Identifying individuals with target mutant phenotypes is a significant procedure in mutant exploitation for implementing genome editing technology in a crop breeding programme. In the present study, a rapid and non-invasive method was proposed to identify CRISPR/Cas9-induced rice mutants from their acceptor lines (huaidao-1 and nanjing46) using hyperspectral imaging in the near-infrared (NIR) range (874.41–1733.91 nm) combined with chemometric analysis. The hyperspectral imaging data were analysed using principal component analysis (PCA) for exploratory purposes, and a support vector machine (SVM) and an extreme learning machine (ELM) were applied to build discrimination models for classification. Meanwhile, PCA loadings and a successive projections algorithm (SPA) were used for extracting optimal spectral wavelengths. The SVM-SPA model achieved best performance, with classification accuracies of 93% and 92.75% being observed for calibration and prediction sets for huaidao-1 and 91.25% and 89.50% for nanjing46, respectively. Furthermore, the classification of mutant seeds was visualized on prediction maps by predicting the features of each pixel on individual hyperspectral images based on the SPA-SVM model. The above results indicated that NIR hyperspectral imaging together with chemometric data analysis could be a reliable tool for identifying CRISPR/Cas9-induced rice mutants, which would help to accelerate selection and crop breeding processes.

To explore the effect of Multi-Disciplinary Team (MDT) mode in the diagnosis and treatment of Coronavirus Disease 2019 (COVID-19) Pneumonia. A total of 65 patients with suspected COVID-19 pneumonia were included. On February 8, 2020, our hospital officially became a designated hospital for the treatment of COVID-19, and the MDT mode was implemented throughout the diagnosis and treatment for newly admitted patients with suspected COVID-19. The patients were divided into control group and observation group according to whether received MDT mode. Our results showed that the diagnosis time in the observation group was significantly shortened than that in the control group (2.51 days vs. 3.47 days) (p < 0.05). The average daily hospitalization costs in the observation group was significantly decreased in comparison with the control group (¥766.1 vs. ¥1190.4) (p < 0.001). The average daily cost of protective materials in the observation group was significantly reduced in comparison with the control group (¥4226.90 vs. ¥5308.20) (p < 0.001). Compared with the control group, the subjective symptoms of patients in the observation group were significantly improved (p < 0.001). In conclusion, the MDT mode shortens the diagnosis time of, reduces the hospitalization costs, and improves the subjective symptoms of COVID-19.