Large-scale serum protein biomarker discovery in Duchenne muscular dystrophy
Bibliographic record
Abstract
Serum biomarkers in Duchenne muscular dystrophy (DMD) may provide deeper insights into disease pathogenesis, suggest new therapeutic approaches, serve as acute read-outs of drug effects, and be useful as surrogate outcome measures to predict later clinical benefit. In this study a large-scale biomarker discovery was performed on serum samples from patients with DMD and age-matched healthy volunteers using a modified aptamer-based proteomics technology. Levels of 1,125 proteins were quantified in serum samples from two independent DMD cohorts: cohort 1 (The Parent Project Muscular Dystrophy-Cincinnati Children's Hospital Medical Center), 42 patients with DMD and 28 age-matched normal volunteers; and cohort 2 (The Cooperative International Neuromuscular Research Group, Duchenne Natural History Study), 51 patients with DMD and 17 age-matched normal volunteers. Forty-four proteins showed significant differences that were consistent in both cohorts when comparing DMD patients and healthy volunteers at a 1% false-discovery rate, a large number of significant protein changes for such a small study. These biomarkers can be classified by known cellular processes and by age-dependent changes in protein concentration. Our findings demonstrate both the utility of this unbiased biomarker discovery approach and suggest potential new diagnostic and therapeutic avenues for ameliorating the burden of DMD and, we hope, other rare and devastating diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".