Sympathetic Fibre Sprouting in the Skin Contributes to Pain-Related Behaviour in Spared Nerve Injury and Cuff Models of Neuropathic Pain
Bibliographic record
Abstract
BACKGROUND: Cuff and spared nerve injury (SNI) in the sciatic territory are widely used to model neuropathic pain. Because nociceptive information is first detected in skin, it is important to understand how alterations in peripheral innervation contribute to pain in each model. Over 16 weeks in male rats, changes in sensory and autonomic innervation of the skin were described after cuff and SNI using immunohistochemistry to label myelinated (neurofilament 200 positive-NF200+) and peptidergic (calcitonin gene-related peptide positive-CGRP+) primary afferents and sympathetic fibres (dopamine β-hydroxylase positive-DBH+) RESULTS: Cuff and SNI caused an early loss and later reinnervation of NF200 and CGRP fibres in the plantar hind paw skin. In both models, DBH+ fibres sprouted into the upper dermis of the plantar skin 4 and 6 weeks after injury. Despite these similarities, behavioural pain measures were significantly different in each model. Sympathectomy using guanethidine significantly alleviated mechanical allodynia 6 weeks after cuff, when peak sympathetic sprouting was observed, having no effect at 2 weeks, when fibres were absent. In SNI animals, mechanical allodynia in the lateral paw was significantly improved by guanethidine at 2 and 6 weeks, and the development of cold hyperalgesia, which roughly paralleled the appearance of ectopic sympathetic fibres, was alleviated by guanethidine at 6 weeks. Sympathetic fibres did not sprout into the dorsal root ganglia at 2 or 6 weeks, indicating their unimportance to pain behaviour in these two models. CONCLUSIONS: Alterations in sympathetic innervation in the skin represents an important mechanism that contributes to pain in cuff and SNI models of neuropathic pain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".