V37I connexin 26 allele in patients with sensorineural hearing loss: Evidence of its pathogenicity
Bibliographic record
Abstract
Sensorineural hearing loss (SNHL) is the most common inherited sensory disorder, reported in 1-3 of every 1,000 births. It has been estimated that 50% of all cases of prelingual SNHL are genetically determined. There is tremendous genetic heterogeneity, with multiple dominant and recessive loci. Mutations of the gap junction beta-2 gene (GJB2) emerge as a leading cause of autosomal recessive non-syndromic SNHL. Over 90 sequence alterations have been reported, the pathogenicity of some of them being unknown or unclear. The status of the V37I allele of connexin 26 (GJB2 amino acid product) with regards to its association with SNHL has been controversial. This study examines the pathogenicity of V37I by comparing the frequency of this allele in 40 patients with SNHL of Chinese and Caucasian descent with the frequency of the allele in 100 anonymized, ethnically matched controls. The V37I allele was identified in 43.75 and 11.5% of the patient and control alleles of Chinese ethnicity, respectively, but was not found in either Caucasian cohort. We also compiled the audiograms of 15 individuals with SNHL homozygous for the V37I allele, and showed that these individuals present with a mild to moderate SNHL. These results indicate that (1) the V37I allele is common in individuals of Chinese descent but rarely present in individuals of Caucasian decent; and (2) the V37I allele is pathogenic, but produces milder hearing loss compared to nonsense mutations of connexin 26 such as the 35delG mutation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".