Hyperexcitability of muscle spindle afferents in jaw‐closing muscles in experimental myalgia: Evidence for large primary afferents involvement in chronic pain
Bibliographic record
Abstract
Abstract The goals of this review are to improve understanding of the aetiology of chronic muscle pain and identify new targets for treatments. Muscle pain is usually associated with trigger points in syndromes such as fibromyalgia and myofascial syndrome, and with small spots associated with spontaneous electrical activity that seems to emanate from fibers inside muscle spindles in EMG studies. These observations, added to the reports that large‐diameter primary afferents, such as those innervating muscle spindles, become hyperexcitable and develop spontaneous ectopic firing in conditions leading to neuropathic pain, suggest that changes in excitability of these afferents might make an important contribution to the development of pathological pain. Here, we review evidence that the muscle spindle afferents (MSAs) of the jaw‐closing muscles become hyperexcitable in a model of chronic orofacial myalgia. In these afferents, as in other large‐diameter primary afferents in dorsal root ganglia, firing emerges from fast membrane potential oscillations that are supported by a persistent sodium current (INaP) mediated by Na+ channels containing the α‐subunit NaV1.6. The current flowing through NaV1.6 channels increases when the extracellular Ca2+ concentration decreases, and studies have shown that INaP‐driven firing is increased by S100β, an astrocytic protein that chelates Ca2+ when released in the extracellular space. We review evidence of how astrocytes, which are known to be activated in pain conditions, might, through their regulation of extracellular Ca2+, contribute to the generation of ectopic firing in MSAs. To explain how ectopic firing in MSAs might cause pain, we review evidence supporting the hypothesis that cross‐talk between proprioceptive and nociceptive pathways might occur in the periphery, within the spindle capsule.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".