Bacterial Colonization on Healthcare Workers' Mobile Phones and Hands in Municipal Hospitals of Chongqing, China: Cross-contamination and Associated Factors.

BACKGROUND: Mobile phones are widely used in clinical settings and could be colonized by potential pathogenic bacteria which may lead to hospital-acquired infections (HAIs) transmission. This study aimed to determine the prevalence of bacterial contamination of healthcare workers' (HCWs) mobile phones, identify bacterial isolates, and assess the factors associated with mobile phone contamination. METHODS: Self-administered questionnaire was used to collect the information on the demographic characteristics and the use of mobile phones. A total of 111 HCWs' hands and their mobile phones were swabbed, then bacterial culture, isolation, and identification were performed. Univariate and multivariable logistic regression were applied to identify factors associated with mobile phone bacterial contamination. RESULTS: Totally 106 (95.5%) of the 111 mobile phones investigated were contaminated with bacteria. Staphylococcus epidermidis (13/111), Acinetobacter baumannii (4/111) and Staphylococcus aureus (3/111) were the predominant bacterial isolates from HCWs' mobile phones. Univariate analyses showed that age, gender, profession and the frequency of mobile phone utilization were significantly associated with the number of bacterial colonization. Frequency of phone utilization (OR 8.366; 95% CI 1.496-46.797) was found to be the most significant factors associated with the qualified rate of mobile phones bacterial load. In addition, phone cover using was associated with the increased risk of mobile phone bacterial contamination. CONCLUSION: There was cross-contamination between hands and phones. It is necessary to develop guidelines for mobile phone cleaning. Special attention needs to be paid to the disinfection of mobile phone covers to reduce contamination and transmission of pathogens.

An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study.

BACKGROUND: Cystoscopy plays an important role in bladder cancer (BCa) diagnosis and treatment, but its sensitivity needs improvement. Artificial intelligence has shown promise in endoscopy, but few cystoscopic applications have been reported. We report a Cystoscopy Artificial Intelligence Diagnostic System (CAIDS) for BCa diagnosis. METHODS: In total, 69 204 images from 10 729 consecutive patients from 6 hospitals were collected and divided into training, internal validation, and external validation sets. The CAIDS was built using a pyramid scene parsing network and transfer learning. A subset (n = 260) of the validation sets was used for a performance comparison between the CAIDS and urologists for complex lesion detection. The diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values and 95% confidence intervals (CIs) were calculated using the Clopper-Pearson method. RESULTS: The diagnostic accuracies of the CAIDS were 0.977 (95% CI = 0.974 to 0.979) in the internal validation set and 0.990 (95% CI = 0.979 to 0.996), 0.982 (95% CI = 0.974 to 0.988), 0.978 (95% CI = 0.959 to 0.989), and 0.991 (95% CI = 0.987 to 0.994) in different external validation sets. In the CAIDS vs urologists' comparisons, the CAIDS showed high accuracy and sensitivity (accuracy = 0.939, 95% CI = 0.902 to 0.964; sensitivity = 0.954, 95% CI = 0.902 to 0.983) with a short latency of 12 seconds, much more accurate and quicker than the expert urologists. CONCLUSIONS: The CAIDS achieved accurate BCa detection with a short latency. The CAIDS may provide many clinical benefits, from increasing the diagnostic accuracy for BCa, even for commonly misdiagnosed cases such as flat cancerous tissue (carcinoma in situ), to reducing the operation time for cystoscopy.

Kidney damage causally affects the brain cortical structure: A Mendelian randomization study.

BACKGROUND: Alterations in the brain cortical structures of patients with chronic kidney disease (CKD) have been reported; however, the cause has not been determined yet. Herein, we used Mendelian randomization (MR) to reveal the causal effect of kidney damage on brain cortical structure. METHODS: Genome-wide association studies summary data of estimated glomerular filtration rate (eGFR) in 480,698 participants from the CKDGen Consortium were used to identify genetically predicted eGFR. Data from 567,460 individuals from the CKDGen Consortium were used to assess genetically determined CKD; 302,687 participants from the UK Biobank were used to evaluate genetically predicted albuminuria. Further, data from 51,665 patients from the ENIGMA Consortium were used to assess the relationship between genetic predisposition and reduced eGFR, CKD, and progressive albuminuria with alterations in cortical thickness (TH) or surficial area (SA) of the brain. Magnetic resonance imaging was used to measure the SA and TH globally and in 34 functional regions. Inverse-variance weighted was used as the primary estimate whereas MR Pleiotropy RESidual Sum and Outlier, MR-Egger and weighted median were used to detect heterogeneity and pleiotropy. FINDINGS: At the global level, albuminuria decreased TH (ß = -0.07 mm, 95% CI: -0.12 mm to -0.02 mm, P = 0.004); at the functional level, albuminuria reduced TH of pars opercularis gyrus without global weighted (ß = -0.11 mm, 95% CI: -0.16 mm to -0.07 mm, P = 3.74×10-6). No pleiotropy was detected. INTERPRETATION: Kidney damage causally influences the cortex structure which suggests the existence of a kidney-brain axis. FUNDING: This study was supported by the Science and Technology Planning Project of Guangdong Province (Grant No. 2020A1515111119 and 2017B020227007), the National Key Research and Development Program of China (Grant No. 2018YFA0902803), the National Natural Science Foundation of China (Grant No. 81825016, 81961128027, 81772719, 81772728), the Key Areas Research and Development Program of Guangdong (Grant No. 2018B010109006), Guangdong Special Support Program (2017TX04R246), Grant KLB09001 from the Key Laboratory of Malignant Tumor Gene Regulation and Target Therapy of Guangdong Higher Education Institutes, and Grants from the Guangdong Science and Technology Department (2020B1212060018).