Immunolocalization of nestin in pancreatic tissue of mice at different ages
Bibliographic record
Abstract
AIM: To localize nestin positive cells (NPC) in pancreatic tissue of mice of different ages. METHODS: Paraffin sections of 6-8 mum of fixed pancreatic samples were mounted on poly-L-lysine coated slides and used for Immunolocalization using appropriate primary antibodies (Nestin, Insulin, Glucagon), followed by addition of a fluorescently labeled secondary antibody. The antigen-antibody localization was captured using a confocal microscope (Leica SP 5 series). RESULTS: In 3-6 d pups, the NPC were localized towards the periphery of the endocrine portion, as evident from immunolocalization of insulin and glucagon, while NPC were absent in the acinar portion. At 2 wk, NPC were localized in both the exocrine and endocrine portions. Interestingly, in 4-wk-old mice NPC were seen only in the endocrine portion, towards the periphery, and were colocalised with the glucagon positive cells. In the pancreas of 8- wk-old mice, the NPC were predominantly localized in the central region of the islet clusters, where immunostaining for insulin was at a maximum. CONCLUSION: We report for the first time the immunolocalization of NPC in the pancreas of mice of different ages (3 d to 8 wk) with reference to insulin and glucagon positive cells. The heterogeneous localization of the NPC observed may be of functional and developmental significance and suggest(s) that mice pancreatic tissue can be a potential source of progenitor cells. NPC from the pancreas can be isolated, proliferated and programmed to differentiate into insulin secreting cells under the appropriate microenvironment.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".