A novel preparation to study rat pancreatic spinal and vagal mechanosensitive afferents <i>in vitro</i>
Bibliographic record
Abstract
The management of pancreatic pain is a significant clinical problem so understanding of how sensory signals are generated in pancreatic tissue is fundamental. We aimed to characterize mechanosensitive and chemosensitive properties of pancreatic spinal and vagal afferents in vitro. Spinal and vagal afferent preparations from Sprague-Dawley rats were established incorporating the left splanchnic nerve or vagus nerves respectively. The common bile duct was cannulated for distension of the pancreatic duct with fluid. Nerve discharge evoked by blunt probing, duct distension or electrical stimulation was obtained from teased nerve bundles using standard extra-cellular recording. Discharge from 197 spinal afferent bundles was recorded, of which 57% displayed spontaneous activity. Blunt probing revealed 61 mechanosensitive receptive fields which were associated primarily with arteries/blood vessels (33/61) and the parenchyma (22/61). All mechanosensitive responses were slowly adapting, with 33% continuing to discharge after termination of the stimulus and 60% displaying a response threshold <10 g. Application of chemical mediators (bradykinin, histamine, 5-hydroxytryptamine, cholecystokinin octapeptide) evoked a response from 31/57 units, with 33% excitatory and 23% inhibitory. Spontaneous discharge was recorded from 72% of 135 vagal bundles. Mechanosensitive receptive fields were not identified in the pancreas but were evident in adjacent organs. No spinal or vagal afferent response to duct distension was obtained. In conclusion, pancreatic mechanosensitive spinal afferents are common, in contrast to pancreatic mechanosensitive vagal afferents indicating that pancreatic sensory innervation is predominantly spinal. Chemosensitive spinal afferent nerve endings are present in the pancreas and respond to a variety of inflammatory and physiological mediators.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".