Clinical and genetic features of eight Chinese autosomal-dominant optic atrophy pedigrees with six novel<i>OPA1</i>pathogenic variants
Bibliographic record
Abstract
BACKGROUND: Autosomal-dominant optic atrophy (ADOA) is one of the most common types of inherited optic atrophy. We identify OPA1 pathogenic variants and assess the clinical features of a cohort of Chinese ADOA patients Materials and Methods: Detailed clinical evaluations were performed and genomic DNA was extracted from peripheral blood for all the participants. Sanger sequencing was used to analyze all exons and exon/intron junctions of OPA1 for eight pedigrees. Target exome capture plus next-generation sequencing (NGS) were applied for one atypical family with photophobia. Reverse transcription polymerase chain reaction was carried out to further characterize the mRNA change of selected splicing alteration. RESULTS: All 17 patients had impaired vision and optic-disk pallor; however, the clinical severity varied markedly. Two patients complicated with hearing loss. Six novel and two reported pathogenic variants in OPA1 (GenBank Accession No. NM_130837.2) were identified including four nonsynonymous variants (c.2400T > G, c.1468T > C, c.1567A > G and c.1466T > C), two splicing variants (c.2984-1_2986delGAGA and c.2983 + 5G > A), one small deletion (c.2960_2968delGCGTTCAAC), and one small insertion (c.3009_3010insA). RNA analysis revealed the splicing variant c.2984-1_2986delGAGA caused small deletion of mRNA (r.2983_2988del). CONCLUSIONS: ADOA patients presented variable clinical manifestations. Novel OPA1 pathogenic variants are the main genetic defect for Chinese ADOA cases. NGS may be a useful molecular testing tool for atypical ADOA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".