Propranolol as a novel adjunctive treatment for head and neck squamous cell carcinoma.
Bibliographic record
Abstract
OBJECTIVE: To investigate propranolol as a novel treatment for head and neck squamous cell carcinoma (HNSCC) in vitro. METHODS: HNSCC cell lines were cultured and treated with propranolol alone and in combination with cisplatin or γ-irradiation. The alamarBlue assay was performed to assess cell viability, and apoptosis was confirmed via Western immunoblot for cleaved poly-ADP-ribose polymerase (PARP) and caspase-3/7 assays. RESULTS: Propranolol reduced cell viability and induced apoptosis. In response to propranolol, ΔNp63α decreased, whereas TAp73β and downstream proapoptotic p53 family target genes increased. Expression of the proangiogenic protein vascular endothelial growth factor (VEGF) also decreased. Combination treatment with propranolol and cisplatin resulted in synergistic effects. Propranolol treatment also enhanced the effects of γ-irradiation on cell viability. CONCLUSIONS: Our results demonstrate that propranolol reduced HNSCC viability, induced apoptosis, and inhibited production of the proangiogenic protein VEGF. These changes may be due to modulation of p53 family proteins, which are critical regulators of chemotherapy-induced apoptosis in HNSCC. Moreover, propranolol is synergistic in combination with cisplatin and reduces HNSCC viability postradiation in vitro, which may have important implications for novel treatments of HNSCC patients.
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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".