Overexpression of tensin homolog deleted on chromosome ten (PTEN) by ciglitazone sensitizes doxorubicin‐resistance leukemia cancer cells to treatment
Bibliographic record
Abstract
Overcoming multidrug resistance (MDR) is a final goal of various recent studies, in which combination of different compounds and conventional chemotherapeutics results in circumventing MDR and hence cancer progression. Therefore, we aimed to investigate the effects of peroxisome proliferator-activated receptors (PPARs)-γ on MDR in doxorubicin-resistant human myelogenous leukemia cells. The effect of doxorubicin on cell viability following treatment with ciglitazone was measured using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. The activity of P-glycoprotein (P-gp), as one of the membrane transporters, was determined by the rhodamine 123 (Rho 123) assay. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and Western blot were used for the measurement of P-gp, and tensin homolog deleted on chromosome ten (PTEN) expression at mRNA and protein, respectively. For evaluation of doxorubicin (DOX)-induced apoptosis by annexin V/PI staining was used. Ciglitazone significantly increases the cytotoxic effects of DOX. In addition, ciglitazone considerably decreased the expression levels and activity of P-gp in DOX-resistant K562 cells. Furthermore, upon the ciglitazone treatment, PTEN expression could be increased in K562/DOX cells in a PPARγ-dependent manner. Moreover, ciglitazone significantly enhanced DOX-induced apoptosis in K562/DOX cells. The combination treatment of K562/DOX leukemia cancer cells with doxorubicin and ciglitazone might be an effective strategy in inducing apoptosis and reversing developed MDR, and more importantly decreasing the adverse side effects of these agents.
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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".