Antibody mediated targeting of autoimmune cells in multiple sclerosis
Bibliographic record
Abstract
Multiple Sclerosis (MS) is a debilitating autoimmune disorder of the central nervous system (CNS) affecting over 77,000 Canadians, with a prevalence rate of almost one out of 300 Canadians, with the highest rates in Saskatchewan. Myelination of neuronal axons is essential for the normal and rapid electrical conduction along an axon. Currently there are few feasible options to treat MS. Here, we propose a novel therapy that specifically targets aberrant immune cells. We created a trimolecular peptide (TPC) that combines and antigen sequence (myelin oligodendrocyte glycoprotein; MOG 35‐55 ), a modified type IV secretatory system (TIVSS) peptide, and an apoptotic protein. As the TPC displays MOG, the TPC is hypothesized to only bind to immune cells that are actively seeking MOG (e.g. in MS). Since the TPC contains TIVSS, the cell that bound the antigen (e.g. MOG 35‐55 ) is forced to take up the TPC. Lastly, the apoptotic protein, which is now inside the cell, causes the cell to undergo apoptosis. Thus eliminating the aberrant cell. In order to test this TPC compound, we induced experimental autoimmune encephalopathy (EAE) using MOG 35‐55 . Test compound was administered 15 days post EAE induction. Administration of the TPC resulted in the suppression of autoimmune cells, as determined via flocytometry. We also found significant improvements in motor function as well as a reduction in MS plaque formation in the cerebellum. By targeting the pathogenic immune response whilst leaving the patient’s normal immune response intact, we believe this strategy will lead to improved clinical outcomes without the side effects seen in current MS therapies. Support or Funding Information Saskatchewan Health Research Foundation
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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 | 0.001 | 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".