Sex-specific involvement of calcitonin gene–related peptide signaling for pain in experimental autoimmune encephalomyelitis
Bibliographic record
Abstract
Introduction: Neuropathic pain (NP) is one of the most devastating and under-managed symptoms of multiple sclerosis (MS). As it stands, NP is a difficult condition to treat, as many commonly used pain therapies are ineffective. Research has now emerged demonstrating sex differences in the mechanism of NP in MS, adding complexity to the search for new treatments. Although it is widely known that female patients are more likely than male patients to develop MS, it is less commonly acknowledged that they are also more likely to experience NP in the disease. Thus, there is an urgent need to develop NP treatments specifically for female patients with MS. Methods: Using the experimental autoimmune encephalomyelitis (EAE) mouse model of MS, we have characterized the outgrowth in culture as well as the spinal innervation pattern of peptidergic primary sensory afferents in both sexes. We then used a receptor antagonist to disrupt neuropeptide signaling from these cells to treat pain. Results: We found structural plasticity in peptidergic nociceptors from female animals with established EAE both in vitro and in vivo, as well as increased antibody stain intensity for the neuropeptide calcitonin gene-related peptide (CGRP). Using a receptor antagonist for CGRP (CGRP8-37), we were able to reverse spontaneous pain in female EAE animals. Conclusions: These results suggest that targeting peptidergic nociceptors and CGRP signaling, specifically in female patients, may be a viable strategy to relieve and possibly reverse pain in female patients with MS.
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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) | 0.003 | 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".