2 research outputs found
Metal-Oxidant-Free Cobalt-Catalyzed C(sp<sup>2</sup>)–H Carbonylation of <i>ortho</i>-Arylanilines: An Approach toward Free (<i>NH</i>)‑Phenanthridinones
A traceless directing
group assisted Co-catalyzed CÂ(sp<sup>2</sup>)–H carbonylation
of <i>ortho</i>-arylanilines for
the synthesis of free (<i>NH</i>)-phenanthridinones in metal-based-oxidant-free
fashion was accomplished. This protocol employs diisopropyl azodicarboxylate
as the CO source and oxygen as the sole oxidant, and provides good
yields with various functional tolerance. The methodology has been
applied for the total synthesis of PARP inhibitor <b>PJ-34</b>. Furthermore, the kinetic isotopic effect experiments reveal the
C–H bond cleavage probably occurred in the rate-determining
step
Data_Sheet_1_Radiomics analysis of pancreas based on dual-energy computed tomography for the detection of type 2 diabetes mellitus.PDF
ObjectiveTo utilize radiomics analysis on dual-energy CT images of the pancreas to establish a quantitative imaging biomarker for type 2 diabetes mellitus.Materials and methodsIn this retrospective study, 78 participants (45 with type 2 diabetes mellitus, 33 without) underwent a dual energy CT exam. Pancreas regions were segmented automatically using a deep learning algorithm. From these regions, radiomics features were extracted. Additionally, 24 clinical features were collected for each patient. Both radiomics and clinical features were then selected using the least absolute shrinkage and selection operator (LASSO) technique and then build classifies with random forest (RF), support vector machines (SVM) and Logistic. Three models were built: one using radiomics features, one using clinical features, and a combined model.ResultsSeven radiomic features were selected from the segmented pancreas regions, while eight clinical features were chosen from a pool of 24 using the LASSO method. These features were used to build a combined model, and its performance was evaluated using five-fold cross-validation. The best classifier type is Logistic and the reported area under the curve (AUC) values on the test dataset were 0.887 (0.73–1), 0.881 (0.715–1), and 0.922 (0.804–1) for the respective models.ConclusionRadiomics analysis of the pancreas on dual-energy CT images offers potential as a quantitative imaging biomarker in the detection of type 2 diabetes mellitus.</p