Probiotics as Potential Tool to Mitigate Nucleotide Metabolism Alterations Induced by DiNP Dietary Exposure in Danio rerio
Bibliographic record
Abstract
Diisononyl phthalate, classified as endocrine disruptor, has been investigate to trigger lipid biosynthesis in both mammalian and teleostean animal models. Despite this, little is known about the effects of DiNP exposure at tolerable daily intake level and the possible mechanisms of its toxicity. Probiotics, on the other hand, were demonstrated to have beneficial effects on the organism’s metabolism and recently emerged as a possible tool to mitigate the EDC toxicity. In the present study, using a metabolomic approach, the potential hepatic sex-related toxicity of DiNP was investigated in adult zebrafish together with the mitigating action of the probiotic formulation SLAB51, which has already demonstrated its ability to ameliorate gastrointestinal pathologies in animals including humans. Zebrafish were exposed for 28 days to 50 µg/kg body weight (bw)/day of DiNP (DiNP) through their diet and treated with 109 CFU/g bw of SLAB51 (P) and the combination of DiNP and SLAB51 (DiNP + P), and the results were compared to those of an untreated control group (C). DiNP reduced AMP, IMP, and GMP in the purine metabolism, while such alterations were not observed in the DiNP + P group, for which the phenotype overlapped that of C fish. In addition, in male, DiNP reduced UMP and CMP levels in the pyrimidine metabolism, while the co-administration of probiotic shifted the DiNP + P metabolic phenotype toward that of P male and closed to C male, suggesting the beneficial effects of probiotics also in male fish. Overall, these results provide the first evidence of the disruptive actions of DiNP on hepatic nucleotide metabolism and mitigating action of the probiotic to reduce a DiNP-induced response in a sex-related manner.
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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) | 0.001 | 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 | 0.001 |
| 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".