P.077 Utility of a next generation sequencing in the diagnosis of Congenital Myasthenic Syndromes
Bibliographic record
Abstract
Background: Congenital Myasthenic Syndromes (CMS) are heterogeneous disorders caused by genetically determined structural or functional differences in proteins involved with the neuromuscular junctions. Clinical and molecular genetics studies of CMS patients have revealed significant locus heterogeneity; there are 21 known genes related to CMS, but other genes may mimic the phenotype, justifying the use of a multi-gene panel for genetic testing Methods: Our group developed custom sequence capture probes designed to flank 27 different genes associated with CMS, including enrichment for all coding exons as well the flanking intronic regions. We enrolled 20 patients from the paediatric and adult neuromuscular clinic with a clinical phenotype of CMS. Using custom analytical, we -assessed the sequence variants and exon-level CNVs for each patient. Results: Thirteen male and seven female patients with median age of 12.25 years (range 1.5-39y) were assessed. We identified missense and CNVs in 17 patients, including established pathogenic mutations confirming the diagnosis in 5 patients Conclusions: The use of Next Generation Sequence with CNV for CMS can help determine the underlying causes of most CMS disorders and allow appropriate medical treatment, refined genetic counseling, and improved understanding of prognosis, justifying the implementation in the standard clinical screening of CMS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".