Clinical biomarker-based biological aging and risk of benign prostatic hyperplasia: A large prospective cohort study.

Objective: Chronological age (CAge), biological age (BAge), and accelerated age (AAge) are all important for aging-related diseases. CAge is a known risk factor for benign prostatic hyperplasia (BPH); However, the evidence of association of BAge and AAge with BPH is limited. This study aimed to evaluate the association of CAge, Bage, and AAge with BPH in a large prospective cohort. Method: A total of 135,933 males without BPH at enrolment were extracted from the UK biobank. We calculated three BAge measures (Klemera-Doubal method, KDM; PhenoAge; homeostatic dysregulation, HD) based on 16 biomarkers. Additionally, we calculated KDM-BAge and PhenoAge-BAge measures based on the Levine method. The KDM-AAge and PhenoAge-AAge were assessed by the difference between CAge and BAge and were standardized (mean = 0 and standard deviation [SD] = 1). Cox proportional hazard models were applied to assess the associations of CAge, Bage, and AAge with incident BPH risk. Results: During a median follow-up of 13.150 years, 11,811 (8.690%) incident BPH were identified. Advanced CAge and BAge measures were associated with an increased risk of BPH, showing threshold effects at a later age (all P for nonlinearity <0.001). Nonlinear relationships between AAge measures and risk of BPH were also found for KDM-AAge (P = 0.041) and PhenoAge-AAge (P = 0.020). Compared to the balance comparison group (-1 SD < AAge < 1 SD), the accelerated aging group (AAge > 2 SD) had a significantly elevated BPH risk with hazard ratio (HR) of 1.115 (95% CI, 1.000-1.223) for KDM-AAge and 1.180 (95% CI, 1.068-1.303) for PhenoAge-AAge, respectively. For PhenoAge-AAge, subgroup analysis of the accelerated aging group showed an increased HR of 1.904 (95% CI, 1.374-2.639) in males with CAge <50 years and 1.233 (95% CI, 1.088-1.397) in those having testosterone levels <12 nmol/L. Moreover, AAge-associated risk of BPH was independent of and additive to genetic risk. Conclusions: Biological aging is an independent and modifiable risk factor for BPH. We suggest performing active health interventions to slow biological aging, which will help mitigate the progression of prostate aging and further reduce the burden of BPH.

Vina-FPGA-Cluster: Multi-FPGA Based Molecular Docking Tool with High-Accuracy and Multi-Level Parallelism.

AutoDock Vina (Vina) stands out among numerous molecular docking tools due to its precision and comparatively high speed, playing a key role in the drug discovery process. Hardware acceleration of Vina on FPGA platforms offers a high energy-efficiency approach to speed up the docking process. However, previous FPGA-based Vina accelerators exhibit several shortcomings: 1) Simple uniform quantization results in inevitable accuracy drop; 2) Due to Vina's complex computing process, the evaluation and optimization phase for hardware design becomes extended; 3) The iterative computations in Vina constrain the potential for further parallelization. 4) The system's scalability is limited by its unwieldy architecture. To address the above challenges, we propose Vina-FPGA-cluster, a multi-FPGA-based molecular docking tool enabling high-accuracy and multi-level parallel Vina acceleration. Standing upon the shoulders of Vina-FPGA, we first adapt hybrid fixed-point quantization to minimize accuracy loss. We then propose a SystemC-based model, accelerating the hardware accelerator architecture design evaluation. Next, we propose a novel bidirectional AG module for data-level parallelism. Finally, we optimize the system architecture for scalable deployment on multiple Xilinx ZCU104 boards, achieving task-level parallelism. Vina-FPGA-cluster is tested on three representative molecular docking datasets. The experiment results indicate that in the context of RMSD (for successful docking outcomes with metrics below 2Å), Vina-FPGA-cluster shows a mere 0.2% lose. Relative to CPU and Vina-FPGA, Vina-FPGA-cluster achieves 27.33× and 7.26× speedup, respectively. Notably, Vina-FPGA-cluster is able to deliver the 1.38× speedup as GPU implementation (Vina-GPU), with just the 28.99% power consumption.

