Treatment with Multiple-linked Myelin Peptides Encapsulated within Nanoparticles Induces Antigen-specific Tolerance in SJL/J Relapsing-remitting Experimental Autoimmune Encephalomyelitis
Bibliographic record
Abstract
Abstract Experimental autoimmune encephalomyelitis (EAE) in SJL/J mice is a demyelinating disease of the central nervous system (CNS), and serves as a fit-for-purpose pre-clinical model of Multiple Sclerosis (MS). Data show that tolerogenic immune-modifying nanoparticles (TIMPs) encapsulating peptides/proteins is an effective therapeutic that induces antigen-specific tolerance to the encapsulated peptide/protein. The effectiveness of this therapeutic platform has been be demonstrated in multiple mouse models, as well as in a recently completed phase 2 double-blinded placebo-controlled clinical trial for the treatment of celiac disease. While previous EAE studies have utilized single peptides, the present study utilized a polypeptide containing the SJL/J mouse dominant encephalitogenic peptides (PLP139–151, PLP178–191, MBP84–104, and MOG92-10) linked together with intervening capsaicin S cleavage sites. The use of the multiple-linked myelin peptides was produced to achieve broader coverage of myelin-derived epitopes, which will be required for the treatment of MS. The present data show that this multiple-linked myelin peptide emulsified in CFA induced both CD4+ T cell responses and EAE in SJL/J mice similar to PLP139–151/CFA. Our data go on to show that treatment of SJL/J mice with multiple-linked myelin peptide TIMP inhibited both PLP139–151/CFA-induced R-EAE, as well as multiple-linked myelin peptide/CFA-induced EAE. Furthermore, treatment of SJL/J mice with multiple-linked myelin peptide TIMP significantly decreased TH17 cell responses and increased Tr1 cell responses. The present findings suggest that utilizing multiple-linked peptides may be clinically translatable for the treatment of 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) | 0.001 | 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 | 0.001 | 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".