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]: Exogenous lipoid pneumonia (ELP), an important cause of interstitial lung disease, often goes unrecognized. We conducted a retrospective study of children with histologically confirmed ELP at Red Cross Children's Hospital, South Africa. Twelve children of Zimbabwean heritage aged 2.1-10.8 months were identified between 2012 and 2017. Repeated oral administration of plant-based oil for cultural reasons was reported by 10 of 11 caregivers. Cough (12/12), tachypnoea (11/12), hypoxia (9/12), and diffuse alveolar infiltrates on chest radiography (12/12) were common at presentation. Chest computed tomography revealed ground-glass opacification with lower zone predominance (9/9) and interlobular septal thickening (8/9). Bronchoalveolar lavage specimens appeared cloudy/milky, with abundant lipid-laden macrophages and extracellular lipid on Oil-Red-O staining (12/12), with polymicrobial (6/12) and Mycobacterium abscessus (2/12) coinfection. Antibiotics, systemic corticosteroids, and therapeutic lavage were interventions in all eight and five patients, respectively. Clinicians should consider ELP in children with non-resolving pneumonia in settings with similar practices.
[EX A]: Exogenous lipoid pneumonia: an important cause of interstitial lung disease in infants

[EX 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.
[EX A]: China through a drug-target interaction deep learning model

[EX Q]: Background: Large RNA molecules are often composed of multiple functional domains whose spatial arrangement strongly influences their function. Pre-mRNA splicing, for instance, relies on the spatial proximity of the splice junctions that can be separated by very long introns. Similar effects appear in the processing of RNA virus genomes. Albeit a crude measure, the distribution of spatial distances in thermodynamic equilibrium harbors useful information on the shape of the molecule that in turn can give insights into the interplay of its functional domains. Result: Spatial distance can be approximated by the graph-distance in RNA secondary structure. We show here that the equilibrium distribution of graph-distances between a fixed pair of nucleotides can be computed in polynomial time by means of dynamic programming. While a naïve implementation would yield recursions with a very high time complexity of O(n 6 D 5 ) for sequence length n and D distinct distance values, it is possible to reduce this to O(n 4 ) for practical applications in which predominantly small distances are of of interest. Further reductions, however, seem to be difficult. Therefore, we introduced sampling approaches that are much easier to implement. They are also theoretically favorable for several real-life applications, in particular since these primarily concern long-range interactions in very large RNA molecules. Conclusions: The graph-distance distribution can be computed using a dynamic programming approach. Although a crude approximation of reality, our initial results indicate that the graph-distance can be related to the smFRET data. The additional file and the software of our paper are available from http://www.rna.uni-jena.de/RNAgraphdist.html. The distance distribution within an RNA molecule is of interest in various contexts. Most directly, the question arises whether panhandle-like structures (in which 3' and 5' ends of long RNA molecules are placed in close proximity) are the rule or an exception. Panhandles have been reported in particular for many RNA virus genomes. Several studies [1-4] agree based on different models that the two ends of single-stranded RNA molecules are typically not far apart. On a more technical level, the problem
[EX A]:
Graph-distance distribution of the Boltzmann ensemble of RNA secondary structures