This review article explores the fascinating realm of fluorescence using organochalcogen molecules, with a particular emphasis on tellurium (Te). The discussion encompasses the underlying mechanisms, structural motifs influencing fluorescence, and the applications of these intriguing phenomena. This review not only elucidates the current state of knowledge but also identifies avenues for future research, thereby serving as a valuable resource for researchers and enthusiasts in the field of fluorescence chemistry with a focus on Te-based molecules. By highlighting challenges and prospects, this review sparks a conversation on the transformative potential of Te-containing compounds across different fields, ranging from environmental solutions to healthcare and materials science applications. This review aims to provide a comprehensive understanding of the distinct fluorescence behaviors exhibited by Te-containing compounds, contributing valuable insights to the evolving landscape of chalcogen-based fluorescence research.
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http://dx.doi.org/10.1039/d3cp05740b | DOI Listing |
The increasing prevalence of diabetes mellitus worldwide necessitates that medical undergraduates acquire a deep understanding of the disease to ensure accurate diagnosis and effective management. Traditional teaching methods, while foundational, often lack the interactive elements that enhance student engagement and knowledge retention. This study aimed to evaluate the effectiveness of a novel educational board game, "Diabe-teach," in enhancing knowledge retention among medical students compared with conventional self-study methods.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
Objective: Rapid on-site evaluation (ROSE) of respiratory cytology specimens is a critical technique for accurate and timely diagnosis of lung cancer. However, in China, limited familiarity with the Diff-Quik staining method and a shortage of trained cytopathologists hamper utilization of ROSE. Therefore, developing an improved deep learning model to assist clinicians in promptly and accurately evaluating Diff-Quik stained cytology samples during ROSE has important clinical value.
View Article and Find Full Text PDFNutr J
January 2025
Division of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Eugeniahemmet T2:02, Stockholm, SE-171 76, Sweden.
Background: mHealth, i.e. mobile-health, strategies may be used as a complement to regular care to support healthy dietary habits in primary care patients.
View Article and Find Full Text PDFJ Nanobiotechnology
January 2025
Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, Guangdong, China.
Extracellular vesicles (EVs) are membrane-bound vesicles that are shed or secreted from the cell membrane and enveloped by a lipid bilayer. They possess stability, low immunogenicity, and non-cytotoxicity, exhibiting extensive prospects in regenerative medicine (RM). However, natural EVs pose challenges, such as insufficient targeting capabilities, potential biosafety concerns, and limited acquisition pathways.
View Article and Find Full Text PDFBMC Neurol
January 2025
Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, School of Medicine, College of Medicine, National Sun Yat-Sen University, No. 123 Ta-Pei Road, Niao-Sung Dist, Kaohsiung, 83305, Taiwan.
Background And Purpose: White matter hyperintensities in brain MRI are key indicators of various neurological conditions, and their accurate segmentation is essential for assessing disease progression. This study aims to evaluate the performance of a 3D convolutional neural network and a 3D Transformer-based model for white matter hyperintensities segmentation, focusing on their efficacy with limited datasets and similar computational resources.
Materials And Methods: We implemented a convolution-based model (3D ResNet-50 U-Net with spatial and channel squeeze & excitation) and a Transformer-based model (3D Swin Transformer with a convolutional stem).
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