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]: publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. An Pediatr (Barc). 2006;65(3):205-10 205 ORIGINALES Papel del rinovirus en las infecciones respiratorias en niños hospitalizados Antecedentes El rinovirus se considera un agente causal de cuadros catarrales banales, sin embargo se ha descrito como un agente inductor de exacerbaciones asmáticas en adultos y niños mayores. En nuestro medio no se ha descrito el papel del rinovirus en infecciones respiratorias de niños hospitalizados. Objetivos Describir las infecciones confirmadas por rinovirus en niños hospitalizados por infección respiratoria en un hospital de segundo nivel. We performed a prospective descriptive study in children admitted to hospital with fever or respiratory tract infection and with a positive isolation of rhinovirus in nasopharyngeal washings between September 2004 and July 2005. Virological diagnosis was made with direct immunofluorescent assay and/or reverse transcription-polymerase chain reaction in specimens obtained from nasopha-Estudio parcialmente financiado por el Fondo de Investigaciones Sanitarias. FIS N.º 98/0310. Correspondencia: Dra. C. Calvo Rey. Viento, 5, 5.º A. 28760 Tres Cantos. Madrid. España. Correo electrónico: ccalvo@mi.madritel.es Recibido en noviembre de 2005. Aceptado para su publicación en marzo de 2006. Calvo Rey C, et al. Rinovirus en las infecciones respiratorias en niños hospitalizados 206 An Pediatr (Barc). 2006;65(3):205-10 ryngeal washings. The clinical characteristics of the patients were analyzed. There were 76 children with rhinovirus infection, representing 25 % of admissions in 304 children with fever or respiratory tract infection. Rhinovirus was the second most frequent viral agent identified after respiratory syncytial virus (RSV) (29.9 % of admissions). Fifty-four children (71.1 %) were under 2 years of age. The most frequent clinical diagnoses were recurrent wheezing in 60.5 %, bronchiolitis in 23.7 %, pneumonia in 7.9 %, and upper respiratory tract infection in 5.3 %. Fever > 38°C was present in 57.9 % of the patients and radiologic infiltrate was found in 23.7 %. Oxygen saturation less than 95 % was found in 43.4 % of the patients. Of 22 children aged more than 2 years, a diagnosis of asthmatic crisis was made in 21. Rhinoviruses were frequently identified in hospitalized children with respiratory tract disease and were the second most common viruses after RSV. In our series, it was the most frequent cause of recurrent wheezing in hospitalized children and the second most common cause in infants.
[EX A]: ROLE OF RHINOVIRUS IN RESPIRATORY TRACT INFECTIONS IN HOSPITALIZED CHILDREN Background

[EX Q]: After human coronaviruses OC43, 229E and NL63, human coronavirus HKU1 (HCoV-HKU1) is the fourth human coronavirus discovered. HCoV-HKU1 is a group 2a coronavirus that is still not cultivable. The G + C contents of HCoV-HKU1 genomes are 32%, the lowest among all known coronaviruses with complete genome sequences available. Among all coronaviruses, HCoV-HKU1 shows the most extreme codon usage bias, attributed most importantly to severe cytosine deamination. All HCoV-HKU1 genomes contain unique tandem copies of a 30-base acidic tandem repeat of unknown function at the N-terminus of nsp3 inside the acidic domain upstream of papain-like protease 1. Three genotypes, A, B and C, of HCoV-HKU1 and homologous recombination among their genomes, are observed. The incidence of HCoV-HKU1 infections is the highest in winter. Similar to other human coronaviruses, HCoV-HKU1 infections have been reported globally, with a median (range) incidence of 0.9 (0 -4.4) %. HCoV-HKU1 is associated with both upper and lower respiratory tract infections that are mostly selflimiting. The most common method for diagnosing HCoV-HKU1 infection is RT-PCR or real-time RT-PCR using RNA extracted from respiratory tract samples such as nasopharyngeal aspirates (NPA). Both the pol and nucleocapsid genes have been used as the targets for amplification. Monoclonal antibodies have been generated for direct antigen detection in NPA. For antibody detection, Escherichia coli BL21 and baculovirus- Viruses 2009, 1 58 expressed recombinant nucleocapsid of HCoV-HKU1 have been used for IgG and IgM detection in sera of patients and normal individuals, using Western blot and enzyme-linked immunoassay.
[EX A]: More and More Coronaviruses: Human Coronavirus HKU1

[EX Q]: Chest CT had high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at early stage, giving it an advantage over nucleic acid detection in time of crisis. Deep learning was reported to discover intricate structures from clinical images and achieve expert-level performance in medical image analysis. To develop and validate an integrated deep learning framework on chest CT images for autodetection of NCP, particularly focusing on differentiating NCP from influenza pneumonia (IP). : 35 confirmed NCP cases were consecutively enrolled as training set from 1138 suspected patients in three NCP designated hospitals together with 361 confirmed viral pneumonia patients from center one including 156 IP patients, from May, 2015 to February, 2020. The external validation set enrolled 57 NCP patients and 50 IP patients from eight centers. Results: 96.6% of NCP lesions were larger than 1 cm and 76.8% were with intensity below -500 Hu, indicating less consolidation than IP lesions which had nodules ranging 5-10 mm. The classification schemes accurately distinguished NCP and IP lesions with area under the receiver operating characteristic curve (AUC) above 0.93. The Trinary scheme was more device-independent and consistent with specialists than the Plain scheme, which achieved a F1 score of 0.847, higher than the Plain scheme (0.774), specialists (0.785) and residents (0.644). Our study potentially provides an accurate early diagnosis tool on chest CT for NCP with high transferability, and shows high efficiency in differentiating NCP and IP, helping to reduce misdiagnosis and contain the pandemic transmission.
[EX A]:
Improved deep learning model for differentiating novel coronavirus pneumonia and influenza pneumonia Running title: Deep learning model for differentiating NCP and IP