Defined distribution and features of lymph node therapies enable recruitment and manipulation of antigen-specific T cell response
Bibliographic record
Abstract
Antigen-specific therapies to treat autoimmune diseases would benefit from improved understanding of the conditions needed to efficiently and selectively modulate inflammatory response. By leveraging the unique features of a spatially restricted platform to deliver polymer depots to lymph nodes (LNs), we establish design and delivery parameters required to locally regulate antigen-specific response. We show depots containing peptides and regulatory or stimulatory cues introduced directly to LNs recruit and engage antigen-specific T cells in treated LN microenvironments. This selectivity is maintained even during the administration of formulations containing multiple antigens, and the nature of this response can be switched between tolerizing or activating responses by defining cues in depots. Notably, in a myelin-driven model of autoimmunity, local depots promote re-polarization of inflammatory antigen-specific T cells into regulatory T cells. Efficacy against autoimmune disease is dose dependent but with low sensitivity to formulation parameters such as cargo-loading density and the ratio of antigen and modulatory cues in depots. This work defines cardinal features and delivery considerations for next-generation antigen-specific immunotherapies targeting autoimmune 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 | 0.000 |
| 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) | 0.001 | 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".