Sequential Measurements of Catalytic Activities of Multi-Drug-Resistance Transporters and Cytochrome P450 Enzymes by Cytometry of Reaction Rate Constant
Bibliographic record
Abstract
ABSTRACT Cytometry of reaction rate constant (CRRC) is an accurate and robust approach to characterize cell-population heterogeneity using rate constants of cellular processes for which kinetic mechanisms are known. We work on a CRRC-based method to develop predictors of tumor chemoresistance driven by two processes: drug extrusion by multi-drug-resistance (MDR) transporters and drug inactivation by cytochrome-P450 enzymes (CYP). Each of the two possess is studied with its specific substrate and the process activity is characterized by a corresponding unimolecular rate constant. Due to the incompatibility of MDR and CYP assays, MDR and CYP activities may be difficult to measure simultaneously suggesting that they may need to be measured sequentially. The sequential measurements may also impose a problem: the results of the second assay may be affected by artifacts exerted by the first assay. The goal of this work was to understand whether the cells have a memory of the first assay that significantly affects the results of the second assay. To achieve this goal, we compared CRRC results for two orders of sequential measurements: the MDR→CYP order in which MDR activity is measured before CYP activity and the CYP→MDR order in which CYP activity is measured before MDR activity. It was found that the results of the CYP assay were similar in both orders; on the contrary, the results of the MDR assay were significantly different. Our findings suggest that MDR and CYP activity can be studied sequentially provided that MDR activity is measured first and CYP activity second.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".