Diabetes mellitus, systemic inflammation and overactive bladder.

Background: Increasing evidence emphasizes the potential relationship between diabetes and OAB (overactive bladder). However, large population epidemiology is still lacking. Methods: This cross-sectional study included six cycle NHANES surveys, with a total of 23863 participants. Logistic regression models were constructed to analyze the association between diabetes mellitus, diabetes-related markers, and inflammatory biomarkers with OAB. Restricted cubic splines were used to analyze the non-linear associations. Mediating analysis was performed to test the effect of inflammatory biomarkers on the relationship between diabetes-related markers and OAB. Finally, machine learning models were applied to predict the relative importance and construct the best-fit model. Results: Diabetes mellitus participants' OAB prevalence increased by 77% compared with non-diabetes. As the quartiles of diabetes-related markers increased, the odds of OAB monotonically increased in three models (all p for trend < 0.001). Glycohemoglobin exhibited a linear association with OAB (p for nonlinearity > 0.05). White blood cells significantly mediated the associations between diabetes-related markers (glycohemoglobin, fasting glucose, and insulin) with OAB, and the proportions were 7.23%, 8.08%, and 17.74%, respectively (all p < 0.0001). Neutrophils partly mediated the correlation between (glycohemoglobin, fasting glucose, and insulin) and OAB at 6.58%, 9.64%, and 17.93%, respectively (all p < 0.0001). Machine learning of the XGBoost model constructs the best fit model, and XGBoost predicts glycohemoglobin is the most important indicator on OAB. Conclusion: Our research revealed diabetes mellitus and diabetes-related markers were remarkably associated with OAB, and systemic inflammation was an important mediator of this association.

Fast detection method for prostate cancer cells based on an integrated ResNet50 and YoloV5 framework.

PURPOSE: To propose a fast detection method for prostate cancer abnormal cells based on deep learning. The purpose of this method is to quickly and accurately locate and identify abnormal cells, so as to improve the efficiency of prostate precancerous screening and promote the application and popularization of prostate cancer cell assisted screening technology. METHOD: The method includes two stages: preliminary screening of abnormal cell images and accurate identification of abnormal cells. In the preliminary screening stage of abnormal cell images, ResNet50 model is used as the image classification network to judge whether the local area contains cell clusters. In the another stage, YoloV5 model is used as the target detection network to locate and recognize abnormal cells in the image containing cell clusters. RESULTS: This detection method aims at the pathological cell images obtained by the membrane method. And the double stage models proposed in this paper are compared with the single stage model method using only the target detection model. The results show that through the image classification network based on deep learning, we can first judge whether there are abnormal cells in the local area. If there are abnormal cells, we can further use the target detection method based on candidate box for analysis, which can reduce the reasoning time by 50% and improve the efficiency of abnormal cell detection under the condition of losing a small amount of accuracy and slightly increasing the complexity of the model. CONCLUSION: This study proposes a fast detection method for prostate cancer abnormal cells based on deep learning, which can greatly shorten the reasoning time and improve the detection speed. It is able to improve the efficiency of prostate precancerous screening.

Au-coated Fe3O4 core-shell nanohybrids with photothermal activity for point-of-care immunoassay for lipoprotein-associated phospholipase A2 on a digital near-infrared thermometer.

A portable photothermal immunoassay based on Au-coated magnetic Fe3O4 core-shell nanohybrids (Au-Fe3O4) was developed for point-of-care (POC) testing of lipoprotein-associated phospholipase A2 (Lp-PLA2) on a digital near-infrared (NIR) thermometer. Au-Fe3O4 photothermal materials were first synthesized through reverse micelle method, and then functionalized with polyclonal rabbit anti-human Lp-PLA2 antibody. A sandwiched immunoreaction was carried out in polyclonal mouse anti-human Lp-PLA2 antibody-coated microplate using Au-Fe3O4-labeled antibody as the detection antibody. With formation of sandwich-type immunocomplex, the captured Au-Fe3O4 on the plate converted the light into heat under an 808-nm laser irradiation (1.5 W cm-2), thereby resulting in the increasing temperature of the detection solution. The temperature variations relative to surrounding temperature was determined on a portable NIR thermometer. Several labeling protocols with gold nanoparticle, Fe3O4 nanoparticle, or Au-Fe3O4 nanohybrids were investigated for determination of Lp-PLA2 and improved analytical features were achieved with the core-shell Au-Fe3O4 nanohybrids. Under optimum conditions, Au-Fe3O4-based immunoassay exhibited good photothermal responses for the detection of Lp-PLA2 with a dynamic linear range of 0.01-100 ng mL-1 at a low detection limit of 8.6 pg mL-1. Good reproducibility and intermediate precision were less than 9.7%. Other biomarkers or proteins did not interfere with responses of this system. An acceptable accuracy was acquired for analysis of human serum sample between Au-Fe3O4-based photothermal immunoassay and commercialized human Lp-PLA2 ELISA kit.