In this study, loop-mediated isothermal amplification (LAMP) assay in conjunction with calcein for visualized detection of Mycobacterium tuberculosis (MTB) was established. Firstly, four LAMP primers were designed according to the region of 16S rDNA sequences of MTB. Secondly, clinical sputum samples were collected, decontaminated and their DNA was extracted. Thirdly, standard MTB strains were used to evaluate the specificity and sensitivity of LAMP. At the same time, electrophoresis was used for products detection and calcein was used for visualized verification. At last, Chi-squared test function in SPSS 17.0 software was used for consistency evaluation of LAMP assay as compared with the gold standard (culture method). Results showed that there was no nonspecific amplification appeared in the specificity assay and the detection limit was 10 copies/tube in the sensitivity assay. In addition, visualized method by calcein had a comparable sensitivity with that of electrophoresis method. After evaluation of clinical practicability, the sensitivity of LAMP was calculated as 94.74% and the specificity was 90%, respectively. And Chi-squared test showed that LAMP and culture method had no statistic difference, and the two methods were in good consistency (P>0.05). In conclusion, LAMP assay introduced in our study has the characteristics of high efficiency and visualized detection so that this technique has great application prospects in the resource-limited environment, such as work field and primary care hospitals.
To solve the problems of noise interference and edge signal weakness for the existing medical image, we used two-dimensional wavelet transform to process medical images. Combined the directivity of the image edges and the correlation of the wavelet coefficients, we proposed a medical image processing algorithm based on wavelet characteristics and edge blur detection. This algorithm improved noise reduction capabilities and the edge effect due to wavelet transformation and edge blur detection. The experimental results showed that directional correlation improved edge based on wavelet transform fuzzy algorithm could effectively reduce the noise signal in the medical image and save the image edge signal. It has the advantage of the high-definition and de-noising ability.
In order to study the effect of light with different wavelengths on the motion behavior of carp robots, phototaxis experiment, anatomical experiment, light control experiment and speed measurement experiment were carried out in this study. Blue, green, yellow and red light with different wavelength were used to conduct phototaxis experiments on carp to observe their movement behavior. By dissecting the skull bones of the carp to determine the appropriate location to carry the light control device, we independently developed a light control carrying device which was suitable for any illumination intensity environment. The experiment of the light-controlled carp robots was carried out. The motion behavior of the carp robot was checked by using computer binocular stereo vision technology. The motion trajectory of the carp robot was tracked and obtained by applying kernel correlation filter (KCF) algorithm. The motion velocity of the carp robot at different wavelengths was calculated according to their motion trajectory. The results showed that carps’ sensitivity to different light changed from strong to weak in the order of blue, red, yellow and green, so that using light with different wavelengths to control the speed of the carp robot has certain laws to follow. A new method to avoid brain damage in carp robots control can be provided in this study.
Existing near-infrared non-invasive blood glucose detection modelings mostly detect multi-spectral signals with different wavelength, which is not conducive to the popularization of non-invasive glucose meter at home and does not consider the physiological glucose dynamics of individuals. In order to solve these problems, this study presented a non-invasive blood glucose detection model combining particle swarm optimization (PSO) and artificial neural network (ANN) by using the 1 550 nm near-infrared absorbance as the independent variable and the concentration of blood glucose as the dependent variable, named as PSO-2ANN. The PSO-2ANN model was based on two sub-modules of neural networks with certain structures and arguments, and was built up after optimizing the weight coefficients of the two networks by particle swarm optimization. The results of 10 volunteers were predicted by PSO-2ANN. It was indicated that the relative error of 9 volunteers was less than 20%; 98.28% of the predictions of blood glucose by PSO-2ANN were distributed in the regions A and B of Clarke error grid, which confirmed that PSO-2ANN could offer higher prediction accuracy and better robustness by comparison with ANN. Additionally, even the physiological glucose dynamics of individuals may be different due to the influence of environment, temper, mental state and so on, PSO-2ANN can correct this difference only by adjusting one argument. The PSO-2ANN model provided us a new prospect to overcome individual differences in blood glucose prediction.
Objective To explore the clinical value of Persyst automatic detection of spike waves in adult patients with temporal lobe epilepsy. Methods EEG recordings were continuously concluded from the Epilepsy Unit of the First Affiliated Hospital of Soochow University during 2019.1.1 to 2019.12.31. Two EEG experts certified by the Chinese Anti-Epileptic Association marked interictal epileptic discharge in the long-time electroencephalogram that meet the criteria. Consistent results of the two experts were seen as the "golden standard". The sensitivity and false positive rates were calculated compared with the automatic test results of Persyst version 11, 13 and 14. Results 7 cases were included, each with a recording time of 24~25 hours and a total of 169 hours. Two expert readers achieved the consistency of 43.09%. Spike waves detected automatically were much more than manually. The sensitivity was as high as 62.26%, 77.0% and 67.28%. The lowest false positive rate was 0.37/min, 0.85/min and 0.46/min respectively. Automatic analysis achieved an average workload reduction of 14.59%~37.05%. Conclusions Persyst automatic spike detection has the acceptable sensitivity and false positive rate. It differs from versions and need to be further combined with expert readers.Less workload and accuracy can be balanced by setting reasonable perception parameter.
