Objective To study the effect of electrical stimulation breath training on lung function of patients following pulmonary lobectomy. Methods 62 patients following pulmonary lobectomy were randomly allocated into experimental group (n=30 )and control group (n=32). The experimental group received a 4-week supervised electrical stimulation breath training program using an electric stimulus feedback trainer (20mins per time, 3 times per week);The control group received postoperative routine nursing. Cadiopulmonary function evaluation of 2 groups were tested before and after the experiment. The evaluation included the 6-min walking test (6MWD), FVC, FEV1,W,AT and VO2max/kg. Results After 4 week training, the value of 6MWD,W,FVC,FEV1 all improved, compared to the baseline value (P < 0.05) and the value of 6MWD,W,FVC,FEV1 were more obvious in experimental group, compared to control group(P<0.05). The AT value and the VO2max/kg value increased than the baseline value (P<0.05)and the improvement degree was more remarkable in experimental group than that in control group (P<0.05). Conclusion Electrical stimulation breath training can improve cardiopulmonary function of the patients following pulmonary lobectomy.

The characterization methods in the field of protein adsorption on biological materials in recent years were reviewed from the aspects of protein adsorption amount, adsorption layer thickness, molecular conformational change after protein adsorption, molecular morphology after protein adsorption, and protein molecule adsorption process simulation. These methods include biochemical analysis, surface plasmon resonance (SPR), dissipative quartz crystal microbalance ( QCM-D ) , ellipsometry ( ELM ) , optical interference reflection ( RIFS ) , attenuated total reflection Fourier transformed infrared spectroscopy (ATR-FTIR), circular dichroism (CD), atomic force microscopy ( AFM ) and computer molecular simulation techniques . In this paper , the basic principles , the advantages and disadvantages of the above characterization methods and related researches were reviewed. This paper provides a comprehensive and reliable basis for the selection of protein experimental characterization methods in protein adsorption, biomaterial design and other research, and provides new ideas for research in the field of protein.

Objective: To investigate the abnormalities of the blood system in landfill workers. Methods: A cohort study was conducted for 224 landfill workers who were followed up for 6 consecutive years with abnormal routine blood test results and a low platelet count as the outcome events. The life-table method was used to analyze the incidence rates of these two outcome events, and the incidence rates were compared between first-and second-line workers. Results: A total of 71 workers had abnormal routine blood test results, among whom 29 had abnormal leukocyte count, 14 had abnormal erythrocyte count, 40 had abnormal platelet count, 17 had abnormal hemoglobin, and 29 had a reduction in platelet count. For these landfill workers, the 6-year abnormal rate of routine blood test results was 43.2%, and the incidence rate of low platelet count within 6 years was 13.5%. The first-line workers had a significantly lower abnormal rate of routine blood test results than the second-line workers (P=0.020) , and the relative risk of abnormal routine blood test results in the first-and second-line workers was 0.592. There was no significant difference in the incidence rate of low platelet count between the two groups of workers (P≥0.05) . Conclusion The landfill has an adverse effect on the blood system of landfill workers, and the second-line workers have greater impairment than the first-line workers.

With the progress of information technology and intelligent technology,the intelligent development of urine testing instruments is facing new opportunities.Using the disease cybernetics theory model to analyze the business process and current urine testing instruments of clinical urine analyzer,a generational theoretical model of urine testing instruments has been constructed,which is conducive to guiding the intelligent development direction of urine testing instruments.The study divides urine testing instruments into one to four generations of products,with the first-generation of products being operated by doctors.The second-generation products are currently available for laboratory technicians to use various urine analyzers.The third-generation products further optimize the testing process and intelligence,without the need for inspectors to operate.The fourth-generation products are unmanned and do not require sampling.It can be seen that with the development of technology,urine analysis has indeed become more convenient,but after all,various instruments have their limitations.Therefore,the establishment of a theoretical model for the generation of urine testing instruments should be applied in clinical urine testing,which can not only improve the efficiency of urine analysis but also improve its quality.

