Macrophage migration inhibitory factor is a potential therapeutic target for cisplatin induced peripheral neuropathy in breast cancer
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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 it