[Numerical study on structural design and mechanical analysis of anti-migration tracheal stent with non-uniform Poisson's ratio].

Stent migration is one of the common complications after tracheal stent implantation. The causes of stent migration include size mismatch between the stent and the trachea, physiological movement of the trachea, and so on. In order to solve the above problems, this study designed a non-uniform Poisson ratio tracheal stent by combining the size and structure of the trachea and the physiological movement of the trachea to improve the migration of the stent, meanwhile ensuring the support of the stent.

Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data.

The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA.

Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset.

Accelerated brain aging (ABA) intricately links with age-associated neurodegenerative and neuropsychiatric diseases, emphasizing the critical need for a nuanced exploration of heterogeneous ABA patterns. This investigation leveraged data from the UK Biobank (UKB) for a comprehensive analysis, utilizing structural magnetic resonance imaging (sMRI), diffusion magnetic resonance imaging (dMRI), and resting-state functional magnetic resonance imaging (rsfMRI) from 31,621 participants. Pre-processing employed tools from the FMRIB Software Library (FSL, version 5.

Exploring Successful Cognitive Aging: Insights Regarding Brain Structure, Function, and Demographics.

In the realm of cognitive science, the phenomenon of "successful cognitive aging" stands as a hallmark of individuals who exhibit cognitive abilities surpassing those of their age-matched counterparts. However, it is paramount to underscore a significant gap in the current research, which is marked by a paucity of comprehensive inquiries that deploy substantial sample sizes to methodically investigate the cerebral biomarkers and contributory elements underpinning this cognitive success. It is within this context that our present study emerges, harnessing data derived from the UK Biobank.

A review of neuroimaging-based data-driven approach for Alzheimer's disease heterogeneity analysis.

Alzheimer's disease (AD) is a complex form of dementia and due to its high phenotypic variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial role in AD diagnosis and monitoring, but interpreting these biomarkers can be problematic due to their spatial and temporal heterogeneity. Therefore, researchers are increasingly turning to imaging-based biomarkers that employ data-driven computational approaches to examine the heterogeneity of AD.

Moderating effects of cognitive reserve on the relationship between brain structure and cognitive abilities in middle-aged and older adults.

The cognitive reserve (CR) hypothesis is reinforced by negative moderating effects, suggesting that those with higher CR are less reliant on brain structure for cognitive function. Previous research on CR's moderating effects yielded inconsistent results, motivating our 3 studies using UK Biobank data. Study I examined five CR proxies' moderating effects on global, lobar, and regional brain-cognition models; study II extended study I by using a larger sample size; and study III investigated age-related moderating effects on the hippocampal regions.

Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults.

Machine learning (ML) has transformed neuroimaging research by enabling accurate predictions and feature extraction from large datasets. In this study, we investigate the application of six ML algorithms (Lasso, relevance vector regression, support vector regression, extreme gradient boosting, category boost, and multilayer perceptron) to predict brain age for middle-aged and older adults, which is a crucial area of research in neuroimaging. Despite the plethora of proposed ML models, there is no clear consensus on how to achieve better performance in brain age prediction for this population.

A review of the application of three-dimensional convolutional neural networks for the diagnosis of Alzheimer's disease using neuroimaging.

Alzheimer's disease (AD) is a degenerative disorder that leads to progressive, irreversible cognitive decline. To obtain an accurate and timely diagnosis and detect AD at an early stage, numerous approaches based on convolutional neural networks (CNNs) using neuroimaging data have been proposed. Because 3D CNNs can extract more spatial discrimination information than 2D CNNs, they have emerged as a promising research direction in the diagnosis of AD.

Dynamic gestational week prediction model for pre-eclampsia based on ID3 algorithm.

Pre-eclampsia (PE) is a type of hypertensive disorder during pregnancy, which is a serious threat to the life of mother and fetus. It is a placenta-derived disease that results in placental damage and necrosis due to systemic small vessel spasms that cause pathological changes such as ischemia and hypoxia and oxidative stress, which leads to fetal and maternal damage. In this study, four types of risk factors, namely, clinical epidemiology, hemodynamics, basic biochemistry, and biomarkers, were used for the initial selection of model parameters related to PE, and factors that were easily available and clinically recognized as being associated with a higher risk of PE were selected based on hospital medical record data.

Dynamic prediction model of fetal growth restriction based on support vector machine and logistic regression algorithm.

Background: This study analyzed the influencing factors of fetal growth restriction (FGR), and selected epidemiological and fetal parameters as risk factors for FGR.

Objective: To establish a dynamic prediction model of FGR.

Methods: This study used two methods, support vector machine (SVM) and multivariate logistic regression, to establish the prediction model of FGR at different gestational weeks.

Automatic generation of retinal optical coherence tomography images based on generative adversarial networks.

Significance: The automatic generation algorithm of optical coherence tomography (OCT) images based on generative adversarial networks (GAN) can generate a large number of simulation images by a relatively small number of real images, which can effectively improve the classification performance.

Aim: We proposed an automatic generation algorithm for retinal OCT images based on GAN to alleviate the problem of insufficient images with high quality in deep learning, and put the diagnosis algorithm toward clinical application.

Approach: We designed a generation network based on GAN and trained the network with a data set constructed by 2014_BOE_Srinivasan and OCT2017 to acquire three models.

[Multimodal high-grade glioma semantic segmentation network with multi-scale and multi-attention fusion mechanism].

