Boosting Gold(I) Catalysis via Weak Interactions: New Fine-Tunable Impy Ligands

An Improved Gold(I) Catalytic System for the Preparation of Coumarins via Intramolecular Cyclization

A catalytic system comprising a gold(I) complex with an N-heterocyclic carbene (NHC) ligand in an ionic liquid as solvent exhibits higher catalytic efficiency compared to state of the art systems in the title reaction, which enables using down to 0.01 mol % gold. A commercial gold(I) catalyst such as IPrAuNTf2 can be employed for this purpose. In the case of less reactive substrates bearing electron-withdrawing substituents at the phenol moiety, a tailor made NHC-gold(I) precatalyst exhibits improved reactivity and can be advantageously employed compared to the commercial one.

Chiral Biaryl N-Heterocyclic Carbene-Palladium Catalysts with Anagostic C-H···Pd Interaction for Enantioselective Desymmetric C-N Cross-Coupling

Novel chiral biaryl imidazo[1,5-a]pyridine carbene-palladium complexes (ImPy-Pd) featuring an anagostic C-H···Pd interaction and a C5-aryl substituent have been developed and successfully applied to the Pd-catalyzed enantioselective desymmetric C-N cross-coupling of malonamide derivatives, providing chiral 3,4-dihydroquinoline-2-ones with quaternary stereocenters in high yields (≤99%) and enantioselectivities (≤97:3 er). The chiral catalyst exerts stereocontrol by restricting the rotation of substituents around the metal center through anagostic interactions with sterically bulky substituents.

A laterally-fused N-heterocyclic carbene framework from polysubstituted aminoimidazo[5,1-b]oxazol-6-ium salts

A polysubstituted 3-aminoimidazo[5,1-b]oxazol-6-ium framework has been accessed from a new nitrenoid reagent by a two-step ynamide annulation and imidazolium ring-formation sequence. Metalation with Au(I), Cu(I) and Ir(I) at the C2 position provides an L-shaped NHC ligand scaffold that has been validated in gold-catalysed alkyne hydration and arylative cyclisation reactions.

[Au(Np#)Cl]: Highly Reactive and Broadly Applicable Au(I)─NHC Catalysts for Alkyne π-Activation Reactions

Cationic Au(I)─NHC (NHC = N-heterocyclic carbene) complexes have become an important class of catalysts for alkyne π-activation reactions in organic synthesis. In particular, these complexes are characterized by high stability of catalytic species engendered by strong σ-donation and metal backbonding. Herein, we report the synthesis and characterization of well-defined [Au(NHC)Cl] complexes featuring recently discovered IPr# family of ligands that hinge upon modular peralkylation of aniline. These ligands have been commercialized in collaboration with MilliporeSigma (IPr#: 915653; Np#: 915912; BIAN-IPr#: 916420). Evaluation of the [Au(NHC)Cl] complexes in a series of Au(I)─NHC-catalyzed π-functionalizations of alkynes, such as hydrocarboxylation, hydroamination and hydration, resulted in the identification of wingtip-flexible [Au(Np#)Cl] as a highly reactive and broadly applicable catalyst with the re-activity outperforming the classical [Au(IPr)Cl] and [Au(IPr*)Cl] complexes. The utility of this catalyst has been demonstrated in the direct late-stage derivatization of complex pharmaceuticals. Structural and computational studies were conducted to determine steric effects, frontier molecular orbitals and bond orders of this class of catalysts. Considering the attractive features of well-defined Au(I)─NHC complexes, we anticipate that this class of bulky and wingtip-flexible Au(I)─NHCs based on the modular peralkylated naphthylamine scaffold will find broad application in π-functionalization of alkynes in various areas of organic synthesis and catalysis.

Predicting hospital readmission risk in patients with COVID-19: A machine learning approach

Introduction

The Coronavirus 2019 (COVID-19) epidemic stunned the health systems with severe scarcities in hospital resources. In this critical situation, decreasing COVID-19 readmissions could potentially sustain hospital capacity. This study aimed to select the most affecting features of COVID-19 readmission and compare the capability of Machine Learning (ML) algorithms to predict COVID-19 readmission based on the selected features.

Material and methods

The data of 5791 hospitalized patients with COVID-19 were retrospectively recruited from a hospital registry system. The LASSO feature selection algorithm was used to select the most important features related to COVID-19 readmission. HistGradientBoosting classifier (HGB), Bagging classifier, Multi-Layered Perceptron (MLP), Support Vector Machine ((SVM) kernel = linear), SVM (kernel = RBF), and Extreme Gradient Boosting (XGBoost) classifiers were used for prediction. We evaluated the performance of ML algorithms with a 10-fold cross-validation method using six performance evaluation metrics.

Results

Out of the 42 features, 14 were identified as the most relevant predictors. The XGBoost classifier outperformed the other six ML models with an average accuracy of 91.7%, specificity of 91.3%, the sensitivity of 91.6%, F-measure of 91.8%, and AUC of 0.91%.

Conclusion

The experimental results prove that ML models can satisfactorily predict COVID-19 readmission. Besides considering the risk factors prioritized in this work, categorizing cases with a high risk of reinfection can make the patient triaging procedure and hospital resource utilization more effective.

Convenient synthesis of tricyclic N(1)-C(2)-fused oxazino-indolones via [Au(I)] catalyzed hydrocarboxylation of allenes

A new [Au(I)] catalyzed intramolecular hydrocarboxylation of allenes is presented as a valuable synthetic route to oxazino-indolones. The employment of 3,5-(CF3)2-C6H3-ImPyAuSbF6 as the optimal catalyst (5 mol%) was necessary to...