Reversal Effects of 20(R)– and 20(S)–Ginsenoside-Rg3 on Daunorubicin Uptake in Multidrug Resistant Leukemia Cells Studied in the Single-Cell Biochip
Bibliographic record
Abstract
Multidrug resistance (MDR), frequently mediated by over-expression of the P-glycoprotein (P-gp/ABCB1) efflux transporter, remains a major challenge in the treatment of leukemia by limiting intracellular accumulation of chemotherapeutic agents such as daunorubicin (DNR). This study evaluates the applicability of a microfluidic-based single-cell biochip to investigate the reversal effects of microgram-level ginsenosides on daunorubicin uptake in multidrug-resistant leukemia cells. Pure ginsenosides are difficult to obtain in bulk and are typically available only in milligram quantities, which restricts their evaluation using conventional MDR assays such as flow cytometry that require large cell populations and substantial amounts of compound. To address this limitation, a microfluidic single-cell biochip (SCB) requiring microgram quantities of ginsenosides (< 100 µg) and fewer than ten cells was employed. Intracellular DNR accumulation was measured in the CEM/VLB1000 leukemia cell line following treatment with DNR alone or in combination with ginsenoside Rg3-R, ginsenoside Rg3-S, 20(S)-protopanaxatriol (PPT), and 20(S)-protopanaxadiol (PPD), in order to compare their relative efficacy in enhancing drug accumulation. Although Rg3-R and Rg3-S share highly similar chemical structures and are glycosylated derivatives of the PPD aglycone, Rg3-S exhibited greater potency in increasing intracellular daunorubicin accumulation than Rg3-R, and both were more effective than PPD. These findings underscore the importance of ginsenoside stereochemistry modulating P-gp associated drug resistance and demonstrate the utility of the SCB platform for quantifying daunorubicin accumulation in multidrug resistant leukemia cells at single cell resolution.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".