Spinal NGF Restores Opioid Sensitivity in Neuropathic Rats: Possible Role of NGF as a Regulator of CCK‐Induced Anti‐Opioid Effects
Bibliographic record
Abstract
The breadth of peripheral effects produced by nerve growth factor (NGF) in nociceptive processing has been well documented. However, less is known about the functional significance of central NGF in nociceptive transmission. The effect of NGF on the nervous system is dependent on the developmental stage. During the prenatal developmental period, NGF is critical for survival of nociceptors; in the postnatal period it regulates the expression of nociceptor phenotype, and in the adult it contributes to pain following an inflammatory insult. The implications for central NGF in the expression and regulation of spinal neuropeptides that are involved in pain mechanisms are reviewed. Knowledge has been gained by studies using peripheral nerve injury models that cause a deprivation of central NGF. These models also give rise to the development of pain syndromes, which encompass spontaneous pain, hyperalgesia and allodynia, routinely referred to as neuropathic pain. These models provide an approach for examining the contribution of central NGF to nociceptive transmission. Chronic pain emanating from a nerve injury is typically refractory to traditional analgesics such as opioids. Recent evidence suggests that supplementation of spinal NGF restores morphine‐induced antinociception in an animal model of neuropathic pain. This effect appears to be mediated by alterations in spinal levels of cholecystokinin. The authors hypothesize that NGF is critical in maintaining neurochemical homeostasis in the spinal cord of nociceptive neurons, and that supplementation may be beneficial in restoring and/or maintaining opioid analgesia in chronic pain conditions resulting from traumatic nerve injury.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".