Integrated Analysis of the Pancreas and Islets Reveals Unexpected Findings in Human Male With Type 1 Diabetes
Bibliographic record
Abstract
Clinical and pathologic heterogeneity in type 1 diabetes is increasingly being recognized. Findings in the islets and pancreas of a 22-year-old male with 8 years of type 1 diabetes were discordant with expected results and clinical history (islet autoantibodies negative, hemoglobin A1c 11.9%) and led to comprehensive investigation to define the functional, molecular, genetic, and architectural features of the islets and pancreas to understand the cause of the donor's diabetes. Examination of the donor's pancreatic tissue found substantial but reduced β-cell mass with some islets devoid of β cells (29.3% of 311 islets) while other islets had many β cells. Surprisingly, isolated islets from the donor pancreas had substantial insulin secretion, which is uncommon for type 1 diabetes of this duration. Targeted and whole-genome sequencing and analysis did not uncover monogenic causes of diabetes but did identify high-risk human leukocyte antigen haplotypes and a genetic risk score suggestive of type 1 diabetes. Further review of pancreatic tissue found islet inflammation and some previously described α-cell molecular features seen in type 1 diabetes. By integrating analysis of isolated islets, histological evaluation of the pancreas, and genetic information, we concluded that the donor's clinical insulin deficiency was most likely the result autoimmune-mediated β-cell loss but that the constellation of findings was not typical for type 1 diabetes. This report highlights the pathologic and functional heterogeneity that can be present in type 1 diabetes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".