Objective: To explore the characteristics of distribution on Chinese adult body mass index (BMI) in different age groups and genders and to provide reference related to obesity and related chronic diseases. Methods: Data from the China Health and Nutrition Survey in 2009 were used. Sequential sample cluster method was used to analyze the characteristics of BMI distribution in different age groups and genders by SAS. Results: Our results showed that the adult BMI in China should be divided into 3 groups according to their age, as 20 to 40 years old, 40 to 65 years old, and> 65 years old, in females or in total when grouped by difference of 5 years. For groupings in male, the three groups should be as 20 to 40, 40 to 60 years old and>60 years old. There were differences on distribution between the male and female groups. When grouped by difference of 10 years, all of the clusters for male, female and total groups as 20-40, 40-60 and>60 years old, became similar for the three classes, respectively, with no differences of distribution between gender, suggesting that the 5-years grouping was more accurate than the 10-years one, and BMI showing gender differences. Conclusions: BMI of the Chinese adults should be divided into 3 categories according to the characteristics of their age. Our results showed that BMI was increasing with age in youths and adolescents, remained unchanged in the middle-aged but decreasing in the elderly.
Adolescent ; Adult ; Age Distribution ; Aged ; Asian People/statistics & numerical data* ; Body Mass Index ; China/epidemiology* ; Female ; Humans ; Male ; Middle Aged ; Nutrition Surveys ; Obesity/ethnology* ; Sex Distribution ; Sex Factors ; Young Adult

Intelligent medical image segmentation methods have been rapidly developed and applied, while a significant challenge is domain shift. That is, the segmentation performance degrades due to distribution differences between the source domain and the target domain. This paper proposed an unsupervised end-to-end domain adaptation medical image segmentation method based on the generative adversarial network (GAN). A network training and adjustment model was designed, including segmentation and discriminant networks. In the segmentation network, the residual module was used as the basic module to increase feature reusability and reduce model optimization difficulty. Further, it learned cross-domain features at the image feature level with the help of the discriminant network and a combination of segmentation loss with adversarial loss. The discriminant network took the convolutional neural network and used the labels from the source domain, to distinguish whether the segmentation result of the generated network is from the source domain or the target domain. The whole training process was unsupervised. The proposed method was tested with experiments on a public dataset of knee magnetic resonance (MR) images and the clinical dataset from our cooperative hospital. With our method, the mean Dice similarity coefficient (DSC) of segmentation results increased by 2.52% and 6.10% to the classical feature level and image level domain adaptive method. The proposed method effectively improves the domain adaptive ability of the segmentation method, significantly improves the segmentation accuracy of the tibia and femur, and can better solve the domain transfer problem in MR image segmentation.
Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Magnetic Resonance Imaging ; Knee ; Knee Joint

Objective To assess early clinical safety and efficacy of transfemoral transcatheter aortic valve replacement (TF-TAVR) for pure aortic regurgitation (PAR). Methods The clinical data of PAR patients who underwent TAVR in Wuhan Asia Heart Hospital and Wuhan Asia General Hospital from January 2018 to October 2022 were retrospectively analyzed. Patients were divided into a TF-TAVR group and a transapical transcatheter aortic valve replacement (TA-TAVR) group. The clinical data of the patients were analyzed. Results A total of 54 patients were enrolled, including 34 males and 20 females with an average age of 74.43±6.87 years. The preoperative N-terminal pro-B-type natriuretic peptide level was lower [808.50 (143.50, 2 937.00) pg/mL vs. 2 245.00 (486.30, 7 177.50) pg/mL, P=0.015], and the left ventricular end-diastolic diameter (56.00±6.92 mm vs. 63.07±10.23 mm, P=0.005) and sinus junction diameter (32.47±4.41 mm vs. 37.65±8.08 mm, P=0.007) were smaller in the TF-TAVR group. There was no death in the two groups during the hospitalization. Only 1 new death within postoperative 1 month in the TF-TAVR group (cerebral hemorrhage). A total of 2 new deaths in the TF-TAVR group (1 patient of sudden cardiac death and 1 of multiple organ failure), and there was no death in the TA-TAVR group within postoperative 3 months. There was 1 new death in the TA-TAVR group (details unknown), and there was no death in the TF-TAVR group within postoperative 6 months. There was no statistical difference between the two groups in the all-cause mortality and the cumulative survival rate during the follow-up period (P>0.05). The incidence of high atrioventricular block was 36.0% in the TF-TAVR group and 10.3% in the TA-TAVR group (P=0.024). There were no significant differences between the two groups in the perivalvular leakage (≥moderate), valve in valve, a second valve implantation, valve migration, cerebrovascular events, major vascular complications, complete left bundle branch block, new permanent pacemaker implantation or transferring to surgery (P>0.05). However, the incidence rates of complete left bundle branch block and new permanent pacemaker implantation were higher in the TF-TAVR group, accounting for 56.0% and 40.0%, respectively. Conclusion TF-TAVR is a safe and feasible treatment for PAR patients, which is comparable to TA-TAVR in the early postoperative safety and efficacy.

