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Shape-morphing uses a single actuation source for complex-task-oriented multiple patterns generation, showing a more promising way than reconfiguration, especially for microrobots, where multiple actuators are typically hardly available. Environmental stimuli can induce additional causes of shape transformation to compensate the insufficient space for actuators and sensors, which enriches the shape-morphing and thereby enhances the function and intelligence as well. Here, making use of the ionic sensitivity of alginate hydrogel microstructures, we present a shape-morphing strategy for microrobotic end-effectors made from them to adapt to different physiochemical environments. Pre-programmed hydrogel crosslinks were embedded in different patterns within the alginate microstructures in an electric field using different electrode configurations. These microstructures were designed for accomplishing tasks such as targeting, releasing and sampling under the control of a magnetic field and environmental ionic stimuli. In addition to structural flexibility and environmental ion sensitivity, these end-effectors are also characterized by their complete biodegradability and versatile actuation modes. The latter includes global locomotion of the whole end-effector by self-trapping magnetic microspheres as a hitch-hiker and the local opening and closing of the jaws using encapsulated nanoparticles based on local ionic density or pH values. The versatility was demonstrated experimentally in both in vitro environments and ex vivo in a gastrointestinal tract. Global locomotion was programmable and the local opening and closing was achieved by changing the ionic density or pH values. This ‘structural intelligence’ will enable strategies for shape-morphing and functionalization, which have attracted growing interest for applications in minimally invasive medicine, soft robotics, and smart materials.

Background Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is inconvenient during follow-up. Objective To quickly predict the severity of CKD using more easily available demographic and blood biochemical features during follow-up, we developed and compared several predictive models using statistical, machine learning and neural network approaches. Methods The clinical and blood biochemical results from 551 patients with proteinuria were collected. Thirteen blood-derived tests and 5 demographic features were used as non-urinary clinical variables to predict the 24-h urinary protein outcome response. Nine predictive models were established and compared, including logistic regression, Elastic Net, lasso regression, ridge regression, support vector machine, random forest, XGBoost, neural network and k-nearest neighbor. The AU-ROC, sensitivity (recall), specificity, accuracy, log-loss and precision of each of the models were evaluated. The effect sizes of each variable were analysed and ranked. Results The linear models including Elastic Net, lasso regression, ridge regression and logistic regression showed the highest overall predictive power, with an average AUC and a precision above 0.87 and 0.8, respectively. Logistic regression ranked first, reaching an AUC of 0.873, with a sensitivity and specificity of 0.83 and 0.82, respectively. The model with the highest sensitivity was Elastic Net (0.85), while XGBoost showed the highest specificity (0.83). In the effect size analyses, we identified that ALB, Scr, TG, LDL and EGFR had important impacts on the predictability of the models, while other predictors such as CRP, HDL and SNA were less important. Conclusions Blood-derived tests could be applied as non-urinary predictors during outpatient follow-up. Features in routine blood tests, including ALB, Scr, TG, LDL and EGFR levels, showed predictive ability for CKD severity. The developed online tool can facilitate the prediction of proteinuria progress during follow-up in clinical practice. Electronic supplementary material The online version of this article (10.1186/s12967-019-1860-0) contains supplementary material, which is available to authorized users.

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Silent information regulator 2 (Sir2) is a nicotinamide adenine dinucleotide- (NAD+-) dependent deacetylase. The homology of SIRT1 and Sir2 has been extensively studied. SIRT1 deacetylates target proteins using the coenzyme NAD+ and is therefore linked to cellular energy metabolism and the redox state through multiple signalling and survival pathways. During the past decade, investigators have reported that SIRT1 activity is essential in cancer, neurodegenerative diseases, diabetes, cardiovascular disease, and other age-related diseases. In the kidneys, SIRT1 may inhibit renal cell apoptosis, inflammation, and fibrosis. Therefore its activation may also become a new therapeutic target in the patients with chronic kidney disease including diabetic nephropathy. In this paper, we would like to review the protective functions of sirtuins and the role of SIRT1 in the onset of kidney disease based on previous studies, including diabetic nephropathy, acute renal injury, chronic kidney disease as well as lupus nephritis.