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    Shrimp Lipids Inhibit Migration, Epithelial–Mesenchymal Transition, and Cancer Stem Cells via Akt/mTOR/c-Myc Pathway Suppression
    (2024-04-01)
    Thepthanee, Chorpaka
    ;
    Ei, Zin Zin
    ;
    Benjakul, Soottawat
    ;
    Zou, Hongbin
    ;
    Petsri, Korrakod
    Shrimp is a rich source of bioactive molecules that provide health benefits. However, the high cholesterol content in shrimp oil may pose a risk. We utilized the cholesterol elimination method to obtain cholesterol-free shrimp lipids (CLs) and investigated their anticancer potential, focusing on cancer stem cells (CSCs) and epithelial-to-mesenchymal transition (EMT). Our study focused on CSCs and EMT, as these factors are known to contribute to cancer metastasis. The results showed that treatment with CLs at doses ranging from 0 to 500 µg/mL significantly suppressed the cell migration ability of human lung cancer (H460 and H292) cells, indicating its potential to inhibit cancer metastasis. The CLs at such concentrations did not cause cytotoxicity to normal human keratinocytes. Additionally, CL treatment was found to significantly reduce the levels of Snail, Slug, and Vimentin, which are markers of EMT. Furthermore, we investigated the effect of CLs on CSC-like phenotypes and found that CLs could significantly suppress the formation of a three-dimensional (3D) tumor spheroid in lung cancer cells. Furthermore, CLs induced apoptosis in the CSC-rich population and significantly depleted the levels of CSC markers CD133, CD44, and Sox2. A mechanistic investigation demonstrated that exposing lung cancer cells to CLs downregulated the phosphorylation of Akt and mTOR, as well as c-Myc expression. Based on these findings, we believe that CLs may have beneficial effects on health as they potentially suppress EMT and CSCs, as well as the cancer-potentiating pathway of Akt/mTOR/c-Myc.
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    A Study of Pulmonary Nodules from Multi-slice Computed Tomography Using 3-D Structure
    (2018-06-21)
    Paing, May Phu
    ;
    Choomchuay, Somsak
    Multi-slice CT screening is an auspicious imaging test to detect the lung cancer. Formation of the pulmonary nodules in the lungs is the first suspicious sign of the lung cancer. The nodules appear as round or irregular shaped spots having size up to 30 mm on the CT scan. As the rate of cancer cases increases day by day, the manual detection of pulmonary nodules becomes a struggling way for the radiologists. The proposed method aims to develop an automated system to detect the pulmonary nodules using image processing techniques. Moreover, the needs of the conventional 2D detection are figured out and compensated by creating 3D structure.
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    Classification of margin characteristics from 3D pulmonary nodules
    (2017-12-19)
    Paing, May Phu
    ;
    Choomchuay, Somsak
    Detection of pulmonary nodules has played a significant role in lung cancer diagnosis because nodules are the first suspicious symptoms for the likelihood of cancer. Margin characteristics of the pulmonary nodules provide essential radiological features to determine the possibility of malignancy. In general, benign nodules hold quite smooth margins whilst malignant ones hold irregular margins. The main objective of this research is to classify different margin types of pulmonary nodules by observing the 3D structure. Nodule candidates from 2D lung CT slices are segmented firstly and then stacked to form a 3D image. Geometric features of the 3D nodule are extracted and fed into the support vector machine (SVM) classifier to classify the margin types. The proposed method can provide the classification accuracy of 90.9%.
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    A computer aided diagnosis system for detection of lung nodules from series of CT slices
    (2017-11-03)
    Paing, May Phu
    ;
    Choomchuay, Somsak
    The proposed system aims to detect the lung nodules from a series of CT scan images. Otsu's thresholding and morphological operations are applied for nodules segmentation. After segmentation, the objects that do not hold the possibility to be nodules are removed. Geometric, histogram as well as texture features are then extracted for benign and malignant nodules classification. Multilayer Perceptron (MLP) is used for classification and the accuracy 95% has been achieved.
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    Ground glass opacity (GGO) nodules detection from lung CT scans
    (2017-07-01)
    Paing, May Phu
    ;
    Choomchuay, Somsak
    Ground glass opacity (GGO) nodules have a higher possibility of malignancy compared to other types of nodules appeared in the lung cancer. They are very effortful to detect due to their hazy structures and unclear margins. 65% of lung cancers are missed by the radiologists with the faint appearance of the GGO. Consequently, the detection of GGO is a critical issue and a striving task for the radiologists. This research proposes an automatic detection of GGO remained after the detection of solid opacity. Simple thresholding based on grey levels and mathematical image subtraction are applied for segmentation. Possible false after segmentation are reduced by the support vector machine (SVM). In total 37 GGOs, only 2 are missed by proposed segmentation and the false reduction performance is 94%.