Polymorphisms of Multidrug Resistance Gene (MDR1) and Cyclosporine Absorption in De Novo Renal Transplant Patients
Bibliographic record
Abstract
BACKGROUND: Several single nucleotide polymorphisms (SNPs) in the multidrug resistance (MDR1) gene may play a role in the interindividual variation of cyclosporine A (CsA) absorption in renal transplant patients. METHODS: An analysis of CsA absorption measured by the dose- and weight-adjusted 4 hr area under the time-concentration curve, AUC(0-4)/mg doseCsA/kg, was conducted on day 3 after transplantation, in 69 de novo renal transplant patients who were genotyped for MDR1 SNPs. Follow-up pharmacogenomic analysis at 1 month posttransplant was performed utilizing dose- and weight-adjusted 2-hour postdose CsA concentration (C2). RESULTS: AUC(0-4)/mg doseCsA/kg was significantly higher (P=0.024) in (C/C)3435 individuals than in a grouped population of (C/T)3435 and (T/T)3435 patients on postoperative day 3. G2677T variants were not significantly correlated with CsA absorption (P=0.084). The number of C3435-G2677 haplotypes was the best predictor of CsA exposure. At 1 month posttransplant, no correlation was seen between MDR1 SNPs and CsA exposure. The frequency of wild-type variants for C3435T and G2677T were 61% and 77.6%, respectively. SNPs at G2677T and C3435T loci were found to be in linkage disequilibrium. CONCLUSIONS: MDR1 polymorphisms are associated with differences in CsA exposure only in the first posttransplant week.
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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 | 0.001 |
| 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".