Screening of common deaf genes in pregnant women and prevention of deafness at birth / 中华医学遗传学杂志

<p><b>OBJECTIVE</b>To determine the carrier rate for common mutations causing deafness among pregnant women in order to prevent births of deaf children.</p><p><b>METHODS</b>For 893 pregnant women, 2 mL peripheral venous blood was taken and DNA was extracted. A deafness DNA microarray screening was applied to such samples, and DNA sequencing was applied to husbands of women with positive screening results.</p><p><b>RESULTS</b>A total of 40 carriers were detected, with the overall mutation rate being 4.48%. Among such carriers, GJB2 235delC was the most common heterozygous mutation (18 cases) and the mutation rate was 2.02%. GJB2 299A-T heterozygous mutation was detected in 7 cases with a mutation rate of 0.78%. IVS7-2A to G heterozygous mutation was detected in 9 cases with a mutation rate of 1.02%. There were 2 cases carrying GJB3 heterozygous mutation and 2 cases of mitochondrial 12S rRNA heterozygous mutation, with a mutation rate of 0.22%. IVS7-2A>G with GJB3 538C>T double heterozygous mutation was detected in 1 case, and IVS7-2A>G with GJB2 299A-T double heterozygous mutation was detected in another case, with the mutation rate of each being 0.11%. DNA sequencing has failed to find presence of mutations in the same gene in the husbands. The results of neonatal hearing follow-up were all normal.</p><p><b>CONCLUSION</b>Applications of the deaf genes screening in pregnant women may play prove to be valuable for the early detection for neonatal deafness.</p>

Study on intra-retinal layers imaged with ultra - high resolution optical coherence tomography / 国际眼科杂志(Guoji Yanke Zazhi)

To evaluate the automated segmentation algorithm for detection of intra - retinal layers to process images obtained from ultra- high resolution optical coherence tomography ( OCT ) . Graph theory and the shortest path search based on dynamic programming were applied to automatically segment the 8 intra - retinal layers. We experimentally verified the accuracy and reliability of the algorithm. The results showed that the intra-retinal layer boundaries between automated and manual segmentations matched well. The algorithm successfully segmented the intra- retinal layers in glaucoma, high myopia, and retinitis pigmentosa patients. The proposed automatic segmentation for intra-retinal layers provides a promising tool for quantitative analysis in clinical diagnosis and treatment.