Background and Objectives: Hereditary hearing loss is the most common genetic disorder in children. Nearly 120 genes associated with auditory impairment have been identified. Although the disease is clinically and genetically complex, the chances of identifying deafness-causing loci increase when studying consanguineous families.Materials and Methods: Whole-exome sequencing was performed to identify genetic variants underlying sensorineural hearing loss in affected individuals from a family with third-degree consanguineous practices.Results: A homozygous deletion of 250.285 kb was identified in the 16p12.2 region encompassing three genes, METTL9, IGSF6, and OTOA, and a partial deletion of the NPIPB4 gene co-segregated within the family.Conclusions: This study highlighted the genetic heterogeneity of hearing loss in consanguineous families. Future research should focus on the OTOA mutational spectrum in South Indian populations with hearing loss.

Background and Aim: Diabetic foot ulcers (DFUs) pose a significant challenge in the management of chronic wounds, characterized by a high risk of infection and severe complications, including potential limb amputation. The limitations of conventional treatments have sparked interest in regenerative therapies, particularly the use of adipose‐derived stem cells (ADSCs). which possess significant potential across various medical fields for enhancing tissue repair and modulating immune responses. This perspective critically evaluates the potential of ADSCs for treating DFUs while exploring their broader implications in regenerative medicine. Methods: This perspective incorporates insights from a comprehensive literature review, with searches conducted on platforms such as Google Scholar, PubMed, and Scopus. The selected studies focus on the regenerative efficacy, safety, and clinical applications of ADSCs in wound healing, emphasizing rigorous methodologies that support reliable conclusions. Results: ADSCs exhibit significant promise in promoting tissue regeneration, stimulating angiogenesis, and modulating immune function—mechanisms that are beneficial not only for patients with DFUs but also for those with other chronic and non‐healing wounds. Nevertheless, challenges persist, including the risk of tumorigenesis, particularly in patients with a history of cancer, as well as variability in therapeutic outcomes due to donor characteristics and processing techniques. Conclusion: While ADSCs represent a promising therapeutic option for DFUs and provide valuable insights for a wider array of chronic wound therapies, their clinical application requires careful consideration of safety concerns, including risks of tumor formation and immune response issues. Future advancements in cell processing and well‐structured clinical trials will be essential to ensuring efficacy across diverse populations in the broader context of regenerative medicine.

Aneuploidy is increasingly recognized globally as a common cause of miscarriage among expectant mothers. The existing prenatal screening techniques for detecting aneuploidy have several limitations. The ability to diagnose aneuploidy early in a non-invasive manner is not feasible with the current screening methods, as they may produce false positive or false negative results. Recently, the widely used gene editing tool CRISPR/Cas has shown great promise in diagnostics. This review summarizes the prenatal screening tests used in the first trimester to assess aneuploidy conditions. Additionally, we discuss the advantages and disadvantages of molecular diagnostic tests, including the benefits and challenges of CRISPR/Cas-based trisomy detection. Thus, the proposed prenatal screening using CRISPR/Cas could provide significant benefits to expectant mothers by potentially enabling the early diagnosis of trisomy, helping to prevent miscarriage and birth defects. Furthermore, it opens new avenues for research, allowing clinicians and researchers to develop, optimize, and implement CRISPR/Cas-based prenatal screening assays in the future.

SARS-CoV-2 Omicron variant of concern (VOC) has rapidly expanded worldwide since it first emerged on November 24, 2021, in Southern Africa and has already reached every continent. Many issues surrounding Omicron remain unresolved, including the severity of the disease and the extent to which currently available vaccinations effectively prevent COVID-19 infection. Compared to the previous VOC (Delta version), more mutations were detected, which may address Omicron’s characteristics.

Consanguineous marriages heighten the risk of recessive genetic disorders, including congenital sensorineural hearing loss (CSHL), by increasing the prevalence of homozygous pathogenic variants. However, limited availability of large-scale annotated variant datasets remains a major challenge in developing accurate machine learning (ML) classifiers for variant pathogenicity prediction in such contexts. In this study, we propose a novel approach leveraging synthetic whole exome sequencing (WES) data to develop a robust ML model for distinguishing pathogenic from non-pathogenic variants specific to consanguineous hearing loss cases. We first applied a rule-based probabilistic framework to simulate clinically realistic distributions of variant features, including chromosomal position, allele frequency, CADD, SIFT, PolyPhen, and ClinVar-like pathogenicity labels. To enhance data diversity and mitigate bias from manually defined rules, we employed rule based probabilistic simulation to generate complex, high-fidelity synthetic variants that preserved conditional dependencies across multiple annotation layers. A balanced dataset of 5000 variants was generated and used to train and evaluate several machine learning models, including XGBoost, random forest, and logistic regression. The models achieved high accuracy and strong discriminative power, as measured by ROC-AUC and F1-score, validating the feasibility of synthetic data in precision genomics. Our results demonstrate the potential of combining rule-based priors with generative models to overcome data scarcity in rare genetic disorders and enable ML-based variant classification in consanguineous populations. This synthetic data–driven pipeline offers a scalable and ethical alternative for training predictive models in underrepresented genetic conditions, ultimately facilitating early diagnosis and personalized healthcare interventions for inherited hearing loss.