Macrophage migration inhibitory factor is a potential therapeutic target for cisplatin induced peripheral neuropathy in breast cancer
Bibliographic record
Abstract
Abstract Background Cisplatin (CP) is an effective chemotherapy drug for several cancers. However, the use of CP is associated with peripheral neuropathy, a painful nerve disorder. Unfortunately, no therapies are available for CP-induced peripheral neuropathy (CisIPN). This study explored the role of a cytokine, the macrophage migration inhibitory factor (MIF), as a potential therapeutic target for CisIPN. Methods The role of neuroinflammation and MIF in CisIPN was evaluated in mice models of CisIPN, with and without breast cancer, after treatment with the anti-inflammatory drug Dexamethasone (Dex). Circulating MIF levels in animals were examined using ELISA. Pharmacological inhibition of MIF was achieved using the small molecule inhibitors, CPSI-1306 and ISO-1. Mechanical and thermal sensitivities of animals were assessed using von frey filament and cold acetone assays. Macrophage infiltration in peripheral nerve tissues was examined using CD68 and Iba-1 staining. Results Our results showed that Dex suppressed mechanical hyperalgesia in CisIPN animals, which was accompanied by downregulation of MIF. We also found that circulating MIF levels were increased in CisIPN animals. Furthermore, direct inhibition of MIF using CPSI-1306 and ISO-1 led to suppression of mechanical hyperalgesia, without compromising the anti-tumor efficacy of CP, in CisIPN animals. We did not find any significant change in macrophage infiltration in the peripheral nerve tissues of CisIPN animals. Immunostaining results indicated that sensory neurons in the DRGs and Schwann Cells in the sciatic nerves are potential sources for increased MIF in CisIPN. Interpretation Overall, our results strongly suggest that MIF is a promising therapeutic target for CisIPN.
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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) | 0.001 | 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".