Screening and Characterization of Some Lactobacillaceae for Detection of Cholesterol-Lowering Activities
Bibliographic record
Abstract
Dyslipidemia, specifically abnormal levels of low-density lipoprotein cholesterol (LDL-C), is an important risk factor of cardiovascular disease. Evidence showing the promising abilities of probiotics to lower total cholesterol or LDL-C has, however, not yet convinced experts to recommend probiotic bacteria as treatment for blood lipid management. Therefore, there are opportunities for the development of new efficient cholesterol-lowering probiotics. Bile salt hydrolase (BSH) and feruloyl esterase (FAE) are bacterial enzymes proposed to explain the cholesterol-lowering capacity of some bacteria and have both been shown to be responsible for lipid reduction in vivo. Here, in order to select for cholesterol-lowering bacteria, 70 strains related to Lactobacillaceae were screened for BSH and FAE activities. Based on this two-way screening approach, two bacteria were selected and assessed for their capacity to assimilate cholesterol in vitro, another suggested mechanism. Lactobacillus acidophilus CL1285 showed BSH and FAE activity as well as capacity to assimilate cholesterol in vitro. Lactiplantibacillus plantarum CHOL-200 exhibited BSH activity and ability to assimilate cholesterol. These properties observed in vitro make both strains good probiotic candidates for the management of dyslipidemia. Further investigation is needed to assess their ability to reduce blood cholesterol in human trial.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".