In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

[EX Q]: Quinone-methide triterpenes, celastrol (1), pristimerin (2), tingenone (3), and iguesterin (4) were isolated from Triterygium regelii and dihydrocelastrol (5) was synthesized by hydrogenation under palladium catalyst. Isolated quinone-methide triterpenes (1-4) and 5 were evaluated for SARS-CoV 3CL pro inhibitory activities and showed potent inhibitory activities with IC 50 values of 10.3, 5.5, 9.9, and 2.6 lM, respectively, whereas the corresponding 5 having phenol moiety was observed in low activity (IC 50 = 21.7 lM). As a result, quinone-methide moiety in A-ring and more hydrophobic E-ring assist to exhibit potent activity. Also, all quinone-methide triterpenes 1-4 have proven to be competitive by the kinetic analysis.
[EX A]: SARS-CoV 3CL pro inhibitory effects of quinone-methide triterpenes from Tripterygium regelii

[EX Q]: Purpose: COVID-19 has become global threaten. CT acts as an important method of 60 diagnosis. However, human-based interpretation of CT imaging is time consuming. 61 More than that, substantial inter-observer-variation cannot be ignored. We aim at 62 developing a diagnostic tool for artificial intelligence (AI)-based classification of CT 63 images for recognizing COVID-19 and other common infectious diseases of the lung. 64 Experimental Design: In this study, images were retrospectively collected and 65 prospectively analyzed using machine learning. CT scan images of the lung that show 66 or do not show COVID-19 were used to train and validate a classification framework 67 based on convolutional neural network. Five conditions including COVID-19 68 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, pulmonary 69 tuberculosis, and normal lung were evaluated. Training and validation set of images 70 were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were 71 collected from Zhongshan Hospital Xiamen University and the fifth Hospital of 72 Wuhan. 73 Results: Accuracy, sensitivity, and specificity of the AI framework were reported. For 74 test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia, 75 non-COVID-19 viral pneumonia, bacterial pneumonia, and pulmonary tuberculosis 76 were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, 77 accuracy, sensitivity, specificity, PPV, and NPV of recognizing COVID-19 were 78 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively. 79 All rights reserved. No reuse allowed without permission. the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
[EX A]: Deep Learning-Based Recognizing COVID-19 Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT 2 Scan Images

[EX Q]: The severe acute respiratory syndrome (SARS) pandemic in Toronto resulted in a large number of autopsies on its victims. We describe the pulmonary pathology of patients who died in the 2003 Toronto outbreak. Autopsy material from the lungs of 20 patients who died between March and July 2003 were characterized by histology, molecular biology, and immunohistochemistry for cytokeratins, thyroid transcription factor-1, CD68, Epstein-Barr virus, cytomegalovirus, and human herpes simplex viruses. Matched controls were obtained from patients who died of other causes over the same interval. The mean duration of illness was 27 days (range 5-108 days). Post-mortem lung tissues from 19 of 20 patients with probable SARS were positive for SARS-associated coronavirus by RT-PCR. Histologically, all patients showed varying degrees of exudative and proliferative phase acute lung injury, evidenced in conventional and immunohistochemical stains by edema, inflammatory infiltrate, pneumocyte hyperplasia, fibrinous exudates, and organization. Eight of 20 patients showed predominantly a diffuse alveolar damage pattern of acute lung injury, six showed predominantly an acute fibrinous and organizing pneumonia pattern, and the remainder showed an admixture of the two patterns. Squamous metaplasia and scattered multinucleate giant cells were present in most cases. Vascular fibrin thrombi were a common finding and were often associated with pulmonary infarcts. Special stains demonstrated vascular endothelial damage of both small-and mid-sized pulmonary vessels. Two cases were complicated by invasive fungal disease consistent with Aspergillosis, and another by coinfection with cytomegalovirus. Our findings indicate that the lungs of patients who die of SARS are almost always positive for the SARS-associated coronavirus by RT-PCR, and may show features of both diffuse alveolar damage and acute fibrinous and organizing pneumonia patterns of acute injury. Cases of SARS may be complicated by coexistent infections and therapy-related lung injury.
[EX A]:
Pulmonary pathology of severe acute respiratory syndrome in Toronto