Estimating the burden of 2009 pandemic influenza A (H1N1) in the United States (April 2009-April 2010)

Cooperation of immune regulators Tollip and surfactant protein A inhibits influenza A virus infection in mice

BACKGROUND

Influenza A virus (IAV) infection is a significant risk factor for respiratory diseases, but the host defense mechanisms against IAV remain to be defined. Immune regulators such as surfactant protein A (SP-A) and Toll-interacting protein (Tollip) have been shown to be involved in IAV infection, but whether SP-A and Tollip cooperate in more effective host defense against IAV infection has not been investigated.

METHODS

Wild-type (WT), Tollip knockout (KO), SP-A KO, and Tollip/SP-A double KO (dKO) mice were infected with IAV for four days. Lung macrophages were isolated for bulk RNA sequencing. Precision-cut lung slices (PCLS) from WT and dKO mice were pre-treated with SP-A and then infected with IAV for 48 h.

RESULTS

Viral load was significantly increased in bronchoalveolar lavage (BAL) fluid of dKO mice compared to all other strains of mice. dKO mice had significantly less recruitment of neutrophils into the lung compared to Tollip KO mice. SP-A treatment of PCLS enhanced expression of TNF and reduced viral load in dKO mouse lung tissue. Pathway analysis of bulk RNA sequencing data suggests that macrophages from IAV-infected dKO mice reduced expression of genes involved in neutrophil recruitment, IL-17 signaling, and Toll-like receptor signaling.

CONCLUSIONS

Our data suggests that both Tollip and SP-A are essential for the lung to exert more effective innate defense against IAV infection.

Detection of Novel Influenza Viruses Through Community and Healthcare Testing: Implications for Surveillance Efforts in the United States

BACKGROUND

Novel influenza viruses pose a potential pandemic risk, and rapid detection of infections in humans is critical to characterizing the virus and facilitating the implementation of public health response measures.

METHODS

We use a probabilistic framework to estimate the likelihood that novel influenza virus cases would be detected through testing in different community and healthcare settings (urgent care, emergency department, hospital, and intensive care unit [ICU]) while at low frequencies in the United States. Parameters were informed by data on seasonal influenza virus activity and existing testing practices.

RESULTS

In a baseline scenario reflecting the presence of 100 novel virus infections with similar severity to seasonal influenza viruses, the median probability of detecting at least one infection per month was highest in urgent care settings (72%) and when community testing was conducted at random among the general population (77%). However, urgent care testing was over 15 times more efficient (estimated as the number of cases detected per 100,000 tests) due to the larger number of tests required for community testing. In scenarios that assumed increased clinical severity of novel virus infection, median detection probabilities increased across all healthcare settings, particularly in hospitals and ICUs (up to 100%) where testing also became more efficient.

CONCLUSIONS

Our results suggest that novel influenza virus circulation is likely to be detected through existing healthcare surveillance, with the most efficient testing setting impacted by the disease severity profile. These analyses can help inform future testing strategies to maximize the likelihood of novel influenza detection.

Viral Pneumonias

Viral pneumonia is usually community acquired and caused by influenza, parainfluenza, respiratory syncytial virus, human metapneumovirus, and adenovirus. Many of these infections are airway centric and chest imaging demonstrates bronchiolitis and bronchopneumonia, With the exception of adenovirus infections, the presence of lobar consolidation usually suggests bacterial coinfection. Community-acquired viral pathogens can cause more severe pneumonia in immunocompromised hosts, who are also susceptible to CMV and varicella infection. These latter 2 pathogens are less likely to manifest the striking airway-centric pattern. Airway-centric pattern is distinctly uncommon in Hantavirus pulmonary syndrome, a rare environmentally acquired infection with high mortality.

Color-Flu Fluorescent Reporter Influenza A Viruses Allow for In Vivo Studies of Innate Immune Function in Zebrafish

Influenza virus infection can cause severe respiratory disease and is estimated to cause millions of illnesses annually. Studies on the contribution of the innate immune response to influenza A virus (IAV) to viral pathogenesis may yield new antiviral strategies. Zebrafish larvae are useful models for studying the innate immune response to pathogens, including IAV, in vivo. Here, we demonstrate how Color-flu, four fluorescent IAV strains originally developed for mice, can be used to study the host response to infection by simultaneously monitoring infected cells, neutrophils, and macrophages in vivo. Using this model, we show how the angiotensin-converting enzyme inhibitor, ramipril, and mitophagy inhibitor, MDIVI-1, improved survival, decreased viral burden, and improved the respiratory burst response to IAV infection. The Color-flu zebrafish larvae model of IAV infection is complementary to other models where the dynamics of infection and the response of innate immune cells can be visualized in a transparent host in vivo.

