Antitumour effects of metformin and curcumin in human papillomavirus positive and negative head and neck cancer cells
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
The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has significantly increased in recent decades due to human papillomavirus (HPV)-mediated oncogenesis. Unfortunately, a growing number of HPV-positive (+) OPSCC survivors are living with the irreversible side effects of treatment. The novel, well-tolerated chemotherapeutics with improved side effect profiles are, therefore, in high demand. Metformin is one such drug, widely used as a first-line oral agent in the treatment of type 2 diabetes mellitus. Curcumin is another well-tolerated agent quickly gaining attention for its medicinal properties. Both metformin and curcumin have been shown to display anticancer properties. This study aimed to determine the antitumor effects of these agents, individually and combined, in HPV+ and HPV-negative (-) head and neck squamous cell carcinoma (HNSCC) cell lines. This was achieved by assessing the efficacy of varying drug concentrations on the overall cell viability, proliferation, and expression of common HNSCC biomarkers. The results from protein and RNA expression data are highly variable, as expected, with multiple pathways being affected in cancer. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assays and immunofluorescence microscopy suggest that both agents are capable of slowing proliferation and inducing apoptosis. We conclude that curcumin and metformin display effective antitumor effects in both HPV+ and HPV- HNSCC cell lines. The curcumin effects appear more pronounced in the HPV- cell lines. Metformin appears to be more effective at reducing the overall cell numbers in HPV+ cell lines. Metformin and curcumin combined did not appear to have synergistic effects on the proliferation or apoptosis of the treated cell lines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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