Though the mechanism of MEA-CO2 system has been widely studied, there is few literature on the detailed mechanism of CO2 capture into MEA solution with different CO2 loading during absorption/desorption processes. To get a clear picture of the process mechanism, (13)C nuclear magnetic resonance (NMR) was used to analyze the reaction intermediates under different CO2 loadings and detailed mechanism on CO2 absorption and desorption in MEA was evaluated in this work. The results demonstrated that the CO2 absorption in MEA started with the formation of carbamate according to the zwitterion mechanism, followed by the hydration of CO2 to form HCO3(-)/CO3(2-), and accompanied by the hydrolysis of carbamate. It is interesting to find that the existence of carbamate will be influenced by CO2 loading and that it is rather unstable at high CO2 loading. At low CO2 loading, carbamate is formed fast by the reaction between CO2 and MEA. At high CO2 loading, it is formed by the reaction of CO3(-)/CO3(2-) with MEA, and the formed carbamate can be easily hydrolyzed by H(+). Moreover, CO2 desorption from the CO2-saturated MEA solution was proved to be a reverse process of absorption. Initially, some HCO3(-) were heated to release CO2 and other HCO3(-) were reacted with carbamic acid (MEAH(+)) to form carbamate, and the carbamate was then decomposed to MEA and CO2.

It was recently brought to our attention that our paper was missing information regarding when the patient chest computed tomography (CT) scans were obtained and that there were some discrepancies in the clinical metadata, associated with the very large image dataset, that we made publicly available through the China National Center for Bioinformation (http://ncov-ai.big.ac.cn/ download?lang=en). All of the chest CT and clinical metadata used in our prognostic analysis were collected from patients at the time of hospital admission, and we have now added this statement to the STAR Methods section of our paper. We believe that the errors in the clinical metadata were introduced when the chest CT images, clinical metadata, and codes were transferred to the web server, and we have now corrected the errors manually. Although these corrections do not alter any of the conclusions made in the paper, we do apologize for these errors and any confusion that they may have caused.

Objectives To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents. Methods A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score. Results Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm 3. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance. Conclusions The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists. Key Points • The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.

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Abnormal metabolism of tumour cells is closely related to the occurrence and development of breast cancer, during which the expression of NF‐E2‐related factor 2 (Nrf2) is of great significance. Metastatic breast cancer is one of the most common causes of cancer death worldwide; however, the molecular mechanism underlying breast cancer metastasis remains unknown. In this study, we found that the overexpression of Nrf2 promoted proliferation and migration of breast cancers cells. Inhibition of Nrf2 and overexpression of Kelch‐like ECH‐associated protein 1 (Keap1) reduced the expression of glucose‐6‐phosphate dehydrogenase (G6PD) and transketolase of pentose phosphate pathway, and overexpression of Nrf2 and knockdown of Keap1 had opposite effects. Our results further showed that the overexpression of Nrf2 promoted the expression of G6PD and Hypoxia‐inducing factor 1α (HIF‐1α) in MCF‐7 and MDA‐MB‐231 cells. Overexpression of Nrf2 up‐regulated the expression of Notch1 via G6PD/HIF‐1α pathway. Notch signalling pathway affected the proliferation of breast cancer by affecting its downstream gene HES‐1, and regulated the migration of breast cancer cells by affecting the expression of EMT pathway. The results suggest that Nrf2 is a potential molecular target for the treatment of breast cancer and targeting Notch1 signalling pathway may provide a promising strategy for the treatment of Nrf2‐driven breast cancer metastasis.