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Abstract Robot-assisted surgery has evolved into a crucial treatment for prostate cancer (PCa). However, from its appearance to today, brain-computer interface, virtual reality, and metaverse have revolutionized the field of robot-assisted surgery for PCa, presenting both opportunities and challenges. Especially in the context of contemporary big data and precision medicine, facing the heterogeneity of PCa and the complexity of clinical problems, it still needs to be continuously upgraded and improved. Keeping this in mind, this article summarized the 5 stages of the historical development of robot-assisted surgery for PCa, encompassing the stages of emergence, promotion, development, maturity, and intelligence. Initially, safety concerns were paramount, but subsequent research and engineering advancements have focused on enhancing device efficacy, surgical technology, and achieving precise multi modal treatment. The dominance of da Vinci robot-assisted surgical system has seen this evolution intimately tied to its successive versions. In the future, robot-assisted surgery for PCa will move towards intelligence, promising improved patient outcomes and personalized therapy, alongside formidable challenges. To guide future development, we propose 10 significant prospects spanning clinical, research, engineering, materials, social, and economic domains, envisioning a future era of artificial intelligence in the surgical treatment of PCa.

To reduce treatment-related side effects in low-risk prostate cancer (PCa), both focal therapy and deferred treatments, including active surveillance (AS) and watchful waiting (WW), are worth considering over radical prostatectomy (RP). Therefore, this study aimed to compare long-term survival outcomes between focal therapy and AS/WW. Data were obtained and analyzed from the Surveillance, Epidemiology, and End Results (SEER) database. Patients with low-risk PCa who received focal therapy or AS/WW from 2010 to 2016 were included. Focal therapy included cryotherapy and laser ablation. Multivariate Cox proportional hazards models were used to compare overall mortality (OM) and cancer-specific mortality (CSM) between AS/WW and focal therapy, and propensity score matching (PSM) was performed to reduce the influence of bias and unmeasured confounders. A total of 19 292 patients with low-risk PCa were included in this study. In multivariate Cox proportional hazards model analysis, the risk of OM was higher in patients receiving focal therapy than those receiving AS/WW (hazard ratio [HR] = 1.35, 95% confidence interval [CI]: 1.02–1.79, P = 0.037), whereas no significant difference was found in CSM (HR = 0.98, 95% CI: 0.23–4.11, P = 0.977). After PSM, the OM and CSM of focal therapy and AS/WW showed no significant differences (HR = 1.26, 95% CI: 0.92–1.74, P = 0.149; and HR = 1.26, 95% CI: 0.24–6.51, P = 0.782, respectively). For patients with low-risk PCa, focal therapy was no match for AS/WW in decreasing OM, suggesting that AS/WW could bring more overall survival benefits.

Prostate cancer (PCa) is the second-most common cancer among men. Both active surveillance or watchful waiting (AS/WW) and focal laser ablation (FLA) can avoid the complications caused by radical treatment. How to make the choice between these options in clinical practice needs further study. Therefore, this study aims to compare and analyze their effects based on overall survival (OS) and cancer-specific survival (CSS) to obtain better long-term benefits. We included patients with low-risk PCa from the Surveillance Epidemiology and End Results database of 2010–2016. Multivariate Cox proportional hazard analyses were conducted for OS and CSS in the two groups. To eliminate bias, this study applied a series of sensitivity analyses. Moreover, Kaplan–Meier curves were plotted to obtain survival status. A total of 18 841 patients with low-risk PCa were included, with a median of 36-month follow-up. According to the multivariate Cox proportional hazard regression, the FLA group presented inferior survival benefits in OS than the AS/WW group (hazard ratio [HR]: 2.13, 95% confidence interval [CI]: 1.37–3.33, P < 0.05). After adjusting for confounders, the result persisted (HR: 1.69, 95% CI: 1.02–2.81, P < 0.05). According to the results of the sensitivity analysis, the inverse probability of the treatment weighing model indicated the same result in OS. In conclusion, AS/WW and FLA have the advantage of fewer side effects and the benefit of avoiding overtreatment compared with standard treatment. Our study suggested that AS/WW provides more survival benefits for patients with low-risk PCa. More relevant researches and data will be needed for further clarity.

Background: Group B streptococcus (GBS) is a leading cause of serious infection in infants. Understanding its regional molecular epidemiology is helpful for regulating efficient prevention practice. Methods: A retrospective study was conducted to collected data from infants and pregnant women with culture-proven GBS disease in the largest women and children's medical center in Shanxi between January 2017 and September 2019. All GBS isolates were analyzed by multi-locus sequence typing (MLST) as well as distribution of pilus island (PI) genes. Results: A total of 54 GBS isolates were obtained from 36 (66.7%) pregnant women and 18 (33.3%) infants with invasive disease. Among invasive GBS strains, the most common sequence type was ST10 (72.2%, P

In the cloud manufacturing environment, the interaction and cooperation between enterprises become more convenient. The most optimal green supplier selection through cloud manufacturing platform can improve the production quality and sustainable development efficiency of enterprises. However, as alternative suppliers are all over the world, there is a risk that the selected supplier is not ideal. Therefore, in this paper, a hybrid supplier selection model based on TODIM method is proposed to choose the optimal green supplier under cloud manufacturing platform. Considering the characteristics of suppliers in the cloud manufacturing environment and green criteria, the comprehensive green supplier evaluation index system is constructed. The creative application of heterogeneous evaluation information including crisp numbers, interval numbers, and probabilistic linguistic values, can express the evaluation information of different subjects comprehensively. In order to consider human judgment and the importance of information provided by raw data, the criteria weights are determined by integrating fuzzy BWM (Best-worst method) weights and objective entropy weights. Then, with the full consideration of decision maker’s risk attitude, the TODIM method is used to process heterogeneous evaluation information and calculate the priority of the green suppliers. The proposed method is novel, and allows multi-subjects to participate in the assessment process and considers the risk attitude of decision-makers in the green supplier selection process. An empirical study of green supplier selection in the cloud manufacturing environment is conducted. Sensitivity analysis and comparative analysis indicate that the proposed selection model for green supplier is reliable and effective.

A simple emergency risk prediction tool should be developed for clinicians to quickly identify the prognosis of patients with acute aortic dissection.We enrolled 280 patients with acute aortic dissection admitted to emergency department between May 2010 and February 2013. Multivariate logistic regression analysis was performed to identify independent predictors of in-hospital death.The in-hospital mortality of our patients with acute aortic dissection was 32.5%, in-hospital deaths with surgery less than the survived (34.1% VS 54.5%). Multivariate analysis identified that age (≥65 years old), Type A, blood pressure (mean systolic blood pressure ≤ 90 mmHg), neutrophil percentage (≥ 80%) and serum D-dimer (≥ 5.0 mg/L) were significant predictors of death. With the simple emergency risk prediction tool, scores of all in-hospital deaths were ≥ 3, whereas almost all of the survivors (97.9%) had scores < 15. A score of 10 offered the best threshold value, with the highest sensitivity (81.3%) and specificity (86.8%).The in-hospital mortality rate of patients with acute aortic dissection is high and can be predicted. Early surgery would be beneficial for in-hospital survive. This tool should be available for clinicians in the emergency department to quickly identify the prognosis of patients with acute aortic dissection.