Spinal NTS2 receptor activation reverses signs of neuropathic pain
Bibliographic record
Abstract
Management of painful peripheral neuropathies remains challenging, since patients with chronic pain respond poorly to the available pharmacopeia. In recent years, the G-protein-coupled receptor neurotensin (NT) type 2 (NTS2) emerged as an attractive target for treating transitory pain states. To date, however, there is no evidence for its role in the regulation of chronic peripheral neuropathies. Here, we found that NTS2 receptors were largely localized to primary afferent fibers and superficial dorsal horns. Changes in the time course of the gene expression profile of NT, NTS1, and NTS2 were observed over a 28-d period following the sciatic nerve constriction [chronic constriction injury (CCI) model]. We next determined the effects of central delivery of selective-NTS2 agonists to CCI-treated rats on both mechanical allodynia (evoked withdrawal responses) and weight-bearing deficits (discomfort and quality-of-life proxies). The NTS2 analogs JMV431, levocabastine, and β-lactotensin were all effective in reducing ongoing tactile allodynia in CCI-treated rats. Likewise, amitriptyline, pregabalin, and morphine significantly attenuated CCI-induced mechanical hypersensitivity. NTS2 agonists were also efficient in reversing weight-bearing and postural deficits caused by nerve damage, unlike reference analgesics currently used in the clinic. Thus, NTS2 agonists may offer new treatment avenues for limiting pain associated with peripheral neuropathies and improve functional rehabilitation and well-being.
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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 | 0.000 | 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) | 0.003 | 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".