Objective
To explore the application of two methods of direct fecal detection ofClostridium difficilein patients with recurrent inflammatory bowel disease (IBD), including nucleic acid amplification test (NAAT) and enzyme immunoassay (EIA), in order to provide support for hospitals to prevent and control clostridium difficile infection (CDI).
Methods
Fresh feces of 48 patients with recurrent IBD treated between November 2014 and April 2015 were collected within 48 hours after admission. Anaerobic culture and identification, NAAT and EIA were used to test the same samples. Statistical analysis was performed using Kappa test.
Results
Among the 48 fecal samples,Clostridium difficilewas negative in 37 and positive in 11 including 2 (4.2%) with toxigenicClostridium difficile characterized as toxin type A+B+. Compared with anaerobic culture and identification, NAAT had a perfect correlation (Kappa=1.00,P<0.05), and EIA had an almost perfect correlation (Kappa=0.89,P<0.05). But EIA toxin test had missed detection of toxigenic samples.
Conclusions
For patients with recurrent IBD combined with CDI, both NAAT and EIA test may be applied to detctClostridium difficile in feces directly, while NAAT may show a better performance. Samples from highly suspected patients with negative toxin result tested by EIA should be confirmed by other methods such as NAAT.
ObjectiveTo use failure mode and effect analysis (FMEA) to check and improve the risk of severe acute respiratory syndrome coronavirus 2 nucleic acid detection, and explore the application effect of FMEA in the emergency inspection items.MethodsFMEA was used to sort out the whole process of severe acute respiratory syndrome coronavirus 2 nucleic acid detection from January 30 to February 21, 2020. By establishing the theme, setting up a team, analyzing the failure mode and potential influencing factors. Then calculate the risk priority number (RPN), formulate preventive measures and implement continuous improvement according to the analysis results.ResultsA total of 2 138 cases were included. After improvement, the number of potential failure modes has been reduced by 2 (17 vs.19); the value of total RPN decreased (3 527.49 vs. 1 858.28). There was significant difference in average RPN before and after improvement [(185.66±74.34) vs. (97.80±37.97); t=6.128, P<0.001].ConclusionsIn the early stage of emergency inspection items, using FMEA can systematically check the risk factors in the process, develop improvement measures. It also can effectively reduce the risk of severe acute respiratory syndrome coronavirus 2 nucleic acid detection in hospital.
A method was proposed to detect pulmonary nodules in low-dose computed tomography (CT) images by two-dimensional convolutional neural network under the condition of fine image preprocessing. Firstly, CT image preprocessing was carried out by image clipping, normalization and other algorithms. Then the positive samples were expanded to balance the number of positive and negative samples in convolutional neural network. Finally, the model with the best performance was obtained by training two-dimensional convolutional neural network and constantly optimizing network parameters. The model was evaluated in Lung Nodule Analysis 2016(LUNA16) dataset by means of five-fold cross validation, and each group's average model experiment results were obtained with the final accuracy of 92.3%, sensitivity of 92.1% and specificity of 92.6%.Compared with other existing automatic detection and classification methods for pulmonary nodules, all indexes were improved. Subsequently, the model perturbation experiment was carried out on this basis. The experimental results showed that the model is stable and has certain anti-interference ability, which could effectively identify pulmonary nodules and provide auxiliary diagnostic advice for early screening of lung cancer.
Multivariate time series problems widely exist in production and life in the society. Anomaly detection has provided people with a lot of valuable information in financial, hydrological, meteorological fields, and the research areas of earthquake, video surveillance, medicine and others. In order to quickly and efficiently find exceptions in time sequence so that it can be presented in front of people in an intuitive way, we in this study combined the Riemannian manifold with statistical process control charts, based on sliding window, with a description of the covariance matrix as the time sequence, to achieve the multivariate time series of anomaly detection and its visualization. We made MA analog data flow and abnormal electrocardiogram data from MIT-BIH as experimental objects, and verified the anomaly detection method. The results showed that the method was reasonable and effective.
The detection of electrocardiogram (ECG) characteristic wave is the basis of cardiovascular disease analysis and heart rate variability analysis. In order to solve the problems of low detection accuracy and poor real-time performance of ECG signal in the state of motion, this paper proposes a detection algorithm based on segmentation energy and stationary wavelet transform (SWT). Firstly, the energy of ECG signal is calculated by segmenting, and the energy candidate peak is obtained after moving average to detect QRS complex. Secondly, the QRS amplitude is set to zero and the fifth component of SWT is used to locate P wave and T wave. The experimental results show that compared with other algorithms, the algorithm in this paper has high accuracy in detecting QRS complex in different motion states. It only takes 0.22 s to detect QSR complex of a 30-minute ECG record, and the real-time performance is improved obviously. On the basis of QRS complex detection, the accuracy of P wave and T wave detection is higher than 95%. The results show that this method can improve the efficiency of ECG signal detection, and provide a new method for real-time ECG signal classification and cardiovascular disease diagnosis.
