Heterogenous expression of nestin in human pancreatic tissue precludes its use as an islet precursor marker
Bibliographic record
Abstract
The discovery of a pancreatic adult stem cell would have significant implications for cell-based replacement therapies for type 1 diabetes mellitus. Nestin, a marker for neural precursor cells, has been suggested as a possible marker for islet progenitor cells. We have characterized the expression and localization of nestin in both the intact human pancreas and clinical human pancreatic islet grafts. Nestin was found to be expressed at different levels in the acinar component of human pancreatic biopsies depending on donor, as well as in ductal structures and islets to some degree. In islets, insulin-producing beta-cells rarely co-expressed the protein, and in the ducts a small percentage (1-2%) of cells co-expressed nestin and cytokeratin 19 (CK19) while most expressed only CK19 (90%) or nestin (5-10%) alone. Assessment of nestin expression in neonatal pancreatic sections revealed an increased number of islet-associated positive cells as compared with adult islets. Nestin immunoreactivity was also found in cells of the pancreatic vasculature and mesenchyme as evidenced by co-localization with smooth muscle actin and vimentin. Samples from post-islet isolation clinical islet grafts revealed a pronounced heterogeneity in the proportion of nestin-positive cells (<1-72%). Co-localization studies in these grafts showed that nestin is not co-expressed in endocrine cells and rarely (<5%) with cytokeratin-positive ductal cells. However, relatively high levels of co-expression were found with acinar cells and cells expressing the mesenchymal marker vimentin. In conclusion we have shown a diffuse and variable expression of nestin in human pancreas that may be due to a number of different processes, including post-mortem tissue remodeling and cellular differentiation. For this reason nestin may not be a suitable marker solely for the identification of endocrine precursor cells 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 | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".