Objective To investigate the characteristics of urinary microflora in women with type 2 diabetic peripheral neuropathy without lower urinary tract symptoms. Methods By completing nerve conduction function and the American Urological Association Symptom Index questionnaire (AUA-SI), a total of 30 cases of women hospitalized with type 2 diabetes and no symptoms of lower urinary tract from May 2017 to August 2018 were included. 17 patients with diabetic peripheral neuropathy were assigned to the DPN group, and 13 patients without diabetic peripheral neuropathy were assigned to the nDPN group. Urine specimens were collected from clean catch midstream urine and processed for extracting DNA. Microbial diversity and composition were analyzed using the Illumina sequencing platform targeting to 16S rDNA gene. Sequencing reads were processed by QIIME. LEfSe algorithm was used to analyze the flora with significant differences between the two groups. Results The duration of diabetes in the DPN group was lower than that in the nDPN group [(4.12 ± 3.28)years vs.(8.03 ± 6.11)years, P = 0.03], and the retinopathy cases were more in the DPN group than those in the nDPN group (6 vs. 0, P=0.03). Except for above two indicators, there was no significant difference in demographic characteristics between DPN group and nDPN group(P>0.05). The urinary microenvironment of DPN was characterized by increased bacterial richness(sobs index, chao index and aec index, 67.24±40.25 vs.108.69±57.18; 81.36±47.99 vs.122.55±55.70; 88.58±55.03 vs.125.78±53.03, all P 0.05). LEfSe analysis showed that at the genus level, the relative abundance of eight genera(e.g., Bacillus, Duganella, Leptotrichia, Proteus, Propionibacterium, Pseudoxanthomonas, Bdellovibrio and uncultured_soil_bacterium) in DPN group decreased at the level of genus(P<0.05). Conclusions Female patients with type 2 diabetes without lower urinary tract symptoms of peripheral neuropathy exhibit a different microbial community compared to nDPN controls. Mycoplasmataceae may be a potential biomarker for patients with DPN. Key words: Diabetic peripheral neuropathy; No lower urinary tract symptoms; Urinary microbiome

At present, the application of artificial intelligence in the diagnosis of renal cell carcinoma (RCC) is still at an early stage. There were more reports of imaging diagnosis than pathology. Studies of imaging diagnosis mainly focused on using artificial intelligence to identify benign and malignant renal tumors and predict pathological types of RCC by computed tomography. However, there were no reports of artificial intelligence in diagnosing RCC by magnetic resonance imaging. Studies of pathological diagnosis were mainly about the classification of the nucleus. In the future, artificial intelligence has great development potential in the diagnosis of RCC, and further research is needed. Key words: Carcinoma, renal cell; Artificial intelligence; Machine learning; Deep learning

In recent years robot system has been widely introduced into prostatic hyperplasia treatment. Rather than pay attention to traditional laparoscopic robot, this paper focused on Image-Guided Robot-Assisted Prostate Ablation Using Water Jet-Hydrodissection. This treatment system was very mini-invasive and easy to learn. Therefore, it reduced learning curve, operation time and related complications. This paper summarized current advance on the above fields. Key words: Benign prostatic hyperplasia; Aquablation; Minimally invasive robotic surgery; Water-jet ablation; Transurethral resection of prostate