Validation of Chemokine Biomarkers in Duchenne Muscular Dystrophy
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is a progressive muscle disease involving complex skeletal muscle pathogenesis. The pathogenesis is triggered by sarcolemma instability due to the lack of dystrophin protein expression, leading to Ca2+ influx, muscle fiber apoptosis, inflammation, muscle necrosis, and fibrosis. Our lab recently used two high-throughput multiplexing techniques (e.g., SomaScan® aptamer assay and tandem mass tag-(TMT) approach) and identified a series of serum protein biomarkers tied to different pathobiochemical pathways. In this study, we focused on validating the circulating levels of three proinflammatory chemokines (CCL2, CXCL10, and CCL18) that are believed to be involved in an early stage of muscle pathogenesis. We used highly specific and reproducible MSD ELISA assays and examined the association of these chemokines with DMD pathogenesis, age, disease severity, and response to glucocorticoid treatment. As expected, we confirmed that these three chemokines were significantly elevated in serum and muscle samples of DMD patients relative to age-matched healthy controls (p-value < 0.05, CCL18 was not significantly altered in muscle samples). These three chemokines were not significantly elevated in Becker muscular dystrophy (BMD) patients, a milder form of dystrophinopathy, when compared in a one-way ANOVA to a control group but remained significantly elevated in the age-matched DMD group (p < 0.05). CCL2 and CCL18 but not CXCL10 declined with age in DMD patients, whereas all three chemokines remained unchanged with age in BMD and controls. Only CCL2 showed significant association with time to climb four steps in the DMD group (r = 0.48, p = 0.038) and neared significant association with patients’ reported outcome in the BMD group (r = 0.39, p = 0.058). Furthermore, CCL2 was found to be elevated in a serum of the mdx mouse model of DMD, relative to wild-type mouse model. This study suggests that CCL2 might be a suitable candidate biomarker for follow-up studies to demonstrate its physiological significance and clinical utility in DMD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".