Tolerogenic nanoparticles inhibit T cell–mediated autoimmunity through SOCS2
Bibliographic record
Abstract
Type 1 diabetes (T1D) is a T cell-dependent autoimmune disease that is characterized by the destruction of insulin-producing β cells in the pancreas. The administration to patients of ex vivo-differentiated FoxP3(+) regulatory T (Treg) cells or tolerogenic dendritic cells (DCs) that promote Treg cell differentiation is considered a potential therapy for T1D; however, cell-based therapies cannot be easily translated into clinical practice. We engineered nanoparticles (NPs) to deliver both a tolerogenic molecule, the aryl hydrocarbon receptor (AhR) ligand 2-(1'H-indole-3'-carbonyl)-thiazole-4-carboxylic acid methyl ester (ITE), and the β cell antigen proinsulin (NPITE+Ins) to induce a tolerogenic phenotype in DCs and promote Treg cell generation in vivo. NPITE+Ins administration to 8-week-old nonobese diabetic mice suppressed autoimmune diabetes. NPITE+Ins induced a tolerogenic phenotype in DCs, which was characterized by a decreased ability to activate inflammatory effector T cells and was concomitant with the increased differentiation of FoxP3(+) Treg cells. The induction of a tolerogenic phenotype in DCs by NPs was mediated by the AhR-dependent induction of Socs2, which resulted in inhibition of nuclear factor κB activation and proinflammatory cytokine production (properties of tolerogenic DCs). Together, these data suggest that NPs constitute a potential tool to reestablish tolerance in T1D and potentially other autoimmune disorders.
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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) | 0.000 | 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 | 0.000 |
| 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".