Therapeutic effects of antibiotic drug mefloquine against cervical cancer through impairing mitochondrial function and inhibiting mTOR pathway
Bibliographic record
Abstract
Targeting mitochondria is an attractive strategy for cancer therapy due to the essential roles of mitochondria in cancer cell energy metabolism. In this study, we show that mefloquine, an antibiotic drug, effectively targets cervical cancer cells through impairing mitochondrial function. Mefloquine dose-dependently induces apoptosis and inhibits proliferation and anchorage-independent colony formation of multiple cervical cancer cell lines. Mefloquine alone inhibits cervical tumor growth in vivo and its combination with paclitaxel is synergistic in inhibiting tumor growth. Mechanistically, mefloquine inhibits mitochondrial function via inhibiting mitochondrial respiration, decreasing membrane potential, increasing ROS generation, and decreasing ATP level. We further show that mefloquine suppresses activation of mTOR signaling pathway in HeLa cells. However, the inhibitory effects of mefloquine on survival, colony formation, and ATP are abolished in mitochondrial respiration-deficient HeLa ρ0 cells, demonstrating that mefloquine acts on cervical cancer cells via targeting mitochondrial respiration. Inhibition of mTOR signaling pathway by mefloquine was also reversed in HeLa ρ0 cells, suggesting deactivation of mTOR pathway as a consequence of mitochondria function disruption. Our work suggests that mefloquine is a potential candidate for cervical cancer treatment. Our work also highlights the therapeutic value of anti-mitochondria and establishes the association of mitochondrial function and the activation of mTOR signaling pathway in cervical cancer cells.
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".