OBJECTIVES: To analyze sex and age distribution of global disease burden of calcific aortic valve disease (CAVD) from 1990 to 2021. METHODS: CAVD data during 1990-2021 were obtained from the IHME website for Global Burden of Disease (GBD). The prevalence, mortality, years lived with disability (YLDs), and disability-adjusted life years (DALYs) were analyzed by gender and age groups. Joinpoint regression was used to calculate annual percentage change (APC) and average annual percentage change (AAPC). RESULTS: In 2021, there were 13.32 million CAVD patients and 142 000 deaths caused by CAVD globally. Age-standardized prevalence was higher in males (193.2/10⁵) than that in females (128.9/10⁵). Patients in 65-<85 age group accounted for 64.0% of total cases, while those ≥85 years old accounted for 16.1%. From 1990 to 2021, prevalence increased in both sexes with an AAPC of 0.72% for males and 0.57% for females, respectively. Prevalence grew fastest from 2000 to 2010, slowed thereafter, and declined from 2015 to 2021. In <65 years old, the mortality of males was 2.4 times higher than that of females, while in ≥85 years old, mortality of females (117.3/10⁵) exceeded that of males (99.1/10⁵). YLD rates increased with age, and were higher in males for all age groups. DALY rates decreased overall but increased in ≥85 years old, with a greater increase in females. CONCLUSIONS There are significant gender and age disparities in global disease burden of CAVD, with the elderly, especially super-elderly females deserving particular attention. It is recommended to develop personalized intervention strategies for these populations.
Humans ; Male ; Female ; Aged ; Calcinosis/mortality* ; Prevalence ; Global Burden of Disease ; Aged, 80 and over ; Middle Aged ; Aortic Valve/pathology* ; Aortic Valve Stenosis/epidemiology* ; Age Distribution ; Adult ; Disability-Adjusted Life Years ; Sex Distribution ; Global Health ; Aortic Valve Disease/epidemiology* ; Sex Factors

Amoenucles A-F (1-6), six previously undescribed nucleoside derivatives, and two known analogs (7 and 8) were isolated from the culture of Aspergillus amoenus TJ507. Their structures were elucidated through spectroscopic analysis, single-crystal X-ray crystallography, and chemical reactions. Notably, 3 and 4 represent the first reported instances of nucleosides with an attached pyrrole moiety. Of particular significance, the absolute configuration of the sugar moiety of 1-4 was determined using nuclear magnetic resonance (NMR), electric circular dichroism (ECD) calculations, and a hydrolysis reaction, presenting a potentially valuable method for confirming nucleoside structures. Furthermore, 1, 2, and 5-8 exhibited potential tumor necrosis factor α (TNF-α) inhibitory activities, which may provide a novel chemical template for the development of agents targeting autoimmune and inflammatory diseases.
Aspergillus/chemistry* ; Tumor Necrosis Factor-alpha/antagonists & inhibitors* ; Molecular Structure ; Nucleosides/isolation & purification* ; Crystallography, X-Ray ; Animals ; Humans ; Mice ; Magnetic Resonance Spectroscopy

