Neurons populating the rectal extrinsic nerves in humans express neuronal and Schwann cell markers
Bibliographic record
Abstract
BACKGROUND: In mice, Schwann cell (SC) progenitors give rise to autonomic ganglion cells and migrate into the gut to become enteric neurons. It is unknown whether SC progenitors have a similar fate in humans. In search of evidence for human SC-derived neurogenesis in the gastrointestinal (GI) tract, we studied the rectums from cadaveric controls and children with anorectal malformations (ARM). METHODS: We analyzed distal rectal tissue taken at autopsy from 10 children with normal GI tracts and resected rectal specimens in 48 cases of ARM. Of these specimens, 6 had neurons within the extrinsic rectal innervation. These were further investigated with immunohistochemistry for neuronal and SC/glial markers. KEY RESULTS: Perirectal tissue from control and ARM contained GLUT1-positive extrinsic nerves, many containing neurons. SC/glial markers (SOX10, CDH19, and PLP1) were expressed by glia in the enteric nervous system and perirectal nerves, while MPZ predominated only in glia of perirectal nerves, in both control and ARM. Neurons in perirectal nerves were 61% larger in ARM samples and co-expressed SOX10 (81%), PLP1 (73%), and CDH19 (56%). In ARM, cytoplasmic SOX10 was co-expressed with neuronal antigens in ~57% of submucosal and myenteric neurons, vs. ~3% in control. Furthermore, intrinsic gut neurons in ARM specimens co-expressed PLP1 (18%) and CDH19 (18%); however, neuronal co-expression of PLP1 and CDH19 was rarely (<2%) observed in controls. CONCLUSIONS & INFERENCES: Dual expression of glial and neuronal markers in rectal and perirectal neurons support a model of Schwann cell-derived neurogenesis in the innervation of the human GI tract.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".