Selective loss of spinal GABAergic or glycinergic neurons is not necessary for development of thermal hyperalgesia in the chronic constriction injury model of neuropathic pain
Bibliographic record
Abstract
GABA and glycine are inhibitory neurotransmitters used by many neurons in the spinal dorsal horn, and intrathecal administration of GABA(A) and glycine receptor antagonists produces behavioural signs of allodynia, suggesting that these transmitters have an important role in spinal pain mechanisms. Several studies have described a substantial loss of GABA-immunoreactive neurons from the dorsal horn in nerve injury models, and it has been suggested that this may be associated with a loss of inhibition, which contributes to the behavioural signs of neuropathic pain. We have carried out a quantitative stereological analysis of the proportions of neurons in laminae I, II and III of the rat dorsal horn that show GABA- and/or glycine-immunoreactivity 2 weeks after nerve ligation in the chronic constriction injury (CCI) model, as well as in sham-operated and nai;ve animals. At this time, rats that had undergone CCI showed a significant reduction in the latency of withdrawal of the ipsilateral hindpaw to a radiant heat stimulus, suggesting that thermal hyperalgesia had developed. However, we did not observe any change in the proportion of neurons in laminae I-III of the ipsilateral dorsal horn that showed GABA- or glycine-immunoreactivity compared to the contralateral side in these animals, and these proportions did not differ significantly from those seen in sham-operated or nai;ve animals. In addition, we did not see any evidence for alterations of GABA- or glycine-immunostaining in the neuropil of laminae I-III in the animals that had undergone CCI. Our results suggest that significant loss of GABAergic or glycinergic neurons is not necessary for the development of thermal hyperalgesia in the CCI model 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 | 0.001 | 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".