Toll like receptor 7 antagonist IRS661 inhibits insulitis and autoimmune diabetes in non-obese diabetic mice (P5227)
Bibliographic record
Abstract
Abstract Toll like receptor 7 (TLR7) stimulation activates DCs and T cells to promote autoimmune diabetes in non-obese diabetic (NOD) mice. The aim of the study was to investigate effects of TLR7 antagonist IRS661 on type 1 diabetes onset, insulitis and immune responses in NOD mice. Female NOD mice were treated with IRS661 at ages of 4, 6 and 8 weeks or with either ODN1982 or PBS as control groups. Blood glucose was measured weekly, and pancreas, pancreatic lymph nodes (PLNs) and spleens were obtained for histology analysis or FACS analysis. This treatment significantly inhibited the onset of diabetes, as 6/15 NOD mice treated with IRS661 developed diabetes by 30 weeks of age, compared to 8/8 ODN1982-treated or 13/15 PBS groups (p<0.001). Further, insulitis scores were lower and pancreatic islets were preserved in NOD mice treated with IRS661. To determine effects of IRS661 upon immune responses, NOD PLN cells or splenocytes were analyzed after the treatment. IRS661 diminished CD86 expression on DCs and IFN-α expression in plasmacytoid DCs in NOD PLNs. Further, IRS661 treatment decreased IGRP V7+ CD8 T cells and CD69 expression on CD8 T cells, and inhibited adoptively transferred BDC 2.5 CD4 T cell proliferation in NOD PLNs. No difference in CD25+Foxp3+ CD4 T regulatory cells between IRS661-treated or ODN1982-treated NOD splenocytes was detected. We conclude that IRS661 administration inhibits activation of DCs and CD8 T cells so as to decrease insulitis and diabetes onsets.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".