Molecular biomarkers for neuromuscular disorders – challenges and future perspectives
Bibliographic record
Abstract
There is an ever-growing need for molecular biomarkers in assessing clinical course and diagnosing neuromuscular disorders, as well as in monitoring drug therapy. With the development of high throughput techniques, there has been an acceleration in the discovery of potential biomarkers. It is quite easy to find potential candidates, but difficult to validate them and translate into a clinical setting. Neuromuscular diseases (NMD) are a major challenge in terms of finding potential molecular biomarkers, mainly because of their heterogeneous aetiology and variability in phenotype, their as yet incompletely understood pathophysiology, and their slow clinical progression. Furthermore, it is challenging to assemble a large cohort of patients, as many NMDs are rare diseases. In this literature review, we provide an update on the latest discoveries in DNA, RNA, miRNA, epigenetic, protein, metabolic and cellular biomarkers for NMD. The advantages and potential difficulties of clinical application and the role of identification of biomarker panels are discussed. We have especially sought to highlight translational biomarkers which can be easily transferred to the clinic, where they may eventually present possible future therapies related to molecular biomarker discoveries.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".