Objective:To investigate the arsenic metabolism pattern and possible influencing factors in the population in drinking-water-borne endemic arsenic poisoning (drinking-water-borne arsenic poisoning for short) areas.Methods:In December 2004, a cluster sampling method was used to select arsenic poisoning population (arsenic poisoning group) and healthy population (control group) in drinking-water-borne arsenic poisoning area of Bayannur City, Inner Mongolia Autonomous Region as the survey subjects. A questionnaire survey was conducted. Arsenic content in drinking water at home of survey subjects, the levels of urinary arsenic and its metabolites, including [trivalent arsenic (As Ⅲ), inorganic arsenic (iAs), monomethylarsenic acid (pentavalent, MMA V), dimethylarsenic acid (pentavalent, DMA V), total arsenic (tAs), percentage of inorganic arsenic (iAs%), percentage of monomethylarsenic acid (MMA%), percentage of dimethylarsenic acid (DMA%), primary methylation index (PMI), secondary methylation index (SMI)] were tested using high performance liquid chromatography-inductively coupled plasma mass spectrometry; nail arsenic and nail selenium levels were tested using atomic fluorescence spectrometer. The influencing factors of arsenic metabolism pattern were analyzed by multiple linear regression. Results:A total of 536 survey subjects were included, including 155 individuals in the arsenic poisoning group and 381 in the control group. The water arsenic level ranged from 0.0 to 825.7 μg/L. Compared with the control group, there was no significant difference in the distribution of gender, education level and dental fluorosis in the arsenic poisoning group ( P > 0.05), but there were significant differences in the distribution of age, marital status, smoking, drinking and water arsenic ( P < 0.05). Compared with the control group, the levels of urinary As Ⅲ, iAs, MMA V, DMA V, tAs, MMA%, MMA/DMA and nail arsenic in the arsenic poisoning group were higher ( P < 0.05), while the levels of urinary DMA%, SMI and nail selenium were lower ( P < 0.05); but there was no statistically significant difference in the levels of urinary iAs% and PMI ( P > 0.05). Gender, education level, depth of wells, water arsenic, total number of wells and nail arsenic were the influencing factors of urinary As Ⅲ (β = - 19.82, - 23.83, 0.61, 0.21, 7.26, 2.98, P < 0.05). Age, depth of wells, water arsenic and nail arsenic were the influencing factors of urinary tAs (β = 3.18, 3.25, 1.31, 15.59, P < 0.05). Gender, education level, depth of wells, water arsenic, total number of wells and nail arsenic were the influencing factors of urinary iAs (β = - 20.47, - 25.90, 0.64, 0.25, 7.87, 3.11, P < 0.05). Age, gender, education level, water arsenic and nail arsenic were the influencing factors of urinary MMA V (β = 0.52, - 17.07, - 21.84, 0.22, 2.77, P < 0.05). Age, depth of wells, water arsenic and nail arsenic were the influencing factors of urinary DMA V (β = 2.35, 2.47, 0.85, 9.22, P < 0.05). Conclusions:Compared with healthy individuals, there are differences in arsenic metabolism pattern among individuals with drinking-water-borne arsenic poisoning. Age, gender, education level, depth of wells, water arsenic, total number of wells and nail arsenic may be influencing factors of different arsenic metabolism patterns.

Objective:To investigate the prediction of the tumor proliferation antigen(Ki-67) expression status in breast cancer patients based on ultrasound radiomics combined with clinicopathologic features.Methods:Breast cancer patients who underwent 2D ultrasound and Ki-67 examination from January 2018 to February 2022 in Changzhou Second People′s Hospital, Nanjing Medical University were retrospectively analyzed. Among them, 427 patients from Chengzhong campus were randomly divided into training and validation sets in the ratio of 8∶2, and 229 patients from Yanghu campus were used as an independent external test set. Radiomics features were extracted from the region of interest of 2D ultrasound images, and the Mann-Whitney U test, recursive feature elimination, and minimum absolute shrinkage and selection operators were used to perform feature dimensionality reduction and to establish a radiomics score(Rad-score). Subsequently, single/multifactor logistic regression regression analyses were used to construct a joint prediction model based on Rad-score and clinicopathological features. Model performance and utility were assessed using the subject operating characteristic area under the curve (AUC), calibration curve, and decision curve analyses. Results:The AUCs of the joint model for predicting Ki-67 expression status in breast cancer in the training, validation, and test sets were 0.858, 0.797, and 0.802, respectively, which were superior to those of the radiomics (0.772, 0.731, and 0.713) and clinical models (0.738, 0.750, and 0.707). Calibration curve and decision curve analyses indicated that the joint model had good calibration and clinical value.Conclusions:A joint model based on ultrasound radiomics and clinicopathological features can effectively predict the Ki-67 expression status of breast cancer, which is expected to become a non-invasive tool for Ki-67 detection and provide clinicians with an important auxiliary diagnostic and therapeutic decision-making basis.