Changes in disease burden and global inequalities in bladder, kidney and prostate cancers from 1990 to 2019: a comparative analysis based on the global burden of disease study 2019.

BACKGROUND: Bladder, kidney and prostate cancers make significant contributors to cancer burdens. Exploring their cross-country inequalities may inform equitable strategies to meet the 17 sustainable development goals before 2030. METHODS: We analyzed age-standardized disability-adjusted life-years (ASDALY) rates for the three cancers based on Global Burden of Diseases Study 2019. We quantified the inequalities using slope index of inequality (SII, absolute measure) and concentration index (relative measure) associated with national sociodemographic index. RESULTS: Varied ASDALY rates were observed in the three cancers across 204 regions. The SII decreased from 35.15 (95% confidence interval, CI: 29.34 to 39.17) in 1990 to 15.81 (95% CI: 7.99 to 21.79) in 2019 for bladder cancers, from 78.94 (95% CI: 75.97 to 81.31) in 1990 to 59.79 (95% CI: 55.32 to 63.83) in 2019 for kidney cancer, and from 192.27 (95% CI: 137.00 to 241.05) in 1990 to - 103.99 (95% CI: - 183.82 to 51.75) in 2019 for prostate cancer. Moreover, the concentration index changed from 12.44 (95% CI, 11.86 to 12.74) in 1990 to 15.72 (95% CI, 15.14 to 16.01) in 2019 for bladder cancer, from 33.88 (95% CI: 33.35 to 34.17) in 1990 to 31.13 (95% CI: 30.36 to 31.43) in 2019 for kidney cancer, and from 14.61 (95% CI: 13.89 to 14.84) in 1990 to 5.89 (95% CI: 5.16 to 6.26) in 2019 for prostate cancer. Notably, the males presented higher inequality than females in both bladder and kidney cancer from 1990 to 2019. CONCLUSIONS: Different patterns of inequality were observed in the three cancers, necessitating tailored national cancer control strategies to mitigate disparities. Priority interventions for bladder and kidney cancer should target higher socioeconomic regions, whereas interventions for prostate cancer should prioritize the lowest socioeconomic regions. Additionally, addressing higher inequality in males requires more intensive interventions among males from higher socioeconomic regions.

Secular trends of morbidity and mortality of thyroid cancer in five Asian countries from 1990 to 2019 and their predictions to 2035.

BACKGROUND: The rising burden of thyroid cancer (TC) is a public health problem in Asia. Predicting the future burden of TC in Asian countries is essential for disease prevention. METHODS: Data were obtained from the Global Burden of Disease 2019 for five Asian countries. We applied Bayesian age-period-cohort models to predict morbidity and mortality to 2035 and calculated age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR). Furthermore, the estimated annual percentage change was calculated to evaluate the variation of ASIR and ASMR. RESULTS: By 2035, predictions suggest that cases of TC will reach 75.56 × 103 in China, 70.22 × 103 in India, 15.78 × 103 in the Republic of Korea, 9.01 × 103 in Japan and 5.55 × 103 in Thailand, respectively. Except Japan, a significant upward trend of ASIR of TC will be observed in five Asian countries. The deaths from TC will increase in five countries and India will become the highest reaching 14.07 × 103 . The ASMR will rise to 0.83/100 000 in India and 1.06/100 000 in the Republic of Korea, while it will drop to 0.35/100 000 in China, 0.43/100 000 in Japan and 0.50/100 000 in Thailand. In further predictions projected by sex, the growth rate of ASIR is reported higher in males than in females among most countries. ASMR of male will exceed that of females in China and Thailand by 2035. CONCLUSION: The disease burden caused by TC will further increase in five Asian countries, especially for men. It is necessary to develop more rational and timely disease prevention and manage strategies facing this disease trend.

Effectiveness Analysis of Multiple Initial States Simulated Annealing Algorithm, a Case Study on the Molecular Docking Tool AutoDock Vina.

