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Background: Prostatitis seriously endangers the health of men. While they have been widely used in recent years, there remains a lack of systematic evaluation of the clinical efficacy of α-receptor blockers (α-RBs)/α-adrenergic receptor blockers (α-ARBs) in its treatment. Based on this, this study was developed to systematically evaluate the clinical effect of α-ARB in the treatment of prostatitis.Methods: Randomized controlled trials (RCTs) studying α-RBs or α-ARBs, placebos, or other measures to treat prostatitis were searched in Cochrane Library, PubMed, Embase, and CBM databases from establishment to December 2020. The quality of included articles was evaluated using the Cochrane System Review Manual and Jadad tools, and a meta-analysis was performed using Review Manager 5.3 software. Results: A total of six articles meeting the requirements were found and included 450 patients. Metaanalysis showed that the National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) score [mean difference (MD) =−1.76, 95% confidence interval (CI): (−3.35 to −0.17), and P=0.03], pain score [MD =−2.24, 95% CI: (−3.65 to −0.83), and P=0.002], voiding symptom score [MD =−1.21, 95% CI: (−2.06 to −0.35), and P=0.006], and quality of life score [MD =−1.40, 95% CI: (−1.48 to −1.33), and P<0.00001] for patients in the experimental group were lower in contrast to those in the control group after the treatment.Discussion: The use of α-ARB could significantly improve the treatment effect of patients with prostatitis and improve their quality of life.

At the edge of the network close to the source of the data, edge computing deploys computing, storage and other capabilities to provide intelligent services in close proximity and offers low bandwidth consumption, low latency and high security. It satisfies the requirements of transmission bandwidth, real-time and security for Internet of Things (IoT) application scenarios. Based on the IoT architecture, an IoT edge computing (EC-IoT) reference architecture is proposed, which contained three layers: The end edge, the network edge and the cloud edge. Furthermore, the key technologies of the application of artificial intelligence (AI) technology in the EC-IoT reference architecture is analyzed. Platforms for different EC-IoT reference architecture edge locations are classified by comparing IoT edge computing platforms. On the basis of EC-IoT reference architecture, an industrial Internet of Things (IIoT) edge computing solution, an Internet of Vehicles (IoV) edge computing architecture and a reference architecture of the IoT edge gateway-based smart home are proposed. Finally, the trends and challenges of EC-IoT are examined, and the EC-IoT architecture will have very promising applications.

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