HPV vaccination strategy for 14-year-old females and economic returns for cervical cancer prevention in Wuxi City, China: a cost effectiveness analysis.

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.

AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms.

Semantic labeling of coronary arterial segments in invasive coronary angiography (ICA) is important for automated assessment and report generation of coronary artery stenosis in computer-aided coronary artery disease (CAD) diagnosis. However, separating and identifying individual coronary arterial segments is challenging because morphological similarities of different branches on the coronary arterial tree and human-to-human variabilities exist. Inspired by the training procedure of interventional cardiologists for interpreting the structure of coronary arteries, we propose an association graph-based graph matching network (AGMN) for coronary arterial semantic labeling. We first extract the vascular tree from invasive coronary angiography (ICA) and convert it into multiple individual graphs. Then, an association graph is constructed from two individual graphs where each vertex represents the relationship between two arterial segments. Thus, we convert the arterial segment labeling task into a vertex classification task; ultimately, the semantic artery labeling becomes equivalent to identifying the artery-to-artery correspondence on graphs. More specifically, the AGMN extracts the vertex features by the embedding module using the association graph, aggregates the features from adjacent vertices and edges by graph convolution network, and decodes the features to generate the semantic mappings between arteries. By learning the mapping of arterial branches between two individual graphs, the unlabeled arterial segments are classified by the labeled segments to achieve semantic labeling. A dataset containing 263 ICAs was employed to train and validate the proposed model, and a five-fold cross-validation scheme was performed. Our AGMN model achieved an average accuracy of 0.8264, an average precision of 0.8276, an average recall of 0.8264, and an average F1-score of 0.8262, which significantly outperformed existing coronary artery semantic labeling methods. In conclusion, we have developed and validated a new algorithm with high accuracy, interpretability, and robustness for coronary artery semantic labeling on ICAs.

Disturbed flow's impact on cellular changes indicative of vascular aneurysm initiation, expansion, and rupture: A pathological and methodological review.

Aneurysms are malformations within the arterial vasculature brought on by the structural breakdown of the microarchitecture of the vessel wall, with aneurysms posing serious health risks in the event of their rupture. Blood flow within vessels is generally laminar with high, unidirectional wall shear stressors that modulate vascular endothelial cell functionality and regulate vascular smooth muscle cells. However, altered vascular geometry induced by bifurcations, significant curvature, stenosis, or clinical interventions can alter the flow, generating low stressor disturbed flow patterns. Disturbed flow is associated with altered cellular morphology, upregulated expression of proteins modulating inflammation, decreased regulation of vascular permeability, degraded extracellular matrix, and heightened cellular apoptosis. The understanding of the effects disturbed flow has on the cellular cascades which initiate aneurysms and promote their subsequent growth can further elucidate the nature of this complex pathology. This review summarizes the current knowledge about the disturbed flow and its relation to aneurysm pathology, the methods used to investigate these relations, as well as how such knowledge has impacted clinical treatment methodologies. This information can contribute to the understanding of the development, growth, and rupture of aneurysms and help develop novel research and aneurysmal treatment techniques.

A Deep Learning-based Method to Extract Lumen and Media-Adventitia in Intravascular Ultrasound Images.

Intravascular ultrasound (IVUS) imaging allows direct visualization of the coronary vessel wall and is suitable for assessing atherosclerosis and the degree of stenosis. Accurate segmentation and lumen and median-adventitia (MA) measurements from IVUS are essential for such a successful clinical evaluation. However, current automated segmentation by commercial software relies on manual corrections, which is time-consuming and user-dependent. We aim to develop a deep learning-based method using an encoder-decoder deep architecture to automatically and accurately extract both lumen and MA border. Inspired by the dual-path design of the state-of-the-art model IVUS-Net, our method named IVUS-U-Net++ achieved an extension of the U-Net++ model. More specifically, a feature pyramid network was added to the U-Net++ model, enabling the utilization of feature maps at different scales. Following the segmentation, the Pearson correlation and Bland-Altman analyses were performed to evaluate the correlations of 12 clinical parameters measured from our segmentation results and the ground truth. A dataset with 1746 IVUS images from 18 patients was used for training and testing. Our segmentation model at the patient level achieved a Jaccard measure (JM) of 0.9080 ± 0.0321 and a Hausdorff distance (HD) of 0.1484 ± 0.1584 mm for the lumen border; it achieved a JM of 0.9199 ± 0.0370 and an HD of 0.1781 ± 0.1906 mm for the MA border. The 12 clinical parameters measured from our segmentation results agreed well with those from the ground truth (all p-values are smaller than .01). Our proposed method shows great promise for its clinical use in IVUS segmentation.