Incidence of Guillain-Barré syndrome in the world between 1985 and 2020: A systematic review

Introduction

Guillain-Barré syndrome (GBS) is an acute inflammatory demyelinating polyradiculoneuropathy that affects the peripheral nervous system. The study aimed to describe the incidence of GBS in the world up to the year 2020.

Methods

A systematic review was conducted. Searches were done in four databases, PUBMED, EMBASE, EBSCO and Biblioteca virtual em Saude (BVS), and in grey literature and manual search in the reference lists of eligible studies.

Results

A total of 72 studies were included. The incidence of GBS among the cohort studies varied from 0.30 to 6.08 cases per 100.000 habitants and 0.42 to 6.58 cases per 100.000 person-years. Among the self-controlled studies, the risk incidence ranged from 0.072 to 1 case per 100.000 habitants and 1.73 to 4.30 cases per 100.000 person-years.

Conclusions

The reported incidence of GBS in the world among the studies included in the review is slightly higher than that reported in previous studies. The highest incidence rates were associated with public health events of international concern.

Multi-spectral Fluorescent Reporter Influenza A Viruses Allow for in vivo Studies of Innate Immune Function in Zebrafish

Influenza virus infection can cause severe respiratory disease and is estimated to cause millions of illnesses annually. Studies of the contribution of the innate immune response to influenza A virus (IAV) to viral pathogenesis may yield new antiviral strategies. Zebrafish larvae are useful models to study the innate immune response to pathogens, including IAV, in vivo. Here, we demonstrate how Color-flu, four fluorescent IAV strains originally developed for mice, can be used to study host-virus interactions by simultaneously monitoring virus particles, neutrophils, and macrophages in vivo. Using this model, we show how the angiotensin-converting enzyme inhibitor, ramipril, and mitophagy inhibitor, MDIVI-1, improved survival, decreased viral burden, and improved the respiratory burst response to IAV infection. The Color-flu zebrafish model of IAV infection is complementary to other models as it is the only model where interactions between virus particles and host cells in an intact vertebrate can be visualized in vivo.

Indigenous communities and influenza: protocol for a systematic review and meta-analysis

BACKGROUND

Several studies have documented that specific Indigenous groups have been disproportionately affected by previous pandemics. The objective of this paper is to describe the protocol to be used in a review and meta-analysis of the literature on Indigenous groups and influenza. Using this protocol as a guide, a future study will provide a comprehensive historical overview of pre-COVID impact of influenza on Indigenous groups by combining data from the last five influenza pandemics and seasonal influenza up to date.

METHODS/PRINCIPLE FINDINGS

The review will include peer-reviewed original studies published in English, Spanish, Portuguese, Swedish, Danish, and Norwegian. Records will be identified through systematic literature search in eight databases: Embase, MEDLINE, CINAHL, Web of Science, Academic Search Ultimate, SocINDEX, ASSIA, and Google Scholar. Results will be summarized narratively and using meta-analytic strategies.

DISCUSSION

To our knowledge, there is no systematic review combining historical data on the impact of both seasonal and pandemic influenza on Indigenous populations. By summarizing results within and across Indigenous groups, different countries, and historical periods, as well as research in six different languages, we aim to provide information on how strong the risk for influenza is among Indigenous groups and how consistent this risk is across groups, regions, time, and seasonal versus the specific pandemic influenza strains.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42021246391.

Multimodeling approach to evaluating the efficacy of layering pharmaceutical and nonpharmaceutical interventions for influenza pandemics

When an influenza pandemic emerges, temporary school closures and antiviral treatment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their implementation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario.

