ObjectiveTo compare the biological features of early and late endothelial progenitor cells (EPCs) by isolating and culturing early and late EPCs from the human peripheral blood so as to find some unique properties of EPCs and to propose a suitable strategy for EPCs identification.
MethodsMononuclear cells were isolated from the human peripheral blood using density gradient centrifugation. Then, the cells were inoculated in human fibronectin-coated culture flasks and cultured in endothelial cell basal medium 2. After 4-7 days and 2-3 weeks culture, early and late EPCs were obtained respectively. The morphology, proliferation potential, surface markers, cytokine secretion, angiogenic ability, and nitric oxide (NO) release were compared between 2 types of EPCs. Meanwhile, the human aortic endothelial cells (HAECs) were used as positive control.
ResultsThe morphology of early and late EPCs was different:early EPCs formed a cell cluster with a spindle shape after 4-7 days of culture, and late EPCs showed a cobblestone appearance. Late EPCs were characterized by high proliferation potential and were able to form capillary tubes on Matrigel, but early EPCs did not have this feature. Both types EPCs could ingest acetylated low density lipoprotein and combine with ulex europaeus Ⅰ. Flow cytometry analysis showed that early EPCs did not express CD34 and CD133, but expressed the CD14 and CD45 of the hematopoietic stem cell markers;however, late EPCs expressed CD31 and CD34 of the endothelial cell markers, but did not express CD14, CD45, and CD133. By RT-PCR analysis, the expressions of vascular endothelial growth receptor 2 and vascular endothelial cadherin in early EPCs were significantly lower than those in the late EPCs and HAECs (P<0.05), but no significant difference was found in the expression of von Willebrand factor and endothelial nitric oxide synthase (eNOS) between 2 type EPCs (P>0.05). The concentrations of vascular endothelial growth factor, granulocyte colony-stimulating factor, and interleukin 8 were significantly higher in the supernatant of early EPCs than late EPCs (P<0.05). Western blot assay indicated eNOS expressed in both types EPCs, while the expression of eNOS in late EPCs was significantly higher than early EPCs at 5 weeks (P<0.05). Both cell types could produce similar amount of NO (P>0.05).
ConclusionThe expression of eNOS and the production of NO could be used as common biological features to identify EPCs, and the strategy of a combination of multiple methods for EPCs identification is more feasible.
In order to develop safe training intensity and training methods for the passive balance rehabilitation training system, we propose in this paper a mathematical model for human standing balance adjustment based on T-S fuzzy identification method. This model takes the acceleration of a multidimensional motion platform as its inputs, and human joint angles as its outputs. We used the artificial bee colony optimization algorithm to improve fuzzy C-means clustering algorithm, which enhanced the efficiency of the identification for antecedent parameters. Through some experiments, the data of 9 testees were collected, which were used for model training and model results validation. With the mean square error and cross-correlation between the simulation data and measured data, we concluded that the model was accurate and reasonable.
In this paper, the research has been conducted by the Microsoft kinect for windows v2 for obtaining the walking trajectory data from hemiplegic patients, based on which we achieved automatic identification of the hemiplegic gait and sorted the significance of identified features. First of all, the experimental group and two control groups were set up in the study. The three groups of subjects respectively completed the prescribed standard movements according to the requirements. The walking track data of the subjects were obtained straightaway by Kinect, from which the gait identification features were extracted: the moving range of pace, stride and center of mass (up and down/left and right). Then, the bayesian classification algorithm was utilized to classify the sample set of these features so as to automatically recognize the hemiplegia gait. Finally, the random forest algorithm was used to identify the significance of each feature, providing references for the diagnose of disease by ranking the importance of each feature. This thesis states that the accuracy of classification approach based on bayesian algorithm reaches 96%; the sequence of significance based on the random forest algorithm is step speed, stride, left-right moving distance of the center of mass, and up-down moving distance of the center of mass. The combination of step speed and stride, and the combination of step speed and center of mass moving distance are important reference for analyzing and diagnosing of the hemiplegia gait. The results may provide creative mind and new references for the intelligent diagnosis of hemiplegia gait.
