Novel Approaches in the Immunotherapy of Multiple Sclerosis: Cyclization of Myelin Epitope Peptides and Conjugation with Mannan
Bibliographic record
Abstract
Multiple Sclerosis (MS) is a serious autoimmune disease. The patient in an advanced state of the disease has restrained mobility and remains handicapped. It is therefore understandable that there is a great need for novel drugs and vaccines for the treatment of MS. Herein we summarise two major approaches applied for the treatment of the disease using peptide molecules alone or conjugated with mannan. The first approach focuses on selective myelin epitope peptide or peptide mimetic therapy alone or conjugated with mannan, and the second on immune-therapy by preventing or controlling disease through the release of appropriate cytokines. In both approaches the use of cyclic peptides offers the advantage of increased stability from proteolytic enzymes. In these approaches, the synthesis of myelin epitope peptides conjugated to mannan is of particular interest as this was found to protect mice against experimental autoimmune encephalomyelitis, an animal model of MS, in prophylactic and therapeutic protocols. Protection was peptide-specific and associated with reduced antigen-specific T cell proliferation. The aim of the studies of these peptide epitope analogs is to understand their molecular basis of interactions with human autoimmune T-cell receptor and a MS-associated human leucocyte antigen (HLA)-DR2b. This knowledge will lead the rational design to new beneficial non-peptide mimetic analogs for the treatment of MS. Some issues of the use of nanotechnology will also be addressed as a future trend to tackle the disease. We highlight novel immunomodulation and vaccine-based research against MS based on myelin epitope peptides and strategies developed in our laboratories.
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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 | 0.001 |
| 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".