ReviewNeuroscience bulletin2026
Gene Editing Technologies for Hereditary Hearing Loss: Prospects and Challenges.
Review in Neuroscience bulletin, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hereditary deafness represents a significant global health challenge with limited therapeutic interventions. Most cases are caused by monogenic mutations inherited in an autosomal dominant or recessive manner, making them suitable targets for gene editing therapies. Recent advances in gene editing technologies have expanded the toolkit for precise genomic modification, including engineered nucleases for gene disruption, base editors (BEs) for point mutations, prime editors (PEs) for substitutions, insertions, and deletions, and mitochondrial editors for modifying mitochondrial DNA (mtDNA). These tools have demonstrated significant efficacy in mouse models of hereditary deafness, highlighting their clinical potential. However, given the high degree of genetic heterogeneity, gene editing technologies in this field remain in an early exploratory stage. In this review, we provide a comprehensive overview of the latest breakthroughs in gene-editing platforms and critically evaluate the potential benefits and existing hurdles to their clinical application for treating congenital hearing impairment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.