Calcein assay for multidrug resistance reliably predicts therapy response and survival rate in acute myeloid leukaemia
Bibliographic record
Abstract
In this study, we evaluated the suitability of the calcein assay as a routine clinical laboratory method for the identification of multidrug-resistant phenotype in acute leukaemia. This study presents the results of the calcein tests obtained in two large haematological centres in Hungary. Assays were performed with blast cells of 93 de novo acute leukaemia patients, including 65 patients with acute myeloid leukaemia (AML). Results were expressed as multidrug resistance activity factor (MAF) values. AML patients were divided into responders and non-responders and MAF values were calculated for each group. In both centres, responder patients displayed significantly lower MAF values than non-responders (P = 0.0045 and P = 0.0454). Cut-off values were established between the MAFR + SEM and MAFNR - SEM values. On the basis of these cut-off levels, multidrug resistance (MDR) negativity showed a 72% predictive value for the response to chemotherapy, whereas MDR positivity was found to have an average predictive value of 69% for therapy failure. MDR activity was a prognostic factor for survival rate and the test was suitable for detecting patients at relapse. The calcein assay can be used as a quantitative, standardized, inexpensive screening test in a routine clinical laboratory setting. The assay detects both P-glycoprotein and multidrug resistance-associated protein activities, and identifies AML patients with unfavourable therapy responses.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".