The Peri-islet Basement Membrane, a Barrier to Infiltrating Leukocytes in Type 1 Diabetes in Mouse and Human
Bibliographic record
Abstract
We provide the first comprehensive analysis of the extracellular matrix (ECM) composition of peri-islet capsules, composed of the peri-islet basement membrane (BM) and subjacent interstitial matrix (IM), in development of type 1 diabetes in NOD mice and in human type 1 diabetes. Our data demonstrate global loss of peri-islet BM and IM components only at sites of leukocyte infiltration into the islet. Stereological analyses reveal a correlation between incidence of insulitis and the number of islets showing loss of peri-islet BM versus islets with intact BMs, suggesting that leukocyte penetration of the peri-islet BM is a critical step. Protease- and protease inhibitor-specific microarray analyses (CLIP-CHIP) of laser-dissected leukocyte infiltrated and noninfiltrated pancreatic islets and confirmatory quantitative real time PCR and protein analyses identified cathepsin S, W, and C activity at sites of leukocyte penetration of the peri-islet BM in association with a macrophage subpopulation in NOD mice and human type 1 diabetic samples and, hence, potentially a novel therapeutic target specifically acting at the islet penetration stage. Interestingly, the peri-islet BM and underlying IM are reconstituted once inflammation subsides, indicating that the peri-islet BM-producing cells are not lost due to the inflammation, which has important ramifications to islet transplantation studies.
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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".