Applications of Polygenic Risk Scores in Psychiatric Genetics.

Polygenic risk score (PRS) is a powerful tool for studying the genetic architecture of complex diseases, including psychiatric disorders. This review highlights the use of PRS in psychiatric genetics, including its application in identifying high-risk individuals, estimating heritability, assessing shared etiology between phenotypes, and personalizing treatment plans. It also explains the methodology for calculating PRS, the challenges associated with their use in clinical settings, and future research directions. The main limitation of PRS is that the current models only account for a small fraction of the heritability of psychiatric disorders. Despite this limitation, PRS represents a valuable tool that has already yielded important insights into the genetic architecture of psychiatric disorders.

[Segmentation of retinal vessels by fusing contour information and conditional generative adversarial].

The existing retinal vessels segmentation algorithms have various problems that the end of main vessels are easy to break, and the central macula and the optic disc boundary are likely to be mistakenly segmented. To solve the above problems, a novel retinal vessels segmentation algorithm is proposed in this paper. The algorithm merged together vessels contour information and conditional generative adversarial nets. Firstly, non-uniform light removal and principal component analysis were used to process the fundus images. Therefore, it enhanced the contrast between the blood vessels and the background, and obtained the single-scale gray images with rich feature information. Secondly, the dense blocks integrated with the deep separable convolution with offset and squeeze-and-exception (SE) block were applied to the encoder and decoder to alleviate the gradient disappearance or explosion. Simultaneously, the network focused on the feature information of the learning target. Thirdly, the contour loss function was added to improve the identification ability of the blood vessels information and contour information of the network. Finally, experiments were carried out on the DRIVE and STARE datasets respectively. The value of area under the receiver operating characteristic reached 0.982 5 and 0.987 4, respectively, and the accuracy reached 0.967 7 and 0.975 6, respectively. Experimental results show that the algorithm can accurately distinguish contours and blood vessels, and reduce blood vessel rupture. The algorithm has certain application value in the diagnosis of clinical ophthalmic diseases.