The ability of virtual noncontrast images to remove intrinsic and external iodine from the thyroid via dual-layer spectral detector computed tomography.

Background: Virtual noncontrast (VNC) images generated by dual-layer spectral computed tomography (DLCT) remove iodine influence from enhanced images to simulate true noncontrast (TNC) images. Previous research has demonstrated the high comparability of abdominal VNC images with TNC images, suggesting their potential as substitutes. Given the thyroid's significant iodine content, this study evaluated the efficacy of VNC images for removing both intrinsic and extrinsic iodine through an analysis of computed tomography (CT) attenuation and iodine density in TNC and enhanced VNC thyroid images.

Repeatability and reproducibility of deep learning features for lung adenocarcinoma subtypes with nodules less than 10 mm in size: a multicenter thin-slice computed tomography phantom and clinical validation study.

Background: Deep learning features (DLFs) derived from radiomics features (RFs) fused with deep learning have shown potential in enhancing diagnostic capability. However, the limited repeatability and reproducibility of DLFs across multiple centers represents a challenge in the clinically validation of these features. This study thus aimed to evaluate the repeatability and reproducibility of DLFs and their potential efficiency in differentiating subtypes of lung adenocarcinoma less than 10 mm in size and manifesting as ground-glass nodules (GGNs).

Modeling long-range dependencies for weakly supervised disease classification and localization on chest X-ray.

Background: Computer-aided diagnosis based on chest X-ray (CXR) is an exponentially growing field of research owing to the development of deep learning, especially convolutional neural networks (CNNs). However, due to the intrinsic locality of convolution operations, CNNs cannot model long-range dependencies. Although vision transformers (ViTs) have recently been proposed to alleviate this limitation, those trained on patches cannot learn any dependencies for inter-patch pixels and thus, are insufficient for medical image detection.

Enhanced mineralization of bisphenol A by eco-friendly BiFeO-MnO composite: Performance, mechanism and toxicity assessment.

An eco-friendly BiFeO-MnO composite with dual functionalities of adsorption and catalysis was successfully constructed by using a simple one-step hydrothermal method for the removal of bisphenol A (BPA) pollution from water. Several characterization methods, including X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS), were applied to verify the combination of BiFeO and MnO. BiFeO-MnO (BFO-MO) exhibited excellent adsorption and catalytic activity compared with those of pure BiFeO.

Fe and Cu co-doped graphitic carbon nitride as an eco-friendly photo-assisted catalyst for aniline degradation.

A novel bimetallic coordinated g-CN with FeO composite catalyst (FeCu-g-CN) was synthesized by simple calcination of Fe/Cu/melamine precursor. Its catalytic performance was analyzed via photodegrading aniline. The X-ray diffraction (XRD), field emission scanning electronic microscopy (FESEM), Fourier transform infrared (FT-IR), and X-ray photoelectron spectroscopy (XPS) were employed to characterize the composition and structure of the catalysts.