Progressive global perception and local polishing network for lung infection segmentation of COVID-19 CT images.

In this paper, a progressive global perception and local polishing (PCPLP) network is proposed to automatically segment the COVID-19-caused pneumonia infections in computed tomography (CT) images. The proposed PCPLP follows an encoder-decoder architecture. Particularly, the encoder is implemented as a computationally efficient fully convolutional network (FCN). In this study, a multi-scale multi-level feature recursive aggregation (mmFRA) network is used to integrate multi-scale features (viz. global guidance features and local refinement features) with multi-level features (viz. high-level semantic features, middle-level comprehensive features, and low-level detailed features). Because of this innovative aggregation of features, an edge-preserving segmentation map can be produced in a boundary-aware multiple supervision (BMS) way. Furthermore, both global perception and local perception are devised. On the one hand, a global perception module (GPM) providing a holistic estimation of potential lung infection regions is employed to capture more complementary coarse-structure information from different pyramid levels by enlarging the receptive fields without substantially increasing the computational burden. On the other hand, a local polishing module (LPM), which provides a fine prediction of the segmentation regions, is applied to explicitly heighten the fine-detail information and reduce the dilution effect of boundary knowledge. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed PCPLP in boosting the learning ability to identify the lung infected regions with clear contours accurately. Our model is superior remarkably to the state-of-the-art segmentation models both quantitatively and qualitatively on a real CT dataset of COVID-19.

Neural-network-based Motion Tracking for Breast Ultrasound Strain Elastography: An Initial Assessment of Performance and Feasibility.

Accurate tracking of tissue motion is critically important for several ultrasound elastography methods. In this study, we investigate the feasibility of using three published convolution neural network (CNN) models built for optical flow (hereafter referred to as CNN-based tracking) by the computer vision community for breast ultrasound strain elastography. Elastographic datasets produced by finite element and ultrasound simulations were used to retrain three published CNN models: FlowNet-CSS, PWC-Net, and LiteFlowNet. After retraining, the three improved CNN models were evaluated using computer-simulated and tissue-mimicking phantoms, and in vivo breast ultrasound data. CNN-based tracking results were compared with two published two-dimensional (2D) speckle tracking methods: coupled tracking and GLobal Ultrasound Elastography (GLUE) methods. Our preliminary data showed that, based on the Wilcoxon rank-sum tests, the improvements due to retraining were statistically significant (p < 0.05) for all three CNN models. We also found that the PWC-Net model was the best neural network model for data investigated, and its overall performance was on par with the coupled tracking method. CNR values estimated from in vivo axial and lateral strain elastograms showed that the GLUE algorithm outperformed both the retrained PWC-Net model and the coupled tracking method, though the GLUE algorithm exhibited some biases. The PWC-Net model was also able to achieve approximately 45 frames/second for 2D speckle tracking data investigated.

Two Closely Spaced Aneurysms of the Supraclinoid Internal Carotid Artery: How Does One Influence the Other?

The objective of this study was to use image-based computational fluid dynamics (CFD) techniques to analyze the impact that multiple closely spaced intracranial aneurysm (IAs) of the supra-clinoid segment of the internal carotid artery (ICA) have on each other's hemodynamic characteristics. The vascular geometry of fifteen (15) subjects with 2 IAs was gathered using a 3D digital subtraction angiography clinical system. Two groups of computer models were created for each subject's vascular geometry: both IAs present (model A) and after removal of one IA (model B). Models were separated into two groups based on IA separation: tandem (one proximal and one distal) and adjacent (aneurysms directly opposite on a vessel). Simulations using a pulsatile velocity waveform were solved by a commercial CFD solver. Proximal IAs altered flow into distal IAs (5 of 7), increasing flow energy and spatial-temporally averaged wall shear stress (STA-WSS: 3-50% comparing models A to B) while decreasing flow stability within distal IAs. Thus, proximal IAs may "protect" a distal aneurysm from destructive remodeling due to flow stagnation. Among adjacent IAs, the presence of both IAs decreased each other's flow characteristics, lowering WSS (models A to B) and increasing flow stability: all changes statistically significant (p < 0.05). A negative relationship exists between the mean percent change in flow stability in relation to adjacent IA volume and ostium area. Closely spaced IAs impact hemodynamic alterations onto each other concerning flow energy, stressors, and stability. Understanding these alterations (especially after surgical repair of one IA) may help uncover risk factor(s) pertaining to the growth of (remaining) IAs.

