Chemo-resistant melanoma sensitized by tamoxifen to low dose curcumin treatment through induction of apoptosis and autophagy
Bibliographic record
Abstract
Melanoma is the deadliest form of skin cancer, which is notoriously aggressive and chemo-resistant, and for which there is little effective treatment available if it goes undetected. Curcumin from the turmeric spice (Curcuma longa) has long been used in Southeast Asian medicine to alleviate ailments and cure an array of diseases and disorders. It possesses anti-inflammatory, anti-oxidant and most importantly anti-carcinogenic activity. There have been contradictory reports discussing the efficacy of curcumin-induced death on melanoma. In this report we show that curcumin does induce apoptosis in A375 and the relatively resistant G361 malignant human melanoma cell lines at higher doses. Tamoxifen is an estrogen receptor (ER) blocker that is used for ER positive breast cancer treatment. Recently, tamoxifen has been shown to directly target the mitochondria. Given that curcumin is a pro oxidant and tamoxifen can act on mitochondria, we ask whether the combinatorial treatment could result in synergistic induction of apoptosis in chemo-resistant melanoma. Our results show a corresponding increase in phosphatidyl serine flipping, mitochondria depolarization and reactive oxygen species (ROS) generation by the combined treatment at lower doses. Interestingly, there was significant induction of autophagy along with apoptosis following the combined treatment. Importantly, non-cancerous cells are unaffected by the combination of these non-toxic compounds. However, once exposed to low doses of this co-treatment, melanoma cells still retain signals to commit suicide even after removal of the drugs. This combination provides a non-toxic option for combinatorial chemotherapy with great potential for future use.
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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 | 0.001 |
| 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".