A hydrogel is a three-dimensional hyperelastic polymer network that swells to a specific volume upon exposure to a penetrating solvent. If mechanical constraints interfere with the swelling process, anisotropic compressive stresses are generated, which may manifest in local or global instabilities. Herein, we employ confocal microscopy for the in situ, three-dimensional study of micron-scale hydrogels that are pinned to a solid substrate. Depending on the initial geometry of the hydrogel, four general modes of swelling-induced deformation were found: lateral differential swelling, local sinusoidal edge buckling, bulk sinusoidal buckling, and surface creasing. The transition between local edge buckling and bulk buckling is consistent with linear elastic theory; however, linear theory cannot be used to predict many details of the swollen structures. Whereas global buckling has a well-defined wavelength that depends on height of the hydrogel structure, edge buckling appears to be independent of height and depends on sample history. Moreover, edge buckling can appear in globally buckled structures, suggesting two different mechanisms for the two instabilities.

Background Functional magnetic resonance imaging (fMRI) is a promising method for quantifying brain recovery and investigating the intervention-induced changes in corticomotor excitability after stroke. This study aimed to evaluate cortical reorganization subsequent to virtual reality-enhanced treadmill (VRET) training in subacute stroke survivors. Methods Eight participants with ischemic stroke underwent VRET for 5 sections per week and for 3 weeks. fMRI was conducted to quantify the activity of selected brain regions when the subject performed ankle dorsiflexion. Gait speed and clinical scales were also measured before and after intervention. Results Increased activation in the primary sensorimotor cortex of the lesioned hemisphere and supplementary motor areas of both sides for the paretic foot (p < 0.01) was observed postintervention. Statistically significant improvements were observed in gait velocity (p < 0.05). The change in voxel counts in the primary sensorimotor cortex of the lesioned hemisphere is significantly correlated with improvement of 10 m walk time after VRET (r = −0.719). Conclusions We observed improved walking and increased activation in cortical regions of stroke survivors after VRET training. Moreover, the cortical recruitment was associated with better walking function. Our study suggests that cortical networks could be a site of plasticity, and their recruitment may be one mechanism of training-induced recovery of gait function in stroke. This trial is registered with ChiCTR-IOC-15006064.

ObjectiveFor the patients who are suffering from type 2 diabetes, blood glucose level could be affected by multiple factors. An accurate estimation of the trajectory of blood glucose is crucial in clinical decision making. Frequent glucose measurement serves as a good source of data to train machine learning models for prediction purposes. This study aimed at using machine learning methods to predict blood glucose for type 2 diabetic patients. We investigated various parameters influencing blood glucose, as well as determined the most effective machine learning algorithm in predicting blood glucose.Patients and methods273 patients were recruited in this research. Several parameters such as age, diet, family history, BMI, alcohol intake, smoking status et al were analyzed. Patients who had glycosylated hemoglobin less than 6.5% after 52 weeks were considered as having achieved glycemic control and the rest as not achieving it. Five machine learning methods (KNN algorithm, logistic regression algorithm, random forest algorithm, support vector machine, and XGBoost algorithm) were compared to evaluate their performances in prediction accuracy. R 3.6.3 and Python 3.12 were used in data analysis.ResultsThe statistical variables for which p&lt; 0.05 was obtained were BMI, pulse, Na, Cl, AKP. Compared with the other four algorithms, XGBoost algorithm has the highest accuracy (Accuracy=99.54% in training set and 78.18% in testing set) and AUC values (1.0 in training set and 0.68 in testing set), thus it is recommended to be used for prediction in clinical practice.ConclusionWhen it comes to future blood glucose level prediction using machine learning methods, XGBoost algorithm scores the highest in effectiveness. This algorithm could be applied to assist clinical decision making, as well as guide the lifestyle of diabetic patients, in pursuit of minimizing risks of hyperglycemic or hypoglycemic events.