Lymphocyte-mediated regulation of β-endorphin in the myenteric plexus
Bibliographic record
Abstract
Lymphocytes are antinociceptive and can modulate visceral pain perception in mice. Previously, we have shown that adoptive transfer of CD4+ T cells to severe combined immune-deficient (SCID) mice normalized immunodeficiency-related visceral hyperalgesia. Pain attenuation was associated with an increase in beta-endorphin release by T cells and an upregulation of beta-endorphin in the enteric nervous system. In this study, we investigated the relationship between T cells and opioid expression in the myenteric plexus. We examined opioid peptide and receptor expression in the myenteric plexus in the presence and absence of mucosal T cells. We found a positive association between T cells and beta-endorphin expression; this was accompanied by a downregulation of the micro-opioid receptor (MOR). In vitro, T helper (Th) type 1 and type 2 cytokine stimulation of CD4+ T cells or isolation of T cells from in vivo Th-polarized mice did not increase T cell release of beta-endorphin or the induction of beta-endorphin expression in the myenteric plexus. However, exogenous beta-endorphin did upregulate beta-endorphin expression, and both cycloheximide and naloxone methiodide inhibited peptide upregulation. Therefore, our results suggest that nonpolarized CD4+ T cells release beta-endorphin, which, through an interaction with MOR, stimulates an upregulation of beta-endorphin expression in the myenteric plexus. Thus, we propose that the mechanism underlying lymphocyte modulation of visceral pain involves T cell modulation of opioid expression in the enteric nervous system.
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) | 0.000 | 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 | 0.001 |
| 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".