Thiopurine methyltransferase as a therapeutic indicator of purine analogues: a preliminary study in southern metropolis
Bibliographic record
Abstract
The purine analogues used to treat certain chronic diseases are occasionally associated with bone marrow suppression and leucopenia. They are metabolized by Thiopurine S-methyltransferase (TPMT) to methyl mercaptopurines. Thus optimal TPMT activity of an individual plays an important role in the treatment. However a wide interindividual variation of TPMT activity may be multi-factorial including inter and intra familial genotypic variation of TPMT gene. The present study was aimed to establish the reference range of TPMT activity and correlated with genotype. 165 healthy blood donors (Male-133. Female-32) were assessed for their TPMT activity by HPLC-UV method. The mean age of study cohort was 30.4±8.7 (19-58) and 150 of them were analyzed for TPMT *2, *3B and *3C polymorphisms by PCR-direct sequencing. The range of TPMT activity was found to be wide (3.93 to 35.80 nmolml -1 h -1 PRBC). The mean of the TPMT activity in total population was 15.98± 7.95, in females 10.99±3.44 and in males it is 15.19±7.64. All the participants studied for TPMT gene polymorphism were found to be wild type for all major polymorphic regions. The range of TPMT activity has a wide range. The wide reference range of enzyme activity may have multi-factorial basis. The frequency of hetero and homozygous variants of screened polymorphisms in TPMT gene may be less frequent in South Indian population.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".