Development and Validation of a Diagnostic Model for Stanford Type B Aortic Dissection Based on Proteomic Profiling.

Purpose: Stanford Type B Aortic Dissection (TBAD), a critical aortic disease, has exhibited stable mortality rates over the past decade. However, diagnostic approaches for TBAD during routine health check-ups are currently lacking. This study focused on developing a model to improve the diagnosis in a population.

Left atrioventricular coupling index assessed with three-dimensional echocardiography: a prognostic marker of short-term outcomes in light-chain cardiac amyloidosis.

Background: Light-chain cardiac amyloidosis (AL-CA) is associated with structural and functional changes in the left atrium and left ventricle. This study aims to assess the value of the left atrioventricular coupling index (LACI) assessed by three-dimensional echocardiography (3DE) for predicting primary outcome in AL-CA.

Methods: Participants with biopsy-confirmed AL-CA from April 2022 to February 2024 were prospectively analysed.

In Utero Perfluorodecanoic Acid Exposure Causes Fetal Leydig Cell Dysfunction via Endoplasmic Reticulum Stress-Mediated Lipid Composition Alteration.

Perfluorodecanoic acid (PFDA), a C10 fluorine-containing compound, is used widely and found to be present anywhere. However, whether it has reproductive toxicity for fetal Leydig cells and the underlying mechanisms remain unknown. PFDA was investigated for its effects on fetal Leydig cells (FLCs) following exposure to 0, 1, 2.

Nanocarrier imaging at single-cell resolution across entire mouse bodies with deep learning.

Efficient and accurate nanocarrier development for targeted drug delivery is hindered by a lack of methods to analyze its cell-level biodistribution across whole organisms. Here we present Single Cell Precision Nanocarrier Identification (SCP-Nano), an integrated experimental and deep learning pipeline to comprehensively quantify the targeting of nanocarriers throughout the whole mouse body at single-cell resolution. SCP-Nano reveals the tissue distribution patterns of lipid nanoparticles (LNPs) after different injection routes at doses as low as 0.

Belt conveyor idler fault detection algorithm based on improved YOLOv5.

The rapid expansion of the coal mining industry has introduced significant safety risks, particularly within the harsh environments of open-pit coal mines. The safe and stable operation of belt conveyor idlers is crucial not only for ensuring efficient coal production but also for safeguarding the lives of coal mine workers. Therefore, this paper proposes a method based on deep learning for real-time detection of conveyor idler faults.