Abrogating HSP Response Augments Cell Death Induced by As2O3 in Glioma Cell Lines
Bibliographic record
Abstract
OBJECTIVES: We previously reported that Arsenic trioxide (ATO) can inhibit glioma growth both in vitro and in vivo. While the use of ATO alone for solid tumor treatment sometimes was found to be ineffective which may be due to the protective pathways including heat shock proteins (HSPs) response induced by ATO. In this study, we modified HSPs expression to investigate whether HSPs had some effect on ATO induced glioma cell death. METHODS: Trypan bule exclusion assay, mitochondrial membrane potential (MMP) Assay, and SubG1 detection were used to evaluate cell viability and western-blot was employed to detect HSPs and some apoptosis markers expression induced by ATO. Heat pre-treatment, HSPs inhibitor, or Heat Shock factor-1 (HSF1) knockdown by SiRNA was employed to modify HSPs levels. RESULTS: It was showed that KNK437 (HSPs inhibitor) or HSF1 knockdown significantly enhanced cell death, MMP disruption, JNK phosphorylation and caspase-3 cleavage induced by ATO, which was accompanied by abrogation of HSPs induction, while heat pre-treatment with clear HSPs induction had strong protection on the effects mentioned above. CONCLUSION: Those data suggested that HSPs play protective roles on ATO induced cell death in glioma. Inhibition of HSPs may have a synergistic effect with ATO on glioma treatment.
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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".