Melatonin prevents mitochondrial dysfunction and promotes neuroprotection by inducing autophagy during oxaliplatin‐evoked peripheral neuropathy
Bibliographic record
Abstract
Oxaliplatin, an organoplatinum compound, is used in the treatment of colorectal cancer, but its clinical use can be limited due to the development of peripheral neuropathy. Whilst mitochondrial dysfunction has been implicated as a major pathomechanism for oxaliplatin-induced neurotoxicity, the prevention of autophagy may also aggravate neuronal cell death. Melatonin, a well-known mitoprotectant and autophagy inducer, was used to examine its neuroprotective role in oxaliplatin-induced peripheral neuropathy (OIPN). Melatonin prevented the loss of mitochondrial membrane potential (Ψm) and promoted neuritogenesis in oxaliplatin-challenged neuro-2a cells. It did not interfere with the cytotoxic activity of oxaliplatin in human colon cancer cell line, HT-29. Melatonin treatment significantly alleviated oxaliplatin-induced pain behavior and neuropathic deficits in rats. It also ameliorated nitro-oxidative stress mediated by oxaliplatin, thus prevented nitrosylation of proteins and loss of antioxidant enzymes, and therefore, it improved mitochondrial electron transport chain function and maintained cellular bioenergetics by improving the ATP levels. The protective effects of melatonin were attributed to preventing oxaliplatin-induced neuronal apoptosis by increasing the autophagy pathway (via LC3A/3B) in peripheral nerves and dorsal root ganglion (DRG). Hence, it preserved the epidermal nerve fiber density in oxaliplatin-induced neuropathic rats. Taken together, we provide detailed molecular mechanisms for the neuroprotective effect of melatonin and suggest it has translational potential for oxaliplatin-induced neuropathy.
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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) | 0.000 | 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 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".