T-cell antigens in pediatric-onset multiple sclerosis: A unique window into early disease mechanisms
Bibliographic record
Abstract
Abstract Multiple sclerosis (MS) involves immune attacks on the CNS, leading to demyelination, axonal injury and increasing neurological dysfunction. Though T cells are implicated, the particular subsets and their antigenic targets remain unknown. In adult-onset MS, distinguishing immune responses that are consequences of, rather than cause of, injury, is difficult. In contrast, pediatric-onset MS offers an early window into disease mechanisms given the narrower gap from biological onset. We aim to identify and characterize disease-relevant antigen-specific effector T cell responses to traditional and novel antigenic targets involved early in the MS disease process. Our group has implicated target antigens and T cell subsets in pediatric-onset MS, by following patients from time of an initial presentation with acquired demyelinating syndrome and comparing those confirmed to have MS with those who remain monophasic. A CSF proteomic study implicated novel axo-glial apparatus molecules as early injury targets, rather than traditional compact myelin antigens. A series of multiparameter flow-cytometry panels applied to pediatric peripheral blood mononuclear cells (PBMC) revealed that MS children harbor abnormally increased frequencies and pro-inflammatory cytokine responses of particular effector T cell subsets compared to controls. We will develop assays, initially in fresh PBMC samples from adult MS and controls, then miniaturize the approach and validate it for use in the small numbers of available cryopreserved pediatric PBMC samples to quantify antigen-specific responses including proliferation and cytokine profiles of distinct disease-implicated T cell subsets to both traditionally and newly implicated antigens.
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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 | 0.000 |
| 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".