Nanoparticle‐based approaches to immune tolerance for the treatment of autoimmune diseases
Bibliographic record
Abstract
Autoimmune diseases are caused by antigenically complex immune responses of the adaptive and innate immune system against specific cells, tissues or organs. Antigen-specific approaches for induction of immune tolerance in autoimmunity, based on the use of antigenic peptides or proteins, have failed to deliver the desired therapeutic results in clinical trials. These approaches, which are largely relying on triggering clonal anergy and/or deletion of defined autoreactive specificities, do not address the overwhelming antigenic, molecular, and cellular complexity of most autoimmune diseases, which involve various immune cells and ever-growing repertoires of antigenic epitopes on numerous self-antigens. Advances in the field of regulatory T-cell (Treg) biology have suggested that Treg cells might be able to afford dominant tolerance provided they are properly activated and expanded in vivo. More recently, nanotechnology has introduced novel technical advances capable of modulating immune responses. Here, we review nanoparticle-based approaches designed to induce immune tolerance, ranging from approaches that primarily trigger clonal T-cell anergy or deletion to approaches that trigger Treg cell formation and expansion from autoreactive T-cell effectors. We will also highlight the therapeutic potential and positive outcomes in numerous experimental models of autoimmunity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".