Retinal fundus photography provides a non-invasive approach for identifying early microcirculatory alterations of chronic diseases prior to the onset of overt clinical complications. Here, we developed neural network models to predict hypertension, hyperglycemia, dyslipidemia, and a range of risk factors from retinal fundus images obtained from a cross-sectional study of chronic diseases in rural areas of Xinxiang County, Henan, in central China. 1222 high-quality retinal images and over 50 measurements of anthropometry and biochemical parameters were generated from 625 subjects. The models in this study achieved an area under the ROC curve (AUC) of 0.880 in predicting hyperglycemia, of 0.766 in predicting hypertension, and of 0.703 in predicting dyslipidemia. In addition, these models can predict with AUC>0.7 several blood test erythrocyte parameters, including hematocrit (HCT), mean corpuscular hemoglobin concentration (MCHC), and a cluster of cardiovascular disease (CVD) risk factors. Taken together, deep learning approaches are feasible for predicting hypertension, dyslipidemia, diabetes, and risks of other chronic diseases.

Polygenic risk scores (PRS) estimate the genetic risk of an individual for a complex disease based on many genetic variants across the whole genome. In this study, we compared a series of computational models for estimation of breast cancer PRS. A deep neural network (DNN) was found to outperform alternative machine learning techniques and established statistical algorithms, including BLUP, BayesA, and LDpred. In the test cohort with 50% prevalence, the Area Under the receiver operating characteristic Curve (AUC) were 67.4% for DNN, 64.2% for BLUP, 64.5% for BayesA, and 62.4% for LDpred. BLUP, BayesA, and LPpred all generated PRS that followed a normal distribution in the case population. However, the PRS generated by DNN in the case population followed a bimodal distribution composed of two normal distributions with distinctly different means. This suggests that DNN was able to separate the case population into a high-genetic-risk case subpopulation with an average PRS significantly higher than the control population and a normal-genetic-risk case subpopulation with an average PRS similar to the control population. This allowed DNN to achieve 18.8% recall at 90% precision in the test cohort with 50% prevalence, which can be extrapolated to 65.4% recall at 20% precision in a general population with 12% prevalence. Interpretation of the DNN model identified salient variants that were assigned insignificant p values by association studies, but were important for DNN prediction. These variants may be associated with the phenotype through nonlinear relationships.

Background Hypoxia is a common microenvironment condition in most malignant tumors and has been shown to be associated with adverse outcomes of cervical cancer patients. In this study, we investigated the effects of hypoxia-related genes on tumor progress to characterize the tumor hypoxic microenvironment. Methods We retrieved a set of hypoxia-related genes from the Molecular Signatures Database and evaluated their prognostic value for cervical cancer. A hypoxia-based prognostic signature for cervical cancer was then developed and validated using tumor samples from two independent cohorts (TCGA-CESC and CGCI-HTMCP-CC cohorts). Finally, we validated the hypoxia prediction of ccHPS score in eight human cervical cancer cell lines treated with the hypoxic and normoxic conditions, and 286 tumor samples with hypoxic category (more or less) from Gene Expression Omnibus (GEO) database with accession GSE72723. Results A risk signature model containing nine hypoxia-related genes was developed and validated in cervical cancer. Further analysis showed that this risk model could be an independent prognosis factor of cervical cancer, which reflects the condition of the hypoxic tumor microenvironment and its remodeling of cell metabolism and tumor immunity. Furthermore, a nomogram integrating the novel risk model and lymphovascular invasion status was developed, accurately predicting the 1-, 3- and 5-year prognosis with AUC values of 0.928, 0.916 and 0.831, respectively. These findings provided a better understanding of the hypoxic tumor microenvironment in cervical cancer and insights into potential new therapeutic strategies in improving cancer therapy.