Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0086543 (cataract)
 29,165 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The gene DST encodes for the large protein BPAG1 involved in hemidesmosomes. Its alternative splicing gives rise to tissue-enriched isoforms in brain, muscle, and skin. The few patients described so far with bi-allelic mutations in the DST gene have either a skin phenotype of epidermolysis bullosa simplex or a neurological phenotype. Here, we report a 17-year-old female individual presenting with a more complex phenotype consisting of both skin and neuronal involvement, in addition to several previously unreported findings, such as iris heterochromia, cataract, hearing impairment, syringomyelia, behavioral, and gastrointestinal issues, osteoporosis, and growth hormone deficiency. Family-trio whole exome sequencing revealed that she was a compound heterozygous for two variants in the DST gene with highly-predicted functional impact, c.3886A>G (p.R1296X) in exon 29 and c.806C>T (p.H269R) in exon 7. Interestingly, exon 7 is included in the neuronal isoform whereas exon 29 is expressed in both skin and neuronal isoforms. The patient we described is the first case with a mutation affecting an exon expressed in both the neuronal and skin isoforms that can explain the more complex phenotype compared to previously reported cases.
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PMID:Expanding the phenotype of DST-related disorder: A case report suggesting a genotype/phenotype correlation. 3163 69

Cataract is the clouding of lens, which affects vision and it is the leading cause of blindness in the world's population. Accurate and convenient cataract detection and cataract severity evaluation will improve the situation. Automatic cataract detection and grading methods are proposed in this paper. With prior knowledge, the improved Haar features and visible structure features are combined as features, and multilayer perceptron with discrete state transition (DST-MLP) or exponential DST (EDST-MLP) are designed as classifiers. Without prior knowledge, residual neural networks with DST (DST-ResNet) or EDST (EDST-ResNet) are proposed. Whether with prior knowledge or not, our proposed DST and EDST strategy can prevent overfitting and reduce storage memory during network training and implementation, and neural networks with these strategies achieve state-of-the-art accuracy in cataract detection and grading. The experimental results indicate that combined features always achieve better performance than a single type of feature, and classification methods with feature extraction based on prior knowledge are more suitable for complicated medical image classification task. These analyses can provide constructive advice for other medical image processing applications.
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PMID:Automatic Cataract Classification Using Deep Neural Network With Discrete State Transition. 3129 10