Tuberous sclerosis complex (TSC) is a genetic disorder caused by mutations in either TSC1 or TSC2 tumor suppressor gene. TSC1 and TSC2 products, Harmatin and Tuberin, form the functional complex to serve as the negative regulator for insulin-induced phosphorylation of S6 kinase and elF4E-binding protein 1. High-risk human papillomavirus (HPV) infection is the necessary cause for cervical cancer. E6 oncoprotein encoded by HPV plays a pivotal role in carcinogenesis by interference with the host intracellular protein functions. In this study, we show that HPV16 E6 interacts with tumor suppressor gene TSC2 product, Tuberin, and results in the phosphorylation of S6 kinase and S6 even in the absence of insulin. The overexpression of Tuberin overcomes the effect of E6 on S6 kinase phosphorylation. Binding with HPV16 E6 causes the proteasome-mediated degradation of Tuberin. A DILG motif and an ELVG motif located in the carboxyl-terminal of Tuberin are required for E6 binding. In addition, the Tuberin interaction region in E6 has been mapped in the amino-terminal portion of HPV16 E6, which is different from the binding domain with p53. These results provide a possible link between E6-induced oncogenesis and the insulin-stimulated cell proliferation signaling pathway. Human papillomaviruses (HPVs)1 infect epithelial cells and induce epithelial tumors or benign lesions (1). Based on their clinical outcomes, HPVs are grouped into high-risk and lowrisk types. The high-risk types, such as HPV16, HPV18, HPV31, HPV33, have been identified as the cause of cervical carcinoma (2). Two oncoproteins encoded by HPVs E6 and E7 are able to immortalize human epithelial cells in vitro (3). The transgenic mice carrying the E6 gene under the control of the keratin promoter developed skin cancer (4). Moreover, E6 and E7 oncoproteins were found in HPV-infected malignant tumors (5), indicating that E6 and E7 functions are required for tumorigenesis.E6 interacts with a variety of host cell proteins and interferes with their functions. The high-risk HPV E6 proteins bind to tumor suppressor protein p53 for degradation (6). This observation provides an important functional clue in which E6 contributes to cell transformation. Several studies showed that additional E6 activities are required to reach full transformation potential (7,8). A growing number of E6 targets involved in the broad spectrum of cellular functions have been identified (9) including the transcriptional coactivator p300/CREB-binding protein (10, 11), hMcm7 (12, 13), , and E6TP1 (15) and others. Thus, E6 interferes with multiple cellular pathways leading to malignant transformation. However, the mechanism that coordinates E6 targeting cellular activities and leads to tumorigenesis currently remains unclear.Tuberous sclerosis complex (TSC) is an inheritable genetic disorder characterized by the formation of benign tumors in multiple organs (16). Genetic studies show that TSC is caused by mutations in either the TSC1 or TSC2 tumor suppressor gene (17,18). TSC1 encodes a 130-kDa pro...
Purpose Dual-energy CT (DECT) enhances tissue characterization because of its basis material decomposition capability. In addition to conventional two-material decomposition from DECT measurements, multi-material decomposition (MMD) is required in many clinical applications. To solve the ill-posed problem of reconstructing multiple-material images from dual-energy measurements, additional constraints are incorporated into the formulation, including volume and mass conservation and the assumptions that at most three materials in each pixel and various material types among pixels. The recently proposed flexible image-domain MMD method decomposes pixels sequentially into multiple basis materials using direct inversion scheme and leads to magnified noise in the material images. In this paper, we propose a statistical image-domain MMD method for DECT to suppress the noise. Methods The proposed method applies penalized weighted least-square (PWLS) reconstruction with a negative log-likelihood term and edge-preserving regularization for each material. The statistical weight is determined by a data-based method accounting for the noise variance of high- and low-energy CT images. We apply the optimization transfer principles to design a serial of pixel-wise separable quadratic surrogates (PWSQS) functions which monotonically decrease the cost function. The separability in each pixel enables simultaneous update of all pixels. Results The proposed method is evaluated on a digital phantom, Catphan©600 phantom and three patients (pelvis, head and thigh). We also implement the direct inversion and low-pass filtration methods for comparison purpose. Compared with the direct inversion method, the proposed method reduces noise standard deviation (STD) in soft-tissue by 95.35% in the digital phantom study, by 88.01% in the Catphan©600 phantom study, by 92.45% in the pelvis patient study, by 60.21% in the head patient study, and by 81.22% in the thigh patient study, respectively. The overall volume fraction accuracy is improved by around 6.85%. Compared with the low-pass filtration method, the root-mean-square percentage error (RMSE(%)) of electron densities in the Catphan©600 phantom is decreased by 20.89%. At modulation transfer function (MTF) magnitude decreased to 50%, the proposed method increases the spatial resolution by an overall factor of 1.64 on the digital phantom, and 2.16 on the Catphan©600 phantom. The overall volume fraction accuracy is increased by 6.15%. Conclusions We proposed a statistical image-domain MMD method using DECT measurements. The method successfully suppresses the magnified noise while faithfully retaining the quantification accuracy and anatomical structure in the decomposed material images. The proposed method is practical and promising for advanced clinical applications using DECT imaging.
COVID-19 has become a public health emergency due to its rapid transmission. The appearance of pneumonia is one of the major clues for the diagnosis, progress and therapeutic evaluation. More and more literatures about imaging manifestations and related research have been reported. In order to know about the progress and prospective on imaging of COVID-19, this review focus on interpreting the CT findings, stating the potential pathological basis, proposing the challenge of patients with underlying diseases, differentiating with other diseases and suggesting the future research and clinical directions, which would be helpful for the radiologists in the clinical practice and research.
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