<i>In vivo</i> and <i>in vitro</i> effects of curcumin on head and neck carcinoma: a systematic review
Bibliographic record
Abstract
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) contributes globally to a great number of deaths and morbidity, in spite of new therapeutic strategies. There is a great need of new drugs that are significantly effective and less deleterious to the patients' general health. In this sense, phytotherapy is a tendency, with results pointing to its use as a chemo-preventive and adjuvant therapy. Therefore, the objective of this systematic review was to investigate the effects of curcumin on proliferation and survival of HNSCC. MATERIALS AND METHODS: The search was conducted on six databases: Cochrane, LILACS, EMBASE, MEDLINE, PubMed, and Web of Science. In vitro and in vivo studies that evaluated the effects of curcumin on cell viability, tumor growth, cell cycle and/or cell death pattern in HNSCC cell lines or animal models were selected. RESULTS: /M phase in HSNCC cell lines. It also reduces tumor measurements in animal models. These events were mostly studied through MTT assay, flow cytometry, and cell cycle- and apoptosis-related proteins expression. CONCLUSION: This systematic review demonstrated that curcumin is effective on HNSCC cell proliferation and survival, reinforcing the currently available evidence that curcumin could be an adjuvant drug in HNSCC 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".