Pain regulation of endokinin A/B or endokinin C/D on chimeric peptide MCRT in mice
Bibliographic record
Abstract
The present study focused on the interactive pain regulation of endokinin A/B (EKA/B, the common C-terminal decapeptide in EKA and EKB) or endokinin C/D (EKC/D, the common C-terminal duodecapeptide in EKC and EKD) on chimeric peptide MCRT (YPFPFRTic-NH2, based on YPFP-NH2 and PFRTic-NH2) at the supraspinal level in mice. Results demonstrated that the co-injection of nanomolar EKA/B and MCRT showed moderate regulation, whereas 30 pmol EKA/B had no effect on MCRT. The combination of EKC/D and MCRT produced enhanced antinociception, which was nearly equal to the sum of the mathematical values of single EKC/D and MCRT. Mechanism studies revealed that pre-injected naloxone attenuated the combination significantly compared with the equivalent analgesic effects of EKC/D alone, suggesting that EKC/D and MCRT might act on two totally independent pathways. Moreover, based on the above results and previous reports, we made two reasonable hypotheses to explain the cocktail-induced analgesia, which may potentially pave the way to explore the respective regulatory mechanisms of EKA/B, EKC/D, and MCRT and to better understand the complicated pain regulation of NK1 and μ opioid receptors, as follows: (1) MCRT and endomorphin-1 possibly activated different μ subtypes; and (2) picomolar EKA/B might motivate the endogenous NPFF system after NK1 activation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".