Lightweight Lung-nodule Detection Model Combined with Multidimensional Attention Convolution.

Background: Early and timely detection of pulmonary nodules and initiation treatment can substantially improve the survival rate of lung carcinoma. However, current detection methods based on convolutional neural networks (CNNs) cannot easily detect pulmonary nodules owing to low detection accuracy and the difficulty in detecting small-sized pulmonary nodules; meanwhile, more accurate CNN-based models are slow and require high hardware specifications.

Objective: The aim of this study is to develop a detection model that achieves both high accuracy and real-time performance, ensuring effective and timely results.

Changes in the Non-targeted Metabolomic Profile of Three-year-old Toddlers with Elevated Exposure to Polycyclic Aromatic Hydrocarbons.

Objective: To investigate changes in the urinary metabolite profiles of children exposed to polycyclic aromatic hydrocarbons (PAHs) during critical brain development and explore their potential link with the intestinal microbiota.

Methods: Liquid chromatography-tandem mass spectrometry was used to determine ten hydroxyl metabolites of PAHs (OH-PAHs) in 36-month-old children. Subsequently, 37 children were categorized into low- and high-exposure groups based on the sum of the ten OH-PAHs.