Growth-associated Protein-43 (GAP-43) Expression In Ganglionic and Aganglionic Colon
Bibliographic record
Abstract
Abstract Objectives: Because of its specificity for nerve fibers of the enteric nervous system, calretinin is an effective adjunctive marker in the assessment for Hirchsprung disease. Growth associated protein (GAP-43) has been shown to be expressed in nerve fibers within the intestinal lamina propria. No prior report compares GAP-43 expression in ganglionic versus aganglionic intestine. Methods: Six consecutive Hirschsprung endorectal pull through specimens were retrieved from our archives. In addition 3 controls were selected from colonic resections for reasons other than Hirschsprung Disease. Immunoperoxidase for GAP-43 was carried out on the ganglionic and aganglionic segments of all cases and controls. Submucosal ganglion soma positivity and nerve fiber positivity within the lamina propria were graded on a subjective scale of 1-3 that incorporated both strength and density. Data: GAP-43 strongly stained submucosal ganglion cells and nerve fibers within the lamina propria in 6/6 of the ganglionic segments and 3/3 of the normally innervated controls . GAP-43 did not show any ganglion cell body positivity within the aganglionic segments; however, all 6 aganglionic segment lamina propria were positive for nerve fiber staining. There was a small subjective increase in the amount of nerve fiber positivity for GAP-43 in ganglionic segments and controls versus aganglionic segments. Conclusion: GAP-43 marks mucosal nerve fibers in ganglionic intestine but also aganglionic intestine and thus is less useful than calretinin as a marker for Hirschsprung Disease. The abundant mucosal nerves highlighted by GAP-43 requires further characterization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".