Influence of Tissue Microstructure on Shear Wave Speed Measurements in Plane Shear Wave Elastography: A Computational Study in Lossless Fibrotic Liver Media.

Shear wave elastography (SWE) has been used to measure viscoelastic properties for characterization of fibrotic livers. In this technique, external mechanical vibrations or acoustic radiation forces are first transmitted to the tissue being imaged to induce shear waves. Ultrasonically measured displacement/velocity is then utilized to obtain elastographic measurements related to shear wave propagation. Using an open-source wave simulator, k-Wave, we conducted a case study of the relationship between plane shear wave measurements and the microstructure of fibrotic liver tissues. Particularly, three different virtual tissue models (i.e., a histology-based model, a statistics-based model, and a simple inclusion model) were used to represent underlying microstructures of fibrotic liver tissues. We found underlying microstructures affected the estimated mean group shear wave speed (SWS) under the plane shear wave assumption by as much as 56%. Also, the elastic shear wave scattering resulted in frequency-dependent attenuation coefficients and introduced changes in the estimated group SWS. Similarly, the slope of group SWS changes with respect to the excitation frequency differed as much as 78% among three models investigated. This new finding may motivate further studies examining how elastic scattering may contribute to frequency-dependent shear wave dispersion and attenuation in biological tissues.

Virtual Breast Quasi-static Elastography (VBQE).

Viscoelasticity Imaging (VEI) has been proposed to measure relaxation time constants for characterization of in vivo breast lesions. In this technique, an external compression force on the tissue being imaged is maintained for a fixed period of time to induce strain creep. A sequence of ultrasound echo signals is then utilized to generate time-resolved strain measurements. Relaxation time constants can be obtained by fitting local time-resolved strain measurements to a viscoelastic tissue model (e.g., a modified Kevin-Voigt model). In this study, our primary objective is to quantitatively evaluate the contrast transfer efficiency (CTE) of VEI, which contains useful information regarding image interpretations. Using an open-source simulator for virtual breast quasi-static elastography (VBQE), we conducted a case study of contrast transfer efficiency of VEI. In multiple three-dimensional (3D) numerical breast phantoms containing various degrees of heterogeneity, finite element (FE) simulations in conjunction with quasi-linear viscoelastic constitutive tissue models were performed to mimic data acquisition of VEI under freehand scanning. Our results suggested that there were losses in CTE, and the losses could be as high as -18 dB. FE results also qualitatively corroborated clinical observations, for example, artifacts around tissue interfaces.

A PDE-Based Regularization Algorithm Toward Reducing Speckle Tracking Noise: A Feasibility Study for Ultrasound Breast Elastography.

Obtaining accurate ultrasonically estimated displacements along both axial (parallel to the acoustic beam) and lateral (perpendicular to the beam) directions is an important task for various clinical elastography applications (e.g., modulus reconstruction and temperature imaging). In this study, a partial differential equation (PDE)-based regularization algorithm was proposed to enhance motion tracking accuracy. More specifically, the proposed PDE-based algorithm, utilizing two-dimensional (2D) displacement estimates from a conventional elastography system, attempted to iteratively reduce noise contained in the original displacement estimates by mathematical regularization. In this study, tissue incompressibility was the physical constraint used by the above-mentioned mathematical regularization. This proposed algorithm was tested using computer-simulated data, a tissue-mimicking phantom, and in vivo breast lesion data. Computer simulation results demonstrated that the method significantly improved the accuracy of lateral tracking (e.g., a factor of 17 at 0.5% compression). From in vivo breast lesion data investigated, we have found that, as compared with the conventional method, higher quality axial and lateral strain images (e.g., at least 78% improvements among the estimated contrast-to-noise ratios of lateral strain images) were obtained. Our initial results demonstrated that this conceptually and computationally simple method could be useful for improving the image quality of ultrasound elastography with current clinical equipment as a post-processing tool.

