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]: Low cost, high throughput, easy quantitation, and rich descriptions have made gene expression profiling generated by DNA microarrays an optimal approach for describing host transcriptional responses (HTRs). However, efforts to characterize the landscape of HTRs to diverse pathogens are far from offering a comprehensive view. Here, we developed an HTR Connectivity Map based on systematic assessment of pairwise similarities of HTRs to 50 clinically important human pathogens using 1353 gene-expression profiles generated from >60 human cells/tissues. These 50 pathogens were further partitioned into eight robust "HTR communities" (i.e., groups with more consensus internal HTR similarities). These communities showed enrichment in specific infection attributes and differential gene expression patterns. Using query signatures of HTRs to external pathogens, we demonstrated four distinct modes of HTR associations among different pathogens types/class, and validated the reliability of the HTR community divisions for differentiating and categorizing pathogens from a host-oriented perspective. These findings provide a first-generation HTR Connectivity Map of 50 diverse pathogens, and demonstrate the potential for using annotated HTR community to detect functional associations among infectious pathogens.
[EX A]: Inferring Infection Patterns Based on a Connectivity Map of Host Transcriptional Responses

[EX Q]: Recent studies suggest an association between the Interferon Inducible Transmembrane 3 (IFITM3) rs12252 variant and the course of influenza infection. However, it is not clear whether the reported association relates to influenza infection severity. The aim of this study was to estimate the hospitalization risk associated with this variant in Influenza Like Illness (ILI) patients during the H1N1 pandemic influenza. A case-control genetic association study was performed, using nasopharyngeal/oropharyngeal swabs collected during the H1N1 pandemic influenza. Laboratory diagnosis of influenza infection was performed by RT-PCR, the IFITM3 rs12252 was genotyped by RFLP and tested for association with hospitalization. Conditional logistic regression was performed to calculate the confounder-adjusted odds ratio of hospitalization associated with IFITM3 rs12252. We selected 312 ILI cases and 624 matched non-hospitalized controls. Within ILI Influenza A(H1N1)pdm09 positive patients, no statistical significant association was found between the variant and the hospitalization risk (Adjusted OR: 0.73 (95%CI: 0.33-1.50)). Regarding ILI Influenza A(H1N1)pdm09 negative patients, CT/CC genotype carriers had a higher risk of being hospitalized than patients with TT genotype (Adjusted OR: 2.54 (95%CI: 1.54-4.19)). The IFITM3 rs12252 variant was associated with respiratory infection hospitalization but not specifically in patients infected with Influenza A(H1N1)pdm09.
[EX A]: Hospitalization Risk Due to Respiratory Illness Associated with Genetic Variation at IFITM3 in Patients with Influenza A(H1N1)pdm09 Infection: A Case-Control Study

[EX Q]: Objective. In treating highly infectious COVID-19 pneumonia, ICU nurses face a high 34 risk of developing somatic symptom disorder (SSD). The present study aims to investigate 35 the symptoms and causes of SSD of ICU nurses treating COVID-19 pneumonia. The research 36 results are expected to provide evidence for the establishment of a better management 37 strategy. Methods. This study enrolled a total of 140 ICU nurses who were selected by Jiangsu 39 Province Hospital to work in Wuhan (the epicenter of the COVID-19 epidemic in China) on 40 3 rd February 2020. A questionnaire "Somatic Symptom Disorders for ICU Nurses in Wuhan 41 No.1 Hospital" was designed based on the "International Classification of Functioning, 42 Disability and Health" (ICF). Exploratory factor analysis was performed to cluster the 43 symptoms, and logistic regression analysis to find the risk factors of the symptoms. 44 Results. Five major symptoms were chest-discomfort-and-palpitation (31.4%), dyspnea 45 (30.7%), nausea (21.4%), headache (19.3%), and dizziness (17.9%). In exploratory factor 46 analysis, the symptoms were classified into three clusters: Cluster A of breathing and sleep 47 disturbances (dizziness, sleepiness, dyspnea); Cluster B of gastrointestinal complaints and 48 pain (nausea, headache), and Cluster C of general symptoms (xerostomia, fatigue, 49 chest-discomfort-and-palpitation). In Cluster A, urine/feces splash, sex, and sputum splash 50 were independent predictive factors. In Cluster B, fall of protective glasses and urine/feces 51 splash were independent predictive factors. In Cluster C, urine/feces splash and urine/feces 52 clearance were independent predictive factors. 53 Conclusion. The ICU nurses in Wuhan showed varying and overlapping SSDs. These 54 SSDs could be classified into three symptom clusters. Based on the characteristics of their 55 SSDs, specific interventions could be implemented to safeguard the health of ICU nurses. 56 57
[EX A]:
Journal Pre-proof Symptom Cluster of ICU nurses treating COVID-19 pneumonia patients in Wuhan, China Running Title: SSDs of ICU nurses treating COVID-19 pneumonia patients Symptom Cluster of ICU nurses treating COVID-19 pneumonia patients in Wuhan, China Somatic Symptom Disorders of ICU Nurses treating COVID-19 pneumonia patients in