The involvement of spinal bovine adrenal medulla 22‐like peptide, the proenkephalin derivative, in modulation of nociceptive processing
Bibliographic record
Abstract
Bovine adrenal medulla 22 (BAM22), one of the cleavage products of proenkephalin A, possesses high affinity for opioid receptors and sensory neuron-specific receptor (SNSR). The present study was designed to examine the expression of BAM22 in the spinal cord and dorsal root ganglion (DRG) of naive rats as well as in a model of inflammation. BAM22-like immunoreactivity (BAM22-IR) was expressed in fibers in the spinal cord, with high density seen in lamina I in naïve rats. The expression of BAM22-IR in the superficial laminae was greatly reduced following dorsal rhizotomy. BAM22-IR was also located in 19% of DRG cells, mainly in the small- and medium-sized subpopulations. Following injection of complete Freund's adjuvant (CFA) in the hindpaw, the expression of BAM22-IR in the superficial laminae of the spinal cord and small-sized DRG neurons on the ipsilateral side was markedly increased. Double labeling showed that the Fos-positive nucleus was surrounded by BAM22-IR cytoplasm in the spinal dorsal horn neurons or closely associated with BAM22-IR fibers in the superficial laminae. Furthermore, CFA-induced mechanical allodynia in the inflamed paw was potentiated by intrathecal administration of anti-BAM22 antibody. Together, these results demonstrate for the first time that BAM22-like peptide is mainly located in the superficial laminae of the spinal cord and mostly originates from nociceptive DRG neurons. BAM22 could thus act as a ligand for presynaptic opioid receptors and SNSR. Our study also provides evidence suggesting that BAM22 plays a role in the modulation of nociceptive processing at the spinal level under normal and inflammatory conditions.
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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".