Ebola in eastern DRC

Perceptions toward Ebola vaccination and correlates of vaccine uptake among high-risk community members in North Kivu, Democratic Republic of the Congo

The tenth Ebola Virus Disease (EVD) outbreak (2018-2020, North Kivu, Ituri, South Kivu) in the Democratic Republic of the Congo (DRC) was the second-largest EVD outbreak in history. During this outbreak, Ebola vaccination was an integral part of the EVD response. We evaluated community perceptions toward Ebola vaccination and identified correlates of Ebola vaccine uptake among high-risk community members in North Kivu, DRC. In March 2021, a cross-sectional survey among adults was implemented in three health zones. We employed a sampling approach mimicking ring vaccination, targeting EVD survivors, their household members, and their neighbors. Outbreak experiences and perceptions toward the Ebola vaccine were assessed, and modified Poisson regression was used to identify correlates of Ebola vaccine uptake among those offered vaccination. Among the 631 individuals surveyed, most (90.2%) reported a high perceived risk of EVD and 71.6% believed that the vaccine could reduce EVD severity; however, 63.7% believed the vaccine had serious side effects. Among the 474 individuals who had been offered vaccination, 397 (83.8%) received the vaccine, 180 (45.3%) of those vaccinated received the vaccine after two or more offers. Correlates positively associated with vaccine uptake included having heard positive information about the vaccine (RR 1.30, 95% CI 1.06-1.60), the belief that the vaccine could prevent EVD (RR 1.23, 95% CI 1.09-1.39), and reporting that religion influenced all decisions (RR 1.13, 95% CI 1.02-1.25). Ebola vaccine uptake was high in this population, although mixed attitudes and vaccine delays were common. Communicating positive vaccine information, emphasizing the efficacy of the Ebola vaccine, and engaging religious leaders to promote vaccination may aid in increasing Ebola vaccine uptake during future outbreaks.

A hybrid resampling algorithms SMOTE and ENN based deep learning models for identification of Marburg virus inhibitors

Background: Marburg virus (MARV) is a sporadic outbreak of a zoonotic disease that causes lethal hemorrhagic fever in humans. We propose a deep learning model with resampling techniques and predict the inhibitory activity of MARV from unknown compounds in the virtual screening process. Methodology & results: We applied resampling techniques to solve the imbalanced data problem. The classifier model comparisons revealed that the hybrid model of synthetic minority oversampling technique - edited nearest neighbor and artificial neural network (SMOTE-ENN + ANN) achieved better classification performance with 95% overall accuracy. The trained SMOTE-ENN+ANN hybrid model predicted as lead molecules; 25 out of 87,043 from ChemDiv, four out of 340 from ChEMBL anti-viral library, three out of 918 from Phytochemical database, and seven out of 419 from Natural products from NCI divsetIV, and 214 out of 1,12,267 from Natural compounds ZINC database for MARV. Conclusion: Our studies reveal that the proposed SMOTE-ENN + ANN hybrid model can improve overall accuracy more effectively and predict new lead molecules against MARV.

High-Throughput Screening Assay to Identify Small Molecule Inhibitors of Marburg Virus VP40 Protein

Marburg virus (MARV) causes sporadic outbreaks of severe disease with high case fatality rates in humans. To date, neither therapeutics nor prophylactic approaches have been approved for MARV disease. The MARV matrix protein VP40 (mVP40) plays central roles in virus assembly and budding. mVP40 also inhibits interferon signaling by inhibiting the function of Janus kinase 1. This suppression of host antiviral defenses likely contributes to MARV virulence and therefore is a potential therapeutic target. We developed and optimized a cell-based high-throughput screening (HTS) assay in 384-well format to measure mVP40 interferon (IFN) antagonist function such that inhibitors could be identified. We performed a pilot screen of 1280 bioactive compounds and identified 3 hits, azaguanine-8, tosufloxacin hydrochloride, and linezolid, with Z scores > 3 and no significant cytotoxicity. Of these, azaguanine-8 inhibited MARV growth at noncytotoxic concentrations. These data demonstrate the suitability of the HTS mVP40 assay for drug discovery and suggest potential directions for anti-MARV therapeutic development.