Nestin expression and clonal analysis of islet-derived epithelial monolayers: insight into nestin-expressing cell heterogeneity and differentiation potential
Bibliographic record
Abstract
There has over the last several years been renewed interest in developing a system for generating new islets and a search for a self-renewing population in the pancreas. In particular, the neural stem cell marker nestin has been implicated as an islet precursor marker and its immunoreactivity has been localized in the islets of Langerhans. This study examines islet-derived epithelial monolayers expanded ex vivo to provide a source of nestin-expressing progenitor cells--a model that will help us understand the role of nestin-expressing cells in islet cell development. When cultured on a type I collagen gel, islets formed confluent monolayers which lacked endocrine phenotypes but were positive for cytokeratin 20 and contained an increased proportion of proliferating nestin-expressing cells, reaching a maximum of 54+/-10%. Co-expression studies demonstrated that the nestin-positive cells are heterogeneous, with some nestin-expressing cells co-localizing with the transcription factor PDX-1 and glucose transporter type 2 or lack of co-expression with vimentin. When clonal populations of nestin-positive cells were expanded and subjected to a differentiation protocol, only a population that expressed the transcription factor PDX-1 at the mRNA level was capable of re-expressing insulin at the mRNA and protein level. In conclusion, these studies demonstrate that expanded nestin-expressing cells in vitro from islet-derived epithelial monolayers are heterogeneous; clonal analysis of nestin-positive cells reveals that a distinct subpopulation of nestin/PDX-1-expressing cells is capable of forming insulin-producing cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".