The increasing prevalence of the aging population, and inadequate and uneven distribution of medical resources, have led to a growing demand for telemedicine services. Gait disturbance is a primary symptom of neurological disorders such as Parkinson's disease (PD). This study proposed a novel approach for the quantitative assessment and analysis of gait disturbance from two-dimensional (2D) videos captured using smartphones. The approach used a convolutional pose machine to extract human body joints and a gait phase segmentation algorithm based on node motion characteristics to identify the gait phase. Moreover, it extracted features of the upper and lower limbs. A height ratio-based spatial feature extraction method was proposed that effectively captures spatial information. The proposed method underwent validation via error analysis, correction compensation, and accuracy verification using the motion capture system. Specifically, the proposed method achieved an extracted step length error of less than 3 cm. The proposed method underwent clinical validation, recruiting 64 patients with Parkinson's disease and 46 healthy controls of the same age group. Various gait indicators were statistically analyzed using three classic classification methods, with the random forest method achieving a classification accuracy of 91%. This method provides an objective, convenient, and intelligent solution for telemedicine focused on movement disorders in neurological diseases.
Humans ; Aged ; Parkinson Disease/diagnosis* ; Aging ; Algorithms ; Gait ; Lower Extremity

The shortage of medical resources promotes medical treatment reform, and smart healthcare is a promising strategy to solve this problem. With the development of Internet, real-time health status is expected to be monitored at home by using flexible healthcare systems, which puts forward new demands on flexible substrates for sensors. Currently, the flexible substrates are mainly traditional petroleum-based polymers, which are not renewable. As a natural polymer, cellulose, owing to its wide range of sources, convenient processing, biodegradability and so on, is an ideal alternative. In this review, the application progress of nanocellulose in flexible sensors is summarized. The structure and the modification methods of cellulose and nanocellulose are introduced at first, and then the application of nanocellulose flexible sensors in real-time medical monitoring is summarized. Finally, the advantages and future challenges of nanocellulose in the field of flexible sensors are discussed.
Cellulose/chemistry* ; Hydrogels/chemistry* ; Polymers

Heart valve disease (HVD) is one of the common cardiovascular diseases. Heart sound is an important physiological signal for diagnosing HVDs. This paper proposed a model based on combination of basic component features and envelope autocorrelation features to detect early HVDs. Initially, heart sound signals lasting 5 minutes were denoised by empirical mode decomposition (EMD) algorithm and segmented. Then the basic component features and envelope autocorrelation features of heart sound segments were extracted to construct heart sound feature set. Then the max-relevance and min-redundancy (MRMR) algorithm was utilized to select the optimal mixed feature subset. Finally, decision tree, support vector machine (SVM) and k-nearest neighbor (KNN) classifiers were trained to detect the early HVDs from the normal heart sounds and obtained the best accuracy of 99.9% in clinical database. Normal valve, abnormal semilunar valve and abnormal atrioventricular valve heart sounds were classified and the best accuracy was 99.8%. Moreover, normal valve, single-valve abnormal and multi-valve abnormal heart sounds were classified and the best accuracy was 98.2%. In public database, this method also obtained the good overall accuracy. The result demonstrated this proposed method had important value for the clinical diagnosis of early HVDs.
Humans ; Heart Sounds ; Heart Valve Diseases/diagnosis* ; Algorithms ; Support Vector Machine ; Signal Processing, Computer-Assisted

For patients with aortic valve disease who require replacement of their native valve, surgical aortic valve replacement (SAVR) has been the standard of care. Due to the hemorrhage and thromboembolic risks of long-term anticoagulation therapy for mechanical prosthesis, bioprosthetic aortic valve replacement (AVR) has a trend to be used in younger patients, which raising the concern for the durability of bioprosthetic valves. The newly published 5-year outcomes of PERIGON trial, with no structural valve deterioration, again demonstrated the favorable durability of the new generation bioprosthetic valves, further providing the evidence of using bioprosthetic AVR in younger patients. At the meantime, the rapid progress of transcatheter aortic valve implantation (TAVI) has brought a new treatment option. For younger patients with low risks, choosing SAVR or TAVI becomes a critical decision. This paper reviews the outcomes of PERIGON trial and its implications to the clinical practice and research of bioprosthetic AVR.

Myasthenia gravies is a common disease in the clinic. Extended thymectomy is an important way to treat myasthenia gravis. Median thoracotomy, thoracoscopy, and robots are important surgical methods. Da Vinci robot-assisted thoracoscopic surgery is more and more widely used in extended thymectomy, with high surgical safety and good stability. The surgical approach includes intercostal approach, subxiphoid approach, etc. Different surgical approaches have their own advantages, and their surgical effects are different. This article introduces the indications, technical steps, and effects of da Vinci robot-assisted thoracoscopic surgery, analyzes the advantages and limitations of treating myasthenia gravis, and looks forward to its development prospects.

Curriculum ; Education, Medical ; methods ; Internationality

No abstract available.
Antibodies* ; Humans ; Leprosy*