Objective:To evaluate the feasibility of 3D reconstruction techniques based on multi-depth cameras for daily patient positioning in radiotherapy.Methods:Through region of interest (ROI) extraction, filtering, registration, splicing and other processes, multi-depth cameras (Intel RealSense D435i) were used to fuse point clouds in real-time manner to obtain the real optical 3D surface of patients. The reconstructed surface was matched with the external contour of the localization CT to complete the positioning. In this article, the feasibility of the system was validated by using multiple models. Clinical feasibility of 5 patients with head and neck radiotherapy, 10 cases of chest radiotherapy and 5 cases of pelvic radiotherapy was also validated. The data of each group were analyzed by paired t-test. Results:The system running time was 0.475 s, which met the requirement of real-time monitoring. The six-dimensional registration errors in the model experiment were (1.00±0.74) mm, (1.69±0.69) mm, (1.36±0.87) mm, 0.15°±0.14°, 0.25°±0.20°, 0.13°±0.13° in the x, y, z, rotational, pitch and roll directions, respectively. In the actual patient positioning, the mean positioning errors were (0.77±0.51) mm, (1.24±0.67) mm, (0.94±0.76) mm, 0.61°±0.41°, 0.69°±0.55°, and 0.52°±0.35° in the x, y, z, rotational, pitch and roll directions, respectively. The translational error was less than 2.8 mm, and the positioning error was the largest in the pelvic region. Conclusions:Real-time 3D reconstruction techniques based on multi-depth cameras is applicable for patient positioning during radiotherapy. The method is accurate in positioning and can detect the small movement of the patient's position, which meets the requirements of radiotherapy.

Objective:To investigate the relationship between the outcome of skin injury and urinary arsenic methylation metabolism levels in people exposed to arsenic through drinking water.Methods:Using cluster sampling method, permanent residents from drinking-water-borne endemic arsenic poisoning areas in Bayannur City, Inner Mongolia Autonomous Region were selected as survey subjects in 2004 (before water improvement). In 2017 (after water improvement), 74 survey subjects from 2004 were tracked and followed up. Urine samples were collected from survey subjects and high-performance liquid chromatography inductively coupled plasma mass spectrometry was used to detect the levels of arsenic methylation metabolites in urine. According to the "Diagnosis of Endemic Arsenic Poisoning" (WS/T 211-2015), the clinical grading (normal, suspicious, mild, moderate and severe) of skin injury of the survey subjects and the outcome of 2017 (improved, unchanged, aggravated) were assessed. A database was established and SPSS 25.0 software was used for statistical analysis.Results:The clinical grading ratios of skin injuries among survey subjects in 2004 and 2017 were compared, the differences were statistically significant (normal, suspicious, mild, moderate and severe: 38, 18, 4, 14 cases in 2004 and 27, 31, 3, 13 cases in 2017, χ 2 = 53.02, P < 0.001). Compared with 2004, in 2017, the levels of total arsenic (tAs), inorganic arsenic (iAs), monomethylarsenic (MMA), dimethylarsenic (DMA), percentage of inorganic arsenic (iAs%), and ratio of monomethylarsenic to dimethylarsenic (MMA/DMA) in the urine of survey subjects were low, and the differences were statistically significant ( Z = - 8.24, - 9.07, - 7.81, - 8.04, - 8.24, - 3.56, P < 0.001). The levels of dimethylarsenic percentage (DMA%), monomethylation rate (PMI) and dimethylation rate (SMI) were higher, and the differences were statistically significant ( Z = - 6.39, - 8.24, - 3.52, P < 0.001). In 2004, patients with different clinical grading of skin injuries had different outcomes in 2017 (χ 2 = 30.80, P < 0.001). There were statistically significant differences in tAs, iAs, MMA and DMA variation in urine among skin injury patients with different outcomes ( H = 10.62, 9.35, 8.80, 9.13, P < 0.05). Conclusions:Improving water can significantly reduce the levels of tAs, iAs, MMA, and DMA in the urine of arsenic exposed individuals. The outcome of skin injury in individuals exposed to arsenic through drinking water is related to the variation of urinary arsenic methylation metabolites tAs, iAs, MMA, and DMA.

