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: Keywords: interferon, interferon-stimulated genes, tumor necrosis factor-related apoptosis-inducing ligand, acute lung injury, innate immunity, influenza, respiratory syncytial virus, coronavirus FiGURe 1 | PRRs and their downstream signaling pathways in virus-induced iFN induction. In viral infection, type I IFNs are induced by TLR, RLR, CLR, and cytosolic nucleic acid sensors. Cell membrane-located TLRs ligate to viral envelope proteins (e.g., TLR2/herpes simplex virus), upon which they recruit MyD88. MyD88 interacts with IRAK kinase (IRAK-1, -2, or -4) that either directly activate IRF1 or interact with TRAF6, which induces IRF7 or assembles with TAK1. TAK1 forms a complex with TAB-1/-2 and -3 and subsequently either activates the MAPK kinases p38 and JNK, leading to AP-1 phosphorylation and nuclear translocation, or induces ubiquitination of NEMO followed by IκB degradation and NFκB activation. Endosomal TLRs recognizing viral nucleic acid and signal via the adaptor protein MyD88 (for TLR7/8/9) or interact with TRIF (for TLR3) followed by TRAF3, TANK, and TBK1 activation. TBK1 and IKKε then phosphorylate and activate IRF3 and IRF7. Additionally, TRIF can interact with TRAF6 to initiate TAK1 signaling. Both RLRs, RIG-I and MDA-5, recognize nucleic acid contents in the cytoplasm and stimulate the mitochondrial anchored IPS-1 for dimerization followed by TRADD recruitment that acts via TRAF3 on IRF3 and IRF7. Additionally, IPS-1 can interact with FADD and RIP1 to activate NFκB via IKK activated by caspase-8 and -10. Also PKR signaling results in NFκB activation and nuclear translocation. The dsDNA sensor cGAS produces cGAMP that activates ER-located STING that via TBK1 induces IRF3 translocation and type I IFN production. CLRs play a minor role in viral recognition; however, DC-SIGN activates the small GTPase Ras and Raf protein kinase, followed by NFκB activation. Type III IFN are induced by TLR3, TLR9, and via RIG-I and peroxisomal-resident IPS-1. Especially IRF1, but also NFκB, IRF3, and IRF7 are implicated in IFN-λ production, with the latter being stabilized by Med23.

A: The impact of the interferon/TNF- Related Apoptosis-inducing Ligand Signaling Axis on Disease Progression in Respiratory viral infection and Beyond
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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
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Q: Background: Previous data suggest that fecal S100A12 has clinical utility as a biomarker of chronic gastrointestinal inflammation (idiopathic inflammatory bowel disease) in both people and dogs, but the effect of gastrointestinal pathogens on fecal S100A12 concentrations is largely unknown. The role of S100A12 in parasite and viral infections is also difficult to study in traditional animal models due to the lack of S100A12 expression in rodents. Thus, the aim of this study was to evaluate fecal S100A12 concentrations in a cohort of puppies with intestinal parasites (Cystoisospora spp., Toxocara canis, Giardia sp.) and viral agents that are frequently encountered and known to cause gastrointestinal signs in dogs (coronavirus, parvovirus) as a comparative model. Methods: Spot fecal samples were collected from 307 puppies [median age (range): 7 (4−13) weeks; 29 different breeds] in French breeding kennels, and fecal scores (semiquantitative system; scores 1−13) were assigned. Fecal samples were tested for Cystoisospora spp. (C. canis and C. ohioensis), Toxocara canis, Giardia sp., as well as canine coronavirus (CCV) and parvovirus (CPV). S100A12 concentrations were measured in all fecal samples using an inhouse radioimmunoassay. Statistical analyses were performed using non-parametric 2-group or multiple-group comparisons, non-parametric correlation analysis, association testing between nominal variables, and construction of a multivariate mixed model. Results: Fecal S100A12 concentrations ranged from < 24−14,363 ng/g. Univariate analysis only showed increased fecal S100A12 concentrations in dogs shedding Cystoisospora spp. (P = 0.0384) and in dogs infected with parvovirus (P = 0.0277), whereas dogs infected with coronavirus had decreased fecal S100A12 concentrations (P = 0.0345). However, shedding of any single enteropathogen did not affect fecal S100A12 concentrations in multivariate analysis (all P > 0.05) in this study. Only fecal score and breed size had an effect on fecal S100A12 concentrations in multivariate analysis (P < 0.0001). Conclusions: An infection with any single enteropathogen tested in this study is unlikely to alter fecal S100A12 concentrations, and these preliminary data are important for further studies evaluating fecal S100A12 concentrations in dogs or when using fecal S100A12 concentrations as a biomarker in patients with chronic idiopathic gastrointestinal inflammation.

A:
Effect of selected gastrointestinal parasites and viral agents on fecal S100A12 concentrations in puppies as a potential comparative model
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