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.

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.
Exogenous lipoid pneumonia: an important cause of interstitial lung disease in infants

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.
China through a drug-target interaction deep learning model

RLadyBug is an S4 package for the simulation, visualization and estimation of stochastic epidemic models in R. Maximum likelihood and Bayesian inference can be performed to estimate the parameters in a susceptible-exposed-infectious-recovered (SEIR) model, which is a stochastic model for describing a single outbreak of an infectious disease. The package is thus one step towards statistical software supporting parameter estimation, calculation of confidence intervals and hypothesis testing for transmission models.

RLadyBug-An R package for stochastic epidemic models