A vertically-stacked MXene/rGO composite membrane for highly efficient H2/CO2 separation.

A vertically-stacked MXene/rGO composite membrane with ultrashort transport channels is reported here, which demonstrated outstanding molecular sieving, i.e., H2/CO2 selectivity of up to 83 together with high H2 permeance of 2.7 × 10-7 mol m-2 s-1 Pa-1 at 120 °C, highlighting its applicability for H2/CO2 separation in CO2 capture and sequestration.

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. For complete details on the use and execution of this protocol, please refer to Sun et al. (2021).

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. The prognosis following surgery varies depending on the different surgical approaches [Pompilio 2001; Kazui 2002; Di Eusanio 2003; Ueda 2003; Moon 2009; Li 2013; Afifi 2016; Zhou 2019; Zhou 2021]. Aortic arch replacement includes total-arch replacement (Sun's operation) and hemi-arch replacement. The comparative analysis of learning curves between the two procedures has not been systematically studied. In this study, we studied and analyzed the learning curves of total-arch replacement and hemi-arch replacement using cumulative sum (CUSUM) analysis. METHODS: From January 2013 to December 2019, a total of 139 Stanford TAAD operations were performed by the same surgeon and two assistants, including 61 cases of hemi-arch replacement and 78 cases of total-arch replacement. Baseline information, including preoperative conditions, intraoperative related data and postoperative prognosis, were collected. Descriptive statistics and CUSUM were used to analyze the total operation time, cardiopulmonary bypass (CPB) time, aortic clamping (AC) time, operative mortality, incidence of postoperative complications, postoperative intensive care unit (ICU) time, hospital stay, and postoperative drainage volume. RESULTS: A total of 139 patients with TAAD (age 48.8 ± 12.3, male, 107, female, 32) underwent emergency aortic arch replacement. A total of 61 patients (43.9%) underwent hemi-arch replacement, and 78 patients (56.1%) underwent total-arch replacement. The total time, cardiopulmonary bypass (CPB) time, and aortic clamping (AC) time of hemi-arch operation were 434.2 ± 137.0 minutes, 243.3 ± 87.2 minutes, and 157.0 ± 60.2 minutes. The total, CPB, and AC times of total-arch operation were 747.8 ± 164.3 minutes, 476.4 ± 121.6 minutes, and 238.5 ± 67.6 minutes. The mortality of hemi-arch operation was 3.3%, and that of total-arch operation was 6.4%. The incidence of complications after hemi-arch operation was 11.3%, and that after total-arch operation was 46.2%. The ICU time and hospital stay after hemi-arch surgery were 7.3 ± 4.4 days and 27.2 ± 16.2 days, respectively, and the ICU time and total hospital stay after total-arch surgery were 7.2 ± 5.9 days and 24.0 ± 10.3 days, respectively. The total drainage volume after hemi-arch operation was 2182.4 ± 1236.4 ml, and that after total-arch operation was 2467.3 ± 1385.7 ml. According to CUSUM analysis, the same cardiovascular surgery team seems to have different learning curves in the time of two operations. CUSUM analysis of intraoperative and postoperative indicators shows that after a certain period of professional and systematic cardiovascular surgery training, aortic hemi-arch replacement has the characteristics of short learning cycle and easy to master for surgeons, while total-arch replacement requires a longer learning cycle. CONCLUSIONS: Although the emergency operation of TAAD is difficult and risky, according to results the of CUSUM analysis, cardiovascular surgeons can achieve better learning results in hemi-arch replacement than total-arch replacement.

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. The discrimination of the model was then shown by the area under the curve (AUC) of the receiver operating characteristic curve. Internal validation was performed by using a 5-fold cross-validation technique, so as to evaluate and optimize the predictive model. Model visualization was performed to reveal the most influential features during the model output. RESULTS: Of the 1011 patients included in the study, 53 (5.24%) suffered ARDS episodes during the first postoperative week. This random forest distinguished ARDS patients from non-ARDS patients with an AUC of 0.932 (95% CI=0.896-0.968) in the training set and 0.864 (95% CI=0.718-0.997) in the final test set. The top 10 variables in the random forest were cardiopulmonary bypass time, transfusion red blood cell, age, EuroSCORE II score, albumin, hemoglobin, operation time, serum creatinine, diabetes, and type of surgery. CONCLUSION: Our findings suggest that machine learning algorithm is highly effective in predicting ARDS in patients undergoing cardiac surgery. The successful application of the generated random forest may guide clinical decision-making and aid in improving the long-term prognosis of patients.