In Vitro Methylene Blue and Carboplatin Combination Triggers Ovarian Cancer Cells Death
Bibliographic record
Abstract
Ovarian cancer presents a dire prognosis and high mortality rates, necessitating the exploration of alternative therapeutic avenues, particularly in the face of platinum-based chemotherapy resistance. Conventional treatments often overlook the metabolic implications of cancer, but recent research has highlighted the pivotal role of mitochondria in cancer pathogenesis and drug resistance. This study delves into the metabolic landscape of ovarian cancer treatment, focusing on modulating mitochondrial activity using methylene blue (MB). Investigating two epithelial ovarian cancer (EOC) cell lines, OV1369-R2 and OV1946, exhibiting disparate responses to carboplatin, we sought to identify metabolic nodes, especially those linked to mitochondrial dysfunction, contributing to chemo-resistance. Utilizing ARPE-19, a normal retinal epithelial cell line, as a control model, our study reveals MB's distinct cellular uptake, with ARPE-19 absorbing 5 to 7 times more MB than OV1946 and OV1369-R2. Treatment with 50 µM MB (MB-50) effectively curtailed the proliferation of both ovarian cancer cell lines. Furthermore, MB-50 exhibited the ability to quell glutaminolysis and the Warburg effect in cancer cell cultures. Regarding mitochondrial energetics, MB-50 spurred oxygen consumption, disrupted glycolytic pathways, and induced ATP depletion in the chemo-sensitive OV1946 cell line. These findings highlight the potential of long-term MB exposure as a strategy to improve the chemotherapeutic response in ovarian cancer cells. The ability of MB to stimulate oxygen consumption and enhance mitochondrial activity positions it as a promising candidate for ovarian cancer therapy, shedding light on the metabolic pressures exerted on mitochondria and their modulation by MB, thus contributing to a deeper understanding of mitochondrial dysregulation and the metabolic underpinnings of cancer cell proliferation.
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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".