Angiographic signs during mechanical thrombectomy as predictors of post-stroke epilepsy: a multicenter retrospective study.

Background: Post-stroke epilepsy (PSE) is a major complication of stroke. However, data about the predictors of PSE in patients with acute ischemic stroke (AIS) undergoing mechanical thrombectomy are limited.

Objective: To evaluate the relationship between intraoperative angiographic signs and PSE risk in patients with anterior circulation AIS who underwent mechanical thrombectomy.

Ionizable cationic lipid nanoparticles loaded with miRNA-125b/BLZ945 for pancreatic cancer treatment.

In prior research, both miRNA-125b and BLZ945 have shown potential in effectively inhibiting M2 macrophage polarization and producing antitumor effects. Nevertheless, their physicochemical characteristics present significant challenges for efficient in vivo delivery. Ionizable cationic lipid nanoparticles (LNPs), recognized for their superior biocompatibility and drug-loading capacity, serve as a novel carrier for nucleic acid-based therapeutics.

Accelerating Brain MR Imaging With Multisequence and Convolutional Neural Networks.

Purpose: Magnetic resonance imaging (MRI) refers to one of the critical image modalities for diagnosis, whereas its long acquisition time limits its application. In this study, the aim was to investigate whether deep learning-based techniques are capable of using the common information in different MRI sequences to reduce the scan time of the most time-consuming sequences while maintaining the image quality.

Method: Fully sampled T1-FLAIR, T2-FLAIR, and T2WI brain MRI raw data originated from 217 patients and 105 healthy subjects.

Arterial Stiffness and Obesity as Predictors of Diabetes: Longitudinal Cohort Study.

Background: Previous studies have confirmed the separate effect of arterial stiffness and obesity on type 2 diabetes; however, the joint effect of arterial stiffness and obesity on diabetes onset remains unclear.

Objective: This study aimed to propose the concept of arterial stiffness obesity phenotype and explore the risk stratification capacity for diabetes.

Methods: This longitudinal cohort study used baseline data of 12,298 participants from Beijing Xiaotangshan Examination Center between 2008 and 2013 and then annually followed them until incident diabetes or 2019.

Association of baseline and dynamic arterial stiffness status with dyslipidemia: a cohort study.

Background And Aims: Dyslipidemia is known to contribute to arterial stiffness, while the inverse association remains unknown. This study aimed to explore the association of baseline arterial stiffness and its changes, as determined by brachial-ankle pulse wave velocity (baPWV), with dyslipidemia onset in the general population.

Methods: This study enrolled participants from Beijing Health Management Cohort using measurements of the first visit from 2012 to 2013 as baseline, and followed until the dyslipidemia onset or the end of 2019.

Triglyceride-glucose index, renal function and cardiovascular disease: a national cohort study.

Background: The triglyceride-glucose (TyG) index is a predictor of cardiovascular diseases; however, to what extent the TyG index is associated with cardiovascular diseases through renal function is unclear. This study aimed to evaluate the complex association of the TyG index and renal function with cardiovascular diseases using a cohort design.

Methods: This study included participants from the China Health and Retirement Longitudinal Study (CHARLS) free of cardiovascular diseases at baseline.

Nanodrugs Reprogram Cancer-Associated Fibroblasts and Normalize Tumor Vasculatures for Sequentially Enhancing Photodynamic Therapy of Hepatocellular Carcinoma.

Background: The failure of cancer photodynamic therapy (PDT) is largely ascribed to excessive stroma and defective vasculatures that restrain the photosensitizer permeation and the oxygen perfusion in tumors.

Method And Results: In this study, a nanodrug that integrated the cancer-associated fibroblast (CAF) regulation with tumor vessel normalization was tailored to sequentially sensitize PDT. The nanodrug exhibited high targeting towards CAFs and efficiently reversed the activated CAFs into quiescence, thus decreasing collagen deposition in the tumor microenvironment (TME), which overcame the protective physical barrier.

Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning model.

Objectives: Our objective was to use deep learning models to identify underlying brain regions associated with depression symptom phenotypes in late-life depression (LLD).

Participants: Diagnosed with LLD ( = 116) and enrolled in a prospective treatment study.

Design: Cross-sectional.

Comparison of Artificial Intelligence-Assisted Compressed Sensing (ACS) and Routine Two-Dimensional Sequences on Lumbar Spine Imaging.

Purpose: To evaluate and compare the image quality and diagnostic accuracy of Artificial Intelligence-assisted Compressed Sensing (ACS) sequences for lumbar disease, as an acceleration method for MRI combining parallel imaging, half-Fourier, compressed sensing and neural network and routine 2D sequences for lumbar spine.

