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: The Middle East respiratory syndrome coronavirus (MERS-CoV) closely resembled severe acute respiratory syndrome coronavirus (SARS-CoV) in disease manifestation as rapidly progressive acute pneumonia with multi-organ dysfunction. Using monocyte-derived-dendritic cells (Mo-DCs), we discovered fundamental discrepancies in the outcome of MERS-CoV-and SARS-CoV-infection. First, MERS-CoV productively infected Mo-DCs while SARS-CoV-infection was abortive. Second, MERS-CoV induced significantly higher levels of IFN-γ, IP-10, IL-12, and RANTES expression than SARS-CoV. Third, MERS-CoV-infection induced higher surface expression of MHC class II (HLA-DR) and the co-stimulatory molecule CD86 than SARS-CoV-infection. Overall, our data suggests that the dendritic cell can serve as an important target of viral replication and a vehicle for dissemination. MERS-CoV-infection in DCs results in the production of a rich combination of cytokines and chemokines, and modulates innate immune response differently from that of SARS-CoV-infection. Our findings may help to explain the apparent discrepancy in the pathogenicity between MERS-CoV and SARS-CoV.

A: Productive replication of Middle East respiratory syndrome coronavirus in monocyte-derived dendritic cells modulates innate immune response
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Q: The infection of a novel coronavirus found in Wuhan of China (2019-nCoV) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for 2019-nCoV, various strategies are being tested in China, including drug repurposing. In this study, we used our pretrained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of 2019-nCoV. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing a inhibitory potency with Kd of 94.94 nM against the 2019-nCoV 3C-like proteinase, followed by efavirenz (199.17 nM), ritonavir (204.05 nM), and dolutegravir (336.91 nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of 2019-nCoV with an inhibitory potency with Kd < 1000 nM. In addition, we also found that several antiviral agents, such as Kaletra, could be used for the treatment of 2019-nCoV, although there is no real-world evidence supporting the prediction. Overall, we suggest that the list of antiviral drugs identified by the MT-DTI model should be considered, when establishing effective treatment strategies for 2019-nCoV.

A: China through a drug-target interaction deep learning model
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Q: Therapeutic resistance remains the principal problem in acute myeloid leukemia (AML). We used area under receiver operator characteristic curves (AUC) to quantify our ability to predict therapeutic resistance in individual patients where AUC=1.0 denotes perfect prediction and AUC=0.5 denotes a coin flip, using data from 4,601 patients with newly diagnosed AML given induction therapy with 3+7 or more intense standard regimens in MRC/NCRI, HOVON, SWOG, and MD Anderson Cancer Center studies. Age, performance status, white blood cell count, secondary disease, cytogenetic risk, and FLT3-ITD/NPM1 mutation status were each independently associated with failure to achieve complete remission despite no early death ("primary refractoriness"). However, the AUC of a bootstrap-corrected multivariable model predicting this outcome was only 0.78, indicating only fair predictive ability. Removal of FLT3-ITD and NPM1 information only slightly decreased the AUC (0.76). Prediction of resistance, Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:defined as primary refractoriness or short relapse-free survival (RFS), was even more difficult. Our ability to forecast resistance based on routinely available pre-treatment covariates provides a rationale for continued randomization between standard and new therapies and supports further examination of genetic and post-treatment data to optimize resistance prediction in AML. Acute myeloid leukemia; area under the receiver operator characteristic curve; prediction; therapeutic resistance Walter et al. [x10 3 /μL], median (range) 15 (0-559) 19 (1-510) 14 (0-370) 8 (1-340) 15 (0-559) Platelets 1 [x10 3 /μL], median (range) 56 (2-983) 66 (3-998) 54 (2-9,300) 41 (3-1,069) 58 (2-9,300) Bone marrow blasts 1 , median% (range) 70 (0-100) 60 (0-98) 67 (3-100) 47 (6-96) 65 (0-100) Performance status 1 , n (%) 0-1 2,081 (80) 1,051 (93) 368 (83) 374 (91) 3,874 (84) 

A:
Resistance Prediction in AML: Analysis of 4,601 Patients from MRC/NCRI, HOVON/SAKK, SWOG, and MD Anderson Cancer Center HHS Public Access
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