Impact of Administering Intravenous Azithromycin within 7 Days of Hospitalization for Influenza Virus Pneumonia: A Propensity Score Analysis Using a Nationwide Administrative Database

The potential antimicrobial and anti-inflammatory effectiveness of azithromycin against severe influenza is yet unclear. We retrospectively investigated the effect of intravenous azithromycin administration within 7 days of hospitalization in patients with influenza virus pneumonia and respiratory failure. Using Japan's national administrative database, we enrolled and classified 5066 patients with influenza virus pneumonia into severe, moderate, and mild groups based on their respiratory status within 7 days of hospitalization. The primary endpoints were total, 30-day, and 90-day mortality rates. The secondary endpoints were the duration of intensive-care unit management, invasive mechanical ventilation, and hospital stay. The inverse probability of the treatment weighting method with estimated propensity scores was used to minimize data collection bias. Use of intravenous azithromycin was proportional to the severity of respiratory failure (mild: 1.0%, moderate: 3.1%, severe: 14.8%). In the severe group, the 30-day mortality rate was significantly lower with azithromycin (26.49% vs. 36.65%, p = 0.038). In the moderate group, the mean duration of invasive mechanical ventilation after day 8 was shorter with azithromycin; there were no significant differences in other endpoints between the severe and moderate groups. These results suggest that intravenous azithromycin has favorable effects in patients with influenza virus pneumonia using mechanical ventilation or oxygen.

Conductive Thread-Based Immunosensor for Pandemic Influenza A (H1N1) Virus Detection

Infectious agents such as viruses pose significant threats to human health, being transmitted via direct contact as well as airborne transmission without direct contact, thus requiring rapid detection to prevent the spread of infectious diseases. In this study, we developed a conductive thread-based immunosensor (CT-IS), a biosensor to easily detect the presence of airborne viruses. CT-IS utilizes an antibody that specifically recognizes the HA protein of the pandemic influenza A (pH1N1) virus, which is incorporated into the conductive thread. The antigen-antibody interaction results in increased strain on the conductive thread in the presence of the pH1N1 virus, resulting in increased electrical resistance of the CT-IS. We evaluated the performance of this sensor using the HA protein and the pH1N1 virus, in addition to samples from patients infected with the pH1N1 virus. We observed a significant change in resistance in the pH1N1-infected patient samples (positive: n = 11, negative: n = 9), whereas negligible change was observed in the control samples (patients not infected with the pH1N1 virus; negative). Hence, the CT-IS is a lightweight fiber-type sensor that can be used as a wearable biosensor by combining it with textiles, to detect the pH1N1 virus in a person's vicinity.

Comparison of Influenza and Coronavirus Disease 2019-Associated Hospitalizations Among Children Younger Than 18 Years Old in the United States: FluSurv-NET (October-April 2017-2021) and COVID-NET (October 2020-September 2021)

BACKGROUND

Influenza virus and SARS-CoV-2 are significant causes of respiratory illness in children.

METHODS

Influenza- and COVID-19-associated hospitalizations among children <18 years old were analyzed from FluSurv-NET and COVID-NET, 2 population-based surveillance systems with similar catchment areas and methodology. The annual COVID-19-associated hospitalization rate per 100 000 during the ongoing COVID-19 pandemic (1 October 2020-30 September 2021) was compared with influenza-associated hospitalization rates during the 2017-2018 through 2019-2020 influenza seasons. In-hospital outcomes, including intensive care unit (ICU) admission and death, were compared.

RESULTS

Among children <18 years, the COVID-19-associated hospitalization rate (48.2) was higher than influenza-associated hospitalization rates: 2017-2018 (33.5), 2018-2019 (33.8), and 2019-2020 (41.7). The COVID-19-associated hospitalization rate was higher among adolescents 12-17 years old (COVID-19: 59.9; influenza range: 12.2-14.1), but similar or lower among children 5-11 (COVID-19: 25.0; influenza range: 24.3-31.7) and 0-4 (COVID-19: 66.8; influenza range: 70.9-91.5) years old. Among children <18 years, a higher proportion with COVID-19 required ICU admission compared with influenza (26.4% vs 21.6%; P < .01). Pediatric deaths were uncommon during both COVID-19- and influenza-associated hospitalizations (0.7% vs 0.5%; P = .28).

CONCLUSIONS

In the setting of extensive mitigation measures during the COVID-19 pandemic, the annual COVID-19-associated hospitalization rate during 2020-2021 was higher among adolescents and similar or lower among children <12 years compared with influenza during the 3 seasons before the COVID-19 pandemic. COVID-19 adds substantially to the existing burden of pediatric hospitalizations and severe outcomes caused by influenza and other respiratory viruses.

Using real-time data to guide decision-making during an influenza pandemic: A modelling analysis

Influenza pandemics typically occur in multiple waves of infection, often associated with initial emergence of a novel virus, followed (in temperate regions) by a resurgence accompanying the onset of the annual influenza season. Here, we examined whether data collected from an initial pandemic wave could be informative, for the need to implement non-pharmaceutical measures in any resurgent wave. Drawing from the 2009 H1N1 pandemic in 10 states in the USA, we calibrated simple mathematical models of influenza transmission dynamics to data for laboratory confirmed hospitalisations during the initial 'spring' wave. We then projected pandemic outcomes (cumulative hospitalisations) during the fall wave, and compared these projections with data. Model results showed reasonable agreement for all states that reported a substantial number of cases in the spring wave. Using this model we propose a probabilistic decision framework that can be used to determine the need for preemptive measures such as postponing school openings, in advance of a fall wave. This work illustrates how model-based evidence synthesis, in real-time during an early pandemic wave, could be used to inform timely decisions for pandemic response.

A stochastic compartmental model to simulate intra- and inter-species influenza transmission in an indoor swine farm

Common in swine production worldwide, influenza causes significant clinical disease and potential transmission to the workforce. Swine vaccines are not universally used in swine production, due to their limited efficacy because of continuously evolving influenza viruses. We evaluated the effects of vaccination, isolation of infected pigs, and changes to workforce routine (ensuring workers moved from younger pig batches to older pig batches). A Susceptible-Exposed-Infected-Recovered model was used to simulate stochastic influenza transmission during a single production cycle on an indoor hog growing unit containing 4000 pigs and two workers. The absence of control practices resulted in 3,957 pigs [0-3971] being infected and a 0.61 probability of workforce infection. Assuming incoming pigs had maternal-derived antibodies (MDAs), but no control measures were applied, the total number of infected pigs reduced to 1 [0-3958] and the probability of workforce infection was 0.25. Mass vaccination (40% efficacious) of incoming pigs also reduced the total number of infected pigs to 2362 [0-2374] or 0 [0-2364] in pigs assumed to not have MDAs and have MDAs, respectively. Changing the worker routine by starting with younger to older pig batches, reduced the number of infected pigs to 996 [0-1977] and the probability of workforce infection (0.22) in pigs without MDAs. In pigs with MDAs the total number of infected pigs was reduced to 0 [0-994] and the probability of workforce infection was 0.06. All other control practices alone, showed little improvement in reducing total infected pigs and the probability of workforce infection. Combining all control strategies reduced the total number of infected pigs to 0 or 1 with a minimal probability of workforce infection (<0.0002-0.01). These findings suggest that non-pharmaceutical interventions can reduce the impact of influenza on swine production and workers when efficacious vaccines are unavailable.

Competitive evolution of H1N1 and H3N2 influenza viruses in the United States: A mathematical modeling study

Seasonal influenza causes vast public health and economic impact globally. The prevention and control of the annual epidemics remain a challenge due to the antigenic evolution of the viruses. Here, we presented a novel modeling framework based on changes in amino acid sequences and relevant epidemiological data to retrospectively investigate the competitive evolution and transmission of H1N1 and H3N2 influenza viruses in the United States during October 2002 and April 2019. To do so, we estimated the time-varying disease transmission rate from the reported influenza cases and the time-varying antigenic change rate of the viruses from the changes in amino acid sequences. By incorporating the time-varying antigenic change rate into the transmission models, we found that the models could capture the evolutionary transmission dynamics of influenza viruses in the United States. Our modeling results also showed that the antigenic change of the virus plays an essential role in seasonal influenza dynamics.

Carboxylic Acid Isostere Derivatives of Hydroxypyridinones as Core Scaffolds for Influenza Endonuclease Inhibitors

Among the most important influenza virus targets is the RNA-dependent RNA polymerase acidic N-terminal (PA_N) endonuclease, which is a critical component of the viral replication machinery. To inhibit the activity of this metalloenzyme, small-molecule inhibitors employ metal-binding pharmacophores (MBPs) that coordinate to the dinuclear Mn²⁺ active site. In this study, several metal-binding isosteres (MBIs) were examined where the carboxylic acid moiety of a hydroxypyridinone MBP is replaced with other groups to modulate the physicochemical properties of the compound. MBIs were evaluated for their ability to inhibit PA_N using a FRET-based enzymatic assay, and their mode of binding in PA_N was determined using X-ray crystallography.

