BackgroundDistant recurrence is one of the most important risk factors in overall survival, and distant recurrence is related to a complex biologic interaction of seed and soil factors. The aim of the study was to investigate the association between the molecular subtypes and patterns of distant recurrence in patients with breast cancer.MethodsIn an investigation of 313 women with breast cancer who underwent surgery from 1994 and 2000, the expressions of estrogen and progestrone receptor (ER/PR), and human epithelial receptor-2 (HER2) were evaluated. The subtypes were defined as luminal-A, luminal-HER2, HER2-enriched, and triple negative breast cancer (TNBC) according to ER, PR, and HER2 status.ResultsBone was the most common site of distant recurrence. The incidence of first distant recurrence site was significantly different among the subtypes. Brain metastasis was more frequent in the luminal-HER2 and TNBC subtypes. In subgroup analysis, overall survival in patients with distant recurrence after 24 months after surgery was significantly different among the subtypes.ConclusionsOrgan-specific metastasis may depend on the molecular subtype of breast cancer. Tailored strategies against distant metastasis concerning the molecular subtypes in breast cancer may be considered.
In PTC patients ≥45 years of age, a higher BMI was associated with more aggressive tumor features, such as lymph node metastasis, lymphatic invasion, and tumor multiplicity.
Computational methods for predicting drug-target interactions have become important in drug research because they can help to reduce the time, cost, and failure rates for developing new drugs. Recently, with the accumulation of drug-related data sets related to drug side effects and pharmacological data, it has became possible to predict potential drug-target interactions. In this study, we focus on drug-drug interactions (DDI), their adverse effects () and pharmacological information (), and investigate the relationship among chemical structures, side effects, and DDIs from several data sources. In this study, data from the STITCH database, from drugs.com, and drug-target pairs from ChEMBL and SIDER were first collected. Then, by applying two machine learning approaches, a support vector machine (SVM) and a kernel-based L1-norm regularized logistic regression (KL1LR), we showed that DDI is a promising feature in predicting drug-target interactions. Next, the accuracies of predicting drug-target interactions using DDI were compared to those obtained using the chemical structure and side effects based on the SVM and KL1LR approaches, showing that DDI was the data source contributing the most for predicting drug-target interactions.
In the treatment of spent high energetic materials, the issues such as environmental pollution, safety as well as working capacity should be carefully considered and well examined. In this regard, incineration has been recommended as one of the most promising processes for the disposal of such explosives. Due to the fact that high energetic materials encompass various types and their different characteristics, the technology development dealing with various materials is not an easy task. In this study, rigorous modeling and dynamic simulation was carried out to predict dynamic physico-chemical phenomena for research department explosive (RDX). Plug flow reactor was employed to describe the incinerator with 263 elementary reactions and 43 chemical species. Simulation results showed that safe operations can be achieved mainly by controlling the reactor temperature. At 1,200 K, only thermal decomposition (combustion) occurred, whereas increasing temperature to 1,300 K, caused the reaction rates to increase drastically, which led to ignition. The temperature further increased to 3,000 K which was the maximum temperature recorded for the entire process. Case studies for different operating temperatures were also executed and it was concluded that the modeling approach and simulation results will serve as a basis for the effective design and operation of RDX incinerator.
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