Simulated Annealing (SA) algorithm is not effective with large optimization problems for its slow convergence. Hence, several parallel Simulated Annealing (pSA) methods have been proposed, where the increase of searching threads can boost the speed of convergence. Although satisfactory solutions can be obtained by these methods, there is no rigorous mathematical analyses on their effectiveness. Thus, this article introduces a probabilistic model, on which a theorem about the effectiveness of multiple initial states parallel SA (MISPSA) has been proven. The theorem also demonstrates that the increasing parallelism in pSA algorithm with the reducing of search depth in each thread could obtain almost the same probability of finding the global optimal solution. We validated our theorem on AutoDock Vina, a widely used molecular docking tool with high accuracy and docking speed. AutoDock Vina uses a pSA strategy to find optimal molecular conformations. Under the premise that the total searching workload (i.e., thread number * iteration depth of each thread) remains unchanged, the docking accuracy from an aggressively parallelized SA searching method is almost the same or even better than those from the default exhaustiveness (parallelism degree) configuration of AutoDock Vina. Taking complex '1hnn' as an example,with the increase (125x) in the number of initial states (from 8 to 1000) and the decrease in the search depth for each thread (from 15540 to 124, or 1/125 of the original search depth), the mean energy is -7.80 and -7.94, while the mean RMSD is 3.4 and 3.14, respectively. The result also implies that a considerable speedup (in this case 125x in theory) can be obtained by a highly parallelized SA algorithm implementation.

Vina-GPU 2.0: Further Accelerating AutoDock Vina and Its Derivatives with Graphics Processing Units.

Modern drug discovery typically faces large virtual screens from huge compound databases where multiple docking tools are involved for meeting various real scenes or improving the precision of virtual screens. Among these tools, AutoDock Vina and its numerous derivatives are the most popular and have become the standard pipeline for molecular docking in modern drug discovery. Our recent Vina-GPU method realized 14-fold acceleration against AutoDock Vina on a piece of NVIDIA RTX 3090 GPU in one virtual screening case. Further speedup of AutoDock Vina and its derivatives with graphics processing units (GPUs) is beneficial to systematically push their popularization in large-scale virtual screens due to their high benefit-cost ratio and easy operation for users. Thus, we proposed the Vina-GPU 2.0 method to further accelerate AutoDock Vina and the most common derivatives with new docking algorithms (QuickVina 2 and QuickVina-W) with GPUs. Caused by the discrepancy in their docking algorithms, our Vina-GPU 2.0 adopts different GPU acceleration strategies. In virtual screening for two hot protein kinase targets, RIPK1 and RIPK3, from the DrugBank database, our Vina-GPU 2.0 reaches an average of 65.6-fold, 1.4-fold, and 3.6-fold docking acceleration against the original AutoDock Vina, QuickVina 2, and QuickVina-W while ensuring their comparable docking accuracy. In addition, we develop a friendly and installation-free graphical user interface tool for their convenient usage. The codes and tools of Vina-GPU 2.0 are freely available at https://github.com/DeltaGroupNJUPT/Vina-GPU-2.0, coupled with explicit instructions and examples.

Accelerating AutoDock Vina with GPUs.

AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing a common scenario of large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will greatly limit the popularity of AutoDock Vina and the flexibility of its usage in modern drug discovery. In this work, we proposed a new method, Vina-GPU, for accelerating AutoDock Vina with GPUs, which is greatly needed for reducing the investment for large virtual screens and also for wider application in large-scale virtual screening on personal computers, station servers or cloud computing, etc. Our proposed method is based on a modified Monte Carlo using simulating annealing AI algorithm. It greatly raises the number of initial random conformations and reduces the search depth of each thread. Moreover, a classic optimizer named BFGS is adopted to optimize the ligand conformations during the docking progress, before a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmark tests show that Vina-GPU reaches an average of 21-fold and a maximum of 50-fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential for pushing the popularization of AutoDock Vina in large virtual screens.

One-pot synthesis of ZnFe2O4/C hollow spheres as superior anode materials for lithium ion batteries.

ZnFe(2)O(4)/C hollow spheres have been synthesized via a facile solvothermal route using low cost raw materials. The resulting composite showed a very high specific capacity of 841 mAh g(-1) after 30 cycles and good rate capability.