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.

[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.
[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

[Q]: doi: bioRxiv preprint molecular biology. Our high-resolution data have enabled a precise study of transcription and translation in both pathogenic and attenuated forms of IBV, and expand our understanding of gammacoronaviral gene expression. We demonstrate that gene expression shows considerable intraspecies variation, with single nucleotide polymorphisms associated with altered production of sgmRNA transcripts, and our RiboSeq data sets enabled us to uncover novel ribosomally occupied ORFs in both strains. We also identify numerous cellular genes and gene networks that are differentially expressed during virus infection, giving insights into the host cell reponse to IBV infection. 3 47 48 49 50 51 52 53 54 55 56 author/funder. All rights reserved. No reuse allowed without permission.
[A]: Comparative analysis of gene expression in virulent and attenuated strains of infectious bronchitis virus at sub-codon resolution

[Q]: Aptamers are specific nucleic acid sequences that can bind to a wide range of non-nucleic acid targets with high affinity and specificity. These molecules are identified and selected through an in vitro process called SELEX (systematic evolution of ligands by exponential enrichment). Proteins are the most common targets in aptamer selection. In diagnostic and detection assays, aptamers represent an alternative to antibodies as recognition agents. Cellular detection is a promising area in aptamer research. One of its principal advantages is the ability to target and specifically differentiate microbial strains without having previous knowledge of the membrane molecules or structural changes present in that particular microorganism. The present review focuses on aptamers, SELEX procedures, and aptamer-based biosensors (aptasensors) for the detection of pathogenic microorganisms and viruses. Special emphasis is placed on nanoparticlebased platforms.
[A]:
Aptasensors for detection of microbial and viral pathogens