Objective To investigate the clinical significance of visual identification and intraoperative neuromonitoring of recurrent laryngeal nerve (RLN) during thyroidectomy. Methods Totally 1 664 patients underwent thyroidectomy with RLN protection from January 2009 to December 2009 were included in this study, in which 1 447 cases were protected by visual identification only, and 217 complex thyroidectomy cases were protected by visual identification and intraoperative monitoring. Results By the “multisites, three steps” RLN exposure method, 1 417 cases （85.16%） were successfully recognized and the recognition time was (3.57±1.26) min. The recognition time in the rest 30 complex cases (2.07%) without intraoperative neuromonitoring was (17.02±5.48) min. By this method, the temporary RLN injury occurred in 23 cases (1.54%) and 15 cases (65.22%) recovered within 2 weeks. In patients undewent intraoperative neuromonitoring, the recognition rate was 100% (217/217) and recognition time was (2.18±0.67) min. The temporary RLN injury occurred in 4 cases (1.84%) and 3 cases (75.00%) recovered within 2 weeks. All temporary RLN injuries recovered within 1 month and no persistent RLN injury occurred. Conclusions Conventional visual identification can reduce the RLN injury, but not meet the needs of the RLN protection during complex thyroidectomy. The combination of visual identification and intraoperative neuromonitoring can further improve the recognition rate and shorten the recovery time of vocal cord dyskinesia.
Intravascular ultrasound (IVUS) is widely used in coronary artery examination. Ultrasonic elastography combined with IVUS is very conspicuous in identifying plaque component and in detecting plaque vulnerability degree. In this study, a simulation model of the blood vessel based on finite element analysis (FEA) was established. The vessel walls generally have radial changes caused by different intravascular pressure. The signals at lower pressures were used as the pre-deformation data and the signals at higher pressure were used as the post-deformation data. Displacement distribution was constructed using the time-domain cross-correlation method, and then strain images. By comparison of elastograms under different pressures, we obtained the optimal pressure step. Furthermore, on the basis of the obtained optimize pressure step, the simulation results showed that this method could effectively distinguish characteristics between different component plaques, and could guide the later experiments and clinical applications.
ObjectiveTo explore value of ultrasound real-time elastography (RTE) technology for identification of benign and malignant solid thyroid nodules.MethodsA retrospective analysis was performed on 125 patients with thyroid nodules who underwent ultrasound RTE in this hospital from February 2018 to August 2019. All patients underwent RTE on the basis of conventional ultrasound. The ultrasound elasticity contrast index (ECI) was used as the evaluation index and the pathological examination result was used as the gold standard. The receiver operating characteristic (ROC) curve analysis was used to evaluate the value of ECI in the identification of benign and malignant solid thyroid nodules. Logistic regression analysis was used to analyze the influencing factors of ECI.ResultsAmong the 125 patients with solid thyroid nodules, 51 were malignant nodules, 74 were benign nodules. The ECI value of patients with benign thyroid nodules was lower than that of patients with malignant nodules (2.71±0.83 versus 3.42±1.14, t=–4.030, P<0.001). The result of ROC analysis showed that the cutoff value of ECI to distinguish benign and malignant solid thyroid nodules was 3.07, area under curve of ROC was 0.806 [95%CI (0.717, 0.894), P<0.001], sensitivity was 80.3%, specificity was 70.4%. The multivariate logistic regression analysis showed that the thyroid nodules with diffuse lesions, calcification, and maximum nodule diameter ≥1 cm were the risk factors for elevated ECI values (P<0.05). For the solid thyroid nodules without diffuse lesions, without calcification, and maximum nodule diameter <1 cm, ECI had the higher sensitivity, specificity, accuracy, and positive predictive value for the differential diagnosis of benign and malignant thyroid nodules (all exceed 80%), but these indexes were lower (under 60%) for the differential diagnosis of solid thyroid nodules with diffuse diseases, with calcification, and maximum nodule diameter ≥1 cm.ConclusionsECI obtained by ultrasound RTE can be used to differentiate solid thyroid nodules from benign ones. The presence or absence of diffuse lesions, calcification, and maximum nodule diameter are the influencing factors for ECI to differentiate solid thyroid nodules. In clinical diagnosis, it should be paid attention to the comprehensive analysis of the above factors.
Objective
To establish a simple and efficient method to isolate and culture the umbilical vein vascular endothelial cells in canine.
Methods
Twelve umbilical cords [(13.0 ± 1.5) cm in length] were taken from 12 newborn pups of Beagles. And then the vascular endothelial cells were isolated from these umbilical cords digested by 1% collagenase type I for 5, 7, and 10 minutes respectively (4 umbilical cords in each group). After cultured, the vascular endothelial cells were identified by morphology, immunofluorescence, and flow cytometry. And the growth curvature of umbilical vein vascular endothelial cells was detected by MTT assay.
Results
Few vascular endothelial cells were collected at 5 and 10 minutes after digestion; many vascular endothelial cells were seen at 7 minutes, and became cobblestone with culture time, with a large nucleus; after passage, cell morphology had no obvious change. Fluorescence microscope results showed that positive von Willebrand factor (vWF) and CD31 cells were observed in most of cells. The flow cytometry test displayed that the positive cell rates of vWF and CD31 were 99.0% ± 0.7% and 98.0% ± 1.2%, respectively. The above results indicated that cultured cells were vascular endothelial cells. MTT assay showed that vascular endothelial cells proliferation increased significantly with culture time.
Conclusion
Enzyme digestion is a convenient method to isolate vascular endothelial cells from canine umbilical vein, and a large number of cells and high purity of cells can be obtained by the method.
Using modular identification methods in gene-drug multiplex networks to infer new gene-drug associations can identify new therapeutic target genes for known drugs. In this paper, based on the gene expression data and drug response data of lung cancer in the genomics of drug sensitivity in cancer (GDSC) database, a multiple network algorithm is proposed. First, a heterogeneous network of genes of lung cancer and drugs in different cell lines is constructed, and then a network module identification method based on graph entropy is used. In this heterogeneous network, network modules are identified, and five lung cancer gene-drug association modules are identified through iterative convergence. Compared with other methods, the algorithm has better results in terms of running time, accuracy and robustness, and the identified modules have obvious biological significance. The research results in this article have guiding significance for the medication and treatment of lung cancer, and can provide references for the treatment of other diseases with the same targeted genes.
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.
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.
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.
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.
