circELP2 reverse-splicing biogenesis and function as a pro-fibrogenic factor by targeting mitochondrial quality control pathway.

Idiopathic pulmonary fibrosis (IPF) is considered as a chronic, fibrosing interstitial pneumonia with unknown mechanism. The present work aimed to explore the function, biogenesis and regulatory mechanism of circELP2 in pulmonary fibrosis and evaluate the value of blocking circELP2-medicated signal pathway for IPF treatment. The results showed that heterogeneous nuclear ribonucleoprotein L initiated reverse splicing of circELP2 resulting in the increase of circELP2 generation. The biogenetic circELP2 activated the abnormal proliferation and migration of fibroblast and extracellular matrix deposition to promote pulmonary fibrogenesis. Mechanistic studies demonstrated that cytoplasmic circELP2 sponged miR-630 to increase transcriptional co-activators Yes-associated protein 1 (YAP1) and transcriptional co-activator with PDZ-binding motif (TAZ). Then, YAP1/TAZ bound to the promoter regions of their target genes, such as mTOR, Raptor and mLST8, which in turn activated or inhibited the genes expression in mitochondrial quality control pathway. Finally, the overexpressed circELP2 and miR-630 mimic were packaged into adenovirus vector for spraying into the mice lung to evaluate therapeutic effect of blocking circELP2-miR-630-YAP1/TAZ-mitochondrial quality control pathway in vivo. In conclusion, blocking circELP2-medicated pathway can alleviate pulmonary fibrosis, and circELP2 may be a potential target to treat lung fibrosis.

Proteomic analysis reveals the aging-related pathways contribute to pulmonary fibrogenesis.

Aging usually causes lung-function decline and susceptibility to chronic lung diseases, such as pulmonary fibrosis. However, how aging affects the lung-fibrosis pathways and leads to the occurrence of pulmonary fibrosis is not completely understood. Here, mass spectrometry-based proteomics was used to chart the lung proteome of young and old mice. Micro computed tomography imaging, RNA immunoprecipitation, dual-fluorescence mRFP-GFP-LC3 adenovirus monitoring, transmission electron microscopy, and other experiments were performed to explore the screened differentially expressed proteins related to abnormal ferroptosis, autophagy, mitochondria, and mechanical force in vivo, in vitro, and in healthy people. Combined with our previous studies on pulmonary fibrosis, we further demonstrated that these biological processes and underlying molecular players were also involved in the aging process. Our work depicted a comprehensive cellular and molecular atlas of the aging lung and attempted to explain why aging is a risk factor for pulmonary fibrosis and the role that aging plays in the progression of pulmonary fibrosis. The abnormalities of aging triggered an increase in mechanical force and ferroptosis, autophagy blockade, and mitochondrial dysfunction, which often appear during pulmonary fibrogenesis. We hope that the elucidation of these anomalies will help to enhance our understanding of senescence-inducing pulmonary fibrosis, thereby guiding the use of anti-senescence as an entry point for early intervention in pulmonary fibrosis and age-related diseases.

Chalcogen Bonding Catalysis of the Cloke-Wilson Rearrangement.

The Cloke-Wilson rearrangement is an important method to construct heterocycles which was conventionally facilitated by strong Lewis acids, Brønsted acids and Lewis bases. A weak interaction catalysis approach to the Cloke-Wilson rearrangement remains a challenging topic and yet no example is reported. Herein, a chalcogen bonding catalysis method to achieve the Cloke-Wilson rearrangement is described that involves activation of carbonyl cyclopropanes by double Se⋅⋅⋅O interactions. This chalcogen bonding catalysis approach afforded a wide range of dihydrofurans with good yields, thus establishing an alternative strategy to catalyze the Cloke-Wilson rearrangement.

A Selenide Catalyst for the Activation of Alkenes through Se⋠⋠⋠π Bonding.

