Research on the control rate of hypertension under family physician-contracted service.

BACKGROUND: Hypertension is one of the global public health problems. Family physician-contracted service (FPCS) is widely used in the health management of hypertension patients in China. The purpose of this study was to assess the effect of FPCS on hypertension control. METHODS: PubMed, Web of Science, the Cochrane Library, China National Knowledge Network, Chinese Scientific and Technological Journal Database (CQVIP), and Wanfang Database were searched for randomized controlled trials related to family physician-contracted service and hypertension control effect, and meta-analysis was performed on the literature meeting the inclusion criteria. The source of heterogeneity was discovered by meta-regression, and it was further investigated by subgroup analysis. The risk difference (RD) and 95% confidence interval (CI) were utilized as effect values. Evaluations of publication bias and sensitivity analysis were also conducted. RESULTS: A total of 46 studies were included, and the pooled RD suggested that FPCS could effectively improve the control rate by 19% (RD = 0.19; 95%CI: 0.16-0.21; P < 0.001; I2 = 59.3%). The average age (ß = 0.28; P = 0.05) and the intervention mode (ß = 0.36; P < 0.001) were found to be heterogeneous sources by the meta-regression. According to subgroup analysis, the hypertension control rates of the elderly and working-age population in the experimental group were 93.6% and 90.1%, respectively; the control rates of the "family physician" mode (FP), "family physician + patient" mode (FPP) and "family physician + patient + family member" mode (FPPF) in the experimental group were 90.1%, 94.4%, and 92.6%, respectively. The sensitivity analysis revealed steady results, with no discernible publication bias. CONCLUSIONS: The FPCS is beneficial to the control of hypertension. The control effect is influenced by average age and intervention mode. The control effect of hypertension in the elderly is better than that in the working-age population, and FPP and FPPF are more beneficial to the management of hypertension than FP. The quality and continuity of FPCS should receive more focus in the future, patient self-management and family support are also essential for managing hypertension.

The role of life satisfaction and living arrangements in the association between chronic disease and depression: a national cross-sectional survey.

Introduction: For middle-aged and older people, depression is a frequent and prevalent illness. The purpose of this study was to examine the moderating function of living arrangements in the mediating model as well as the mediating role of life satisfaction in the association between chronic diseases and depressive symptoms. Methods: The China Health and Retirement Longitudinal Study (CHARLS) provided the data for this investigation (2018). Respondents were grouped according to depression status to compare the differences between middle-aged and older people with different depression statuses. The moderating effect of living arrangements and the mediating effect of life satisfaction were tested using the Bootstrap program and the simple slope approach. Results: The population's total prevalence of depressive symptoms was 30.3%. According to the mediating effect research, middle-aged and older people with chronic diseases experienced substantial direct effects on depressive symptoms (ß = 1.011, p < 0.001). It has been established that life satisfaction has an 18.6% mediation effect between depressive symptoms and chronic diseases. Regarding the further moderating influence, it was discovered that chronic diseases had a more significant impact on the life satisfaction of middle-aged and older people who are in live alone than those who are living with others (ß = 0.037, p < 0.05). Conclusion: In middle-aged and older people, chronic diseases have a major influence on depressive symptoms. Life satisfaction mediated the relationship between chronic diseases and depressive symptoms, and living arrangements moderated the first part of the route in the mediation model. Therefore, life satisfaction and living arrangements should be important considerations to decrease the prevalence of depressive symptoms in middle-aged and older people.

Systematic exploration of optimized base editing gRNA design and pleiotropic effects with BExplorer.

Base editing technology is being increasingly applied in genome engineering, but the current strategy for designing guide RNA (gRNA) relies substantially on empirical experience rather than a dependable and efficient in silico design. Furthermore, the pleiotropic effect of base editing on disease treatment remains unexplored, which prevents its further clinical usage. Here, we presented BExplorer, an integrated and comprehensive computational pipeline to optimize the design of gRNAs for 26 existing types of base editors in silico. Using BExplorer, we described its results for two types of mainstream base editors, BE3 and ABE7.10, and evaluated the pleiotropic effect of the corresponding base editing loci. BExplorer revealed 524 and 900 editable pathogenic Single Nucleotide Polymorphism (SNP) loci in the human genome together with the selected optimized gRNAs for BE3 and ABE7.10, respectively. In addition, the impact of 707 edited pathogenic SNP loci following base editing on 151 diseases was systematically explored by revealing their pleiotropic effects, indicating that base editing should be carefully utilized given the potential pleiotropic effects. Collectively, the systematic exploration of optimized base editing gRNA design and the corresponding pleiotropic effects with BExplorer provides a computational basis for applying base editing in disease treatment.

