Objective:To investigate the value of chest CT quantitative index in clinical classification and lung injury severity evaluation of COVID-19.Methods:The current study retrospectively analyzed the clinical and CT data of 438 patients with COVID-19 between January 2020 and March 2020 in Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology. The clinical types included common type ( n=146), severe type ( n=247) and critical type ( n=45). The chest CT indexes of all patients were quantitatively analyzed by artificial intelligence (AI) deep learning, including whole lung volume, CT lung opacification, ground glass opacification volume (GGO volume; CT value<-300 HU), solid opacification volume (SO volume; CT value ≥-300 HU) and the ratio of volume to the whole lung volume, the ratio of SO volume to GGO volume (SO volume/GGO volume). Kruskal-Wallis test was used to conduct statistical analysis of the differences in quantitative parameters among clinical types, and multiple ordered logistic regression was used to analyze the correlation between quantitative parameters and clinical types. Results:Among the 438 patients diagnosed with COVID-19, severe and critical patients were older ( P<0.05), and most of the critical patients were male ( P<0.05). The main clinical manifestations of all clinical types were fever, followed by cough, fatigue, chest tightness, dyspnea, gastrointestinal symptoms and so on. GGO volume was the main CT manifestation of all the three clinical subtypes. The whole-lung opacification volume, GGO volume, SO volume and their proportions in whole-lung volume significantly increased from common, severe to critical types (all P<0.05). SO volume/GGO volume increased with the severity of clinical type [common type 0.12 (0.03, 0.34), severe type 0.29 (0.11, 0.59), critical type 0.61 (0.39, 0.97)]. Multiple ordered logistic regression analysis showed that whole-lung opacification volume (OR=1.009), SO volume/GGO volume (OR=1.866), GGO volume (OR=1.008) and SO volume (OR=1.016) had a significant positive effect on the severity of clinical typing ( P<0.01). Conclusion:Quantitative indicators of chest CT based on deep learning algorithm (SO volume, GGO volume, SO volume/GGO volume) are closely related to the clinical severity of COVID-19.

BACKGROUND:The in vitro construction of intestinal smooth muscle layer,as an important component of the intestinal wall,has attracted much attention in the bionic construction of tissue-engineered intestinal canal. OBJECTIVE:To explore the effects of cyclic mechanical stretching on the growth activity of intestinal smooth muscle cells and the expression of functional genes within collagen strips. METHODS:The collagen band culture system of intestinal smooth muscle cells was constructed using a self-designed collagen strip stretching culture device with self-made rat tail collagen as a scaffold and primary rat intestinal smooth muscle cells as seed cells.EthD-1/Calcein-AM cell activity staining,magenta staining,cytoskeleton-Ki67 immunofluorescence staining were used to observe the growth activity and proliferation of the cells,and quantitative RT-PCR was used to detect the expression of desmin,α-sma,and vimentin functional genes. RESULTS AND CONCLUSION:The collagen band culture system of intestinal smooth muscle cells was successfully constructed,and intestinal smooth muscle cells in the band had good cell activity.The number of Ki67 positive cells increased and desmin,α-sma and vimentin were significantly overexpressed under cyclic stretching and dynamic culture conditions(P＜0.001).To conclude,mechanical stimulation is beneficial to maintain the growth phenotype of smooth muscle cells and promote their functional differentiation during three-dimensional culture in vitro.