Objective:To investigate the relationship between sarcopenia and the maximum diaphragm excursion(Dmax)observed on ultrasound in the elderly.Methods:Elderly volunteers(age≥60 years)were recruited from family members of patients at Guangdong Provincial People's Hospital.Their Dmax during forced inhalation was measured via ultrasound.The parameters for the diagnosis of sarcopenia included the appendicular skeletal muscle mass index(ASMI), handgrip strength and usual gait speed.We compared the differences in physical characteristics, pulmonary ventilation, physical performance and Dmax between patients with and without sarcopenia, and evaluated the relationship between sarcopenia and DEmax in the elderly via linear regression.Results:A total of 145 elderly volunteers[age(69.47±5.15)years]were included, and 28(19.31%)were diagnosed with sarcopenia.Body weight, ASMI, maximum inspiratory pressure(Pinmax), maximal power output(Wmax)and Dmax of patients with sarcopenia were significantly lower than those of patients without sarcopenia(all P<0.05).Dmax in the elderly was correlated with sex, height, ASMI, handgrip strength, usual gait speed, Pinmax and Wmax( r=0.181, 0.130, 0.322, 0.373, 0.401, 0.134, and 0.388, P=0.012, 0.037, 0.009, 0.002, 0.022, 0.009, and 0.002, respectively).After adjusting for sex, age, height and forced vital capacity(FVC), there was still a negative correlation between sarcopenia and Dmax in the elderly( β=-0.310, P=0.021). Conclusions:Dmax is related to Pinmax and physical performance in the elderly, and sarcopenia increases the risk of decline in the maximum diaphragm excursion in the elderly as observed on ultrasound.

Seizures during sleep increase the probability of complication and sudden death. Effective prediction of seizures in sleep allows doctors and patients to take timely treatments to reduce the aforementioned probability. Most of the existing methods make use of electroencephalogram (EEG) to predict seizures, which are not specific developed for the sleep. However, EEG during sleep has its characteristics compared with EEG during other states. Therefore, in order to improve the sensitivity and reduce the false alarm rate, this paper utilized the characteristics of EEG to predict seizures during sleep. We firstly constructed the feature vector including the absolute power spectrum, the relative power spectrum and the power spectrum ratio in different frequencies. Secondly, the separation criterion and branch-and-bound method were applied to select features. Finally, support vector machine classifier were trained, which is then employed for online prediction. Compared with the existing method that do not consider the characteristics of sleeping EEG (sensitivity 91.67%, false alarm rate 9.19%), the proposed method was superior in terms of sensitivity (100%) and false alarm rate (2.11%). This method can improve the existing epilepsy prediction methods and has important clinical value.

The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.
Humans ; Adult ; Imagination ; Neural Networks, Computer ; Imagery, Psychotherapy/methods* ; Electroencephalography/methods* ; Algorithms ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted

Aiming at the limitations of clinical diagnosis of Parkinson's disease (PD) with rapid eye movement sleep behavior disorder (RBD), in order to improve the accuracy of diagnosis, an intelligent-aided diagnosis method based on few-channel electroencephalogram (EEG) and time-frequency deep network is proposed for PD with RBD. Firstly, in order to improve the speed of the operation and robustness of the algorithm, the 6-channel scalp EEG of each subject were segmented with the same time-window. Secondly, the model of time-frequency deep network was constructed and trained with time-window EEG data to obtain the segmentation-based classification result. Finally, the output of time-frequency deep network was postprocessed to obtain the subject-based diagnosis result. Polysomnography (PSG) of 60 patients, including 30 idiopathic PD and 30 PD with RBD, were collected by Nanjing Brain Hospital Affiliated to Nanjing Medical University and the doctor's detection results of PSG were taken as the gold standard in our study. The accuracy of the segmentation-based classification was 0.902 4 in the validation set. The accuracy of the subject-based classification was 0.933 3 in the test set. Compared with the RBD screening questionnaire (RBDSQ), the novel approach has clinical application value.
Electroencephalography ; Humans ; Intelligence ; Parkinson Disease/diagnosis* ; Polysomnography ; REM Sleep Behavior Disorder/diagnosis*