PACAP and orphanin FQ/nociceptin -Distribution, importance and regulation in sensory neurons and spinal cord
Bibliographic record
Abstract
Neuropeptide expression in the nervous system is abundant and plastic, and an altered expression after injury is an example of a functional response, helping the neuron to cope with adverse changes, and involves effects on differentiation, synthesis, repair/regeneration, survival, and modulation of signal transmission. I have investigated the distribution/expression of the neuropeptides, orphanin FQ/nociceptin (OFQ/N) and pituitary adenylate cyclase activating polypeptide (PACAP), under normal conditions, in response to nerve injuries, and also studied sensory responses in mice deficient for the PAC1 receptor (PAC1-/-). Expression of OFQ/N and its receptor is demonstrated in neurons in spinal cord dorsal and ventral horns, DRG and SCG. Expression in specific neurons in these tissues gives a morphological basis supporting their suggested role as modulators of sensory, especially nociceptive, transmission. Further modulatory roles are suggested by expression in motor neurons and SCG. Expression of PACAP in spinal cord dorsal and ventral horn neurons, and induced expression in motor neurons in response to sciatic nerve injury, is demonstrated. Further, PACAP expression is induced in DRG neurons in response to nerve transection, and compression injury. Intrathecal anti-BDNF infusion mitigates this injury induced expression, suggesting that endogenous BDNF can regulate PACAP expression. The injury induced PACAP expression, in DRG and motor neurons, indicates a possible role for PACAP in repair/regeneration and modulation of the sensory/nociceptive and motor transmission. More direct evidence for this is the finding of a pronounced decrease in pain behaviour in PAC1–/– mice, strongly suggesting a role for PACAP in modulation of inflammatory 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) | 0.000 | 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 | 0.000 |
| 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".