Hepatocyte growth factor regulates proliferation and differentiation of epithelial monolayers derived from islets of postnatal rat pancreas
Bibliographic record
Abstract
Hepatocyte growth factor (HGF) has been suggested to be a potent regulator of beta-cell function and proliferation. The purpose of this study was to investigate whether HGF could regulate the proliferation and differentiation of islet-derived epithelial monolayers into insulin-producing cells. We have generated islet-derived epithelial monolayers that are enriched with cells expressing c-Kit, a tyrosine kinase receptor and putative marker, from isolated postnatal rat islets. Monolayers were cultured on type I collagen gel and treated in defined differentiation medium with or without HGF (50 ng/ml) for 7 days. Subsequently, the expression of transcription factors and pancreatic endocrine cell markers as well as c-Kit expression were compared between the HGF (HGF+), no HGF treatment (HGF-) and monolayers without differentiation medium (control) groups, using immunocytochemical and RT-PCR approaches. We observed that the number of c-Kit-, glucose transport type 2 (Glut2)- and the transcription factor pancreatic duodenal homeobox-1 (PDX-1)-expressing cells were significantly increased in the HGF+ group. The expression of insulin at the mRNA and protein level was also increased in this treatment group with a 1.7-fold increase in basal insulin release and a 2.3-fold increase in insulin content in comparison with the HGF- group. A high proliferative capacity was also found in the HGF+ group. Co-localization of insulin and PDX-1 or Glut2 was revealed frequently in cells treated with HGF+ with occasional co-staining of c-Kit and insulin observed. This study showed that HGF can activate the proliferation and differentiation of islet-derived epithelial monolayer into insulin-producing cells. However, no formation of islet-like clusters was observed. Taken together, this study implies that HGF mediates differentiation of immature cell types into insulin-expressing cells; however, HGF supplementation alone is insuffcient in restoring full beta-cell function.
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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) | 0.001 | 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".