Peak density of immature nerve cells occurs with high‐grade dysplasia in intraductal papillary mucinous neoplasms of the pancreas
Bibliographic record
Abstract
The development of neural structures within tumors is now considered vital for carcinogenesis. However, the time course of this development in human pre-invasive neoplasia has been incompletely described. Therefore, we performed a detailed analysis of nerves across the neoplastic spectrum in resected intraductal papillary mucinous neoplasms (IPMNs) of the pancreas. Histology and multiplexed immunochemistry demonstrated that nerve density increased from low-grade (LG) to high-grade dysplasia (HG) but did not further increase once invasive IPMN (INV IPMN) was present. Higher nerve density correlated with increasing expression of nerve growth factor (NGF) by the tumor cells. Intra-tumoral nerves were immature and lacked markers of sympathetic, parasympathetic, and sensory lineages. Here, we show for the first time the presence of neural precursor cells (NPCs) within the stroma of pancreatic tumors. The density of these doublecortin (DCX)-positive NPCs increased from LG to HG, but not from HG to INV IPMN. We conclude that peak neural density of tumors is reached in high-grade dysplasia (often termed carcinoma in situ) rather than after invasion. These findings suggest that nerve-tumor interactions are important in IPMN progression and may serve as the basis for future mechanistic studies and novel therapeutic modalities. © 2022 The Pathological Society of Great Britain and Ireland.
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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 | 0.000 |
| 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".