Pronounced proliferation of non-beta cells in response to beta-cell mitogens in isolated human islets of Langerhans
Bibliographic record
Abstract
The potential to treat diabetes by increasing beta-cell mass is driving a major effort to identify beta-cell mitogens. Demonstration of mitogen activity in human beta cells is frequently performed in ex vivo assays. However, reported disparities in the efficacy of beta-cell mitogens led us to investigate the sources of this variability. We studied 35 male (23) and female (12) human islet batches covering a range of donor ages and BMI. Islets were kept intact or dispersed into single cells and cultured in the presence of harmine, glucose, or heparin-binding epidermal growth factor-like growth factor (HB-EGF), and subsequently analyzed by immunohistochemistry or flow cytometry. Proliferating cells were identified by double labeling with EdU and Ki67 and glucagon, c-peptide or Nkx6.1, and cytokeratin-19 to respectively label alpha, beta, and ductal cells. Harmine and HB-EGF stimulated human beta-cell proliferation, but the effect of glucose was dependent on the assay and the donor. Harmine potently stimulated alpha-cell proliferation and both harmine and HB-EGF increased proliferation of insulin- and glucagon-negative cells, including cytokeratin 19-positive cells. Given the abundance of non-beta cells in human islet preparations, our results suggest that assessment of beta-cell mitogens requires complementary approaches and rigorous identification of cell identity using multiple markers.
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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 | 0.000 | 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) | 0.002 | 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".