Role of transient receptor potential vanilloid subetype 1 in the increase of thermal pain threshold by moxibustion.

UNLABELLED: OBTECTIVE: To explore the role of transient receptor potential vanilloid subetype 1 (TRPV1) in the increase of the thermal pain threshold by moxibustion. METHODS: Forty Kunming mice (20 ± 2) g were randomized into control group, capsaicin group, capsazepine group, moxibustion group and moxibustion + capsazepine (MC) group with 8 mice in each, and 16 C57BL/6 wild-type mice (18 ± 2) g were randomized into wild-type (WT) control group and WT moxibustion group with 8 mice in each, and 14 TRPV1 knockout mice (18 ± 2) g were randomized into knockout (KO) control group and KO moxibustion-group with 7 in each. Each mouse in the capsaicin group was subcutaneously injected with the amount of 0.1 mL/10 g into L5 and L6 spinal cords; each mouse in the capsazepine group was intraperitoneally injected with the amount of 0.1 mL/10 g. Similarly, each mouse in the moxibustion group was given a suspended moxibustion with specially-made moxa-stick for 20 min on L5 and L6 spinal cords. Each mouse in MC group was intraperitoneally injected with the amount of 0.1 mL/1 0 g first, then after 15 min was given a suspended moxibustion for 20 min on L5 and L6 spinal cords. Each mouse in WT moxibustion group and KO moxibustion group was given a suspended moxibustion with specially-made moxa-stick for 20 min on L5 and L6 spinal cords. The control group, WT control group and KO control group were of no treatment in any way. After all treatments were completed, the digital-display measurement instrument for thermal pain was used to measure the threshold of thermal pain in each group respectively. RESULTS: Compared with the control group, the thresholds of thermal pain in the moxibustion group and MC group were significantly increased (P <0.01); no significant changes in the thresholds in the capsaicin group and the capsazepine group (P > 0.05); compared with moxibustion group, he threshold of thermal in MC group was obviously decreased (P < 0.01). Compared with WT control group, the threshold of thermal pain in WT moxibustion group was significantly increased (P < 0.01); compared with KO control group, no changes in the threshold in KO moxibustion group (P > 0.05). CONCLUSION: TRPV1 participated in the process of increasing the threshold of thermal pain by stimulating L5 and L6 of mice spinal cord with burning mosa-stick.

Induction of Controllable Vortical Flow in a Dual-Stenosis Aorta Model: A Replication of Disordered Eddies Flow in Aneurysms.

This paper presents a two-stenosis aorta model mimicking vortical flow in vascular aneurysms. More specifically, we propose to virtually induce two adjacent stenoses in the abdominal aorta to develop various vortical flow zones post stenoses. Computational fluid dynamics (CFD) simulations were conducted for the virtual two-stenosis model based on physiological and anatomical data (i.e., diameters, flow rate waveforms) from adult rabbits. The virtual model includes adult rabbits' infra-renal portion of the aorta and iliac arteries. 3D CFD simulations in five different dual-stenosis configurations were performed using a commercial CFD package (FLUENT). In-house software assessed the evolution of flow vortices. Notably, spatial-temporally averaged wall shear stress (STA-WSS) and oscillatory shear index (OSI), the total volume of vortex flow, the number of vortices, and the phase-to-phase overlap of vortex flow within each region were evaluated. In all models, we found consistent patterns of the vortex flow parameters, indicating that the adjacent stenoses induced three different hemodynamic zones, namely, stable vortical flow (after the first stenosis), transient vortical flow (after the second stenosis), and unstable vortical flow (further distal to the second stenosis). Also, different degrees of flow disturbance can be achieved in these three zones. It is significant to note that, although the 'dual-stenosis' geometry is completely hypothetical, it allows us to create various vortical flows in consecutive vessel segments for the first time. As a result, if implemented as a pre-clinical model, the proposed two-stenosis model offers an attractive, tunable environment to investigate the interplays between subject-specific hemodynamics and vascular remodeling. This aspect remains in our future directions.