Objective:To investigate the CT and clinical features of COVID-19.Methods:Chest CT and clinical data of 103 patients who were confirmed as COVID-19 in January 2020 were collected retrospectively. According to diagnosis and treatment of COVID-19 (trial version 5), all the patients were classified into common( n=58), severe ( n=36) and critical ( n=9) types, and their clinical findings, laboratory examination and CT finding were analyzed. CT features included distribution, location, size, shape, edge, number and density of the lesion, percentage of pneumonia lesions of the whole lung and extra-pulmonary manifestations. The CT features among different clinical types were compared using χ 2 test or Fisher's exact probability. Comparisons of age, duration from onset to CT examination, and percentage of pneumonic lesions to total lung volume among different types were performed by using analysis of variance (normal distribution) or Kruskal-Wallis rank sum test (non-normal distribution). Results:In terms of clinical manifestations, the patients with critical COVID-19 were more common in elderly men, with a median age of 65 years. Fever was the first symptom in 49 (84%) of 58 common patients, and also the first symptom in both severe and critical COVID-19 patients. The incidence of coughing in severe (25/36, 69%) and critical (6/9, 67%) COVID-19 patients was higher than that in common patients (20/58, 34%). All critical patients had dyspnea. CT showed the common COVID-19 was located in bilateral lung (40/58, 71%)with multiple (40/58, 69%), ground glass (31/58, 52%) or mixed (25/58, 43%)opacities (56/58, 97%), while all the severe and critical COVID-19 were located in bilateral lung(100%) with multiple (34/36, 96%), patchy (33 /36, 92%), or mixed opacities (26/36, 72%) in severe patients, and with mixed opacities more than 3 cm in critical patients. As for the percentage of pneumonia focus in the whole lung volume, the common type (12.5%±6.1%) was significantly lower than the severe type (25.9%± 10.7%) and the critical type (47.2%±19.2%), with statistically significant differences( P< 0.001 and 0.002 respectively), and the severe type COVID-19 was also significantly lower than the critical type ( P= 0.032). Conclusions:CT and clinical features of different clinical types of COVID-19 pneumonia are different. Chest CT findings are characteristic, which can not only help the early diagnosis but also evaluate the clinical course and severity.

Objective: To investigate the CT and clinical features of 2019 novel coronavirus (NCP) pneumonia. Methods: Chest CT and clinical data of confirmed 103 patients with 2019 novel coronavirus pneumonia in January 2020, retrospectively. According to diagnosis and treatment of NCP infected pneumonia (trial version 5), all the patients were classified into mild(n=58), severe (n=36) and very severe (n=9) type, and their clinical findings, laboratory examination and CT finding were analyzed. CT features included lesions’ distribution, location, size, shape, edge, number, density, percentage of pneumonia lesions of the whole lung and extra-pulmonary manifestations. The CT features of different clinical subtypes were compared using χ²test or Fisher's exact probability. Comparisons between the percentage of pneumonic lesions to total lung volume were computed by using analysis of variance (normal distribution) or Kruskal-Wallis rank sum test (non-normal distribution). Results: In terms of clinical manifestations, the patients with severe NCP were more common in elderly men, with a median age of 65 years. Fever was the first symptom in 49 (84%) of 58 patients with NCP, and fever was the first symptom in both severe and critical NCP patients. The incidence of cough in severe (25 / 36, 69%) and critical (6 /9, 67%) NCP patients was higher than that in general (20 /58, 34%). All critical patients have dyspnea. In terms of CT findings, common NCP showed double lung (40/58,71%) multiple (40 / 58,69%) ground glass (31/58,52%) or mixed (25 / 58,43%) lesions (56 / 58,97%); severe and critical NCP showed double lung lesions, heavy NCP mainly showed multiple (34 / 36,96%) patches (33 / 36,92%) mixed density lesions (26 / 36,72%); 9 severe NCP lesions were more than 3 cm Mixed density lesions. The percentage of pneumonia focus in the whole lung volume: the common type (12.5% ± 6.1%) was significantly lower than the severe type (25.9% ± 10.7%) and the critical type (47.2% ± 19.2%) NCP, the difference was statistically significant (P values were < 0.001 and 0.002 respectively), and the severe type NCP was also significantly lower than the critical type (P = 0.032). Conclusions CT and clinical features of different clinical types of NCP pneumonia are different. Chest CT findings have unique characteristic, which can not only make early diagnosis, but also evaluate its clinical course and severity.