Genetic evidence suggesting the predicted causality between osteoarthritis and cardiovascular diseases

Background:Epidemiological studies have shown a close association between osteoarthritis(OA)and cardiovascular disease(CVD),but reliable evidence needs to be provided.We performed a two-sample Mendelian randomization(MR)study to examine the potential causal effect between OA and CVD.Methods:Exposures were self-reported OA,knee osteoarthritis(KOA),and hip osteoarthritis(HOA).The outcomes were 12 CVDs,including heart failure,atrial fibrillation,coronary artery disease,pulmonary embolism,stroke and its subtypes,myocardial infarction,coronary heart disease,and primary hypertension.All outcomes were obtained from published genomewide association studies.The inverse-variance weighted method was used as the primary MR analysis.Heterogeneity tests and sensitivity analyses were conducted to validate the accuracy of the MR results.Results:Self-reported OA increased the incidence of small vessel stroke(odds ratio[OR]=1.25,95%confidence interval[CI]:1.02–1.52,p=0.03)and primary hypertension(1.01[1.00–1.02],p<0.01).HOA increased the incidence of stroke(1.06[1.01–1.11],p=0.02)and two subtypes(cardioembolic stroke:1.12[1.02–1.23],p=0.02;ischemic stroke:1.06[1.01–1.11],p=0.03).Patients with KOA had an increased risk of heart failure(1.10[1.04–1.16],p<0.01),atrial fibrillation(1.08[1.02–1.13],p<0.01),small vessel stroke(1.21[1.06–1.39],p=0.01),and primary hypertension(1.01[1.01–1.02],p<0.01).Conclusions:Patients with OA have an increased risk of several CVDs.The causality of this relationship may have clinical implications for improving the quality of prevention and treatment.

Genetic-algorithm-based deep neural networks for highly efficient photonic device design

While deep learning has demonstrated tremendous potential for photonic device design,it often demands a large amount of labeled data to train these deep neural network models.Preparing these data requires high-resolution numerical simulations or experimental measurements and cost significant,if not prohibitive,time and resources.In this work,we present a highly efficient inverse design method that combines deep neural networks with a genetic algorithm to optimize the geometry of photonic devices in the polar coordinate system.The method requires significantly less training data compared with previous inverse design methods.We implement this method to design several ultra-compact silicon photonics devices with challenging properties including power splitters with uncommon splitting ratios,a TE mode converter,and a broadband power splitter.These devices are free of the features beyond the capability of photolithography and generally in compliance with silicon photonics fabrication design rules.