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Abstract Intracranial aneurysms (IA) pose significant health risks and are often challenging to manage. Computational fluid dynamics (CFD) simulation has emerged as a powerful tool for understanding lesion-specific hemodynamics in and around IAs, aiding in the clinical management of patients with an IA. However, the current workflow of CFD simulations is time-consuming, complex, and labor-intensive and, thus, does not fit the clinical environment. To address these challenges, we have developed a semi-automated pipeline integrating multiple open-source software packages to streamline the CFD simulation process. Specifically, the study utilized medical angiography data from 18 patients. An in-house open-source DL image segmentation model (ARU-Net) was employed to generate 3D computer models of the anterior circulation. The segmented intracranial vasculature models, including IAs, were further refined using the Vascular Modeling Toolkit (VMTK), an open-source Python package. This step involved smoothing the surface of the models and extending the inlet and outlet regions to ensure a realistic representation of the vascular geometry. The refined vascular models were then converted into computational meshes using an open-source mesh generator known as TetGen. This process was nearly automated and required minimal user interaction(s). Blood flow simulations of the cerebral vascular models were performed using established SimVascular solvers (an open-source finite element platform for vascular applications) through an application programming interface (API). The CFD simulation process was also conducted using the manual workflow for comparative purposes. The initial assessment compared the geometries derived from manual and DL-based segmentation. The DL-based segmentation demonstrated reliable performance, closely aligning with manually segmented results, evidenced by excellent Pearson correlation coefficient (PCC) values and low relative difference (RD) values ranging from 3% to 10% between the computed geometrical variables derived from both methods. The statistical analysis of the computed hemodynamic variables, including velocity informatics and WSS-related variables, indicated good to excellent reliability for most parameters (e.g., ICC of 0.85–0.95). Given the data investigated, the proposed automated workflow streamlines the process of conducting CFD simulations. It generates results consistent with the current standard manual CFD protocol while minimizing dependence on user input.

Abstract Background Since December 2021, Wuxi, China has offered a two-dose human papillomavirus (HPV) vaccination to 14-year-old females for free. This study evaluated the costs and benefits of this vaccination scheduled in the Expanded Program on Immunization in Wuxi from the perspective of the cities’ demographic characteristics, economic development, and policy support. Methods The model-based economic evaluation used TreeAge Pro software to construct a decision tree-Markov model for the vaccination strategy in which 100,000 14-year-old females received two doses of bivalent HPV vaccine or no vaccination. Costs and effects of the strategy were assessed from a societal perspective through literature research and data obtained from the Wuxi Centre for Disease Control and Prevention. Univariate, multivariate, and probabilistic sensitivity analyses assessed the stability of the findings. Results The cost of the bivalent HPV vaccine in Wuxi is 711.3 CNY. The two-dose of bivalent HPV vaccine for 100,000 14-year-old females would cost an additional 658,016 CNY compared to no vaccination, but would result in 1,960 Quality Adjustment Years of Life (QALYs). Using the per capita gross domestic product of 187,415 CNY in 2021 in Wuxi as the willingness-to-pay threshold, the vaccination strategy costs 3,357.37 CNY per QALY gained, which is much lower than the threshold, suggesting that it is a very cost-effective strategy. In addition, the vaccine strategy reduced the incidence of cervical cancer by 300 cases and cervical cancer deaths by 181 cases, representing a benefit-cost ratio of 2.86 (> 1) when health output outcomes were measured in monetary terms. These results suggested that the vaccination strategy was advantageous. Sensitivity analyses showed that changes in the parameters did not affect the conclusions and that the findings were robust. Conclusions Compared to no vaccination, the delivery of two doses of bivalent HPV vaccine for 14-year-old females was a more highly cost-effective and optimal strategy.