The existing one-time identity authentication technology cannot continuously guarantee the legitimacy of user identity during the whole human-computer interaction session, and often requires active cooperation of users, which seriously limits the availability. This study proposes a new non-contact identity recognition technology based on cardiac micro-motion detection using ultra wideband (UWB) bio-radar. After the multi-point micro-motion echoes in the range dimension of the human heart surface area were continuously detected by ultra wideband bio-radar, the two-dimensional principal component analysis (2D-PCA) was exploited to extract the compressed features of the two-dimensional image matrix, namely the distance channel-heart beat sampling point (DC-HBP) matrix, in each accurate segmented heart beat cycle for identity recognition. In the practical measurement experiment, based on the proposed multi-range-bin & 2D-PCA feature scheme along with two conventional reference feature schemes, three typical classifiers were selected as representatives to conduct the heart beat identification under two states of normal breathing and breath holding. The results showed that the multi-range-bin & 2D-PCA feature scheme proposed in this paper showed the best recognition effect. Compared with the optimal range-bin & overall heart beat feature scheme, our proposed scheme held an overall average recognition accuracy of 6.16% higher (normal respiration: 6.84%; breath holding: 5.48%). Compared with the multi-distance unit & whole heart beat feature scheme, the overall average accuracy increase was 27.42% (normal respiration: 28.63%; breath holding: 26.21%) for our proposed scheme. This study is expected to provide a new method of undisturbed, all-weather, non-contact and continuous identification for authentication.
In this research a strain of isolated Pseudomonas alcaligenes which causes degradation of dexamethasone was acclimated further and its proteins of every position in the bacterium were separated by the osmotic shock method. The separated intracellular proteins which had the highest enzyme activity were extracted by the salting out with ammonium sulfate and were purified with the cation exchange chromatography and gel chromatography. The purified proteins which was active to cause degradation of dexamethasone had been detected were cut with enzyme and were analyzed with mass spectrometry. The results showed that the degradation rate to dexamethasone by acclimated Pseudomonas alcaligenes were increased from 23.63% to 52.84%. The degrading enzymes were located mainly in the intracellular of the bacteria and its molecular weight was about 41 kD. The specific activity of the purified degrading enzymes were achieved to 1.02 U·mg-1. Its 5-peptide amino acid sequences were consistent with some sequences of the isovaleryl-CoA dehydrogenase. The protein enzyme may be a new kind degrading enzyme of steroidal compounds. Our experimental results provided new strategies for cleanup of dexamethasone in water environment with microbial bioremediation technique.
ObjectiveTo introduce a new method for identifying intersegmental planes during thoracoscopic segmentectomy using pulmonary circulation single-blocking in the target segment. MethodsTo retrospectively analyze the clinical data of 83 patients who underwent thoracoscopic pulmonary segmentectomy from January 2019 to March 2020 using the pulmonary circulation single-blocking method. There were 33 males and 50 females, with a median age of 54 (46-65) years, and they were divided into a single vein group (SVG, n=31) and a single artery group (SAG, n=52), and the clinical data of two groups were compared. ResultsThe intersegmental planes were identified successfully in both groups and there were no statistically significant differences between the two groups in terms of intersegmental plane management (P=0.823), operating time (P=0.786), intraoperative blood loss (P=0.775), chest drainage time (P=0.659), postoperative hospital stay (P=0.824) or the incidence of postoperative complications (P=1.000). ConclusionThe use of pulmonary circulation single-blocking for intersegmental plane identification during thoracoscopic segmentectomy is safe and feasible, and the intersegmental plane can be satisfactorily identified by the single-blocking of arteries or veins.
