. RESULTS: Samples taken at day 0 and day 56 indicated that the numbers of lactobacilli and bifidobacteria significantly decreased after consumption of all experimental milks; total aerobes, total anaerobes, enterobacteria, and enterococci + streptococci did not change. At day 56, the activities of beta-glucosidase, nitroreductase, and urease enzymes had decreased compared to samples taken on day 0 for all treatments. beta-glucuronidase activity did not change. Fecal pH and ammonia content did not change. CONCLUSION: It was concluded that observed changes could have been attributed to increased milk intake; no differences could be attributed to consumption of the different CLAs.
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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".