Maternal separation influences hepatic drug-metabolizing CYP450 gene expression without pathological changes in adult mice
Bibliographic record
Abstract
OBJECTIVES: The principal motive of this study is to explore the influence maternal separation (MS) exhibits on the mRNA expression of major drug metabolizing-cyp450s in parallel with the assessment of pathological changes that can be induced by MS in the livers of experimental mice. METHODS: . RESULTS: The study demonstrated that MS markedly downregulated (p<0.05) the mRNA expression of all tested drug-metabolizing cyp450s in livers of female and male mice. Furthermore, the mRNA levels of major drug-metabolizing cyp450s were notably lower (p<0.05) in livers of female MS mice as compared with male MS mice. It was found that values of the total body weight and liver weight of MS mice did not vary significantly (p>0.05) from those of the control groups. Additionally, histological examination revealed that the hepatic tissue of MS mice was normal, similar to that of the control mice. CONCLUSIONS: In summary, MS downregulates the gene expression of major hepatic drug-metabolizing cyp450s without inducing pathological alterations in the livers of mice. These findings provide an explanation for the heterogeneity in pharmacokinetics and drug response of patients with early life stress.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".