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The classification equation developed from the molecular descriptors indicates that estrogens react with the receptor through hydrophobic and π-n electron interactions. At the same time, molecular ionization, polarity, and hydrogen bonding ability can also affect the chemical ER activity. A decision tree developed from chemical structures and their applications reveals that many hormones, proton pump inhibitors, PAHs, progestin, insecticides, fungicides, steroid and chemotherapy medications are active ER agonists/antagonists. On the other hand, many monocyclic/nonaromatic chain compounds and herbicides are inactive ER compounds. The decision tree and binomial equation developed here are valuable tools to predict active and inactive ER compounds. Corona virus disease 2019 (COVID-19) has been an extremely difficult pandemic to contain and it has affected more than 148 countries worldwide. The main aim of this systematic review is to provide a comprehensive summary of clinical and laboratory parameters that are associated with and indicative of increased severity among COVID-19 patients. All the available data from high-quality research articles relevant to the epidemiology, demographics, trends in hospitalization and outcomes, clinical signs and symptoms, diagnostic methods and treatment methods of COVID-19 were retrieved and evaluated for inclusion. As per our review, the mean age of patients in the severe group was 59.3 years compared to 46.5 years in non severe group. COVID-19 was more severe among men than women. Clinical presentation was variable among different studies. and dyspnea was the factor indicating severe disease. Laboratory parameters associated with increased severity were lymphopenia <0.8×10 /L, thrombocytopenia 100×10 /L, leucocytosis TC>11×10 /L, procalcitonin >0.5ng/mL, d dimer >2 mcg/mL, aspartate transaminase elevation >150U/L, LDH >250U/L. This systematic review suggests that COVID-19 is a disease with varied clinical presentation and laboratory parameters. The commonest clinical symptoms were fever, cough and dyspnea. The laboratory parameters associated with severe disease were lymphopenia, elevated LDH, D dimer and Procalcitonin. This systematic review suggests that COVID-19 is a disease with varied clinical presentation and laboratory parameters. The commonest clinical symptoms were fever, cough and dyspnea. The laboratory parameters associated with severe disease were lymphopenia, elevated LDH, D dimer and Procalcitonin. To determine the risk factors and patterns of recurrence after radiofrequency ablation (RFA) for hepatocellular carcinoma (HCC) meeting the up-to-seven criteria and to develop a nomogram to predict the recurrence free survival (RFS). This retrospective study included 481 HCC patients meeting the up-to-seven criteria and who received RFA as the primary therapy at three Chinese hospitals from January 2013 to December 2016. All clinical variables were assessed by univariate and multivariate Cox regression analyses and a nomogram was constructed to predict the probability of RFS. The recurrence rate was 50.7 % (244/481). Age > 60 years, male gender, and multiple tumors were independent risk factors of recurrence. The incidence of early and late recurrence was 68.03 % (n = 166) and 31.97 % (n = 78), respectively. Seven patterns of spatial recurrence were identified local tumor progression (LTP) alone (n = 18, 7.38 %), intrahepatic distant recurrence (IDR) alone (n = 136, 55.74 %), extrahepatic recurrence (ER) alone (n = 21, 8.61 %), IDR + ER (n = 45, 18.44 %), LTP + IDR (n = 16, 6.56 %), LTP + ER (n = 4, 1.64 %) and LTP + IDR + ER (n = 4, 1.64 %). The 1-, 2-, and 3-year RFS rates were 79.63 %, 65.23 %, and 51.03 %, respectively. A well-discriminated and calibrated nomogram was constructed. The factors affecting recurrence after RFA were age, gender, and the number of tumors. IDR was the most common type of recurrence after complete ablation. The factors affecting recurrence after RFA were age, gender, and the number of tumors. IDR was the most common type of recurrence after complete ablation. We propose a 3-D tumor computer-aided diagnosis (CADx) system with U-net and a residual-capsule neural network (Res-CapsNet) for ABUS images and provide a reference for early tumor diagnosis, especially non-mass lesions. A total of 396 patients with 444 tumors (226 malignant and 218 benign) were retrospectively enrolled from Sun Yat-sen University Cancer Center. In our CADx, preprocessing was performed first to crop and resize the tumor volumes of interest (VOIs). Then, a 3-D U-net and postprocessing were applied to the VOIs to obtain tumor masks. Finally, a 3-D Res-CapsNet classification model was executed with the VOIs and the corresponding masks to diagnose the tumors. Finally, the diagnostic performance, including accuracy, sensitivity, specificity, and area under the curve (AUC), was compared with other classification models and among three readers with different years of experience in ABUS review. For all tumors, the accuracy, sensitivity, specificity, and AUC of the proposed CADx were 84.9 %, 87.2 %, 82.6 %, and 0.9122, respectively, outperforming other models and junior reader. https://www.selleckchem.com/products/blu-451.html Next, the tumors were subdivided into mass and non-mass tumors to validate the system performance. For mass tumors, our CADx achieved an accuracy, sensitivity, specificity, and AUC of 85.2 %, 88.2 %, 82.3 %, and 0.9147, respectively, which was higher than that of other models and junior reader. For non-mass tumors, our CADx achieved an accuracy, sensitivity, specificity, and AUC of 81.6 %, 78.3 %, 86.7 %, and 0.8654, respectively, outperforming the two readers. The proposed CADx with 3-D U-net and 3-D Res-CapsNet models has the potential to reduce misdiagnosis, especially for non-mass lesions. The proposed CADx with 3-D U-net and 3-D Res-CapsNet models has the potential to reduce misdiagnosis, especially for non-mass lesions. We aim to develop survival predictive tools to inform clinical decision-making in perihilar cholangiocarcinoma (pCCA). A total of 184 patients who had curative resection and magnetic resonance imaging (MRI) examination for pCCA between January 2010 and December 2018 were enrolled. 110 patients were randomly selected for model development, while the other 74 patients for model testing. Preoperative clinical, laboratory, and imaging data were analyzed. Preoperative clinical predictors were used independently or integrated with radiomics signatures to construct different preoperative models through the multivariable Cox proportional hazards method. The nomograms were constructed to predict overall survival (OS), and the performance of which was evaluated by the discrimination ability, time-dependent receiver operating characteristic curve (ROC), calibration curve, and decision curve. The clinical model (Model ) was constructed based on three independent variables including preoperative CEA, cN stage, and invasion of hepatic artery in images.
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