Effects of serum from patients with chronic renal failure on rat hepatic cytochrome P450
Bibliographic record
Abstract
1. In humans, chronic renal failure (CRF) is associated with decreased hepatic drug metabolism, particularly that mediated by the cytochrome P450 (P450). The mechanisms remain poorly understood. The present study aimed to investigate the effects of the serum of patients with CRF on liver P450, and to evaluate whether renal replacement therapies (dialysis or transplantation) impede the inhibition of CRF serum on P450. 2. Rat hepatocytes were incubated for 24 h with serum from patients with severe CRF and from controls to measure (1) P450 level, (2) protein expression and mRNA levels of P450 isoforms and (3) metabolic activities of CYP3A and CYP1A. Similar experiments were performed with serum of patients once on chronic hemodialysis and after kidney transplantation. 3. In rat hepatocytes incubated for 24 h with serum from patients with CRF, P450 level and protein expression, as well as mRNA levels of P450 isoforms (CYP1A2, 2C6, 2C11, 2D1/2D2, 3A2 and 4A1/4A3), were decreased by more than 45% (P<0.001) compared to control serum, while the levels of CYP2E1 were not modified. CYP3A and CYP1A activities were decreased by 51 and 59% (P<0.001), respectively. The inhibitory effect of serum obtained from patients before first dialysis was similar after 1 or 6 months on chronic hemodialysis but was lost after successful kidney transplantation. In CRF serum, the fraction containing proteins between 10 and 15 kDa decreases P450. 4. Human uremic serum contains mediator(s) that decreases rat hepatic P450 activity and expression secondary to reduced gene expression. The inhibitory effect of serum persists even after initiation of dialysis, but disappears after normalization of renal function following kidney transplantation.
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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".