Understanding the signaling and transmission of visceral nociceptive events
Bibliographic record
Abstract
Visceral pain can be considered as part of the defense reactions of the body against harmful stimuli, particularly of those that impinge on the mucosal lining of hollow organs. It is a problem of considerable clinical relevance, and its neurobiological mechanisms differ from those of somatic nociceptive or neuropathic pain. Much progress had been made in recent years in the understanding of the functional properties of the visceral nociceptors that trigger pain states, their molecular mechanisms of activation and sensitization and on their central actions. Some molecular targets have been identified as key players in the activation and sensitization of visceral nociceptors, notably ASICs, TTX-resistant Na channels and the TRPV1 receptor. Some nonneural elements of visceral organs, such as the urothelium have been shown to play active roles in the transduction of visceral sensory events by mechanisms involving ATP release by the urothelial cells. Certain well-known neurotransmitters, such as the tachykinin family of neuropeptides, likely play an important role in the peripheral and central activation of visceral nociceptive afferents and in the generation of visceral hyperalgesia. This article reviews current evidence on the mechanisms of activation and sensitization of visceral nociceptive afferents and on their role in the triggering and maintenance of clinically relevant visceral pain states.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".