Immunohistochemical Profile of Human Pancreatic Pacinian Corpuscles
Bibliographic record
Abstract
OBJECTIVES: To analyze the immunohistochemical profile of the human pancreatic pacinian corpuscles in comparison with that of the cutaneous pacinian corpuscles. In addition, we studied a Pacinilike corpuscle found in the adventitia of a pancreatic artery. METHODS: We used immunohistochemistry to detect specific antigens for corpuscular constituents, specific antibodies for the identification of Adelta- and C-sensory fibers and for the detection of several growth factor receptors, and some members of the degenerin/epithelial Na channel superfamily of proteins. RESULTS: Approximately 62% of pancreatic pacinian corpuscles have 2 to 10 axonic profiles each enclosed by its own inner core: 1 or 2 of these axonic profiles displayed RT-97 immunoreactivity (specific marker of mechanical axons). The cutaneous pacinian corpuscles showed not more than 2 axonic profiles with identical immunohistochemical characteristics. The expression of glial fibrillary acidic protein, epithelial membrane antigen, and tyrosine receptor kinase B was different between pancreatic and cutaneous pacinian corpuscles; the pattern of distribution of degenerin/epithelial Na channel proteins was identical in both cases. The arterial Pacinilike corpuscles displayed a specific immunohistochemical profile. CONCLUSIONS: Pancreatic pacinian corpuscles slightly differ from the cutaneous ones, and these differences could be related to topography, growth factor requirements, or function of pacinian corpuscles in the pancreas.
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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) | 0.002 | 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".