Closing the Gap on Autosomal Dominant Connexin-26 and Connexin-43 Mutants Linked to Human Disease
Bibliographic record
Abstract
Cells within the vast majority of human tissues communicate directly through clustered arrays of intercellular channels called gap junctions. Gene ablation studies in mouse models have revealed that these intercellular channels are necessary for a variety of organ functions and that some of these genes are essential for survival. Molecular genetics has uncovered that germ line mutations in nearly half of the genes that encode the 21-member connexin family of gap junction proteins are linked to one or more human diseases. Frequently, these mutations are autosomal recessive, whereas in other cases, autosomal dominant mutations manifest as disease. Given the broad and overlapping distribution of connexins in a wide arrangement of tissues, it is hard to predict where connexin-linked diseases will clinically manifest. For instance, the most prevalent connexin in the human body is connexin-43 (Cx43), yet autosomal dominant mutations in the GJA1 gene, which encodes Cx43, exhibit modest developmental disorders resulting in a disease termed oculodentodigital dysplasia. Autosomal recessive mutations in the gene encoding Cx26 result in moderate to severe sensorineural hearing loss, whereas autosomal dominant mutations produce hearing loss and a wide range of skin diseases, including palmoplantar keratoderma. Here, we will focus on autosomal dominant mutations of the genes encoding Cx26 and Cx43 in relation to models that link genotypes to phenotypic outcomes with particular reference to how these approaches provide insight into human disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".