Comprehensive databases of microRNA–disease associations are continuously demanded in biomedical researches. The recently launched version 3.0 of Human MicroRNA Disease Database (HMDD v3.0) manually collects a significant number of miRNA–disease association entries from literature. Comparing to HMDD v2.0, this new version contains 2-fold more entries. Besides, the associations have been more accurately classified based on literature-derived evidence code, which results in six generalized categories (genetics, epigenetics, target, circulation, tissue and other) covering 20 types of detailed evidence code. Furthermore, we added new functionalities like network visualization on the web interface. To exemplify the utility of the database, we compared the disease spectrum width of miRNAs (DSW) and the miRNA spectrum width of human diseases (MSW) between version 3.0 and 2.0 of HMDD. HMDD is freely accessible at http://www.cuilab.cn/hmdd. With accumulating evidence of miRNA–disease associations, HMDD database will keep on growing in the future.

Regular health screening plays a crucial role in the early detection of common chronic diseases and prevention of their progression. An AI system capable of recapitulating early disease detection, staging and incidence prediction would help to improve healthcare access and delivery, particularly in resource-poor or remote settings. Using a total of 115,344 retinal fundus photographs from 57,672 patients (with data split into mutually exclusive training, internal testing, and external validation sets), we first developed AI models capable of identifying chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM) based on fundus images. The AI system was shown to be capable of predicting the clinical indicators of CKD and T2DM (including eGFR and blood glucose levels), which indicates its potential for extracting quantitative clinical metrics embedded subtly within retinal fundus images. We further developed an AI system to predict the risk of disease progression using baseline images of 10,269 patients for whom longitudinal clinical data were available for up to 6 years, which demonstrated potential utility in optimizing health screening intervals and clinical management. The generalizability of the AI system in identifying and predicting the progression of CKD and T2DM was evaluated using population-based external validation cohorts. Moreover, a prospective pilot study with 3,081 patients was also conducted to demonstrate the broader applicability of the AI system at the 'point-of-care' using fundus images captured with smartphones. The results provide proof-of-concept for a reliable and non-invasive AI-based clinical screening tool based on fundus photographs for the early detection and incidence prediction of two common systemic diseases.

Aging is a complex biological process that is far from being completely understood. Analyzing transcriptional differences across age might help uncover genetic bases of aging. In this study, 1573 differentially expressed genes, related to chronological age, from the Genotype-Tissue Expression (GTEx) project, were categorized as upregulated age-associated genes (UAGs) and downregulated age-associated genes (DAGs). Characteristics in evolution, expression, function and molecular networks were comprehensively described and compared for UAGs, DAGs and other genes. Analyses revealed that UAGs are more clustered, more quickly evolving, more tissue specific and have accumulated more single-nucleotide polymorphisms (SNPs) and disease genes than DAGs. DAGs were found with a lower evolutionary rate, higher expression level, greater homologous gene number, smaller phyletic age and earlier expression in body development. UAGs are more likely to be located in the extracellular region and to occur in both immune-relevant processes and cancer-related pathways. By contrast, DAGs are more likely to be located intracellularly and to be enriched in catabolic and metabolic processes. Moreover, DAGs are also critical in a protein–protein interaction (PPI) network, whereas UAGs have more influence on a signaling network. This study highlights characteristics of the aging transcriptional landscape in a healthy population, which may benefit future studies on the aging process and provide a broader horizon for age-dependent precision medicine.

SummarySelf-renewal and differentiation of neural stem cells is essential for embryonic neurogenesis, which is associated with cell autophagy. However, the mechanism by which autophagy regulates neurogenesis remains undefined. Here, we show that Eva1a/Tmem166, an autophagy-related gene, regulates neural stem cell self-renewal and differentiation. Eva1a depletion impaired the generation of newborn neurons, both in vivo and in vitro. Conversely, overexpression of EVA1A enhanced newborn neuron generation and maturation. Moreover, Eva1a depletion activated the PIK3CA-AKT axis, leading to the activation of the mammalian target of rapamycin and the subsequent inhibition of autophagy. Furthermore, addition of methylpyruvate to the culture during neural stem cell differentiation rescued the defective embryonic neurogenesis induced by Eva1a depletion, suggesting that energy availability is a significant factor in embryonic neurogenesis. Collectively, these data demonstrated that EVA1A regulates embryonic neurogenesis by modulating autophagy. Our results have potential implications for understanding the pathogenesis of neurodevelopmental disorders caused by autophagy dysregulation.

Background Deep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts. Methods We established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively. Results The AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81–0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83–0.95) and 0.88 (0.79–0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88–0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81–0.92) and 0.88 (0.83–0.94) in external test sets 1 and 2, respectively. Conclusion Our study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression. FUNDING National Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC.