Neurotrophin-3 Suppresses Thermal Hyperalgesia Associated with Neuropathic Pain and Attenuates Transient Receptor Potential Vanilloid Receptor-1 Expression in Adult Sensory Neurons
Bibliographic record
Abstract
Neurotrophin-3 (NT-3) negatively modulates nerve growth factor (NGF) receptor expression and associated nociceptive phenotype in intact neurons, suggesting a beneficial role in treating aspects of neuropathic pain mediated by NGF. We report that NT-3 is effective at suppressing thermal hyperalgesia associated with chronic constriction injury (CCI); however, NT-3 does not alter the mechanical hypersensitivity that also develops with CCI. Thermal hyperalgesia is critically linked to expression and activation of the capsaicin receptor, transient receptor potential vanilloid receptor-1 (TRPV1). Thus, its modulation by NT-3 after CCI was examined. CCI results in elevated TRPV1 expression at both the mRNA and protein levels in predominantly small-to-medium neurons, with the percentage of neurons expressing TRPV1 remaining unchanged at approximately 56%. Attenuation of thermal hyperalgesia mediated by NT-3 correlates with decreased TRPV1 expression such that only approximately 26% of neurons ipsilateral to CCI expressed detectable TRPV1 mRNA. NT-3 effected a decrease in expression of the activated component of the signaling pathway linked to regulation of TRPV1 expression, phospho-p38 MAPK (Ji et al., 2002), in neurons ipsilateral to CCI. Exogenous NT-3 could both prevent the onset of thermal hyperalgesia and reverse established thermal hyperalgesia and elevated TRPV1 expression 1 week after CCI. Continuous infusion is required for suppression of both thermal hyperalgesia and TRPV1 expression, because removal of NT-3 resulted in a prompt reestablishment of the hyperalgesic state and corresponding CCI-associated TRPV1 phenotype. In conclusion, although NGF drives inflammation-associated thermal hyperalgesia via its regulation of TRPV1 expression, NT-3 is now identified as a potent negative modulator of this state.
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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 | 0.001 |
| 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".