A simple and fast microfluidic approach of same-single-cell analysis (SASCA) for the study of multidrug resistance modulation in cancer cells
Bibliographic record
Abstract
Due to the cellular heterogeneity in multidrug resistance (MDR) cell populations, positive drug effects on the modulation of MDR can be obscured in conventional methods, especially when only a small number of cells are available. To address cellular variations among different MDR cells, we report a new microfluidic approach to study drug effect on MDR modulation, by investigating drug accumulation of daunorubicin in MDR leukemia cells. We have demonstrated that the new approach of same-single-cell analysis by accumulation (denoted as SASCA-A) is not only superior to different-single-cell analysis, but also has key advantages over our previous approach of same-single-cell analysis. First, SASCA-A is much simpler as it does not require multiple cycles of drug uptake and drug efflux. Second, it is faster, only taking about one fourth of the time used in the previous approach. Third, it provides a more 'identical' and reliable control because it compares the time points just before MDR modulator tests. To help understand the dynamics of drug accumulation in MDR cells, we also developed a mathematical model to describe the kinetics of drug accumulation conducted in individual cells. The SASCA-A method will benefit drug resistance research in minor cell subpopulations (e.g., cancer "stem" cells) because this method requires only a small number of cells in identifying the MDR reversal effect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".