Objective To propose a lightweight end-to-end neural network model for automated Korotkoff sound phase recognition and subsequent blood pressure (BP) measurement, aiming to improve measurement accuracy and population adaptability. Methods We developed a streamlined architecture integrating depthwise separable convolution (DSConv), multi-head attention (MHA), and bidirectional gated recurrent unit (BiGRU). The model directly processes Korotkoff sound time-series signals to identify auscultatory phases. Systolic BP (SBP) and diastolic BP (DBP) were determined using Phase Ⅰ and PhaseⅤdetections, respectively. Given the clinical relevance of phase Ⅳ for specific populations (e.g., children and pregnant women, denoted as DBPIV), BP values from this phase were also recorded. Results The study enrolled 106 volunteers with 70 males, 36 females at mean age of (40.0±12.0) years. The model achieved 94.25% phase recognition accuracy. Measurement errors were (0.1±2.5) mm Hg (SBP), (0.9±3.4) mm Hg (DBPIV), and (0.8±2.6) mm Hg (DBP). Conclusion Our method enables precise phase recognition and BP measurement, demonstrating potential for developing population-adaptive blood pressure monitoring systems.
Carbapenemase producing Enterobacteriaceae (CPE) has emerged as a significant global public health challenge and placing infected patients at risk of potentially untreatable infections. When resistance to carbapenems occurs, there are often few alternative treatments available. Numerous international guidelines have performed systematic and evidence review to identify new strategies to prevent the entry and spread of CPE in healthcare settings. Several key strategies have been shown to be highly effective. Firstly a new strategy that is proven to be effective is the early identification of the CPE carrier patients through active surveillance cultures. While waiting for the screening results, suspected CPE carriers will be put on preemptive isolation in single room and healthcare worker will at the same time practice contact precautions. The active surveillance culture and prompt preemptive isolation will limit the entry and spread of CPE from getting into hospital. Secondly, it is of utmost importance to incorporate enforcement of the basic infection prevention and control best practices in the hospital including, full compliance to hand hygiene, appropriate use of personal protective equipment, execute antibiotic stewardship program to control abuse of antibiotics, effective environmental cleaning and decontamination, staff education and feedback, as well as surveillance of healthcare-associated infections. Such a holistic approach has been shown to be effective in inhibiting CPE from gaining foothold in the hospital.
Characteristics in pulse wave signals (PWSs) include the information of physiology and pathology of human cardiovascular system. Therefore, identification of characteristic points in PWSs plays a significant role in analyzing human cardiovascular system. Particularly, the characteristic points show personal dependent features and are easy to be affected. Acquiring a signal with high signal-to-noise ratio (SNR) and integrity is fundamentally important to precisely identify the characteristic points. Based on the mathematical morphology theory, we design a combined filter, which can effectively suppress the baseline drift and remove the high-frequency noise simultaneously, to preprocess the PWSs. Furthermore, the characteristic points of the preprocessed signal are extracted according to its position relations with the zero-crossing points of wavelet coefficients of the signal. In addition, the differential method is adopted to calibrate the position offset of characteristic points caused by the wavelet transform. We investigated four typical PWSs reconstructed by three Gaussian functions with tunable parameters. The numerical results suggested that the proposed method could identify the characteristic points of PWSs accurately.
Biometrics plays an important role in information society. As a new type of biometrics, electroencephalogram (EEG) signals have special advantages in terms of versatility, durability, and safety. At present, the researches on individual identification approaches based on EEG signals draw lots of attention. Identity feature extraction is an important step to achieve good identification performance. How to combine the characteristics of EEG data to better extract the difference information in EEG signals is a research hotspots in the field of identity identification based on EEG in recent years. This article reviewed the commonly used identity feature extraction methods based on EEG signals, including single-channel features, inter-channel features, deep learning methods and spatial filter-based feature extraction methods, etc. and explained the basic principles application methods and related achievements of various feature extraction methods. Finally, we summarized the current problems and forecast the development trend.