DeepReac+: deep active learning for quantitative modeling of organic chemical reactions.

Various computational methods have been developed for quantitative modeling of organic chemical reactions; however, the lack of universality as well as the requirement of large amounts of experimental data limit their broad applications. Here, we present DeepReac+, an efficient and universal computational framework for prediction of chemical reaction outcomes and identification of optimal reaction conditions based on deep active learning. Under this framework, DeepReac is designed as a graph-neural-network-based model, which directly takes 2D molecular structures as inputs and automatically adapts to different prediction tasks. In addition, carefully-designed active learning strategies are incorporated to substantially reduce the number of necessary experiments for model training. We demonstrate the universality and high efficiency of DeepReac+ by achieving the state-of-the-art results with a minimum of labeled data on three diverse chemical reaction datasets in several scenarios. Collectively, DeepReac+ has great potential and utility in the development of AI-aided chemical synthesis. DeepReac+ is freely accessible at https://github.com/bm2-lab/DeepReac.

Dynamic Disintegration of Explosively-Driven Metal Cylinder with Internal V-Grooves.

Machining V-shaped grooves to the internal surface of cylindrical shells is one of the most common technologies of controlled fragmentation for improving warhead lethality against targets. The fracture strain of grooved shells is a significant concern in warhead design. However, there is as yet no reasonable theory for predicting the fracture strain of a specific grooved shell; existing approaches are only able to predict this physical regularity of non-grooved shells. In this paper, through theoretical analysis and numerical simulations, a new model was established to study the fracture strain of explosively driven cylindrical shells with internal longitudinal V-grooves. The model was built based on an energy conservation equation in which the energy consumed to create a new fracture surface in non-grooved shells was provided by the elastic deformation energy stored in shells. We modified the energy approach so that it can be applicable to grooved shells by adding the elastic energy liberated for crack penetration and reducing the required fracture energy. Cylinders with different groove geometric parameters were explosively expanded to the point of disintegration to verify the proposed model. Theoretical predictions of fracture strain showed good agreement with experimental results, indicating that the model is suitable for predicting the fracture strain of explosively driven metal cylinders with internal V-grooves. In addition, this study provides an insight into the mechanism whereby geometric defects promote fracturing.

FL-QSAR: a federated learning-based QSAR prototype for collaborative drug discovery.

MOTIVATION: Quantitative structure-activity relationship (QSAR) analysis is commonly used in drug discovery. Collaborations among pharmaceutical institutions can lead to a better performance in QSAR prediction, however, intellectual property and related financial interests remain substantially hindering inter-institutional collaborations in QSAR modeling for drug discovery. RESULTS: For the first time, we verified the feasibility of applying the horizontal federated learning (HFL), which is a recently developed collaborative and privacy-preserving learning framework to perform QSAR analysis. A prototype platform of federated-learning-based QSAR modeling for collaborative drug discovery, i.e. FL-QSAR, is presented accordingly. We first compared the HFL framework with a classic privacy-preserving computation framework, i.e. secure multiparty computation to indicate its difference from various perspective. Then we compared FL-QSAR with the public collaboration in terms of QSAR modeling. Our extensive experiments demonstrated that (i) collaboration by FL-QSAR outperforms a single client using only its private data, and (ii) collaboration by FL-QSAR achieves almost the same performance as that of collaboration via cleartext learning algorithms using all shared information. Taking together, our results indicate that FL-QSAR under the HFL framework provides an efficient solution to break the barriers between pharmaceutical institutions in QSAR modeling, therefore promote the development of collaborative and privacy-preserving drug discovery with extendable ability to other privacy-related biomedical areas. AVAILABILITY AND IMPLEMENTATION: The source codes of FL-QSAR are available on the GitHub: https://github.com/bm2-lab/FL-QSAR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Benchmarking and integrating genome-wide CRISPR off-target detection and prediction.

