Dual localization of neuropeptide FF receptors in the rat dorsal horn
Bibliographic record
Abstract
Although neuropeptide FF (NPFF) is generally considered an anti-opioid, its intrathecal administration produces analgesia. In the present study, the stable analog 1DMe ([D.Tyr(1), (NMe)Phe(3)]neuropeptide FF) was used in quantitative autoradiographic experiments in combination with surgical and chemical lesions to precisely localize NPFF receptors in the rat spinal cord. Ligation of lumbar dorsal spinal roots revealed the presence of NPFF receptors in dorsal root fibers and it induced a significant accumulation of [(125)I]1DMe-specific binding on the side peripheral to the ligature, demonstrating that a population of NPFF receptors is synthesized in dorsal root ganglia and migrates anterogradely towards primary afferent nerve endings. Complete mid-thoracic spinal cord transection failed to modify the [(125)I]1DMe labeling density in the dorsal horn, indicating that NPFF receptors are not located on the descending fiber terminals. In contrast, unilateral microinjections of kainic acid into the dorsal horn dramatically reduced [(125)I]1DMe-specific binding in the superficial layers, revealing localization of a population of NPFF receptors on the spinal intrinsic neurons. NPFF receptor binding was not modified during the development of spinal opioid tolerance. The pre- and postsynaptic localization of spinal NPFF receptors provide further support for heterogeneity in the pain modulation by NPFF and related agonists.
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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".