Alternating direction method of multipliers for displacement estimation in ultrasound strain elastography.

BACKGROUND: Ultrasound strain imaging, which delineates mechanical properties to detect tissue abnormalities, involves estimating the time delay between two radio-frequency (RF) frames collected before and after tissue deformation. The existing regularized optimization-based time-delay estimation (TDE) techniques suffer from at least one of the following drawbacks: (1) The regularizer is not aligned with the tissue deformation physics due to taking only the first-order displacement derivative into account; (2) The L 2 $L2$ -norm of the displacement derivatives, which oversmooths the estimated time-delay, is utilized as the regularizer; (3) The modulus function defined mathematically should be approximated by a smooth function to facilitate the optimization of L 1 $L1$ -norm. PURPOSE: Our purpose is to develop a novel TDE technique that resolves the aforementioned shortcomings of the existing algorithms. METHODS: Herein, we propose employing the alternating direction method of multipliers (ADMM) for optimizing a novel cost function consisting of L 2 $L2$ -norm data fidelity term and L 1 $L1$ -norm first- and second-order spatial continuity terms. ADMM empowers the proposed algorithm to use different techniques for optimizing different parts of the cost function and obtain high-contrast strain images with smooth backgrounds and sharp boundaries. We name our technique ADMM for totaL variaTion RegUlarIzation in ultrasound STrain imaging (ALTRUIST). ALTRUIST's efficacy is quantified using absolute error (AE), Structural SIMilarity (SSIM), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and strain ratio (SR) with respect to GLUE, OVERWIND, and L 1 $L1$ -SOUL, three recently published energy-based techniques, and UMEN-Net, a state-of-the-art deep learning-based algorithm. Analysis of variance (ANOVA)-led multiple comparison tests and paired t $t$ -tests at 5 % $5\%$ overall significance level were conducted to assess the statistical significance of our findings. The Bonferroni correction was taken into account in all statistical tests. Two simulated layer phantoms, three simulated resolution phantoms, one hard-inclusion simulated phantom, one multi-inclusion simulated phantom, one experimental breast phantom, and three in vivo liver cancer datasets have been used for validation experiments. We have published the ALTRUIST code at http://code.sonography.ai. RESULTS: ALTRUIST substantially outperforms the four state-of-the-art benchmarks in all validation experiments, both qualitatively and quantitatively. ALTRUIST yields up to 573 % ∗ ${573\%}^{*}$ , 41 % ∗ ${41\%}^{*}$ , and 51 % ∗ ${51\%}^{*}$ SNR improvements and 443 % ∗ ${443\%}^{*}$ , 53 % ∗ ${53\%}^{*}$ , and 15 % ∗ ${15\%}^{*}$ CNR improvements over L 1 $L1$ -SOUL, its closest competitor, for simulated, phantom, and in vivo liver cancer datasets, respectively, where the asterisk (*) indicates statistical significance. In addition, ANOVA-led multiple comparison tests and paired t $t$ -tests indicate that ALTRUIST generally achieves statistically significant improvements over GLUE, UMEN-Net, OVERWIND, and L 1 $L1$ -SOUL in terms of AE, SSIM map, SNR, and CNR. CONCLUSIONS: A novel ultrasonic displacement tracking algorithm named ALTRUIST has been developed. The principal novelty of ALTRUIST is incorporating ADMM for optimizing an L 1 $L1$ -norm regularization-based cost function. ALTRUIST exhibits promising performance in simulation, phantom, and in vivo experiments.

Teacher-student guided knowledge distillation for unsupervised convolutional neural network-based speckle tracking in ultrasound strain elastography.