COVID-19 vaccine hesitancy remains prevalent globally. However, national data on this issue in the general population after the termination of the zero-COVID policy in China are limited. In March 2023, we conducted a nationwide cross-sectional survey among Chinese adults using a self-administered questionnaire. Descriptive statistics and multivariate logistic regressions were employed. Among 4,966 participants, 43.8% reported COVID-19 vaccine hesitancy following the end of the zero-COVID policy in China. Higher rates of vaccine hesitancy were associated with being married (married: OR 1.36, 95%CI 1.17–1.57; other marital status: OR 1.86, 95%CI 1.36–2.55), working in healthcare (OR 1.64, 95%CI 1.38–1.96), having both minors and older adults in the household (OR 1.45, 95%CI 1.20–1.75), having no minors and older adults in the household (OR 1.44, 95%CI 1.17–1.77), having chronic diseases (OR 1.42, 95%CI 1.23–1.64), experiencing adverse events post-vaccination (OR 1.39, 95%CI 1.19–1.61), and uncertainty about previous COVID-19 infection (OR 1.45, 95%CI 1.13–1.86). Conversely, participants who had received the influenza vaccine in the past three years (OR 0.62, 95%CI 0.54–0.72), had previously taken the COVID-19 vaccine (OR 0.44, 95%CI 0.32–0.59), and had higher confidence in vaccines (OR 0.63, 95%CI 0.60–0.67) were less likely to exhibit hesitancy. Our findings indicate a significant level of vaccine hesitancy, underscoring the urgent need for tailored public health strategies to address vaccine hesitancy and improve uptake post-zero-COVID policy in China. A comprehensive understanding of public concerns and related factors is essential for developing effective vaccine communication strategies.

Abstract Aneurysm hemodynamics is known for its crucial role in the natural history of abdominal aortic aneurysms (AAA). However, there is a lack of well-developed quantitative assessments for disturbed aneurysmal flow. Therefore, we aimed to develop innovative metrics for quantifying disturbed aneurysm hemodynamics and evaluate their effectiveness in predicting the growth status of AAAs, specifically distinguishing between fast-growing and slowly-growing aneurysms. The growth status of aneurysms was classified as fast (≥ 5 mm/year) or slow (

Objective On 20 July 2021, after the outbreak of COVID-19 at Nanjing Lukou International Airport, several universities started closed management and online teaching. This had a large impact on students’ daily life and study, which may lead to mental health problems. The purpose of this study is to study the effect of screen time on mental health status of university students and the possible mediating effect of sleep status.Methods This was a cross-sectional study. A web-based questionnaire survey was employed that included demographic characteristics, sleep status and mental health status (depression, anxiety and loneliness). The Pittsburgh Sleep Quality Index scale was used to assess sleep status, while the Centre for Epidemiologic Studies Depression (CES-D) scale, Generalised Anxiety Disorder-7 (GAD-7) scale and Emotional versus Social Loneliness Scale (ESLS) were used to assess depression, anxiety and loneliness, respectively. Linear and logistic regression models were developed and adjusted for confounding factors, and finally the mediating effects were tested using the Karlson-Holm-Breen method.Results Finally, 1070 valid questionnaires were included. Among these, 604 (56.45%) indicated depressive symptoms (CES-D score ≥16) and 902 (84.30%) indicated anxiety symptoms (GAD-7 score ≥10). The mean ESLS score (for loneliness) was 26.51±6.64. The relationship between screen time and depressive symptoms (OR 1.118, 95% CI 1.072 to 1.166) and anxiety symptoms (OR 1.079, 95% CI 1.023 to 1.138) remained significant after adjusting for confounding factors. Meanwhile, sleep status plays an intermediary role in screen time and mental health status (depression and anxiety) and accounts for 13.73% and 19.68% of the total effects, respectively. We did not find a significant association between screen time and loneliness.Conclusion During the outbreak of COVID-19, screen time is inevitably prolonged among university students. There is a relationship between mental health and screen time, and sleep status plays a mediating role.

With the success of U-Net or its variants in automatic medical image segmentation, building a fully convolutional network (FCN) based on an encoder-decoder structure has become an effective end-to-end learning approach. However, the intrinsic property of FCNs is that as the encoder deepens, higher-level features are learned, and the receptive field size of the network increases, which results in unsatisfactory performance for detecting low-level small/thin structures such as atrial walls and small arteries. To address this issue, we propose to keep the different encoding layer features at their original sizes to constrain the receptive field from increasing as the network goes deeper. Accordingly, we develop a novel S-shaped multiple cross-aggregation segmentation architecture named S-Net, which has two branches in the encoding stage, i.e., a resampling branch to capture low-level fine-grained details and thin/small structures and a downsampling branch to learn high-level discriminative knowledge. In particular, these two branches learn complementary features by residual cross-aggregation; the fusion of the complementary features from different decoding layers can be effectively accomplished through lateral connections. Meanwhile, we perform supervised prediction at all decoding layers to incorporate coarse-level features with high semantic meaning and fine-level features with high localization capability to detect multi-scale structures, especially for small/thin volumes fully. To validate the effectiveness of our S-Net, we conducted extensive experiments on the segmentation of cardiac wall and intracranial aneurysm (IA) vasculature, and quantitative and qualitative evaluations demonstrated the superior performance of our method for predicting small/thin structures in medical images.

