Spontaneous mitochondrial membrane potential change during apoptotic induction by quercetin in K562 and K562/adr cells
Bibliographic record
Abstract
Natural products from plants such as flavonoids are potential drugs to overcome multidrug resistance (MDR) in cancer treatments. However, their modes of action are still unclear. In this study, the effects of quercetin on mitochondrial membrane potential (DeltaPsim) change as well as quercetin's ability to induce apoptosis and inhibit Pgp-mediated efflux of 99mTc-MIBI in K562/adr cells were investigated. Quercetin exhibits cytotoxicity against erythroleukemic cells: IC50 are 11.0 +/- 2.0 micromol/L and 5.0 +/- 0.4 micromol/L for K562 and K562/adr, respectively. Quercetin induces cell death via apoptosis in both K562 and K562/adr cells and does not inhibit Pgp-mediated efflux of 99mTc-MIBI. Quercetin (10 micromol/L, 3 h) and etoposide (100 micromol/L, 24 h) induce similar levels of apoptosis in K562 and K562/adr cells. Quercetin induces an increase followed by a decrease in |DeltaPsim| value depending on its concentration. A decrease in the |DeltaPsim| value is associated with an increase in the percentage of early apoptotic cells. It is clearly shown that quercetin results in a spontaneous DeltaPsim change during apoptotic induction. Therefore, quercetin is potentially an apoptotic-inducing agent, which reacts at the mitochondrial level.
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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) | 0.001 | 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".