RESUMO
Castration-resistant prostate cancer (CRPC) is still a major concern in men's health, with 375,000 cancer deaths annually. Hypoxia, which is a marked characteristic of advanced solid tumors, has been suggested to induce prostate cancer towards CRPC, metastasis and treatment resistance. To evaluate the effect of hypoxia on prostate cancer, two and five cycles of hypoxia and reoxygenation were administered using 22Rv1 cell lines and denominated as 22Rv1-CI and 22Rv1-PCI, respectively. Cancer cell migration was promoted in 22Rv1-CI compared to controls, and the expression of COL13A1 was significantly up-regulated in 22Rv1-CI according to differentially expressed gene analysis of RNA sequencing among groups. Cancer cell migration was impeded in a wound healing assay after transfecting si-COL13A1. Moreover, the expression of COL13A1 was also higher in the cell line originating from bone metastatic prostate cancer compared to other cell lines. Using the open database GEO, we also confirmed that the expression of COL13A1 was higher in bone metastatic prostate cancer tissue than in localized prostate cancer tissue in patients. Therefore, COL13A1 may be closely related to the bony metastasis of prostate cancer, and our findings may provide valuable information on the pathophysiology of the metastatic niche induced by hypoxia in patients with CRPC.
RESUMO
Bone mineral density (BMD) is a key feature in diagnosing bone diseases. Although computational tomography (CT) is a common imaging modality, it seldom provides bone mineral density information in a clinic owing to technical difficulties. Thus, a dual-energy X-ray absorptiometry (DXA) is required to measure bone mineral density at the expense of additional radiation exposure. In this study, a deep learning framework was developed to estimate the bone mineral density from an axial cut of the L1 bone on computational tomography. As a result, the correlation coefficient between bone mineral density estimates and dual-energy X-ray absorptiometry bone mineral density was .90. When the samples were categorized into abnormal and normal groups using a standard (T-score = - 1.0 ), the maximum F1 score in the diagnostic test was .875. In addition, it was identified using explainable artificial intelligence techniques that the network intensively sees a local area spanning tissues around the vertebral foramen. This method is well suited as an auxiliary tool in clinical practice and as an automatic screener for identifying latent patients in computational tomography databases.
RESUMO
We report on the measurement of the pull-off force on nanoscale patterns that are formed by thermal nanoimprint lithography (t-NIL). Various patterns with feature sizes in the range of 50-900 nm were fabricated on silicon substrates using a rigiflex polymeric mold of ultraviolet curable polyurethane acrylate (PUA, Young's modulus approximately 1 GPa) or perfluoropolyether (PFPE, Young's modulus approximately 10.5 MPa) and a resist layer of polystyrene (PS) of three different molecular weights (M(w) = 18,100, 211,600 and 2043,000). The pull-off force was measured in non-polar, non-reactive perfluorodecalin (PFD) solvent between a sharp atomic force microscopy (AFM) tip and an imprinted pattern. Our experimental data demonstrated that the measured pull-off forces were in good agreement with a simple adhesion model based on Lifshitz theory. Also, the force on the pressed region (valley) is higher than that on the cavity region (hill), with the ratio (hill/valley) decreasing with the decrease of pattern size and the increase of molecular weight. The confinement effects were more pronounced for smaller patterns (<300 nm) and higher molecular weights (M(w) = 211,600 and 2043,000) presumably due to sluggish movement of polymer chains into nano-cavities. Finally, the experimental observations were compared with molecular dynamic simulations based on a simplified amorphous polyethylene model.