Systematic evaluation of genome-wide Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) off-target profiles is a fundamental step for the successful application of the CRISPR system to clinical therapies. Many experimental techniques and in silico tools have been proposed for detecting and predicting genome-wide CRISPR off-target profiles. These techniques and tools, however, have not been systematically benchmarked. A comprehensive benchmark study and an integrated strategy that takes advantage of the currently available tools to improve predictions of genome-wide CRISPR off-target profiles are needed. We focused on the specificity of the traditional CRISPR SpCas9 system for gene knockout. First, we benchmarked 10 available genome-wide off-target cleavage site (OTS) detection techniques with the published OTS detection datasets. Second, taking the datasets generated from OTS detection techniques as the benchmark datasets, we benchmarked 17 available in silico genome-wide OTS prediction tools to evaluate their genome-wide CRISPR off-target prediction performances. Finally, we present the first one-stop integrated Genome-Wide Off-target cleavage Search platform (iGWOS) that was specifically designed for the optimal genome-wide OTS prediction by integrating the available OTS prediction algorithms with an AdaBoost ensemble framework.

Acceleration Characteristics of Discrete Fragments Generated from Explosively-Driven Cylindrical Metal Shells.

The acceleration characteristics of fragments generated from explosively-driven cylindrical shells are important issues in warhead design. However, there is as yet no reasonable theory for predicting the acceleration process of a specific metallic shell; existing approaches either ignore the effects of shell disintegration and the subsequent gas leakage on fragment acceleration or treat them in a simplified manner. In this paper, a theoretical model was established to study the acceleration of discrete fragments under the combined effect of shell disintegration and gas leakage. Firstly, an equation of motion was developed, where the acceleration of a cylindrical shell and the internal detonation gas was determined by the motive force impacting the inner surface of the metallic cylinder. To account for the force decrease induced by both the change in fragment area after the shell disintegrates and the subsequent drop in gas pressure due to gas leakage, the equation of motion was then associated with an equation for the locally isentropic expansion of the detonation gas and a modified gas-leakage equation. Finally, theoretical analysis was conducted by solving the associated differential equations. The proposed model showed good agreement with experimental data and numerical simulations, indicating that it was suitable for predicting the acceleration of discrete fragments generated from a disintegrated warhead shell. In addition, this study facilitated a better understanding of the complicated interaction between fragment acceleration and gas outflow.

DeepCRISPR: optimized CRISPR guide RNA design by deep learning.

A major challenge for effective application of CRISPR systems is to accurately predict the single guide RNA (sgRNA) on-target knockout efficacy and off-target profile, which would facilitate the optimized design of sgRNAs with high sensitivity and specificity. Here we present DeepCRISPR, a comprehensive computational platform to unify sgRNA on-target and off-target site prediction into one framework with deep learning, surpassing available state-of-the-art in silico tools. In addition, DeepCRISPR fully automates the identification of sequence and epigenetic features that may affect sgRNA knockout efficacy in a data-driven manner. DeepCRISPR is available at http://www.deepcrispr.net/ .

A 'new lease of life': FnCpf1 possesses DNA cleavage activity for genome editing in human cells.

Cpf1 nucleases were recently reported to be highly specific and programmable nucleases with efficiencies comparable to those of SpCas9. AsCpf1 and LbCpf1 require a single crRNA and recognize a 5'-TTTN-3' protospacer adjacent motif (PAM) at the 5' end of the protospacer for genome editing. For widespread application in precision site-specific human genome editing, the range of sequences that AsCpf1 and LbCpf1 can recognize is limited due to the size of this PAM. To address this limitation, we sought to identify a novel Cpf1 nuclease with simpler PAM requirements. Specifically, here we sought to test and engineer FnCpf1, one reported Cpf1 nuclease (FnCpf1) only requires 5'-TTN-3' as a PAM but does not exhibit detectable levels of nuclease-induced indels at certain locus in human cells. Surprisingly, we found that FnCpf1 possesses DNA cleavage activity in human cells at multiple loci. We also comprehensively and quantitatively examined various FnCpf1 parameters in human cells, including spacer sequence, direct repeat sequence and the PAM sequence. Our study identifies FnCpf1 as a new member of the Cpf1 family for human genome editing with distinctive characteristics, which shows promise as a genome editing tool with the potential for both research and therapeutic applications.