Pancreatic islet cell phenotype and endocrine function throughout diabetes development in non-obese diabetic mice
Bibliographic record
Abstract
Type 1 diabetes (T1D) results from a T cell dependent, autoimmune destruction of insulin producing beta cells in pancreatic islets of Langerhans, which results in insulin deficiency despite attempts at beta cell replacement by the emergence of newly-differentiated beta cells throughout T1D development. The origin of these cells has been difficult to assess as these are rapidly destroyed by the underlying autoimmunity. The identification of islets of Langerhans is typically assessed by either immunochemistry or immunofluorescence using antibodies directed against the different signature hormones and surface markers of various endocrine cells. However, the limited number of markers that can be used simultaneously and the uneven spatial distribution of endocrine cells within islets, limit the use of these histological analyses. To circumvent these caveats, we developed a novel approach using multi-parametric flow cytometry to assess the phenotype and function of pancreatic islet cell populations throughout T1D development. Using such strategy, we show that while beta cells undergo autoimmune destruction, insulin-producing cells arise from trans-differentiated alpha or delta cells, an outcome that was not solely the result of beta cell self-renewal. Moreover, we show that CD4(+)T cell-mediated inflammation correlates with the emergence of this insulin-producing beta cell-like cell.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".