Cholesterol Assimilation by<i>Lactobacillus</i>Probiotic Bacteria: An<i>In Vitro</i>Investigation
Bibliographic record
Abstract
Excess cholesterol is associated with cardiovascular diseases (CVD), an important cause of mortality worldwide. Current CVD therapeutic measures, lifestyle and dietary interventions, and pharmaceutical agents for regulating cholesterol levels are inadequate. Probiotic bacteria have demonstrated potential to lower cholesterol levels by different mechanisms, including bile salt hydrolase activity, production of compounds that inhibit enzymes such as 3-hydroxy-3-methylglutaryl coenzyme A, and cholesterol assimilation. This work investigates 11 Lactobacillus strains for cholesterol assimilation. Probiotic strains for investigation were selected from the literature: Lactobacillus reuteri NCIMB 11951, L. reuteri NCIMB 701359, L. reuteri NCIMB 702655, L. reuteri NCIMB 701089, L. reuteri NCIMB 702656, Lactobacillus fermentum NCIMB 5221, L. fermentum NCIMB 8829, L. fermentum NCIMB 2797, Lactobacillus rhamnosus ATCC 53103 GG, Lactobacillus acidophilus ATCC 314, and Lactobacillus plantarum ATCC 14917. Cholesterol assimilation was investigated in culture media and under simulated intestinal conditions. The best cholesterol assimilator was L. plantarum ATCC 14917 (15.18±0.55 mg/10(10) cfu) in MRS broth. L. reuteri NCIMB 701089 assimilated over 67% (2254.70±63.33 mg/10(10) cfu) of cholesterol, the most of all the strains, under intestinal conditions. This work demonstrates that probiotic bacteria can assimilate cholesterol under intestinal conditions, with L. reuteri NCIMB 701089 showing great potential as a CVD therapeutic.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".