Autophagy-driven regulation of cisplatin response in human cancers: Exploring molecular and cell death dynamics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the challenges posed by drug resistance and side effects, chemotherapy remains a pivotal strategy in cancer treatment. A key issue in this context is macroautophagy (commonly known as autophagy), a dysregulated cell death mechanism often observed during chemotherapy. Autophagy plays a cytoprotective role by maintaining cellular homeostasis and recycling organelles, and emerging evidence points to its significant role in promoting cancer progression. Cisplatin, a DNA-intercalating agent known for inducing cell death and cell cycle arrest, often encounters resistance in chemotherapy treatments. Recent studies have shown that autophagy can contribute to cisplatin resistance or insensitivity in tumor cells through various mechanisms. This resistance can be mediated by protective autophagy, which suppresses apoptosis. Additionally, autophagy-related changes in tumor cell metastasis, particularly the induction of Epithelial-Mesenchymal Transition (EMT), can also lead to cisplatin resistance. Nevertheless, pharmacological strategies targeting the regulation of autophagy and apoptosis offer promising avenues to enhance cisplatin sensitivity in cancer therapy. Notably, numerous non-coding RNAs have been identified as regulators of autophagy in the context of cisplatin chemotherapy. Thus, therapeutic targeting of autophagy or its associated pathways holds potential for restoring cisplatin sensitivity, highlighting an important direction for future clinical research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it