The emerging relationship between mucosal-associated invariant T cell populations and the onset and progression of type 1 diabetes
Bibliographic record
Abstract
T cells are unlikely to be the only cells involved in diabetes. Relatively recently described and still enigmatic, Mucosal-associated invariant T (MAIT) cells, innate-like T cells that recognize microbial-derived peptides, exist in the blood and tissues and are implicated in early immune responses. Immunological differences, some of which implicate MAIT cells, exist between individuals at different stages of T1D progression. This review explores the emerging relationship between gastrointestinal and pancreatic MAIT cell populations and the onset and progression of T1D. Early microbial colonization is critical for immune maturation, homeostasis, and MAIT cell development, and disruptions such as Caesarean delivery or antibiotic-induced dysbiosis correlate with increased T1D incidence. Diabetes progression in the diabetes-prone NOD mice is associated with reduced gut mucosal integrity, impairing the protective IL-17 and IL-22 responses of gut-resident MAIT cells and exacerbating systemic inflammation. MAIT cells recruited to the inflamed pancreas during T1D onset likely contribute to β-cell destruction through IFN-γ and granzyme B production. This hypothesis is supported by altered MAIT cell frequencies and phenotypes in individuals with T1D; MAIT cells are reduced in the blood of children recently diagnosed with T1D, potentially corresponding to pancreatic migration, while adults with long-term T1D have persistent, circulating MAIT cells with exhaustion markers. MAIT cells appear to have dual protective and pathogenic roles impacted by microbiome interactions. Understanding these relationships may inform non-invasive biomarkers for the disease.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".