Online application for the diagnosis of atherosclerosis by six genes.

BACKGROUND: Atherosclerosis (AS) is a primary contributor to cardiovascular disease, leading to significant global mortality rates. Developing effective diagnostic indicators and models for AS holds the potential to substantially reduce the fatalities and disabilities associated with cardiovascular disease. Blood sample analysis has emerged as a promising avenue for facilitating diagnosis and assessing disease prognosis. Nonetheless, it lacks an accurate model or tool for AS diagnosis. Hence, the principal objective of this study is to develop a convenient, simple, and accurate model for the early detection of AS. METHODS: We downloaded the expression data of blood samples from GEO databases. By dividing the mean values of housekeeping genes (meanHGs) and applying the comBat function, we aimed to reduce the batch effect. After separating the datasets into training, evaluation, and testing sets, we applied differential expression analyses (DEA) between AS and control samples from the training dataset. Then, a gradient-boosting model was used to evaluate the importance of genes and identify the hub genes. Using different machine learning algorithms, we constructed a prediction model with the highest accuracy in the testing dataset. Finally, we make the machine learning models publicly accessible by shiny app construction. RESULTS: Seven datasets (GSE9874, GSE12288, GSE20129, GSE23746, GSE27034, GSE90074, and GSE202625), including 403 samples with AS and 325 healthy subjects, were obtained by comprehensive searching and filtering by specific requirements. The batch effect was successfully removed by dividing the meanHGs and applying the comBat function. 331 genes were found to be related to atherosclerosis by the DEA analysis between AS and health samples. The top 6 genes with the highest importance values from the gradient boosting model were identified. Out of the seven machine learning algorithms tested, the random forest model exhibited the most impressive performance in the testing datasets, achieving an accuracy exceeding 0.8. While the batch effect reduction analysis in our study could have contributed to the increased accuracy values, our comparison results further highlight the superiority of our model over the genes provided in published studies. This underscores the effectiveness of our approach in delivering superior predictive performance. The machine-learning models were then uploaded to the Shiny app's server, making it easy for users to distinguish AS samples from normal samples. CONCLUSIONS: A prognostic Shiny application, built upon six potential atherosclerosis-associated genes, has been developed, offering an accurate diagnosis of atherosclerosis.

Three-Component Coupling of Anilines, Amines, and Difluorocarbene to Access Formamidines.

A one-pot three-component reaction of two anilines (or one aniline and one alkylamine) and in situ-generated difluorocarbene is developed herein to enable efficient construction of formamidines. Crucial formimidoyl fluoride intermediate RN═CHF is proposed from the reaction of a primary aniline and difluorocarbene. Ensuing nucleophilic iminyl substitution of this intermediate with a second amine allows cross-condensation of the two amines to produce formamidines. When only one type of primary aniline is used as the substrate, the difluoromethylated homo-condensation products can also be produced under a 1:1 molar ratio of aniline/difluorocarbene. Intramolecular variant of this method allows concise synthesis of benzimidazoquinazolines and nitrogen-fused/spirocyclic compounds, showing the potential of this method in organic synthesis. More interesting reactions are anticipated by exploiting the reactivity of difluorocarbene and primary amines to isocyanides or the formimidoyl fluoride intermediates.

Synthesis of Isocyanides by Reacting Primary Amines with Difluorocarbene.

A general, convenient, and friendly route for preparing a versatile building block of isocyanides from primary amines is developed. Difluorocarbene, generated in situ from decarboxylation of chlorodifluoroacetate, reacts efficiently with primary amines to produce isocyanides. Various primary amines are well tolerated, including aryl, heteroaryl, benzyl, and alkyl amines, as well as amine residues in amino acids and peptides. Late-stage functionalization of biologically active amines is demonstrated, showing its practical capacity in drug design and peptide modification.

An aerobic and green C-H cyanation of terminal alkynes.

This study describes a benign C-H cyanation of terminal alkynes with α-cyanoesters serving as a nontoxic cyanide source. In situ generation of the key copper cyanide intermediate is proposed by a sequence of α-C-H oxidation and copper-mediated ß-carbon elimination of α-cyanoesters, releasing the α-ketoester byproduct observed experimentally. The ensuing reaction of copper cyanide with terminal alkynes delivers preferentially cyanoalkynes and surpasses the possible Glaser type dimerization of terminal alkynes or the undesired accumulation of HCN under protic conditions. The presence of the co-oxidant K2S2O8 is crucial to this selectivity, probably by promoting oxidative transmetalation and the resulting formation of the Cu(iii)(acetylide)(CN) intermediate. All the reagents and salts used are commercially available, cheap and nontoxic, avoiding the use of highly toxic cyanide salts typically required in cyanation studies. The scope of this reaction is demonstrated with a set of alkynes and α-cyanoesters. The application of this method to late-stage functionalization of the terminal alkyne group in an estrone derivative is also feasible, showing its practical value for drug design.