Accurate and efficient motion estimation is a crucial component of real-time ultrasound elastography (USE). However, obtaining radiofrequency ultrasound (RF) data in clinical practice can be challenging. In contrast, although B-mode (BM) data is readily available, elastographic data derived from BM data results in sub-optimal elastographic images. Furthermore, existing conventional ultrasound devices (e.g., portable devices) cannot provide elastography modes, which has become a significant obstacle to the widespread use of traditional ultrasound devices. To address the challenges above, we developed a teacher-student guided knowledge distillation for an unsupervised convolutional neural network (TSGUPWC-Net) to improve the accuracy of BM motion estimation by employing a well-established convolutional neural network (CNN) named modified pyramid warping and cost volume network (MPWC-Net). A pre-trained teacher model based on RF is utilized to guide the training of a student model using BM data. Innovations outlined below include employing spatial attention transfer at intermediate layers to enhance the guidance effect of the model. The loss function consists of smoothness of the displacement field, knowledge distillation loss, and intermediate layer loss. We evaluated our method on simulated data, phantoms, and in vivo ultrasound data. The results indicate that our method has higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) values in axial strain estimation than the model trained on BM. The model is unsupervised and requires no ground truth labels during training, making it highly promising for motion estimation applications.

Automatic segmentation of intraluminal thrombosis of abdominal aortic aneurysms from CT angiography using a mixed-scale-driven multiview perception network (M2Net) model.

Intraluminal thrombosis (ILT) plays a critical role in the progression of abdominal aortic aneurysms (AAA). Understanding the role of ILT can improve the evaluation and management of AAAs. However, compared with highly developed automatic vessel lumen segmentation methods, ILT segmentation is challenging. Angiographic contrast agents can enhance the vessel lumen but cannot improve boundary delineation of the ILT regions; the lack of intrinsic contrast in the ILT structure significantly limits the accurate segmentation of ILT. Additionally, ILT is not evenly distributed within AAAs; its sparsity and scattered distributions in the imaging data pose challenges to the learning process of neural networks. Thus, we propose a multiview fusion approach, allowing us to obtain high-quality ILT delineation from computed tomography angiography (CTA) data. Our multiview fusion network is named Mixed-scale-driven Multiview Perception Network (M2Net), and it consists of two major steps. Following image preprocessing, the 2D mixed-scale ZoomNet segments ILT from each orthogonal view (i.e., Axial, Sagittal, and Coronal views) to enhance the prior information. Then, the proposed context-aware volume integration network (CVIN) effectively fuses the multiview results. Using contrast-enhanced computed tomography angiography (CTA) data from human subjects with AAAs, we evaluated the proposed M2Net. A quantitative analysis shows that the proposed deep-learning M2Net model achieved superior performance (e.g., DICE scores of 0.88 with a sensitivity of 0.92, respectively) compared with other state-of-the-art deep-learning models. In closing, the proposed M2Net model can provide high-quality delineation of ILT in an automated fashion and has the potential to be translated into the clinical workflow.

Slimmable transformer with hybrid axial-attention for medical image segmentation.

The transformer architecture has achieved remarkable success in medical image analysis owing to its powerful capability for capturing long-range dependencies. However, due to the lack of intrinsic inductive bias in modeling visual structural information, the transformer generally requires a large-scale pre-training schedule, limiting the clinical applications over expensive small-scale medical data. To this end, we propose a slimmable transformer to explore intrinsic inductive bias via position information for medical image segmentation. Specifically, we empirically investigate how different position encoding strategies affect the prediction quality of the region of interest (ROI) and observe that ROIs are sensitive to different position encoding strategies. Motivated by this, we present a novel Hybrid Axial-Attention (HAA) that can be equipped with pixel-level spatial structure and relative position information as inductive bias. Moreover, we introduce a gating mechanism to achieve efficient feature selection and further improve the representation quality over small-scale datasets. Experiments on LGG and COVID-19 datasets prove the superiority of our method over the baseline and previous works. Internal workflow visualization with interpretability is conducted to validate our success better; the proposed slimmable transformer has the potential to be further developed into a visual software tool for improving computer-aided lesion diagnosis and treatment planning.

COVID-19 vaccination and concerns regarding vaccine hesitancy after the termination of the zero-COVID policy in China: A nationwide cross-sectional study.

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.

Pair of Functional Polyesters That Are Photo-Cross-Linkable and Electrospinnable to Engineer Elastomeric Scaffolds with Tunable Structure and Properties.

