1,605 research outputs found

    Solubility enhancement of decitabine as anticancer drug via green chemistry solvent: Novel computational prediction and optimization

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    Nowadays, supercritical fluid technology (SFT) has been an interesting scientific subject in disparate industrial-based activities such as drug delivery, chromatography, and purification. In this technology, solubility plays an incontrovertible role. Therefore, achieving more knowledge about the development of promising numerical/computational methods of solubility prediction to validate the experimental data may be advantageous for increasing the quality of research and therefore, the efficacy of novel drugs. Decitabine with the chemical formula Cā‚ˆHā‚ā‚‚Nā‚„Oā‚„ is a chemotherapeutic agent applied for the treatment of disparate bone-marrow-related malignancies such as acute myeloid leukemia (AML) by preventing DNA methyltransferase and activation of silent genes. This study aims to predict the optimum value of decitabine solubility in COā‚‚SCF by employing different machine learning-based mathematical models. In this investigation, we used AdaBoost (Adaptive Boosting) to boost three base models including Linear Regression (LR), Decision Tree (DT), and GRNN. We used a dataset that has 32 sample points to make solubility models. One of the two input features is P (bar) and the other is T (k). ADA-DT (Adaboost Algorithm Decision Tree), ADA-LR (Adaboost Algorithm-Linear Regresion), and ADA-GRNN (Generative Regression Neural Network) models showed MAE of 6.54 Ė£ 10Ė‰āµ, 4.66 10 Ė‰āµ, and 8.35 10 Ė‰āµ, respectively. Also, in terms of R-squared score, these models have 0.986, 0.983, and 0.911 scores, respectively. ADA-LR was selected as the primary model according to numerical and visual analysis. Finally, the optimal values are (P = 400 bar, T = 3.38 K 102, Y = 1.064 10Ė‰Ā³ mol fraction) using this model

    Search for flavour-changing neutral currents in processes with one top quark and a photon using 81 fbāˆ’1 of pp collisions at s=13TeV with the ATLAS experiment

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    A search for flavour-changing neutral current (FCNC) events via the coupling of a top quark, a photon, and an up or charm quark is presented using 81 fbāˆ’1 of protonā€“proton collision data taken at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC. Events with a photon, an electron or muon, a b-tagged jet, and missing transverse momentum are selected. A neural network based on kinematic variables differentiates between events from signal and background processes. The data are consistent with the background-only hypothesis, and limits are set on the strength of the tqĪ³ coupling in an effective field theory. These are also interpreted as 95% CL upper limits on the cross section for FCNC tĪ³ production via a left-handed (right-handed) tuĪ³ coupling of 36 fb (78 fb) and on the branching ratio for tā†’Ī³u of 2.8Ɨ10āˆ’5 (6.1Ɨ10āˆ’5). In addition, they are interpreted as 95% CL upper limits on the cross section for FCNC tĪ³ production via a left-handed (right-handed) tcĪ³ coupling of 40 fb (33 fb) and on the branching ratio for tā†’Ī³c of 22Ɨ10āˆ’5 (18Ɨ10āˆ’5)

    Measurement of J/Ļˆ production in association with a W Ā± boson with pp data at 8 TeV

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    A measurement of the production of a prompt J/Ļˆ meson in association with a WĀ± boson with WĀ± ā†’ Ī¼Ī½ and J/Ļˆ ā†’ Ī¼+Ī¼āˆ’ is presented for J/Ļˆ transverse momenta in the range 8.5ā€“150 GeV and rapidity |yJ/Ļˆ| < 2.1 using ATLAS data recorded in 2012 at the LHC. The data were taken at a proton-proton centre-of-mass energy of s = 8 TeV and correspond to an integrated luminosity of 20.3 fbāˆ’1. The ratio of the prompt J/Ļˆ plus WĀ± cross-section to the inclusive WĀ± cross-section is presented as a differential measurement as a function of J/Ļˆ transverse momenta and compared with theoretical predictions using different double-parton-scattering cross-sections. [Figure not available: see fulltext.]
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