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Hepatocellular carcinoma (HCC) is characterized by a lack of obvious clinical features in the early stages and is likely to progress to advanced HCC. Advanced HCC is a highly malignant tumor. However, there are few treatment options for advanced HCC. Therefore, screening for new drugs that target HCC will provide a new approach to the treatment of HCC. The CCK8 assay was performed to screen compounds inhibiting HCC cell proliferation and to evaluate the IC50 (half-maximal inhibitory concentration) of compounds on cell lines. Colony formation assay was used to determine HCC cell proliferation. The effect of compounds on HCC cell migration and invasion were analyzed using wound healing and transwell assays, respectively. Tumor growth and metastasis were assessed in vivo in a xenograft mouse model. Flow cytometry was carried out to measure apoptotic cells. Reverse transcription and quantitative real-time polymerase chain reaction (RT‒qPCR) and Western blot were performed to examine the expression of epithelial-mesenchymal transition (EMT)- and apoptosis-related genes. Through large-scale screening, we have discovered the anti-tumor activity of cetylpyridinium chloride (CPC) against HCC cells. CPC inhibited the proliferation, invasion and metastasis of HCC cells. Cancer cells are more sensitive to CPC than normal cells. CPC suppressed HCC tumor growth and metastasis in vivo. Mechanistically, CPC promoted apoptosis of HCC cells by affecting the expression of apoptosis-related genes, and inhibited HCC invasion and metastasis by suppressing EMT and expression of EMT markers. Our investigation showed that CPC significantly inhibited HCC cell proliferation, invasion and metastasis in vivo and in vitro, by inducing the expression of apoptosis-related genes and inhibiting expression of EMT markers, suggesting that CPC is a potential agent for HCC treatment. [ABSTRACT FROM AUTHOR]

The development of automated tools using advanced technologies like deep learning holds great promise for improving the accuracy of lung nodule classification in computed tomography (CT) imaging, ultimately reducing lung cancer mortality rates. However, lung nodules can be difficult to detect and classify, from CT images since different imaging modalities may provide varying levels of detail and clarity. Besides, the existing convolutional neural network may struggle to detect nodules that are small or located in difficult-to-detect regions of the lung. Therefore, the attention pyramid pooling network (APPN) is proposed to identify and classify lung nodules. First, a strong feature extractor, named vgg16, is used to obtain features from CT images. Then, the attention primary pyramid module is proposed by combining the attention mechanism and pyramid pooling module, which allows for the fusion of features at different scales and focuses on the most important features for nodule classification. Finally, we use the gated spatial memory technique to decode the general features, which is able to extract more accurate features for classifying lung nodules. The experimental results on the LIDC-IDRI dataset show that the APPN can achieve highly accurate and effective for classifying lung nodules, with sensitivity of 87.59%, specificity of 90.46%, accuracy of 88.47%, positive predictive value of 95.41%, negative predictive value of 76.29% and area under receiver operating characteristic curve of 0.914. [ABSTRACT FROM AUTHOR]

The aging population and increasing prevalence of diseases among younger individuals have heightened the pressing demand for wearable health monitoring devices among the general public. In comparison to self‐health monitoring at physical signal level, wearable biochemical monitoring can provide more comprehensive and insightful information regarding one's own health status. Wearable sweat‐sensing technologies hold great potential for non‐invasive and continuous biochemical monitoring, and have emerged as a focal point of wearable health monitoring research. The effective management of sweat, encompassing convenient induction, reliable collection, and efficient flow control and storage, is fundamental in wearable sweat sensing. To elucidate the research progression in these aspects, this review first summarizes sweat secretion mechanisms, induction method, and sampling mode. Additionally, detailed summaries and discussion on sweat sampling and transport modes are provided. Furthermore, within the context of epidermal microfluidics, the technologies on sweat flow control and storage management based on microvalves are discussed. Lastly, current commercialization and key challenges are summarized. [ABSTRACT FROM AUTHOR]