A pair of alkyne- and thiol-functionalized polyesters are designed to engineer elastomeric scaffolds with a wide range of tunable material properties (e.g., thermal, degradation, and mechanical properties) for different tissues, given their different host responses, mechanics, and regenerative capacities. The two prepolymers are quickly photo-cross-linkable through thiol-yne click chemistry to form robust elastomers with small permanent deformations. The elastic moduli can be easily tuned between 0.96 ± 0.18 and 7.5 ± 2.0 MPa, and in vitro degradation is mediated from hours up to days by adjusting the prepolymer weight ratios. These elastomers bear free hydroxyl and thiol groups with a water contact angle of less than 85.6 ± 3.58 degrees, indicating a hydrophilic nature. The elastomer is compatible with NIH/3T3 fibroblast cells with cell viability reaching 88 ± 8.7% relative to the TCPS control at 48 h incubation. Differing from prior soft elastomers, a mixture of the two prepolymers without a carrying polymer is electrospinnable and UV-cross-linkable to fabricate elastic fibrous scaffolds for soft tissues. The designed prepolymer pair can thus ease the fabrication of elastic fibrous conduits, leading to potential use as a resorbable synthetic graft. The elastomers could find use in other tissue engineering applications as well.

Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations.

Prior studies have shown that computational fluid dynamics (CFD) simulations help assess patient-specific hemodynamics in abdominal aortic aneurysms (AAAs); patient-specific hemodynamic stressors are frequently used to predict an AAA's growth. Previous studies have utilized both laminar and turbulent simulation models to simulate hemodynamics. However, the impact of different CFD simulation models on the predictive modeling of AAA growth remains unknown and is thus the knowledge gap that motivates this study. Specifically, CFD simulations were performed for 70 AAA models derived from 70 patients' computed tomography angiography (CTA) data with known growth status (i.e., fast-growing [> 5 mm/yr] or slowly growing [< 5 mm/yr]). We used laminar and large eddy simulation (LES) models to obtain hemodynamic parameters to predict AAAs' growth status. Predicting the growth status of AAAs was based on morphological, hemodynamic, and patient health parameters in conjunction with three classical machine learning (ML) classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and generalized linear model (GLM). Our preliminary results estimated aneurysmal flow stability and wall shear stress (WSS) were comparable in both laminar and LES flow simulations. Moreover, computed WSS and velocity-related hemodynamic variables obtained from the laminar and LES simulations showed comparable abilities in differentiating the growth status of AAAs. More importantly, the predictive modeling performance of the three ML classifiers mentioned above was similar, with less than a 2% difference observed (p-value > 0.05). In closing, our findings suggest that two different flow simulations investigated did not significantly affect outcomes of computational hemodynamics and predictive modeling of AAAs' growth status, given the data investigated.

USING CONVOLUTIONAL NEURAL NETWORK-BASED SEGMENTATION FOR IMAGE-BASED COMPUTATIONAL FLUID DYNAMICS SIMULATIONS OF BRAIN ANEURYSMS: INITIAL EXPERIENCE IN AUTOMATED MODEL CREATION.

"Image-based" computational fluid dynamics (CFD) simulations provide insights into each patient's hemodynamic environment. However, current standard procedures for creating CFD models start with manual segmentation and are time-consuming, hindering the clinical translation of image-based CFD simulations. This feasibility study adopts deep-learning-based image segmentation (hereafter referred to as Artificial Intelligence (AI) segmentation) to replace manual segmentation to accelerate CFD model creation. Two published convolutional neural network-based AI methods (MIScnn and DeepMedic) were selected to perform CFD model extraction from three-dimensional (3D) rotational angiography data containing intracranial aneurysms. In this study, aneurysm morphological and hemodynamic results using models generated by AI segmentation methods were compared with those obtained by two human users for the same data. Interclass coefficients (ICC), Bland-Altman plots, and Pearson's correlation coefficients (PCC) were combined to assess how well AI-generated CFD models were performed. We found that almost perfect agreement was obtained between the human and AI results for all eleven morphological and five out of eight hemodynamic parameters, while a moderate agreement was obtained from the remaining three hemodynamic parameters. Given this level of agreement, using AI segmentation to create CFD models is feasible, given more developments.