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Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, underlying mechanisms and target genes are largely unknown, as most risk-associated variants might regulate gene expression in a context-specific manner. Here, we generate a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation. Identified candidate cis-regulatory elements (cCREs) are largely cell type-specific, with 37% detected in one cell type. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs combined with transcription factor footprinting prioritize the variants for 68% of the GWAS loci. CCV-colocalization and trait relevance score indicate that epithelial and immune cell categories, including rare cell types, contribute to lung cancer susceptibility the most. A multi-level cCRE-gene linking system identifies candidate susceptibility genes from 57% of the loci, where most loci display cell-category-specific target genes, suggesting context-specific susceptibility gene function. Multiple genetic loci are associated with lung cancer risk, but the underlying genetic mechanisms remain poorly understood. Here, the authors perform single-cell RNA-seq and ATAC-seq analyses of lung cells from ever- and never-smokers; they report candidate cis-regulatory elements that colocalise with candidate causal variants in lung cancer risk loci and potential susceptibility genes. [ABSTRACT FROM AUTHOR]

Tissue-based functional genomics resources including molecular quantitative trait loci datasets lack diversity in ancestry and tissue types and thus are inadequate for comprehensively investigating gene regulation. Global efforts to increase the tissue diversity will help achieve more equitable medical care. [ABSTRACT FROM AUTHOR]

A challenge of chronic diseases that remains to be solved is how to liberate patients and medical resources from the burdens of long-term monitoring and periodic visits. Precise management based on artificial intelligence (AI) holds great promise; however, a clinical application that fully integrates prediction and telehealth computing has not been achieved, and further efforts are required to validate its real-world benefits. Taking congenital cataract as a representative, we used Bayesian and deep-learning algorithms to create CC-Guardian, an AI agent that incorporates individualized prediction and scheduling, and intelligent telehealth follow-up computing. Our agent exhibits high sensitivity and specificity in both internal and multi-resource validation. We integrate our agent with a web-based smartphone app and prototype a prediction-telehealth cloud platform to support our intelligent follow-up system. We then conduct a retrospective self-controlled test validating that our system not only accurately detects and addresses complications at earlier stages, but also reduces the socioeconomic burdens compared to conventional methods. This study represents a pioneering step in applying AI to achieve real medical benefits and demonstrates a novel strategy for the effective management of chronic diseases. [ABSTRACT FROM AUTHOR]

There are many image classification methods, but it remains unclear which methods are most helpful for analyzing and intelligently identifying ophthalmic images. We select representative slit-lamp images which show the complexity of ocular images as research material to compare image classification algorithms for diagnosing ophthalmic diseases. To facilitate this study, some feature extraction algorithms and classifiers are combined to automatic diagnose pediatric cataract with same dataset and then their performance are compared using multiple criteria. This comparative study reveals the general characteristics of the existing methods for automatic identification of ophthalmic images and provides new insights into the strengths and shortcomings of these methods. The relevant methods (local binary pattern +SVMs, wavelet transformation +SVMs) which achieve an average accuracy of 87% and can be adopted in specific situations to aid doctors in preliminarily disease screening. Furthermore, some methods requiring fewer computational resources and less time could be applied in remote places or mobile devices to assist individuals in understanding the condition of their body. In addition, it would be helpful to accelerate the development of innovative approaches and to apply these methods to assist doctors in diagnosing ophthalmic disease. [ABSTRACT FROM AUTHOR]

Capsular outcomes of anterior/posterior capsulorhexis opening (ACO/PCO) are essential for performing a secondary in-the-bag intraocular lens implantation. To compare the capsular outcomes with different primary capsulorhexis sizes, Thirty-eight eligible patients (45 eyes) were randomly assigned to three groups by anterior capsulorhexis diameter (Group A: 3.0-3.9, Group B: 4.0-5.0, and Group C: 5.1-6.0 mm). The areas of ACO/PCO and posterior capsule opening opacity (PCOO) as primary outcomes, while, the incidence of visual axis opacity (VAO) as secondary outcome were measured at follow-up visits. Among the thirty eyes included in the final analysis, the mean area of the ACO decreased significantly, whereas the PCO enlarged with time. Group A had the highest anterior capsule constriction and percentage reduction, which increased with time. There were significant differences in the percentage reductions at 6 months and 1 year compared to 1 month in Group A and B. Group C had the highest posterior capsule enlargement. The percentage of PCOO to PCO area and the incidence of VAO was highest in Group A and lowest in Group C. Thus, Capsulorhexis diameter of 4.0-5.0 mm may yield better capsular outcomes, considering moderate contraction of ACO, moderate enlargement of PCO, and lower percentage of PCOO and VAO. [ABSTRACT FROM AUTHOR]