Celastrol and an EGCG pro-drug exhibit potent chemosensitizing activity in human leukemia cells
Bibliographic record
Abstract
Chemotherapy remains the staple of treatment for many types of leukemia. Despite the positive impact on extending overall survival in patients with hematological malignancies, new treatment strategies are needed to reduce the nonspecific toxicity and improve the efficacy of treatment. Celastrol, derived from the 'Thunder God Vine' and Pro-EGCG, a pre-drug version of green tea polyphenol EGCG have shown potent biological activity in vitro and in vivo. Whether these natural products augment the efficacy of conventional chemotherapy in the treatment of leukemia cells has yet to be demonstrated. Here we demonstrate that these natural products could sensitize the effect of chemotherapy in both K-562 and Jurkat T human leukemia cells. Accordingly, this potent biological activity was associated with increased levels of leukemia cell killing, caspase 3 activation, and poly(ADP-ribose) polymerase cleavage. Furthermore, the higher levels of apoptotic indices were associated with decreased levels of Bcr-Abl oncoprotein in K-562 cells. Taken together, our findings present a compelling rationale for the development of combination strategies using natural products in the treatment of hematological malignancies.
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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) | 0.002 | 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".