A procedure for gram‐scale synthesis of monodisperse Cu2O nanocubes by a simple polyol process is demonstrated. The nanocubes are subsequently oxidized to form CuO hollow cubes, spheres, and urchin‐like particles, through a sequential dissolution–precipitation process. The CuO urchin‐like particles exhibited excellent electrochemical performance and stability, superior to those of hollow structures, for lithium‐ion battery anode materials.

A flexible single-crystalline PMN-PT piezoelectric energy harvester is demonstrated to achieve a self-powered artificial cardiac pacemaker. The energy-harvesting device generates a short-circuit current of 0.223 mA and an open-circuit voltage of 8.2 V, which are enough not only to meet the standard for charging commercial batteries but also for stimulating the heart without an external power source.

Metabolic syndrome is defined as a cluster of glucose intolerance, hypertension, dyslipidemia and central obesity with insulin resistance as the source of pathogenesis. Although several different combinations of criteria have been used to define metabolic syndrome, a recently published consensus recommends the use of ethnic-specific criteria, including waist circumference as an indicator of central obesity, triglyceride and high-density lipoprotein (HDL) cholesterol as indicators of dyslipidemia, and blood pressure greater than 130/85 mmHg. The definition of dysglycemia, and whether central obesity and insulin resistance are essential components remain controversial. Regardless of the definition, the prevalence of metabolic syndrome is increasing in Western and Asian countries, particularly in developing areas undergoing rapid socioenvironmental changes. Numerous clinical trials have shown that metabolic syndrome is an important risk factor for cardiovascular disease (CVD), type 2 diabetes mellitus and all-cause mortality. Therefore, metabolic syndrome might be useful as a practical tool to predict these two major metabolic disorders. Comprehensive management of risk factors is very important to the improvement of personal and public health. However, recent studies have focused on the role metabolic syndrome plays as a risk factor for CVD; its importance in the prediction of incident diabetes is frequently overlooked. In the present review, we summarize the known evidence supporting metabolic syndrome as a predictor for type 2 diabetes mellitus and CVD. Additionally, we suggest how metabolic syndrome might be useful in clinical practice, especially for the prediction of diabetes. (J Diabetes Invest,

High variability in lipid levels is associated with adverse health-related outcomes. These findings suggest that lipid variability is an important risk factor in the general population.

BackgroundTo determine whether the TyG index, a product of the levels of triglycerides and fasting plasma glucose (FPG) might be a valuable marker for predicting future diabetes.MethodsA total of 5,354 nondiabetic subjects who had completed their follow-up visit for evaluating diabetes status were selected from a large cohort of middle-aged Koreans in the Chungju Metabolic Disease Cohort study. The risk of diabetes was assessed according to the baseline TyG index, calculated as ln[fasting triglycerides (mg/dL) × FPG (mg/dL)/2]. The median follow-up period was 4.6 years.ResultsDuring the follow-up period, 420 subjects (7.8%) developed diabetes. The baseline values of the TyG index were significantly higher in these subjects compared with nondiabetic subjects (8.9±0.6 vs. 8.6±0.6; P<0.0001) and the incidence of diabetes increased in proportion to TyG index quartiles. After adjusting for age, gender, body mass index, waist circumference, systolic blood pressure, high-density lipoprotein (HDL)-cholesterol level, a family history of diabetes, smoking, alcohol drinking, education level and serum insulin level, the risk of diabetes onset was more than fourfold higher in the highest vs. the lowest quartile of the TyG index (relative risk, 4.095; 95% CI, 2.701–6.207). The predictive power of the TyG index was better than the triglyceride/HDL-cholesterol ratio or the homeostasis model assessment of insulin resistance.ConclusionsThe TyG index, a simple measure reflecting insulin resistance, might be useful in identifying individuals at high risk of developing diabetes.

