GJB2 Gene Testing, Etiologic Diagnosis and Genetic Counseling in Romanian Persons With Prelingual Hearing Loss
Bibliographic record
Abstract
Background: Nowadays, molecular genetic tests provide insights into the etiologic diagnosis of hearing loss (HL). Specific gene mutations are known to cause sensorineural HL of early onset. Previously published studies showed the allelic heterogeneity of GJB2 gene as main genetic cause of isolated congenital HL. The aims of the present study were to provide an extended and updated spectrum of mutations in GJB2 gene and to identify the most prevalent mutations in the Romanian population for testing prevention strategy in people with sensorineural HL of early onset. Methods: To overcome our aims, we used clinical data from 125 unrelated persons with congenital HL and performed ARMS-PCR and DNA sequencing techniques for detection of known mutations or identification of mutations within GJB2 gene. Results: The most prevalent mutation was c.35delG found in both homozygotic and heterozygotic forms. The second mutant allele was c.71G>A (p.W24X) found in homo- or heterozygotic forms as well, followed by c.-23+1G>A and c.380G>A (p.R127H) mutations with lower frequencies. Conclusion: The study reveals the c.35delG mutation as having the highest prevalence, further highlights the genetic background of congenital HL in a local population, and supports improvement of genetic testing such as newborn and carrier screening on which to base genetic counseling services. Int J Clin Pediatr. 2015;4(1):121-126 doi: http://dx.doi.org/10.14740/ijcp194w
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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 | 0.002 |
| 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 | 0.000 | 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".