Objective To analyze and summarize the clinical features of delayed occurrence of senile asthma,to add the knowledge and understanding of the disease. Methods To retrospectively analyze clinical data of the cases of senile asthma treated in our hospital,all the cases were divided into groups of early onset senile asthma and delayed occurrence senile asthma,according to the age of first onset.Then related indexes were analyzed,to summarize the clinical features of group of delayed occurrence of senile asthma. Results There were 28 cases of delayed occurrence of senile asthma,accounting for 34.6% of total cases.Compared to early onset group,there was no significant difference (all P＞0.05) between two groups for the following items such as age,allergic history and positive rate of family's history,disease causes,clinical symptoms,basic diseases and complications,proportion of severe cases,rate of misdiagnosis and mistreatment,proportion of standardized treatment and un-standardized treatment,prognosis of diseases and mortality.Both groups had low rate of knowledge and application on PEF monitoring equipment and ACT score.The period of misdiagnosis and mistreatment for delayed occurrence group was shorter than the early onset group (P＜0.05＝; the seasonal nature and day and night pattern was significant in delayed occurrence group (P＜0.05＝. Conclusion Late onset elderly asthma had the features such as shorter course of the disease,relatively obvious onset rule during day and night,and obvious symptoms during night,which are different from that of early onset group.

Support vector machine (SVM) has shown its excellent learning and generalization ability for the binary classification of real problems and has been extensively employed in many areas. In this paper, SVM, K-Nearest Neighbor, Decision Tree C4.5 and Artificial Neural Network were applied to identify cancer patients and normal individuals using the concentrations of 6 elements including macroelements (Ca, Mg) and microelements (Ba, Cu, Se, Zn) in human blood. It was demonstrated, by using the normalized features instead of the original features, the classification performances can be improved from 91.89% to 95.95%, from 83.78% to 93.24%, and from 90.54% to 94.59% for SVM, K-NN and ANN respectively, whereas that of C4.5 keeps unchangeable. The best average accuracy of SVM with linear dot kernel by using 5-fold cross validation reaches 95.95%, and is superior to those of other classifiers based on K-NN (93.24%), C4.5 (79.73%), and ANN (94.59%). The study suggests that support vector machine is capable of being used as a potential application methodology for SVM-aided clinical cancer diagnosis.
Algorithms ; Barium ; blood ; Calcium ; blood ; Computational Biology ; methods ; Copper ; blood ; Diagnosis, Computer-Assisted ; methods ; Humans ; Neoplasms ; blood ; diagnosis ; Neural Networks (Computer) ; Trace Elements ; blood

Objective:To explore the function of esophageal small balloon or papillary sphincter knife in the treatment of stent implantation for colorectal malignant obstruction,and to improve the success rate of colonic stent placement in such patients.Methods:A total of 49 patients with colorectal cancer complicated with almost complete obstruction or colorectal cancer were enrolled for this study.The esophageal small balloon or papillary sphincter knife was used in the guide wires.The guide wires gradually crossed the tumor gap and they were placed in the contralateral intestinal cavity with balloon progression.X-ray was then used to confirm whether the guide wire was inserted in the lesion intestinal cavity,and then the metal bare stent was inserted.Results:The guide wires was successfully inserted with conventional methods in these 49 cases,while they were also successfully placed the guide wire and the stent in the new way.Conclusion:For the patients with colorectal cancer complicated with complete obstruction or colorectal cancer located in obviously angled location,the use of esophageal small balloon or papillary sphincter knife can help the guide wire insert.They greatly improve the success rate of stent implantation.

Cardiovascular disease is one of the important factors that threaten the health of residents, ranking the first among various causes of death, so the monitoring and diagnosis of human cardiovascular health is particularly important. Compared with traditional brachial artery pressure, central arterial pressure (CAP) has a higher correlation with the occurrence of many cardiovascular events. The measurement of CAP can more accurately reflect the real situation of human blood pressure, and provide an important basis for diagnosis and disease prevention. Therefore, the realization of high-precision, high-generalization ability and low-cost non-invasive measurement of CAP has always been the research focus in this field. This article combines the relevant literature in China and abroad to summarize the current status of CPA measurement, introduces related research progress from two aspects, namely parameter measurement and waveform measurement, and discusses the characteristics of the existing methods and the future development.

