Intrathecal administration of<scp>NTS</scp>1 agonists reverses nociceptive behaviors in a rat model of neuropathic pain
Bibliographic record
Abstract
Chronic neuropathic pain arising from peripheral nerve damage is a severe clinical issue where there is a major unmet medical need. We previously demonstrated that both neurotensin (NT) receptor subtypes 1 (NTS1) and 2 (NTS2) are involved in mediating the naloxone-insensitive antinociceptive effects of neurotensin in different analgesic tests including hotplate, tail-flick, and tonic pain. However, the role of these receptors in neuropathic pain management has been poorly investigated. In the present study, we therefore examined whether intrathecal delivery of NTS1 agonists was effective in reducing neuropathic pain symptoms in rats. Neuropathy was induced by sciatic nerve constriction (CCI model), and the development of mechanical allodynia and thermal hyperalgesia on the ipsi- and contralateral hind paws was examined 3, 7, 14, 21, and 28 days post-surgery. CCI-operated rats exhibited significant increases in thermal and mechanical hypersensitivities over a 28-day testing period. Spinal injection of NT to CCI rats alleviated the behavioral responses to radiant heat and mechanical stimuli, with a maximal reversal of 91% of allodynia at 6 μg/kg. Intrathecal administration of the NTS1-selective agonist, PD149163 (30-90 μg/kg) also produced potent anti-allodynic and anti-hyperalgesic effects in nerve-injured rats. Likewise, heat hyperalgesia and tactile allodynia produced by CCI of the sciatic nerve were fully reversed by the NTS1 agonist, NT69L (5-25 μg/kg). Altogether, these results support the idea that the NTS1 receptor subtype is involved in pain modulation, and the potential use of NTS1 agonists for the treatment of painful neuropathies.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".