Objective To identify genes associated with juvenile open-angle glaucoma (JOAG) by screening for gene mutation loci and clinical phenotype analysis in a JOAG family. Methods In January 2021, an ophthalmic examination was performed on members of a family with JOAG. Whole-exome sequencing was done on the proband to look for pathogenic genes. Family members were validated using Sanger sequencing, and a long-term follow-up was conducted. Results Three generations of the family comprised eight individuals, including three patients with JOAG. All patients carried a missense mutation in the MYOC gene c.1130C>G (p.Thr377Arg), which showed autosomal dominant inheritance. Other unaffected family members were not found to have the mutation. Conclusion The c.1130C>G (p.Thr377Arg) mutation in the MYOC gene may be responsible for the pathogenesis of this JOAG family.
Glaucoma stands as the leading irreversible cause of blindness worldwide. Regular visual field examinations play a crucial role in both diagnosing and treating glaucoma. Predicting future visual field changes can assist clinicians in making timely interventions to manage the progression of this disease. To integrate temporal and spatial features from past visual field examination results and enhance visual field prediction, a convolutional long short-term memory (ConvLSTM) network was employed to construct a predictive model. The predictive performance of the ConvLSTM model was validated and compared with other methods using a dataset of perimetry tests from the Humphrey field analyzer at the University of Washington (UWHVF). Compared to traditional methods, the ConvLSTM model demonstrated higher prediction accuracy. Additionally, the relationship between visual field series length and prediction performance was investigated. In predicting the visual field using the previous three visual field results of past 1.5~6.0 years, it was found that the ConvLSTM model performed better, achieving a mean absolute error of 2.255 dB, a root mean squared error of 3.457 dB, and a coefficient of determination of 0.960. The experimental results show that the proposed method effectively utilizes existing visual field examination results to achieve more accurate visual field prediction for the next 0.5~2.0 years. This approach holds promise in assisting clinicians in diagnosing and treating visual field progression in glaucoma patients.