Probiotic<i>Lactobacillus Acidophilus</i>and<i>L. Casei</i>Mix Sensitize Colorectal Tumoral Cells to 5-Fluorouracil-Induced Apoptosis
Bibliographic record
Abstract
To assess the potential of Lactobacillus acidophilus and Lactobacillus casei strains to increase the apoptosis of a colorectal cancer cell line in the presence of 5-fluorouracil (5-FU), LS513 colorectal cancer cells were treated for 48 h with increasing concentrations of these lactic acid bacteria (LAB) in the presence of 100 mu g/ml of 5-FU. In the presence of 10(8) CFU/ml of live LAB, the apoptotic efficacy of the 5-FU increased by 40%, and the phenomenon was dose dependent. Moreover, irradiation-inactivated LAB caused the same level of induction, whereas microwave-inactivated LAB reduced the apoptotic capacity of the 5-FU. When cells were treated with a combination of live LAB and 5-FU, a faster activation of caspase-3 protein was observed, and the p21 protein seems to be downregulated. These results suggest that live L. acidophilus and L. casei are able to increase the apoptosis-induction capacity of 5-FU. The mechanisms of action are still not elucidated, and more research is needed to understand them. This is the first set of experiments demonstrating that some strains of LAB can enhance the apoptosis-induction capacity of the 5-FU. Based on these results, it is possible to speculate that LAB or probiotics could be used as an adjuvant treatment during anticancer chemotherapy.
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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".