Chalcogen bonding catalysis with divalent chalcogenides required using heteroatoms as electron donors to give reactivity, while the activation of hydrocarbons such as alkenes by this concept remains an unresolved challenge. Herein, we discovered a powerful selenide catalyst that showed unprecedented capability in the activation of alkenes. The Se⋅⋅⋅π interactions were capable of catalyzing a broad range of transformations, including intermolecular cyclization and coupling reactions. Significantly, the Se⋅⋅⋅π bonding activation mode can be exploited to achieve intermolecular enyne cyclizations and controlled cross-coupling of triple alkenes. The activation of alkenes by divalent selenides opens up a new avenue for supramolecular catalysis.

Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension.

Risk stratification of young patients with hypertension remains challenging. Generally, machine learning (ML) is considered a promising alternative to traditional methods for clinical predictions because it is capable of processing large amounts of complex data. We, therefore, explored the feasibility of an ML approach for predicting outcomes in young patients with hypertension and compared its performance with that of approaches now commonly used in clinical practice. Baseline clinical data and a composite end point-comprising all-cause death, acute myocardial infarction, coronary artery revascularization, new-onset heart failure, new-onset atrial fibrillation/atrial flutter, sustained ventricular tachycardia/ventricular fibrillation, peripheral artery revascularization, new-onset stroke, end-stage renal disease-were evaluated in 508 young patients with hypertension (30.83±6.17 years) who had been treated at a tertiary hospital. Construction of the ML model, which consisted of recursive feature elimination, extreme gradient boosting, and 10-fold cross-validation, was performed at the 33-month follow-up evaluation, and the model's performance was compared with that of the Cox regression and recalibrated Framingham Risk Score models. An 11-variable combination was considered most valuable for predicting outcomes using the ML approach. The C statistic for identifying patients with composite end points was 0.757 (95% CI, 0.660-0.854) for the ML model, whereas for Cox regression model and the recalibrated Framingham Risk Score model it was 0.723 (95% CI, 0.636-0.810) and 0.529 (95% CI, 0.403-0.655). The ML approach was comparable with Cox regression for determining the clinical prognosis of young patients with hypertension and was better than that of the recalibrated Framingham Risk Score model.

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data.

The outcomes of hypertension refer to the death or serious complications (such as myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes of hypertension are very concerning for patients and doctors, and are ideally avoided. However, there is no satisfactory method for predicting the outcomes of hypertension. Therefore, this paper proposes a prediction method for outcomes based on physical examination indicators of hypertension patients. In this work, we divide the patients' outcome prediction into two steps. The first step is to extract the key features from the patients' many physical examination indicators. The second step is to use the key features extracted from the first step to predict the patients' outcomes. To this end, we propose a model combining recursive feature elimination with a cross-validation method and classification algorithm. In the first step, we use the recursive feature elimination algorithm to rank the importance of all features, and then extract the optimal features subset using cross-validation. In the second step, we use four classification algorithms (support vector machine (SVM), C4.5 decision tree, random forest (RF), and extreme gradient boosting (XGBoost)) to accurately predict patient outcomes by using their optimal features subset. The selected model prediction performance evaluation metrics are accuracy, F1 measure, and area under receiver operating characteristic curve. The 10-fold cross-validation shows that C4.5, RF, and XGBoost can achieve very good prediction results with a small number of features, and the classifier after recursive feature elimination with cross-validation feature selection has better prediction performance. Among the four classifiers, XGBoost has the best prediction performance, and its accuracy, F1, and area under receiver operating characteristic curve (AUC) values are 94.36%, 0.875, and 0.927, respectively, using the optimal features subset. This article's prediction of hypertension outcomes contributes to the in-depth study of hypertension complications and has strong practical significance.

Metal-free iodine(iii)-promoted synthesis of 2,5-diaryloxazoles.

A nonmetal-catalyzed oxidative cyclization to achieve 2,5-disubstituted oxazoles from inexpensive and readily available substituted chalcone, (diacetoxyiodo)benzene (PIDA) and ammonium acetate (NH4OAc) at room temperature is described. The reaction forms a variety of 2,5-diaryloxazoles in good to excellent yields with broad substrate scope under mild conditions without the requirement of ligands and additional bases.