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BackgroundBrain-derived neurotrophic factor (BDNF) is a neurotrophin involved in angiogenesis and maintenance of endothelial integrity. Whether circulating BDNF levels are associated with von Willebrand factor (vWF) levels, which are indicators of endothelial dysfunction is not known. This study investigated the association between plasma BNDF and vWF levels and whether these biomarkers could predict cardiovascular events at a 12-month follow-up in patients with stable coronary artery disease (CAD).MethodsWe recruited 234 patients with suspected angina pectoris. Subjects were divided into CAD (n = 143) and control (n = 91) groups based on coronary angiography. Plasma BDNF and vWF levels were measured using ELISA. Patients were followed-up for one year, and information on adverse cardiac events was collected.ResultsCAD patients exhibited significantly lower plasma BDNF and higher vWF levels than those of control patients. High vWF levels were associated with low BDNF levels even after adjustment for age, gender, low-density lipoprotein (LDL) levels, and the presence of diabetes mellitus. A receiver operating characteristic curve was used to determine whether low BDNF and high vWF levels could predict adverse cardiovascular events. The area under the curve for vWF and the inverse of BDNF were 0.774 and 0.804, respectively.ConclusionsThese findings suggest that endothelial dysfunction is an important determinant of the impaired circulating BDNF levels, and they further reflected cardiovascular prognosis in stable CAD patients.

Cardiovascular disease (CVD) is a leading cause of mortality and morbidity among patients with diabetes. Endothelial dysfunction is an early physiological event in CVD. Metformin, a common oral antihyperglycemic agent, has been demonstrated to directly affect endothelial cell function. Brain-derived neurotrophic factor (BDNF), originally discovered in the brain as a neurotrophin, has also been reported to play a protective role in the cardiovascular system. In our study, we demonstrated that high glucose (HG) reduced cell proliferation and induced cell apoptosis via changes in BDNF expression and that metformin reversed the effects of HG injury by upregulating BDNF expression. Furthermore, we found that cyclic AMP response element binding (CREB) phosphorylation was reduced in HG-treated human umbilical vein endothelial cells (HUVECs), and this effect was reversed by the metformin treatment. However, the metformin effect on BDNF levels in HG-incubated HUVECs was blocked by a CREB inhibitor, which indicated that BDNF expression is regulated by metformin through CREB activation. In addition, we found that adenosine monophosphate-activated protein kinase (AMPK) activation is involved in CREB/BDNF regulation in HG-incubated HUVECs treated with metformin and that an AMPK inhibitor impaired the protective effects of metformin on HG-treated HUVECs. In conclusion, this study demonstrated that metformin affects cell proliferation and apoptosis via the AMPK/CREB/BDNF pathway in HG-incubated HUVECs.

The enhanced cardiovascular fitness after aerobic cycling training in Chinese patients with chronic stroke is not associated with the increased walking ability. Unparallel improvements in these parameters related different determinants may have implications for intervention strategy.

In this study, a wearable prototype system was developed for multiple-gesture rehabilitation using electrical stimulation controlled by a volitional surface electromyography (sEMG) scan of a healthy forearm. The purpose of the prototype system is to reconstruct multiple gestures of a paralysed limb and to simplify the positioning of sEMG detection sites on a healthy forearm. A self-designed eight-channel sEMG detection armband was used to detect the sEMG signal distributions of the muscle groups in healthy forearms. Linear discriminant analysis (LDA) was used to classify the sEMG signal distributions corresponding to different gestures, and then the classification results were mapped to corresponding stimulation channels. The sEMG signal with the maximum root mean square (RMS) was used as the source of stimulus coding for each gesture. Our proposed mean absolute value (MAV)/number of slope sign changes (NSS) dual-coding (MNDC) algorithm was used to encode the sEMG signal into an electrical stimulus with a dynamic pulse width and frequency. The constant-current stimulation armband electrically stimulated multiple muscles in the affected forearm by means of a circuit designed with a time-division multiplexed stimulation channel. An experiment involving 6 able-bodied volunteers showed that when the detection armband was located near the middle of the forearm, the gesture classification accuracy was greater than 90%, and each active sEMG signal was high. Gesture bridge experiments, including grasping, wrist flexion, wrist extension and finger extension, were carried out among six hemiplegic subjects and between one able-bodied volunteer acting as a controller and each of six stroke patients as the controllee. Both sets of results show that the proposed system can reconstruct these four gestures in the controlled subject with a delay of at most 360 ms and with a correlation coefficient of > 0.72.