A vertically-stacked MXene/rGO composite membrane for highly efficient H/CO separation.

A vertically-stacked MXene/rGO composite membrane with ultrashort transport channels is reported here, which demonstrated outstanding molecular sieving, , H/CO selectivity of up to 83 together with high H permeance of 2.7 × 10 mol m s Pa at 120 °C, highlighting its applicability for H/CO separation in CO capture and sequestration.

Random Forest-Based Prediction of Acute Respiratory Distress Syndrome in Patients Undergoing Cardiac Surgery.

Background: To develop a machine learning-based model for predicting the risk of acute respiratory distress syndrome (ARDS) after cardiac surgery.

Methods: Data were collected from 1011 patients, who underwent cardiac surgery between February 2018 and September 2019. We developed a predictive model on ARDS by using the random forest algorithm of machine learning.

Inferring disease progression and gene regulatory networks from clinical transcriptomic data using PROB_R.

Due to a lack of explicit temporal information, it can be challenging to infer gene regulatory networks from clinical transcriptomic data. Here, we describe the protocol of PROB_R for inferring latent temporal disease progression and reconstructing gene regulatory networks from cross-sectional clinical transcriptomic data. We illustrate the protocol by applying it to a breast cancer dataset to demonstrate its use in recovering pseudo-temporal dynamics of gene expression alongside disease progression, reconstructing gene regulatory networks, and identifying key regulatory genes.

Learning Curve of Aortic Arch Replacement Surgery in Chinese Mainland with Stanford Type A Aortic Dissection.

Background: Stanford type A aortic dissection (TAAD) is the most common cause of death caused by aortic disease in the Chinese mainland. Patients suffering TAAD need immediate surgical treatment [Pompilio 2001; Di Eusanio 2003; Ueda 2003; Li 2013; Afifi 2016; Zhou 2019; Zhou 2021]. Emergency aortic arch replacement is difficult and risky.