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 Input:
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.

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

Ex Input:
The global spread of SARS-CoV-2 requires an urgent need to find effective therapeutics for the treatment of COVID-19. We developed a data-driven drug repositioning framework, which applies both machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. The retrospective study using the past SARS-CoV and MERS-CoV data demonstrated that our machine learning based method can successfully predict effective drug candidates : bioRxiv preprint against a specific coronavirus. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 is able to suppress the CpG-induced IL-6 production in peripheral blood mononuclear cells, suggesting that it may also have anti-inflammatory effect that is highly relevant to the prevention immunopathology induced by SARS-CoV-2 infection. Further pharmacokinetic and toxicokinetic evaluation in rats and monkeys showed a high concentration of CVL218 in lung and observed no apparent signs of toxicity, indicating the appealing potential of this drug for the treatment of the pneumonia caused by SARS-CoV-2 infection. Moreover, molecular docking simulation suggested that CVL218 may bind to the N-terminal domain of nucleocapsid (N) protein of SARS-CoV-2, providing a possible model to explain its antiviral action. We also proposed several possible mechanisms to explain the antiviral activities of PARP1 inhibitors against SARS-CoV-2, based on the data present in this study and previous evidences reported in the literature. In summary, the PARP1 inhibitor CVL218 discovered by our data-driven drug repositioning framework can serve as a potential therapeutic agent for the treatment of COVID-19. author/funder. All rights reserved. No reuse allowed without permission. : bioRxiv preprint known evidences reported in the literature, we also discussed several putative mechanisms of the anti-SARS-CoV-2 effects for CVL218 or other PARP1 inhibitors to be involved in the treatment of COVID-19. Overall, our results indicated that the PARP1 inhibitor CVL218 identified by our drug repositioning pipeline may serve as an effective therapeutic agent against COVID-19. Results The overview of our data-driven drug repositioning framework is shown in Figure 1A . We first constructed a virus related knowledge graph consisting of drug-target interactions, protein-protein interactions and similarity networks from publically available databases (Methods). Three different types of nodes (i.e., drugs, human targets and virus targets) within the knowledge graph were connected through edges describing their interactions, associations or similarities to establish bridges of information aggregation and knowledge mining. We then applied a network-based knowledge mining algorithm to predict an initial list of drug candidates that can be potentially used to treat SARS-CoV-2 infection ( Figure 1B and Methods). Next, we further narrowed down the list of drug candidates with the previously reported evidences of antiviral activities based on the text mining results from the large-scale literature texts, which were derived through a deep learning based relation extraction method named BERE [4] ( Figure 1C and Methods), followed by a minimum of manual checking. After that, we used the connectivity map analysis approach [5] with the gene expression profiles of ten SARS-CoV-infected patients [6] to further refine the list of drug candidates against SARS-CoV-2 ( Figure 1D , Table 1, Table S1 and Methods). The above screening process revealed that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor PJ-34 could potentially have the antiviral activities against SARS-CoV-2. 4 author/funder. All rights reserved. No reuse allowed without permission.

Ex Output:
A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

Ex Input:
Antimicrobial stewardship program aims to reduce antibiotic use. Periodic measurement and monitoring of antibiotic use and comparison within the institution as well as with other organizations are important indicators. We analyzed antibiotic usage in a general hospital in Saudi Arabia. Antibiotic data were collected retrospectively for 2011 and from 2013 to 2015, and only adult patients (>15 year of age) were included in the study. Data were presented as days of therapy (DOT) and defined daily dose (DDD). DDD was adjusted per 100 bed-days and according to the case mix index (CMI). The total DDD was 37,557 in 2013, 36,550 in 2014 and 38,738 in 2015. The DDD per 100 patient-days was 90.7-94.5. There was a discordant findings of antibiotic measurements based on the DDD compared to DOT, and DDD/100 beddays compared to DOT/100 bed-days. There was a negative correlation between CMI and DDD per 100 bed days (r 0.696), but a positive correlation of CMI with DOT (r +0.93). Adjusted DDD/100 bed-days showed decrease in the usage of antibiotics, reflecting activities of the antibiotic stewardship program. The increase in DOT/100 bed-days may indicate the favorable utilization of combination therapy. Antibiotic usage needs to be adjusted per 100 bed-days and correlated with CMI for better reflection of optimal antibiotic utilization, activities of the antibiotic stewardship program, and to allow benchmarking.

Ex Output:
Benchmarking of antibiotic usage: An adjustment to reflect antibiotic stewardship program outcome in a hospital in Saudi Arabia