Background: Limited information is available regarding the changes in blood culture utilization following the COVID-19 pandemic. Blood culture utilization rate is a critical indicator of diagnostic efficiency for infectious diseases. This study aims to describe the impact of the COVID-19 pandemic on blood culture utilization rate in Shanghai.
View Article and Find Full Text PDFThis paper propose a significantly enhanced YOLOv8 model specifically designed for detecting tongue fissures and teeth marks in Traditional Chinese Medicine (TCM) diagnostic images. By integrating the C2f_DCNv3 module, which incorporates Deformable Convolutions (DCN), replace the original C2f module, enabling the model to exhibit exceptional adaptability to intricate and irregular features, such as fine fissures and teeth marks. Furthermore, the introduction of the Squeeze-and-Excitation (SE) attention mechanism optimizes feature weighting, allowing the model to focus more accurately on key regions of the image, even in the presence of complex backgrounds.
View Article and Find Full Text PDFBackground: Online platforms are an efficient means to detect early cognitive decline, but few studies have investigated the relationship between remotely collected subjective cognitive change and cognitive decline. We hypothesized that the Everyday Cognition Scale (ECog), a subjective change measure, predicts longitudinal change in cognition in Brain Health Registry (BHR), an online registry for neuroscience research.
Method: From the BHR database, we included participants aged 55+ who completed both the baseline ECog and repeated administrations of the CANTAB® Paired Associates Learning (PAL) test.
Chrysanthemi Flos has been consumed as floral tea for centuries, but the effects of stir-frying on its chemical profile, sensory characteristics, and bioactivity remain unclear. This study used untargeted metabolomics, sensory assessment (E-eye, E-nose, E-tongue), and antioxidant activity evaluation to investigate compositional changes and their effects. In the metabolomics analysis, a total of 101 non-volatile and 306 volatile differential metabolites were identified.
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