Nanomaterial-based optical-and electrochemical-biosensors for urine glucose detection:A comprehensive review

Urine glucose detection is an important diagnostic tool for screening and early diagnosis of diabetes mellitus.Detection of urine glucose has many advantages over blood glucose,such as non-invasive,easy-to-detect,simple sampling and being well accepted by patients.Therefore,it is commonly used to monitor diabetes progression,assist in therapeutic intervention as well as in point-of-care testing(POCT).In recent years,with the development of material science,electrochemistry and miniaturization technology,novel applications of natural enzymes,nanozymes as well as nanomaterials,such as metal(Au,Pt,Ni,Co,etc.),alloy,grapheme,in the analysis of urine glucose level have been increasing sharply.In particular,different types of nanozymes-based biosensors,inspired by natural enzymes,have been developed with improved characteristics of being low-cost,stable,and mass-produced.On the other hand,making use of portable devices,such as smartphones and microfluidic paper-based analytical devices,has facilitated on site accurate urine glucose monitoring in real time.All these rapid advancements in nanotechnologies and devices have contributed greatly to the development of cost effective,highly sensitive,user friendly urine glucose biosensors.This review summarizes the most recent improvements in two major types of urine glucose biosensors:the optical-and electrochemical-biosensors.We also discuss the limitations,challenges and perspectives of these biosensors.Finally,we propose future research directions,development trends and potential clinical applications of nanomaterial-based biosensors developed for urine glucose detection.

Artificial intelligence-assisted colorimetry for urine glucose detection towards enhanced sensitivity,accuracy,resolution,and anti-illuminating capability

Colorimetry often suffers from deficiency in quantitative determination,susceptibility to ambient illuminance,and low sensitivity and visual resolution to tiny color changes.To offset these deficiencies,we incorporate deep machine learning into colorimetry by introducing a convolutional neural network(CNN)with powerful parallel processing,self-organization,and self-learning capabilities.As a proof of concept,a plasmonic nanosensor is proposed for the colorimetric detection of glucose by coupling Benedict’s reagent with gold nanoparticles(AuNPs),which relies on the assemble of AuNPs into dendritic nanochains by Cu2O.The distinct difference of refractive index between Cu2O and Au and the localized surface plasmon resonance coupling effect among AuNPs leads to a broad spectral shift as well as abundant color changes,thereby providing sufficient data for selflearning enabled by machine learning.The CNN is then used to fully diversify the learning and training of the images from standard samples under different ambient conditions and to obtain a classifier that can not only recognize tiny color changes that are imperceptible to human eyes,but also exhibit high accuracy and excellent anti-environmental interference capability.This classifier is then compiled as an application(APP)and implanted into a smartphone with Android environment.306 clinical urine samples were detected using the proposed method and the results showed a satisfactory correlation(87.6%)with that of a standard blood glucose test method.More importantly,this method can be generalized to other applications in colorimetry,and more broadly,in other scientific domains that involve image analysis and quantification.

Urine glucose levels are disordered before blood glucose level increase was observed in Zucker diabetic fatty rats

Many patients with diabetes are not diagnosed at all or are diagnosed too late to be effectively treated, resulting in nonspecific symptoms and a long period of incubation of the disease. Pre-diabetes is an early warning signal of diabetes, and the change of urine glucose in this period has been ignored even though urine has long been related with diabetes. In this study, Zucker diabetic fatty (ZDF) rats were used to test if there were changes in urine glucose before blood glucose increases. Six 8-week-old male ZDF rats (fa/fa) and Zucker lean (ZL) rats (fa/+) were fed with Purina 5008 high-fat diet and tested for fasting blood glucose and urine glucose. After 12 weeks of feeding, the urine glucose values of the ZL rats were normal (0–10 mmol L^(-1)), but the values of the ZDF model rats increased 10 weeks before their blood glucose levels elevated. The urine glucose values of the ZDF model rats showed a state of disorder that was frequently elevated (>10 mmol L^(-1)) and occasionally normal (0–10 mmol L^(-1)). This finding may provide an easy early screening for diabetes by long-term monitoring of urine glucose levels: pre-diabetes may be revealed by frequently disordered urine glucose levels over a period.