Revitalizing health with <i>Lactobacillus</i> isolates: mitigating aflatoxin B1 toxicity in HT-29 cell line and Balb/c mice
Bibliographic record
Abstract
AIM: Aflatoxins (AFs), carcinogenic mycotoxins found in fermented foods, animal gastrointestinal tracts (GITs), and the environment, can be detoxified by probiotic lactobacilli. The aim of this study was to test whether probiotic lactobacilli can detoxify AFs, carcinogenic mycotoxins prevalent in fermented foods, animal GITs, and the environment. METHODS AND RESULTS: Five candidate Lactobacillus strains [Lacticaseibacillus rhamnosus 195 (G1), Lactiplantibacillus plantarum 42 (G2), Levilactobacillus brevis 205 (G3), L. plantarum 165 (G4), and Limosilactobacillus reuteri 100 (G5)] were prepared to compare their ability to reduce aflatoxicosis (AFB1) in vitro and in vivo by evaluating apoptotic factors, inflammatory markers, tight junction, and nutrient transport genes. G1, G2, G3, G4, and G5 bound AFB1 well (with no notable variations). These probiotic isolates lowered Bax and caspase-3 expression in AFB1-treated HT-29 cells and raised Bcl-2 expression. In AFB1-treated cells, these probiotics increased occludin and zonula occludens-1 expression as tight junction markers. In contrast, Lactobacillus strains + AFB1-treated cells expressed fewer nutritional transport genes (amino acid transporter 2, glucose transporter 2, peptide transporter 1, sodium-dependent glucose cotransporter 1) and interleukin-6, an inflammatory marker. In animal models, oral Lactobacillus supplementation reduced AFB1-induced liver injury and increased GST A3 expression via the Nrf2 pathway. These Lactobacillus isolates also reduced CYP 450 1A2 and 3A4 expression. CONCLUSIONS: The results of this study suggest that Lactobacillus strains could be used as a protective strategy against mycotoxin cytotoxicity by promoting the maintenance of standard intestinal cell structure and function and the health of animal models.
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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) | 0.001 | 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 | 0.001 | 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".