Gingerol acts as a potent radiosensitizer in head and neck squamous cell carcinoma
Bibliographic record
Abstract
Treatment options for advanced head and neck squamous cell carcinoma (HNSCC) are limited and often cause severe toxicity and debilitating long-term impacts. Developing effective and safer treatments is warranted. Several plant extracts have shown their effectiveness, but a comprehensive comparison between plant extracts in HNSCC has not been reported. Our aim was to investigate the effect of different plant extracts on the proliferation and viability of HNSCC cell lines. In addition, we investigated the efficacy of combining cytotoxic plant extracts with radiation. Since RT is a cornerstone in the treatment and management of HNSCC, it is desirable to enhance its efficacy through combination with cytotoxic agents that have minimal side effects. HNSCC cell lines were treated with various plant extracts at different concentrations. MTT assays were performed to identify the most potent anti-tumor plant extract. Colony-formation assays were performed to determine the radiosensitization effect. To investigate the effect on migration, transwell migration assays were performed. Annexin V staining was performed to analyze cell apoptosis. 6-gingerol resulted in the most significant dose-dependent inhibition in all cell lines compared to other plant extracts. Colony-formation assays showed a significant radiosensitizing effect when 6-gingerol was combined with radiation. In addition, the combination of 6-gingerol with radiation resulted in a significant decrease in HNSCC cell migration. Mechanistically, Annexin V staining showed that the combination of 6-gingerol and RT induces a synergistic apoptotic effect in MOC1, MOC2 and SCC9 cells compared to RT alone. In conclusion, 6-gingerol enhances the effect of radiation in HNSCC cell lines and could be a suitable candidate for combination therapy in HNSCC.
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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 | 0.001 | 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 | 0.001 |
| 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".