TASK DEFINITION: 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.
PROBLEM: This study was de,figned mainly to determine the relationships between the expression and distribution of the cellular receptor proteins for coxsackievirus B3 (CVB3) and susceptibility of mouse brain cells during fetal development of Balb/c mice. Immunoblot analysis of fetal extracts demonstrated that the CVB3 receptor proteins were first expressed at day 14 of the fetal stage, and that maximal expression of the cellular receptor occurred at near term or newborn stage. Results also suggested that newborn mouse brain tissue expressed much larger quantities of viral receptor proteins, compared to other tissues. In vitro studies showed that both mouse neurons and astrocytes could be infected by two CVB3 strains, pantropic CVB3 Nancy strain (CVB3N) and myocardiotropic CVB3 Woodruff strain (CVB3W). CVB3N, however, replicated and grew to high titer in primary astrocyte cultures and in primary neuron cultures, whereas, primary astrocyte cultures were relatively resistant to CVB3W. Virus binding assays revealed that CVB3N bound faster and in greater amounts to mouse brain cells than CVBW. These two virus strains, however, were found to share the same receptor specificity by virus competition assays. The number of virus binding sites for CVB3 on newborn mouse brain cells was approximately 1.8 x 104 per cell. The data suggested that preferential expression of the cellular receptors on newborn mouse brain cells may be related to their high susceptibilities to CVB3 infection.

SOLUTION: Expression and distribution of the receptors for coxsackievirus B3 during fetal development of the Balb/c mouse and of their brain cells in culture

PROBLEM: medRxiv preprint Interpretation Parameters that successfully reproduce the observed number of cases indicate that both R 0 and the prevalence of the virus might be underestimated. This is in concordance with the newest research on undocumented COVID-19 cases. Consequently, the actual mortality rate is putatively lower than estimated. Confirmation of the pandemic characteristic by further refinement of the model and screening tests is crucial for developing an effective strategy for the global epidemiological crisis. 1 Balcan D, Gonçalves B, Hu H, Ramasco JJ, Colizza V, Vespignani A. Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model. J Comput Sci 2010; 1 : 132-45. 2 Van den Broeck W, Gioannini C, Gonçalves B, Quaggiotto M, Colizza V, Vespignani A. The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC Infect Dis 2011; 11 : 37. 3 Tizzoni M, Bajardi P, Poletto C, et al. Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm. BMC Med 2012;

SOLUTION: From a single host to global spread. The global mobility based modelling of the COVID-19 pandemic implies higher infection and lower detection rates than current estimates

PROBLEM: An outbreak of a novel coronavirus, named CoVID-19, was first reported in China on 31 December 2019. As of 9 February 2020, cases have been reported in 25 countries, including probable cases of human-to-human transmission in England. We adapted an existing national-scale metapopulation model to capture the spread of CoVID-19 in England and Wales. We used 2011 census data to capture population sizes and population movement, together with parameter estimates from the current outbreak in China. We predict that a CoVID-19 outbreak will peak 126 to 147 days (~4 months) after the start of person-to-person transmission in England and Wales in the absence of controls, assuming biological parameters remain unchanged. Therefore, if person-to-person transmission persists from February, we predict the epidemic peak would occur in June. The starting location has minimal impact on peak timing, and model stochasticity varies peak timing by 10 days. Incorporating realistic parameter uncertainty leads to estimates of peak time ranging from 78 days to 241 days after person-to-person transmission has been established. Seasonal changes in transmission rate substantially impact the timing and size of the epidemic peak, as well as the total attack rate. We provide initial estimates of the potential course of CoVID-19 in England and Wales in the absence of control measures. These results can be refined with improved estimates of epidemiological parameters, and permit investigation of control measures and cost effectiveness analyses. Seasonal changes in transmission rate could shift the timing of the peak into winter months, which will have important implications for health-care capacity planning.

SOLUTION:
A spatial model of CoVID-19 transmission in England and Wales: early spread and peak timing