Percutaneous interventions are gaining rapid acceptance in cardiology and revolutionizing the treatment of structural heart disease (SHD). As new percutaneous procedures of SHD are being developed, their associated complexity and anatomical variability demand a high-resolution special understanding for intraprocedural image guidance. During the last decade, three-dimensional (3D) transesophageal echocardiography (TEE) has become one of the most accessed imaging methods for structural interventions. Although 3D-TEE can assess cardiac structures and functions in real-time, its limitations (e.g., limited field of view, image quality at a large depth, etc.) must be addressed for its universal adaptation, as well as to improve the quality of its imaging and interventions. This review aims to present the role of TEE in the intraprocedural guidance of percutaneous structural interventions. We also focus on the current and future developments required in a multimodal image integration process when using TEE to enhance the management of congenital and SHD treatments.

With the rate of cardiovascular diseases in the U.S increasing throughout the years, there is a need for developing more advanced treatment plans that can be tailored to specific patients and scenarios. The development of 3D printing is rapidly gaining acceptance into clinical cardiology.In this review, key technologies used in 3D printing are briefly summarized, particularly, the use of artificial intelligence (AI), open-source tools like MeshLab and MeshMixer, and 3D printing techniques such as fused deposition molding (FDM) and polyjet are reviewed. The combination of 3D printing, multiple image integration, and augmented reality may greatly enhance data visualization during diagnosis, treatment planning, and surgical procedures for cardiology.

In this work, a super-resolution approach based on generative adversary network (GAN) was used to interpolate (up-sample) ultrasound radio-frequency (RF) echo data along the lateral (perpendicular to the acoustic beam direction) direction before motion estimation. Our primary objective was to investigate the feasibility of using a GAN-based super-solution approach to improve lateral resolution in the RF data as a means of improving strain image quality in quasi-static ultrasound strain elastography (QUSE). Unlike natural scene photographs, axial (parallel to the acoustic beam direction) resolution is significantly higher than that of lateral resolution in ultrasound RF data. To better handle RF data, we first modified a super-resolution generative adversary network (SRGAN) model developed by the computer vision community. We named the modified SRGAN model as super-resolution radio-frequency neural network (SRRFNN) model. Our preliminary experiments showed that, compared with axial strain elastograms obtained using the original ultrasound RF data, axial strain elastograms using ultrasound RF data up-sampled by the proposed SRRFNN model were improved. Based on the Wilcoxon rank-sum tests, such improvements were statistically significant ($p

Now, with the availability of 3-D ultrasound data, a lot of research efforts are being devoted to developing 3-D ultrasound strain elastography (USE) systems. Because 3-D motion tracking, a core component in any 3-D USE system, is computationally intensive, a lot of efforts are under way to accelerate 3-D motion tracking. In the literature, the concept of Sum-Table has been used in a serial computing environment to reduce the burden of computing signal correlation, which is the single most computationally intensive component in 3-D motion tracking. In this study, parallel programming using graphics processing units (GPU) is used in conjunction with the concept of Sum-Table to improve the computational efficiency of 3-D motion tracking. To our knowledge, sum-tables have not been used in a GPU environment for 3-D motion tracking. Our main objective here is to investigate the feasibility of using sum-table-based normalized correlation coefficient (ST-NCC) method for the above-mentioned GPU-accelerated 3-D USE. More specifically, two different implementations of ST-NCC methods proposed by Lewis et al. and Luo-Konofagou are compared against each other. During the performance comparison, the conventional method for calculating the normalized correlation coefficient (NCC) was used as the baseline. All three methods were implemented using compute unified device architecture (CUDA; Version 9.0, Nvidia Inc., CA, USA) and tested on a professional GeForce GTX TITAN X card (Nvidia Inc., CA, USA). Using 3-D ultrasound data acquired during a tissue-mimicking phantom experiment, both displacement tracking accuracy and computational efficiency were evaluated for the above-mentioned three different methods. Based on data investigated, we found that under the GPU platform, Lou-Konofaguo method can still improve the computational efficiency (17–46%), as compared to the classic NCC method implemented into the same GPU platform. However, the Lewis method does not improve the computational efficiency in some configuration or improves the computational efficiency at a lower rate (7–23%) under the GPU parallel computing environment. Comparable displacement tracking accuracy was obtained by both methods.