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.

Global, regional, and national time trends in incidence for depressive disorders, from 1990 to 2019: an age-period-cohort analysis for the GBD 2019.

Background: Even with advances in primary health care, depressive disorders remain a major global public health problem. We conducted an in-depth analysis of global, regional and national trends in depressive disorders incidence over the past 30 years.

Methods: Data on the incidence of depressive disorders were obtained by sex (female, male, and both), location (204 countries), age (5-84 years), year (1990-2019) from the Global Burden of Disease Study (GBD) 2019.

Advances in animal models of Parkinson's disease.

Parkinson's disease is a complex neurodegenerative disease characterized by progressive movement impairments. Predominant symptoms encompass resting tremor, bradykinesia, limb rigidity, and postural instability. In addition, it also includes a series of non-motor symptoms such as sleep disorders, hyposmia, gastrointestinal dysfunction, autonomic dysfunction and cognitive impairment.

Selective ensemble method for anomaly detection based on parallel learning.

Anomaly detection is a highly important task in the field of data analysis. Traditional anomaly detection approaches often strongly depend on data size, structure and features, while introducing the idea of ensemble into anomaly detection can greatly improve the generalization ability. Ensemble-based anomaly detection methods still face some challenges, however, such as data imbalance, time and space demand and the selection of base detectors.

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.

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.

In vitro and in vivo biological evaluation of newly synthesized multi-target 20(R)-panaxadiol derivatives for treating Alzheimer's disease.

An extensive study was performed to discover a series of novel 20(R)-panaxadiol derivatives with various substituents at the 3-OH position as nontoxic, brain-permeable, multi-target leads for treating Alzheimer's disease. In vitro analysis revealed that a compound bearing benzyl-substituted carbamate, which we denoted compound 14a, exhibited the most potent neuroprotective activity, with an EC of 13.17 μM.

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.

Effect of freeze-thaw cycle aging and high-temperature oxidation aging on the sorption of atrazine by microplastics.

This study aims to better understand the aging characteristics of microplastics in the environment and the influence of aging microplastics on the migration and transformation of organic pollutants. In this study, polyvinyl chloride (PVC) and polyethylene (PE) were chosen as research objects, and the effects of two aging methods (freeze-thaw cycle aging and high-temperature oxidation aging) on their surface properties and atrazine (ATZ) sorption were investigated. The crystallinity of PE increased after freeze-thaw cycling and decreased after high-temperature oxidation.

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.

Ocular manifestations and SARS-CoV-2 detection in tears and conjunctival scrape from non-severe COVID-19 patients.

Aim: To explore the ocular features of corona virus disease (COVID)-19 and severe acute respiratory syndrome coronavirus (SARS-CoV)-2 detection in tears and conjunctival scrapes in non-severe COVID-19 patients.

Methods: This is a multicenter observational clinical study with no intervention conducted from Jan 25 to March 1, 2020. Clinical data and samples of tears and conjunctival scraping were collected in consecutive laboratory-confirmed, non-severe COVID-19 patients from three hospitals.

Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification.

The worldwide spread of coronavirus disease (COVID-19) has become a threat to global public health. It is of great importance to rapidly and accurately screen and distinguish patients with COVID-19 from those with community-acquired pneumonia (CAP). In this study, a total of 1,658 patients with COVID-19 and 1,027 CAP patients underwent thin-section CT and were enrolled.

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.

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.

Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning.

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of infected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease.

Segmenting Brain Tumor Using Cascaded V-Nets in Multimodal MR Images.

In this work, we propose a novel cascaded V-Nets method to segment brain tumor substructures in multimodal brain magnetic resonance imaging. Although V-Net has been successfully used in many segmentation tasks, we demonstrate that its performance could be further enhanced by using a cascaded structure and ensemble strategy. Briefly, our baseline V-Net consists of four levels with encoding and decoding paths and intra- and inter-path skip connections.

CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions.

Background: To retrospectively validate CT-based radiomics features for predicting the risk of anterior mediastinal lesions.

Methods: A retrospective study was performed through February 2013 to March 2018 on 298 patients who had pathologically confirmed anterior mediastinal lesions. The patients all underwent CT scans before their treatment, including 130 unenhanced computed tomography (UECT) and 168 contrast-enhanced CT (CECT) scans.