Objective To construct models of viral transfection and hypoxia/reoxygenation induced cellular injury in adult mouse cardiomyocytes(AMCMs)isolated using a non-Langendorff method.Methods AMCMs were isolated,extracted,sedimented,and plated using a non-Langendorff method.The morphology and survival rate of the isolated cells were evaluated 2,24,48 and 72 h after plating,and their integrity was observed by immunofluorescence staining for α-actinin.The isolated AMCMs were infected with adenoviruses carrying an RFP-expressing vector and fluorescence images were obtained at 36 and 48 h post-infection and used to calculate transfection efficiency.The cells were cultured under hypoxic conditions for 45 min,reoxygenated for 24 h,and then stained with propidium iodide(PI)to verify establishment of the hypoxia/reoxygenation injury model.Results The survival rates of AMCMs at 2,24 and 48 h after plating were comparable,but survival was significantly reduced at 72 h.The integrity of the AMCMs was good and＞80%of the cells were transfected with adenovirus at 48 h.After hypoxia/reoxygenation treatment,42%of cells were stained by PI,suggesting successful establishment of the AMCM injury model.Conclusions In this study,we developed a non-Langendorff method for the fast and easy isolation of AMCMs with high cell viability.The isolated cells can be efficiently infected with adenovirus and respond to hypoxia/reoxygenation injury.These findings provide a systematic method for isolating AMCMs and for applying gene modification and hypoxia/reoxygenation injury in these cells.

Medical image is a powerful tool to assist doctors in the diagnosis and treatment planning.Nowadays,the segmentation of medical images is no longer limited to manual segmentation methods.Traditional methods and deep learning methods have been used to achieve more accurate results in medical image segmentation.Herein some innovative medical image segmentation methods in recent years are reviewed.By elaborating on the innovations of deep learning methods(SAM,SegNet,Mask R-CNN,and U-NET)and traditional methods(active contour model and threshold segmentation model),the differences and similarities between them are compared.The summary of medical image segmentation methods and the prospect is expected to help researchers better grasp and familiarize themselves with research status and development trend.

Objective:To develop the real-time radiotherapy monitoring system of three-dimensional (3D) point cloud by using depth camera and verify its feasibility.Methods:Taking the depth camera coordinate system as the world coordinate system, the conversion relationship between the simulation CT coordinate system and the world coordinate system was obtained from the calibration module. The patient's simulation CT point cloud was transformed into the world coordinate system through the above relationship, and registered with the patient's surface point cloud obtained in real-time manner by the depth camera to calculate the six-dimensional (6D) error, and complete the positioning verification and fractional internal position error monitoring in radiotherapy. Mean and standard deviation of 6D calculation error, Hausdorff distance of point cloud after registration and the running time of each part of the program were calculated to verify the feasibility of the system. Fifteen real patients were selected to calculate the 6D error between the system and cone beam CT (CBCT).Results:In the phantom experiment, the errors of the system in the x, y and z axes were (1.292±0.880)mm, (1.963±1.115)mm, (1.496±1.045)mm, respectively, and the errors in the rotation, pitch and roll directions were 0.201°±0.181°, 0.286°±0.326°, 0.181°±0.192°, respectively. For real patients, the translational error of the system was within 2.6 mm, the rotational error was approximately 1°, and the program run at 1-2 frames/s. The precision and speed met the radiotherapy requirement. Conclusion:The 3D point cloud radiotherapy real-time monitoring system based on depth camera can automatically complete the positioning verification before radiotherapy, real-time monitoring of body position during radiotherapy, and provide error visual feedback, which has potential clinical application value.

In the current clinical diagnosis, medical images have become an important basis for diagnosis, and different modes of medical images provide different tissue information and functional information. Single-mode images can only provide single diagnostic information, by which difficult and complicated diseases cannot be diagnosed, and comprehensive and accurate diagnostic results can be obtained only with the help of multiple diagnostic information. The multimodal fusion technology fuses multiple modes of medical images into single-mode images, and thus the single-mode images contain complementary information between multiple modes of images, so that sufficient information for clinical diagnosis can be obtained in a single image. In this paper, the multimodal medical image fusion methods are sorted into two types, namely the traditional fusion method and the fusion method based on deep learning.