Bibliographic record
Abstract
T1DM (Type I diabetes mellitus) results from selective destruction of the insulin-producing beta-cells of the pancreas by the immune system, and is characterized by hyperglycaemia and vascular complications arising from suboptimal control of blood glucose levels. The discovery of animal models of T1DM in the late 1970s and early 1980s, particularly the NOD (non-obese diabetic) mouse and the BB (BioBreeding) diabetes-prone rat, had a fundamental impact on our ability to understand the genetics, aetiology and pathogenesis of this disease. NOD and BB diabetes-prone rats spontaneously develop a form of diabetes that closely resembles the human counterpart. Early studies of these animals quickly led to the realization that T1DM is caused by autoreactive T-lymphocytes and revealed that the development of T1DM is controlled by numerous polymorphic genetic elements that are scattered throughout the genome. The development of transgenic and gene-targeting technologies during the 1980s allowed the generation of models of T1DM of reduced genetic and pathogenic complexity, and a more detailed understanding of the immunogenetics of T1DM. In this review, we summarize the contribution of studies in animal models of T1DM to our current understanding of four fundamental aspects of T1DM: (i) the nature of genetic elements affording T1DM susceptibility or resistance; (ii) the mechanisms underlying the development and recruitment of pathogenic autoreactive T-cells; (iii) the identity of islet antigens that contribute to the initiation and/or progression of islet inflammation and beta-cell destruction; and (iv) the design of avenues for therapeutic intervention that are rooted in the knowledge gained from studies of animal models. Development of new animal models will ensure continued progress in these four areas.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".