Multi-layer perceptron (MLP) neural network belongs to multi-layer feedforward neural network, and has the ability and characteristics of high intelligence. It can realize the complex nonlinear mapping by its own learning through the network. Bipolar disorder is a serious mental illness with high recurrence rate, high self-harm rate and high suicide rate. Most of the onset of the bipolar disorder starts with depressive episode, which can be easily misdiagnosed as unipolar depression and lead to a delayed treatment so as to influence the prognosis. The early identification of bipolar disorder is of great importance for patients with bipolar disorder. Due to the fact that the process of early identification of bipolar disorder is nonlinear, we in this paper discuss the MLP neural network application in early identification of bipolar disorder. This study covered 250 cases, including 143 cases with recurrent depression and 107 cases with bipolar disorder, and clinical features were statistically analyzed between the two groups. A total of 42 variables with significant differences were screened as the input variables of the neural network. Part of the samples were randomly selected as the learning sample, and the other as the test sample. By choosing different neural network structures, all results of the identification of bipolar disorder were relatively good, which showed that MLP neural network could be used in the early identification of bipolar disorder.
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.
Objective To identify glial cell line-derived neurotrophic factor (GDNF) recombinant retroviral vector and to establish its packaging cell line PA317. Methods PA317 cells were transfected with recombinant retroviral vector pLXSN-GDNF using liposomes. The recombinant retroviral particles were then harvested from culture media of G418 resistant transfected cells and analyzed using RT-PCR. Virus titers in supernatants were investigated. Results Sequencing date indicated that GDNF gene was exactly identical to the sequence in the GeneBank. PA317 cells were transfected with recombinant retroviral vector pLXSN-GDNF using liposomes, and virus titers insupernatants harvested from culture media of G418 resistant transfected cells were 104-105 CFU/ml. Conclusion Packaging cell line PA317/pLXSN-GDNF was established.
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.
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.
Forced oscillation technique (FOT) is an active method to test pulmonary function, which can derive the mechanical characteristics of the respiratory system with liner system identification theory by pushing in an oscillation air signal and measuring the changes of output pressure and flow. A pulmonary function determination system was developed based on the FOT in this paper. Several critical technologies of this determination system were analyzed, including the selection criteria of oscillation air generator, pressure and flow sensor, the signal design of oscillation air generator, and the synchronous sampling of pressure and flow data. A software program on LabVIEW platform was set up to control the determination system and get the measuring data. The performance of sensors and oscillation air generator was verified. According to the frequency response curve of the pressure, the amplitude of driving signal to the oscillation air generator was corrected at the frequency range between 4~40 Hz. A simulation experiment was carried out to measure the respiratory impedance of the active model lung ASL5000 and the results were close to the setting values of the model lung. The experiment testified that the pulmonary function determination system based on FOT had performance good enough to provide a tool for the in-depth research of the mechanical properties of the respiratory system.
Real-time free breathing cardiac cine imaging is a reproducible method with shorter acquisition time and without breath-hold for cardiac magnetic resonance imaging. However, the detection of end-diastole and end-systole frames of real-time free breathing cardiac cine imaging for left ventricle function analysis is commonly completed by visual identification, which is time-consuming and laborious. In order to save processing time, we propose a method for semi-automatic identification of end-diastole and end-systole frames. The method fits respiratory motion signal and acquires the expiration phase, end-diastole and end-systole frames by cross correlation coefficient. The procedure successfully worked on ten healthy volunteers and validated by the analysis of left ventricle function compared to the standard breath-hold steady-state free precession cardiac cine imaging without any significant statistical differences. The results demonstrated that the present method could correctly detect end-diastole and end-systole frames. In the future, this technique may be used for rapid left ventricle function analysis in clinic.
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.
ObjectiveTo summarize the latest progress of parathyroid gland identification in thyroid surgery, and to provide some reference for improving the clinical efficacy.MethodThe literatures about the identification of parathyroid gland in thyroid surgery in recent years were collected to make an review.ResultsThere were many methods for identifying parathyroid gland in thyroid surgery, such as naked eye identification method, intraoperative frozen section, intraoperative staining identification method, intraoperative optical identification method, intraoperative parathyroid hormone assay, γ-detector, and histological identification, each method had its own advantages and disadvantages.ConclusionThe identification of parathyroid gland does not only depend on a certain method, but also require surgeons to enhance their ability to distinguish parathyroid gland.
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.