Importance of 1918 virus reconstruction to current assessments of pandemic risk

One hundred years after the 1918 pandemic: new concepts for preparing for influenza pandemics

PURPOSE OF REVIEW

In the 100 years since the influenza pandemic of 1918-1919, the most deadly event in human history, we have made substantial progress yet we remain vulnerable to influenza pandemics This article provides a brief overview of important advances in preparing for an influenza pandemic, viewed largely from the perspective of the healthcare system.

RECENT FINDINGS

We have gained insights into influenza pathogenicity, the animal reservoir and have improved global surveillance for new strains and tools for assessing the pandemic risk posed by novel strains. Public health has refined plans for severity assessment, distribution of countermeasures and nonpharmaceutical approaches. Modest improvements in vaccine technology include cell culture-based vaccines, adjuvanted vaccine and recombinant technology. Conventional infection control tools will be critical in healthcare settings. New evidence suggests that influenza virus may be present in aerosols; the contribution of airborne transmission and role of N95 respirators remains unknown. Baloxavir and pimodivir are new antivirals that may improve treatment, especially for severely ill patients. Optimal use and the risk of resistance require further study.

SUMMARY

Despite the progress in pandemic preparedness, gaps remain including important scientific questions, adequate resources and most importantly, the ability to rapidly deliver highly effective vaccines.

Induction of a 5-lipoxygenase product by daidzein is involved in the regulation of influenza virus replication

This study was conducted to evaluate the regulation mechanism of influenza virus replication following treatment of Madin-Darby canine kidney cells with the soy isoflavone daidzein. We performed comparative qualitative and quantitative analyses of lipid peroxide between mock-infected and virus-infected cells treated with or without daidzein, as it had been reported that daidzein was an antioxidant and lipid peroxide levels increased upon virus infection. Contrary to our belief, lipid peroxides were not elevated in virus-infected cells and no decrease in lipid peroxides was observed in daidzein-treated cells. In daidzein-treated cells, 5-hydroxyeicosatetraenoic acid, the 5-lipoxygenase product derived from arachidonate, was significantly elevated compared to other lipid peroxides. Zileuton (5-lipoxygenase inhibitor) and 5-lipoxygenase knockdown reduced the daidzein-induced antiviral effect. Moreover, virus replication was regulated by treatment with 5-hydroperoxyeicosatetraenoic acid, a precursor of 5-hydroxyeicosatetraenoic acid and 5-lipoxygenase primary product. These results suggest that daidzein regulates virus replication via signal transduction through 5-lipoxygenase products.

Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses

Influenza A viruses are one of the most significant viral groups globally with substantial impacts on human, domestic animal and wildlife health. Wild birds are the natural reservoirs for these viruses, and active surveillance within wild bird populations provides critical information about viral evolution forming the basis of risk assessments and countermeasure development. Unfortunately, active surveillance programs are often resource-intensive, and thus, enhancing programs for increased efficiency is paramount. Machine learning, a branch of artificial intelligence applications, provides statistical learning procedures that can be used to gain novel insights into disease surveillance systems. We use a form of machine learning, gradient boosted trees, to estimate the probability of isolating avian influenza viruses (AIV) from wild bird samples collected during surveillance for AIVs from 2006 to 2011 in the United States. We examined several predictive features including age, sex, bird type, geographic location and matrix gene rRT-PCR results. Our final model had high predictive power and only included geographic location and rRT-PCR results as important predictors. The highest predicted viral isolation probability was for samples collected from the north-central states and the south-eastern region of Alaska. Lower rRT-PCR Ct-values are associated with increased likelihood of AIV isolation, and the model estimated 16% probability of isolating AIV from samples declared negative (i.e., ≥35 Ct-value) using the rRT-PCR screening test and standard protocols. Our model can be used to prioritize previously collected samples for isolation and rapidly evaluate AIV surveillance designs to maximize the probability of viral isolation given limited resources and laboratory capacity.