Glioma is a primary brain tumor with high incidence rate. High-grade gliomas (HGG) are those with the highest degree of malignancy and the lowest degree of survival. Surgical resection and postoperative adjuvant chemoradiotherapy are often used in clinical treatment, so accurate segmentation of tumor-related areas is of great significance for the treatment of patients.

A computational analysis of potential aortic dilation induced by the hemodynamic effects of bicuspid aortic valve phenotypes.

Background And Objectives: The bicuspid aortic valve (BAV) is a major risk factor for the progression of aortic dilation (AD) because of the induced abnormal blood flow environment in aorta. The differences in the development of AD induced by BAV phenotypes remains unclear. Therefore, the objective of this study was to assess the potential locations of AD induced by different phenotypes of BAV.

Concordance of Alzheimer's Disease Subtypes Produced from Different Representative Morphological Measures: A Comparative Study.

Background: Gray matter (GM) density and cortical thickness (CT) obtained from structural magnetic resonance imaging are representative GM morphological measures that have been commonly used in Alzheimer's disease (AD) subtype research. However, how the two measures affect the definition of AD subtypes remains unclear.

Methods: A total of 180 AD patients from the ADNI database were used to identify AD subgroups.

Risk prevention of different forms of gestational diabetes mellitus based on energy metabolism prior to diagnosis.

Background: Gestational diabetes mellitus (GDM) is a metabolic disease that seriously endangers the health of mothers and children. It is important to monitor GDM in real-time before diagnosis and to prevent it effectively.

Objective: GDM was divided into the second trimester diagnosed diabetes mellitus (GDM_24) and the third trimester diagnosed diabetes mellitus (GDM_30).

[Application of hyperbolic heat transfer model in atrial fibrillation microwave ablation].

The effect of relaxation time in hyperbolic heat transfer model on the temperature field of microwave ablation of atrial fibrillation was investigated. And the results were compared with those calculated by Pennes model. A three-dimensional model of microwave ablation of atrial fibrillation was constructed.

[Structural design and biomechanical numerical analysis of body-fitted stent in stenotic vessels].

To solve the problem of stent malapposition of intravascular stents, explore the design method of intravascular body-fitted stent structure and to establish an objective apposition evaluation method, the support and apposition performance of body-fitted stent in the stenotic vessels with different degrees of calcified plaque were simulated and analyzed. The traditional tube-mesh-like stent model was constructed by using computational aided design tool SolidWorks, and based on this model, the body-fitted stent model was designed by means of projection algorithm. Abaqus was used to simulate the crimping-expansion-recoil process of the two stents in the stenotic vessel with incompletely calcified plaque and completely calcified plaque respectively.

Effect of Valve Height on the Opening and Closing Performance of the Aortic Valve Under Aortic Root Dilatation.

Patients with aortic valve disease can suffer from valve insufficiency after valve repair surgery due to aortic root dilatation. The paper investigates the effect of valve height (Hv) on the aortic valve opening and closing in order to select the appropriate range of Hv for smoother blood flow through the aortic valve and valve closure completely in the case of continuous aortic root dilatation. A total of 20 parameterized three-dimensional models of the aortic root were constructed following clinical surgical guidance.

Branch flow distribution approach and its application in the calculation of fractional flow reserve in stenotic coronary artery.

Objective: To calculate fractional flow reserve (FFR) based on computed tomography angiography (i.e., FFRCT) by considering the branch flow distribution in the coronary arteries.

[Experimental study on temperature dependence of dielectric properties of biological tissues at 2 450 MHz].

The temperature dependence of relative permittivity and conductivity of pig liver, lung and heart at 2 450 MHz was studied. The relative permittivity and conductivity of three kinds of biological tissues were measured by the open-end coaxial line method. The dielectric model was fitted according to the principle of least square method.

Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer's disease diagnosis.

Alzheimer's Disease (AD) is a chronic neurodegenerative disease without effective medications or supplemental treatments. Thus, predicting AD progression is crucial for clinical practice and medical research. Due to limited neuroimaging data, two-dimensional convolutional neural networks (2D CNNs) have been commonly adopted to differentiate among cognitively normal subjects (CN), people with mild cognitive impairment (MCI), and AD patients.

[A review on the application of UK Biobank in neuroimaging].

UK Biobank (UKB) is a forward-looking epidemiological project with over 500, 000 people aged 40 to 69, whose image extension project plans to re-invite 100, 000 participants from UKB to perform multimodal brain magnetic resonance imaging. Large-scale multimodal neuroimaging combined with large amounts of phenotypic and genetic data provides great resources to conduct brain health-related research. This article provides an in-depth overview of UKB in the field of neuroimaging.

Gestational diabetes mellitus prediction model: A risk factors analysis of pregnant women with gestational diabetes mellitus but have normal oral glucose tolerance test results in the second trimester of pregnancy.

Background: Oral glucose tolerance test (OGTT) is a standard for the diagnosis of gestational diabetes mellitus (GDM). However, clinically, some cases with normal results were diagnosed as GDM in the third trimester.

Objective: To establish a risk model based on energy metabolism, epidemiology, and biochemistry that could predict the GDM pregnant women with normal OGTT results in the second trimester.

A study of a fetal heart rate calculation system based on R-R interval.

Background: Fetal electrocardiogram (FECG) can be obtained in a non-invasive manner to monitor fetal growth status.

Objective: In this study, a fetal heart rate (FHR) calculation system was proposed, which consists of the FECG recorder (MF-HOLTER) and the FECG monitoring software (FECG-MS). The abdomen electrocardiogram (AECG) of pregnant woman is acquired through the MF-HOLTER.