Possible type 1 diabetes risk prediction: Using ultrasound imaging to assess pancreas inflammation in the inducible autoimmune diabetes BBDR model
Bibliographic record
Abstract
BACKGROUND/AIMS: Studies of human cadaveric pancreas specimens indicate that pancreas inflammation plays an important role in type 1 diabetes pathogenesis. Due to the inaccessibility of pancreas in living patients, imaging technology to visualize pancreas inflammation is much in need. In this study, we investigated the feasibility of utilizing ultrasound imaging to assess pancreas inflammation longitudinally in living rats during the progression leading to type 1 diabetes onset. METHODS: The virus-inducible BBDR type 1 diabetes rat model was used to systematically investigate pancreas changes that occur prior to and during development of autoimmunity. The nearly 100% diabetes incidence upon virus induction and the highly consistent time course of this rat model make longitudinal imaging examination possible. A combination of histology, immunoblotting, flow cytometry, and ultrasound imaging technology was used to identify stage-specific pancreas changes. RESULTS: Our histology data indicated that exocrine pancreas tissue of the diabetes-induced rats underwent dramatic changes, including blood vessel dilation and increased CD8+ cell infiltration, at a very early stage of disease initiation. Ultrasound imaging data revealed significant acute and persistent pancreas inflammation in the diabetes-induced rats. The pancreas micro-vasculature was significantly dilated one day after diabetes induction, and large blood vessel (superior mesenteric artery in this study) dilation and inflammation occurred several days later, but still prior to any observable autoimmune cell infiltration of the pancreatic islets. CONCLUSIONS: Our data demonstrate that ultrasound imaging technology can detect pancreas inflammation in living rats during the development of type 1 diabetes. Due to ultrasound's established use as a non-invasive diagnostic tool, it may prove useful in a clinical setting for type 1 diabetes risk prediction prior to autoimmunity and to assess the effectiveness of potential therapeutics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".