Methods: We collected data from 82 healthy subjects and 213 patients who used 2D ACS accelerated sequences to examine the lumbar spine while 95 healthy subjects and 234 patients used routine 2D sequences. Acquisitions included axial T2WI, sagittal T2WI, T1WI, and T2-fs sequences.

Nomograms predict prognosis and hospitalization time using non-contrast CT and CT perfusion in patients with ischemic stroke.

Background: Stroke is a major disease with high morbidity and mortality worldwide. Currently, there is no quantitative method to evaluate the short-term prognosis and length of hospitalization of patients.

Purpose: We aimed to develop nomograms as prognosis predictors based on imaging characteristics from non-contrast computed tomography (NCCT) and CT perfusion (CTP) and clinical characteristics for predicting activity of daily living (ADL) and hospitalization time of patients with ischemic stroke.

Common feature learning for brain tumor MRI synthesis by context-aware generative adversarial network.

Multi-modal structural Magnetic Resonance Image (MRI) provides complementary information and has been used widely for diagnosis and treatment planning of gliomas. While machine learning is popularly adopted to process and analyze MRI images, most existing tools are based on complete sets of multi-modality images that are costly and sometimes impossible to acquire in real clinical scenarios. In this work, we address the challenge of multi-modality glioma MRI synthesis often with incomplete MRI modalities.

Accelerating Knee MRI: 3D Modulated Flip-Angle Technique in Refocused Imaging with an Extended Echo Train and Compressed Sensing.

Purpose: The three-dimensional (3D) sequence of magnetic resonance imaging (MRI) plays a critical role in the imaging of musculoskeletal joints; however, its long acquisition time limits its clinical application. In such conditions, compressed sensing (CS) is introduced to accelerate MRI in clinical practice. We aimed to investigate the feasibility of an isotropic 3D variable-flip-angle fast spin echo (FSE) sequence with CS technique (CS-MATRIX) compared to conventional 2D sequences in knee imaging.

Detection of Incidental Esophageal Cancers on Chest CT by Deep Learning.

Objective: To develop a deep learning-based model using esophageal thickness to detect esophageal cancer from unenhanced chest CT images.

Methods: We retrospectively identified 141 patients with esophageal cancer and 273 patients negative for esophageal cancer (at the time of imaging) for model training. Unenhanced chest CT images were collected and used to build a convolutional neural network (CNN) model for diagnosing esophageal cancer.

Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning.

The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development.

Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high-order functional connectivity.

Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment and may progress to dementia. However, the brain functional mechanism of T2DM-related dementia is still less understood. Recent resting-state functional magnetic resonance imaging functional connectivity (FC) studies have proved its potential value in the study of T2DM with cognitive impairment (T2DM-CI).

Computing infection distributions and longitudinal evolution patterns in lung CT images.

Background: Spatial and temporal lung infection distributions of coronavirus disease 2019 (COVID-19) and their changes could reveal important patterns to better understand the disease and its time course. This paper presents a pipeline to analyze statistically these patterns by automatically segmenting the infection regions and registering them onto a common template.

Methods: A VB-Net is designed to automatically segment infection regions in CT images.

A Deep Spatial Context Guided Framework for Infant Brain Subcortical Segmentation.

Accurate subcortical segmentation of infant brain magnetic resonance (MR) images is crucial for studying early subcortical structural growth patterns and related diseases diagnosis. However, dynamic intensity changes, low tissue contrast, and small subcortical size of infant brain MR images make subcortical segmentation a challenging task. In this paper, we propose a spatial context guided, coarse-to-fine deep convolutional neural network (CNN) based framework for accurate infant subcortical segmentation.

Hypergraph learning for identification of COVID-19 with CT imaging.

The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features.

Lung volume reduction and infection localization revealed in Big data CT imaging of COVID-19.

The ongoing worldwide COVID-19 pandemic has become a huge threat to global public health. Using CT image, 3389 COVID-19 patients, 1593 community-acquired pneumonia (CAP) patients, and 1707 nonpneumonia subjects were included to explore the different patterns of lung and lung infection. We found that COVID-19 patients have a significant reduced lung volume with increased density and mass, and the infections tend to present as bilateral lower lobes.

Wandering spleen with splenic torsion in a toddler: A case report and literature review.

Rationale: Wandering spleen (WS) is a rare clinical entity characterized by splenic hypermobility caused by absent or abnormal laxity of the suspensory ligaments, which fix the spleen in its normal position. Due to abnormal attachment, the spleen is predisposed to torsion and a series of complications. Pediatric WS is mostly reported in children aged <10 years, especially among infants aged <1 year; it is uncommon among toddlers between 1 and 3 years.

Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT.

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images.

Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia.

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT).