Unsupervised clustering reveals a unique Treg profile in slow progressors to type 1 diabetes
Bibliographic record
Abstract
Abstract Objective To profile CD4 + regulatory T cells (Tregs) in a well-characterised cohort of slow progressors to type 1 diabetes, individuals positive for multiple islet autoantibodies who remain diabetes-free for at least 10 years. Research Design and Methods Peripheral blood samples were obtained from extreme slow progressor individuals (n=8), with up to 32 years follow-up, and age and gender-matched to healthy donors. One participant in this study was identified with a raised HbA1c at the time of assessment, and was individually evaluated in the data analysis. PBMCs were isolated, from donors, and to assess frequency, phenotype and function of Tregs, multi-parameter flow cytometry and T cell suppression assays were performed. Unsupervised clustering analysis, FlowSOM and CITRUS, was used to evaluate Treg phenotypes. Results Treg mediated suppression of CD4 + effector T cells, from slow progressors was significantly impaired, compared to healthy donors (P<0.05). Effector CD4 T cells, from slow progressors, were more responsive to Treg suppression, compared to healthy donors, demonstrated by increased suppression of CD25 expression on effector CD4 T cells (P<0.05). Unsupervised clustering on memory CD4 T cells, from slow progressors, showed an increased frequency of activated-memory CD4 Tregs associated with increased expression of GITR, compared to healthy donors (P<0.05). The participant with a raised HbA1c had a different Treg profile, compared to slow progressors and the matched controls. Conclusions CD4 + Tregs from slow progressor individuals have a unique Treg signature. This report highlights the need for further study of Treg heterogeneity in individuals at-risk of developing 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".