Background: Variability in metabolic parameters, such as fasting blood glucose and cholesterol concentrations, blood pressure, and body weight can affect health outcomes. We investigated whether variability in these metabolic parameters has additive effects on the risk of mortality and cardiovascular outcomes in the general population. Methods: Using nationally representative data from the Korean National Health Insurance System, 6 748 773 people who were free of diabetes mellitus, hypertension, and dyslipidemia and who underwent ≥3 health examinations from 2005 to 2012 were followed to the end of 2015. Variability in fasting blood glucose and total cholesterol concentrations, systolic blood pressure, and body mass index was measured using the coefficient of variation, SD, variability independent of the mean, and average real variability. High variability was defined as the highest quartile of variability. Participants were classified numerically according to the number of high-variability parameters (eg, a score of 4 indicated high variability in all 4 metabolic parameters). Cox proportional hazards models adjusting for age, sex, smoking, alcohol, regular exercise, income, and baseline levels of fasting blood glucose, systolic blood pressure, total cholesterol, and body mass index were used. Results: There were 54 785 deaths (0.8%), 22 498 cases of stroke (0.3%), and 21 452 myocardial infarctions (0.3%) during a median follow-up of 5.5 years. High variability in each metabolic parameter was associated with a higher risk for all-cause mortality, myocardial infarction, and stroke. Furthermore, the risk of outcomes increased significantly with the number of high-variability metabolic parameters. In the multivariable-adjusted model comparing a score of 0 versus 4, the hazard ratios (95% CIs) were 2.27 (2.13–2.42) for all-cause mortality, 1.43 (1.25–1.64) for myocardial infarction, and 1.41 (1.25–1.60) for stroke. Similar results were obtained when modeling the variability using the SD, variability independent of the mean, and average real variability, and in various sensitivity analyses. Conclusions: High variability of fasting blood glucose and total cholesterol levels, systolic blood pressure, and body mass index was an independent predictor of mortality and cardiovascular events. There was a graded association between the number of high-variability parameters and cardiovascular outcomes.

Deep brain stimulation (DBS) is widely used for neural prosthetics and brain-computer interfacing. Thus far in vivo implantation of a battery has been a prerequisite to supply necessary power.Although flexible energy harvesters have recently emerged as an alternative to the battery, they generate insufficient energy for operating brain stimulation. Herein, we report a high performance flexible piezoelectric energy harvester enabling self-powered DBS in mice. This device adopts an indium modified crystalline Pb(In1/2Nb1/2)O3 -Pb(Mg1/3Nb2/3)O3 -PbTiO3 (PIMNT) thin film on a plastic substrate to transform tiny mechanical motions to electricity. By slight bending, it generates an extremely high current reaching 0.57 mA which satisfies high threshold current for real-time DBS of the motor cortex and thereby could efficiently induce forearm movements in mice. The PIMNT based flexible energy harvester could open a new direction for future in vivo healthcare technology using self-powered biomedical devices. Broader contextImplantable biomedical devices have attracted a great attention in light of improving the quality of life and prolonging the life expectancy of human. The implantable electronics are widely used in various parts in the patient's body as medical remedy tools such as deep brain stimulation (DBS), cardiac pacemaker, visual prosthesis, and cochlear implant, by electric stimulation of nerve/muscle and monitoring of health condition. However, the conventional implanted batteries have limited lifetime, fixed energy density, and large volume. Recently, many research teams have explored energy harvesting technology which scavenges electricity from biomechanical energy sources to eliminate the implantable batteries or directly stimulate nerve/muscle. Flexible piezoelectric energy harvester is a promising candidate to realize the self-powered implantable bioelectronics, since it can harvest electric energy from inexhaustible slight motions of organs such as heart, lung, and diaphragm. This work has introduced a new platform of self-powered DBS via a high-performance flexible piezoelectric harvester to directly excite neuron of living brain for inducing behavioural changes.A self-powered deep brain stimulation was demonstrated by a flexible piezoelectric PIMNT energy harvester to induce behavioural change of mouse.

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