Copy number variation in pediatric multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Pediatric onset multiple sclerosis (MS) accounts for 2-4% of all MS. It is unknown whether the disease shares the same underlying pathophysiology found in adult patients or an extreme early onset phenotype triggered by distinct biological mechanisms. It has been hypothesized that copy number variations (CNVs) may result in extreme early onset diseases because CNVs can have major effects on many genes in large genomic regions. OBJECTIVES AND METHODS: The objective of the current research was to identify CNVs, with a specific focus on de novo CNVs, potentially causing early onset MS by competitively hybridizing 30 white non-Hispanic pediatric MS patients with each of their parents via comparative genomic hybridization (CGH) analysis on the Agilent 1M CGH array. RESULTS AND DISCUSSION: We identified 10 CNVs not overlapping with any CNV regions currently reported in the Database of Genomic Variants (DGV). Fifty-five putatively de novo CNVs were also identified: all but one common in the DGV. We found the single rare CNV was a private variation harboring the SACS gene. SACS mutations cause autosomal-recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) disease. Additional clinical review revealed that the patient with the SACS gene CNV shared some features of both MS and ARSACS. CONCLUSIONS: This is the first reported study analyzing pediatric MS CNVs. While not yielding causal variation in our initial pediatric dataset, our approach confirmed diagnosis of an ARSACS-like disease in addition to MS in the affected individual, which led to a more complete understanding of the patient's disease course and prognosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".