Methylene Blue Metabolic Therapy Restrains In Vivo Ovarian Tumor Growth
Bibliographic record
Abstract
Ovarian cancer remains a significant challenge, especially in platinum-resistant cases where treatment options are limited. In this study, we investigated the potential of methylene blue (MB) as a metabolic therapy and complementary treatment approach for ovarian cancer. Our findings demonstrated a significant in vivo reduction in the proliferation of TOV112D-based ovarian-cell-line xenografts. In this preclinical study, which used a carboplatin-resistant ovarian cancer tumor model implanted into mice, MB-mediated metabolic therapy exhibited superior tumor slowdown compared to carboplatin treatment alone. This indicates, for the first time, MB's potential as an alternative or adjuvant treatment, especially for resistant cases. Our in vitro study on TOV112D and ARPE-19 sheds light on the impact of such an MB-based metabolic therapy on mitochondrial energetics (respiration and membrane potential). MB showed a modulatory role in the oxygen consumption rate and the mitochondrial membrane potential. These results revealed, for the first time, that MB specifically targets TOV112D mitochondria and probably induces cell apoptosis. The differential response of normal (ARPE-19) and cancer (TOV112D) cells to the MB treatment suggests potential alterations in cancer cell mitochondria, opening avenues for therapeutic approaches that target the mitochondria. Overall, our findings suggest the efficacy of MB as a possible treatment for ovarian cancer and provide valuable insights into the mechanisms underlying the efficacy of methylene blue metabolic therapy in ovarian cancer treatment.
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.
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".