To evaluate the feasibility and safety of colon stenting as bridge surgery for colorectal cancer obstruction.
 Methods: A total of 30 patients (stent group), who underwent colonic stenting for colorectal obstruction at the Third Xiangya Hospital, Central South University from September 2015 to June 2017, were selected to receive the preoperative bridge surgery. Technical success rates, clinical success rates, and stent-related complications were observed. A total of 38 patients (emergency surgery group), who underwent surgical operation for colorectal obstruction, served as a control. The tumor resection rate at Stage I, ostomy rate, hospitalization time, and hospitalization cost were compared between the 2 groups.
 Results: The technical success and clinical success rates were 100.0% and 90.0% in the stent group, respectively. The stent-related complications included bleeding in 2 cases (6.7%), micro-perforation in 1 case (3.3%), stent displacement in 1 case (3.3%), and stent occlusion in 3 cases (10.0%) in the stent group. The Stage I tumor resection rate in the stent group was significantly higher than that in the emergency surgery group (90.0% vs 68.4%, P<0.01). The incidence of anastomotic leakage in the stent group was lower than that in the emergency surgery group (3.3% vs 10.5%, P<0.05); the stoma rate in the stent group was lower than that in the emergency surgery group (13.3% vs 44.7%, P<0.01). The surgical complications occurred in the stent group were significantly lower than those in the emergency surgery group (20.0% vs 47.3%, P<0.01). The average hospital stay in the stent group was lower than that in the emergency surgery group (20.0 vs 24.5 days, P<0.05). There was no significant difference in hospitalization costs between the 2 groups (P>0.05).
 Conclusion: Preoperative colonic stenting for colorectal obstruction surgery as a bridge is feasible in terms of methods, and which can significantly increase the Stage I tumor resection rate, reduce the ostomy rate, decrease surgical complications, shorten the average length of hospital stay, and reduce patient suffering.
Colorectal Neoplasms ; complications ; Humans ; Intestinal Obstruction ; etiology ; therapy ; Stents ; Treatment Outcome

Lung diseases such as lung cancer and COVID-19 seriously endanger human health and life safety, so early screening and diagnosis are particularly important. computed tomography (CT) technology is one of the important ways to screen lung diseases, among which lung parenchyma segmentation based on CT images is the key step in screening lung diseases, and high-quality lung parenchyma segmentation can effectively improve the level of early diagnosis and treatment of lung diseases. Automatic, fast and accurate segmentation of lung parenchyma based on CT images can effectively compensate for the shortcomings of low efficiency and strong subjectivity of manual segmentation, and has become one of the research hotspots in this field. In this paper, the research progress in lung parenchyma segmentation is reviewed based on the related literatures published at domestic and abroad in recent years. The traditional machine learning methods and deep learning methods are compared and analyzed, and the research progress of improving the network structure of deep learning model is emphatically introduced. Some unsolved problems in lung parenchyma segmentation were discussed, and the development prospect was prospected, providing reference for researchers in related fields.
COVID-19 ; Humans ; Lung/diagnostic imaging* ; Machine Learning ; SARS-CoV-2 ; Tomography, X-Ray Computed

Corona virus disease 2019 (COVID-19) is an acute respiratory infectious disease with strong contagiousness, strong variability, and long incubation period. The probability of misdiagnosis and missed diagnosis can be significantly decreased with the use of automatic segmentation of COVID-19 lesions based on computed tomography images, which helps doctors in rapid diagnosis and precise treatment. This paper introduced the level set generalized Dice loss function (LGDL) in conjunction with the level set segmentation method based on COVID-19 lesion segmentation network and proposed a dual-path COVID-19 lesion segmentation network (Dual-SAUNet++) to address the pain points such as the complex symptoms of COVID-19 and the blurred boundaries that are challenging to segment. LGDL is an adaptive weight joint loss obtained by combining the generalized Dice loss of the mask path and the mean square error of the level set path. On the test set, the model achieved Dice similarity coefficient of (87.81 ± 10.86)%, intersection over union of (79.20 ± 14.58)%, sensitivity of (94.18 ± 13.56)%, specificity of (99.83 ± 0.43)% and Hausdorff distance of 18.29 ± 31.48 mm. Studies indicated that Dual-SAUNet++ has a great anti-noise capability and it can segment multi-scale lesions while simultaneously focusing on their area and border information. The method proposed in this paper assists doctors in judging the severity of COVID-19 infection by accurately segmenting the lesion, and provides a reliable basis for subsequent clinical treatment.
Humans ; COVID-19/diagnostic imaging* ; Respiratory Rate ; Tomography, X-Ray Computed