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 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.
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.
Objective To establish a method for quality control of Astragalus Radix and Scutellariae Radix in Biqiaotong granules and provide basis for the establishment of quality standard. Methods The single-factor test method was used to investigate the factors of thin layer chromatography (TLC) conditions, including different extract method and solvents, developing system, comogemc agents, temperature, humidity, drawing amounts and thin layer boards, and to screen the best TLC conditions of Astragalus Radix and Scutellariae Radix . Results The TLC conditions of Astragalus Radix were used trichloromethane-methanel-water (13:7:2) as developing solvent, separated on silica gel G, heatd under 105℃ until the spots bacame clear. The TLC conditions of Scutellariae Radix were used methylbenzene-ethy acetate- formic acid-methanel (9:3:2:2) as developing solvent, separated on silica gel G, observed after 30 minutes under daylight until the spots were clear. Conclusions The spot features are clear, and with good separating degree, strong specificity, and good repeatability without the inference of negative control. The TLC method is simple, sensitive and accurate, which can be adopted for the quality control of Biqiaotong granules.
Identification of real-time uterine contraction status is very significant to labor analgesia, but the traditional uterine contraction analysis algorithms and systems cannot meet the requirement. According to the situations mentioned above, this paper designs a set of algorithms for the real-time analysis of uterine contraction status. The algorithms include uterine contraction signal preprocessing, uterine contraction baseline extraction based on histogram and linear iteration and an algorithm for the real-time analysis of uterine contraction status based on finite state machines theory. It uses the last uterine status and a series of state transfer conditions to identify the current uterine contraction status, as well as a buffer mechanism to avoid false status transitions. To evaluate the performance of the algorithm, we compare it with an existing uterine contraction analysis algorithm used in the electronic fetal monitor. The experiments show that our algorithm can analyze the uterine contraction status while monitoring the uterine contraction signal in a real-time. Its sensitivity reaches 0.939 9 and its positive predictive value is 0.869 3, suggesting that the algorithm has high accuracy and meets the need of clinical monitoring.
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.
ObjectiveTo investigate the status of knowledge, attitude, and practice of patient identification in nurses, and provide a basis for clinical managers to carry out targeted training.MethodsA total of 3 696 nurses of tertiary, secondary, and primary hospitals in Guizhou Province were recruited and investigated for the status of knowledge, attitude, and practice of patient identification with a questionnaire by using convenient sampling in May 2019.ResultsThe scores of identification knowledge, attitude, and practice of the 3 696 nurses were 47.87±6.10, 27.39±3.15, and 57.19±4.86, respectively. Logistic regression analysis showed that the higher the educational level was, the higher the score of nurses’ knowledge of patient identification was [odds ratio (OR)=1.592, 95% confidence interval (CI) (1.084, 2.338), P=0.018]; the higher the personal monthly income was, the more positive the nurses’ attitude towards patient identification was [OR=1.570, 95%CI (1.005, 2.453), P=0.048].ConclusionsThe general situation of patient identification in nurses is good, but there are still differences among nurses with different characteristics. It is suggested that managers should pay special attention to the training of nurses with low educational level and low income, make them master the knowledge of patient identification, at the same time, improve their enthusiasm and standardize their behavior, so as to ensure the safety of patients.
Acute respiratory distress syndrome (ARDS) is a serious threat to human life and health disease, with acute onset and high mortality. The current diagnosis of the disease depends on blood gas analysis results, while calculating the oxygenation index. However, blood gas analysis is an invasive operation, and can’t continuously monitor the development of the disease. In response to the above problems, in this study, we proposed a new algorithm for identifying the severity of ARDS disease. Based on a variety of non-invasive physiological parameters of patients, combined with feature selection techniques, this paper sorts the importance of various physiological parameters. The cross-validation technique was used to evaluate the identification performance. The classification results of four supervised learning algorithms using neural network, logistic regression, AdaBoost and Bagging were compared under different feature subsets. The optimal feature subset and classification algorithm are comprehensively selected by the sensitivity, specificity, accuracy and area under curve (AUC) of different algorithms under different feature subsets. We use four supervised learning algorithms to distinguish the severity of ARDS (P/F ≤ 300). The performance of the algorithm is evaluated according to AUC. When AdaBoost uses 20 features, AUC = 0.832 1, the accuracy is 74.82%, and the optimal AUC is obtained. The performance of the algorithm is evaluated according to the number of features. When using 2 features, Bagging has AUC = 0.819 4 and the accuracy is 73.01%. Compared with traditional methods, this method has the advantage of continuously monitoring the development of patients with ARDS and providing medical staff with auxiliary diagnosis suggestions.