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]: Acute respiratory tract infections (ARI) are a major burden in pediatric long-term care. We analyzed the financial impact of ARI in 2012-2013. Costs associated with ARI during the respiratory viral season were ten times greater than during the non-respiratory viral season, $31 224 and $3242 per 1000 patient-days, respectively (P < 0Á001). ARI are burdensome for pediatric long-term care facilities not only because of the associated morbidity and mortality, but also due to the great financial costs of prevention. Please cite this paper as: Murray et al. (2016) Direct costs of acute respiratory infections in a pediatric long-term care facility. Influenza and Other Respiratory Viruses 10(1), 34-36.
[A]: Direct costs of acute respiratory infections in a pediatric long-term care facility


[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]: Renal ischemia-reperfusion injury (IRI) is a major cause of acute kidney injury (AKI), which could induce the poor prognosis. The purpose of this study was to characterize the molecular mechanism of the functional changes of CD11b + /Ly6C intermediate macrophages after renal IRI. The gene expression profiles of CD11b + /Ly6C intermediate macrophages of the sham surgery mice, and the mice 4 h, 24 h and 9 days after renal IRI were downloaded from the Gene Expression Omnibus database. Analysis of mRNA expression profiles was conducted to identify differentially expressed genes (DEGs), biological processes and pathways by the series test of cluster. Protein-protein interaction network was constructed and analysed to discover the key genes. A total of 6738 DEGs were identified and assigned to 20 model profiles. DEGs in profile 13 were one of the predominant expression profiles, which are involved in immune cell chemotaxis and proliferation. Signet analysis showed that Atp5a1, Atp5o, Cox4i, Cdc42, Rac2 and Nhp2 were the key genes involved in oxidation-reduction, apoptosis, migration, M1-M2 differentiation, and proliferation of macrophages. RPS18 may be an appreciate reference gene as it was stable in macrophages. The identified DEGs and their enriched pathways investigate factors that may participate in the functional changes of CD11b + /Ly6C intermediate macrophages after renal IRI. Moreover, the vital gene Nhp2 may involve the polarization of macrophages, which may be a new target to affect the process of AKI.
[A]:
Bioinformatics Analysis of Genes and Pathways of CD11b + /Ly6C intermediate Macrophages after Renal Ischemia-Reperfusion Injury *