Long-read sequence analysis of the MECP2 gene in Rett syndrome patients: correlation of disease severity with mutation type and location
Bibliographic record
Abstract
Mutations in the methyl-CpG-binding protein gene MECP2 at Xq28 cause Rett syndrome (RTT), an X-linked dominant neurodevelopmental disorder characterized by a period of stagnation followed by regression in the development of young girls. Mutations were sought in MECP2 in 48 females with classical sporadic RTT, seven families with possible familial RTT and five sporadic females with features suggestive, but not diagnostic of RTT. Long distance PCR coupled with long-read direct sequencing was employed to sequence the entire MECP2 gene coding region in all cases. Mutations were identified in 44/55 (80%) unrelated classical sporadic and familial RTT patients, but only 1/5 (20%) sporadic cases with suggestive but non-diagnostic features of RTT. Twenty-one different mutations were identified (12 missense, four nonsense and five frame-shift mutations); 14 of these were novel. All missense mutations were located either in the methyl-CpG-binding domain or in the transcription repression domain. Nine recurrent mutations were characterized in a total of 33 unrelated cases (73% of all cases with MECP2 mutations). Significantly milder disease was noted in patients carrying missense mutations as compared with those with truncating mutations ( P = 0. 0023), and milder disease was associated with late as compared with early truncating mutations ( P = 0.0190).
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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 | 0.001 |
| 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 | 0.000 | 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".