Data from Transporter-Mediated Protection against Thiopurine-Induced Hematopoietic Toxicity
Bibliographic record
Abstract
Abstract Thiopurines are effective immunosuppressants and anticancer agents, but intracellular accumulation of their active metabolites (6-thioguanine nucleotides, 6-TGN) causes dose-limiting hematopoietic toxicity. Thiopurine S-methyltransferase deficiency is known to exacerbate thiopurine toxicity. However, many patients are highly sensitive to thiopurines for unknown reasons. We show that multidrug-resistance protein 4 (Mrp4) is abundant in myeloid progenitors and tested the role of the Mrp4, an ATP transporter of monophosphorylated nucleosides, in this unexplained thiopurine sensitivity. Mrp4-deficient mice experienced Mrp4 gene dosage-dependent toxicity caused by accumulation of 6-TGNs in their myelopoietic cells. Therefore, Mrp4 protects against thiopurine-induced hematopoietic toxicity by actively exporting thiopurine nucleotides. We then identified a single-nucleotide polymorphism (SNP) in human MRP4 (rs3765534) that dramatically reduces MRP4 function by impairing its cell membrane localization. This SNP is common (>18%) in the Japanese population and indicates that the increased sensitivity of some Japanese patients to thiopurines may reflect the greater frequency of this MRP4 SNP. [Cancer Res 2008;68(13):4983–9]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.008 | 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".