Searching for the efficient and sustainable inhibitors to defend metal corrosion in the marine environment has received a great deal attention in both academia and industry. In this work, a new strategy was developed to encapsulate benzimidazole (BI) inhibitor guest molecules into the micropores of bio-MOF-1, which was constructed with the other natural adenine inhibitor. Electrochemical characterizations revealed that the slowly sustained release of BI and adenine inhibitors from the BI@bio-MOF-1 composite presented excellent anti-corrosion durability in the whole mild steel corrosion process. The impedance arc value attained approximately 5000 Ω•cm 2 at initially 2 h and further increased to 6000 Ω•cm 2 after 5 days immersion in 0.5 M NaCl solution, and the inhibition efficiency reached 85.36% due to the synergistic dual inhibitive effect of BI@bio-MOF-1 composite. The dual inhibition-based bio-MOF-1 composite equipped strong metal-chelating capability and response durability, exhibiting high potentiality for metal anti-corrosion applications.
BackgroundThe coronavirus disease 2019 (COVID-19) is an acute infectious pneumonia caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection previously unknown to humans. However, predictive studies of acute respiratory distress syndrome (ARDS) in patients with COVID-19 are limited. In this study, we attempted to establish predictive models to predict ARDS caused by COVID-19 via a thorough analysis of patients' clinical data and CT images.MethodThe data of included patients were retrospectively collected from the intensive care unit in our hospital from April 2022 to June 2022. The primary outcome was the development of ARDS after ICU admission. We first established two individual predictive models based on extreme gradient boosting (XGBoost) and convolutional neural network (CNN), respectively; then, an integrated model was developed by combining the two individual models. The performance of all the predictive models was evaluated using the area under receiver operating characteristic curve (AUC), confusion matrix, and calibration plot.ResultsA total of 103 critically ill COVID-19 patients were included in this research, of which 23 patients (22.3%) developed ARDS after admission; five predictive variables were selected and further used to establish the machine learning models, and the XGBoost model yielded the most accurate predictions with the highest AUC (0.94, 95% CI: 0.91–0.96). The AUC of the CT-based convolutional neural network predictive model and the integrated model was 0.96 (95% CI: 0.93-0.98) and 0.97 (95% CI: 0.95–0.99), respectively.ConclusionAn integrated deep learning model could be used to predict COVID-19 ARDS in critically ill patients.
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