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X-linked juvenile retinoschisis (XLRS) is an inherited retinal disease caused by mutations in the RS1 gene, resulting in splitting of the retinal layers and visual disturbances. To provide insights on this disease in our cohort, genetic examination, clinical presentation, and functional analysis were performed. We observed three main RS1 mutations in our cohort of six unrelated patients: RS1-D126G, RS1-R209H, and RS1-R213W. The RS1-D126G mutation, exclusively reported in Thai patients so far, showed the highest prevalence. Phenotypically, the D126G mutation manifested early (0.3–4 years old), presenting as asymmetrical visual acuity and schisis. Functional analysis revealed that the molecular pathomechanism of D126G was the failure of protein secretion attributable to endoplasmic reticulum retention. The understanding of the genotype-phenotype relationship and the pathomechanisms of specific mutations in a particular population could immensely benefit the pipeline of personalised treatment design for XLRS. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a type of neurodegenerative disorder that is characterized by the progressive degeneration of brain cells, leading to cognitive decline and memory loss. It is the most common cause of dementia and affects millions of people worldwide. While there is currently no cure for Alzheimer's disease, early detection and treatment can help to slow the progression of symptoms and improve quality of life. This research presents a diagnostic tool for classifying mild cognitive impairment and Alzheimer's diseases using feature-based machine learning applied to optical coherence tomographic angiography images (OCT-A). Several features are extracted from the OCT-A image, including vessel density in five sectors, the area of the foveal avascular zone, retinal thickness, and novel features based on the histogram of the range-filtered OCT-A image. To ensure effectiveness for a diverse population, a large local database for our study was collected. The promising results of our study, with the best accuracy of 92.17,% will provide an efficient diagnostic tool for early detection of Alzheimer's disease. [ABSTRACT FROM AUTHOR]