How do El Niño Southern Oscillation (ENSO) and local meteorological factors affect the incidence of seasonal influenza in New York state

Background

Research is lacking in examining how multiple climate factors affect the incidence of seasonal influenza. We investigated the associations between El Niño Southern Oscillation (ENSO), meteorological factors, and influenza incidence in New York State, United States.

Method

We collected emergency department visit data for influenza from the New York State Department of Health. ENSO index was obtained from the National Oceanic and Atmospheric Administration. Meteorological factors, Google Flu Search Index (GFI), and Influenza-like illness (ILI) data in New York State were also collected. Wavelet analysis was used to quantitatively estimate the coherence and phase difference of ENSO, temperature, precipitation, relative humidity, and absolute humidity with emergency department visits of influenza in New York State. Generalized additive models (GAM) were employed to examine the exposure-response relationships between ENSO, weather, and influenza. GFI and ILI data were used to simulate synchronous influenza visits.

Results

The influenza epidemic in New York State had multiple periodic and was primarily on the 1-year scale. The incidence of influenza closely followed the low ENSO index by an average of two months, and the lag period of ENSO on influenza was shorter during 2015-2018. Low temperature in the previous 2 weeks and low absolute humidity in the prior week were positively associated with influenza incidence in New York State. We found an l-shaped association between ENSO index and influenza, a parabolic relationship between temperature in the previous two weeks and influenza, and a linear negative association between absolute humidity in the previous week and influenza. The simulation models including GFI and ILI had higher accuracy for influenza visit estimation.

Conclusions

Low ENSO index, low temperature, and low absolute humidity may drive the influenza epidemics in New York State. The findings can help us deepen the understanding of the climate-influenza association, and help to develop an influenza forecasting model.

A comparison of conventional and advanced electroanalytical methods to detect SARS-CoV-2 virus: A concise review

Respiratory viruses are a serious threat to human wellbeing that can cause pandemic disease. As a result, it is critical to identify virus in a timely, sensitive, and precise manner. The present novel coronavirus-2019 (COVID-19) disease outbreak has increased these concerns. The research of developing various methods for COVID-19 virus identification is one of the most rapidly growing research areas. This review article compares and addresses recent improvements in conventional and advanced electroanalytical approaches for detecting COVID-19 virus. The popular conventional methods such as polymerase chain reaction (PCR), loop mediated isothermal amplification (LAMP), serology test, and computed tomography (CT) scan with artificial intelligence require specialized equipment, hours of processing, and specially trained staff. Many researchers, on the other hand, focused on the invention and expansion of electrochemical and/or bio sensors to detect SARS-CoV-2, demonstrating that they could show a significant role in COVID-19 disease control. We attempted to meticulously summarize recent advancements, compare conventional and electroanalytical approaches, and ultimately discuss future prospective in the field. We hope that this review will be helpful to researchers who are interested in this interdisciplinary field and desire to develop more innovative virus detection methods.

Missing science: A scoping study of COVID-19 epidemiological data in the United States

Systematic approaches to epidemiologic data collection are critical for informing pandemic responses, providing information for the targeting and timing of mitigations, for judging the efficacy and efficiency of alternative response strategies, and for conducting real-world impact assessments. Here, we report on a scoping study to assess the completeness of epidemiological data available for COVID-19 pandemic management in the United States, enumerating authoritative US government estimates of parameters of infectious transmission, infection severity, and disease burden and characterizing the extent and scope of US public health affiliated epidemiological investigations published through November 2021. While we found authoritative estimates for most expected transmission and disease severity parameters, some were lacking, and others had significant uncertainties. Moreover, most transmission parameters were not validated domestically or re-assessed over the course of the pandemic. Publicly available disease surveillance measures did grow appreciably in scope and resolution over time; however, their resolution with regards to specific populations and exposure settings remained limited. We identified 283 published epidemiological reports authored by investigators affiliated with U.S. governmental public health entities. Most reported on descriptive studies. Published analytic studies did not appear to fully respond to knowledge gaps or to provide systematic evidence to support, evaluate or tailor community mitigation strategies. The existence of epidemiological data gaps 18 months after the declaration of the COVID-19 pandemic underscores the need for more timely standardization of data collection practices and for anticipatory research priorities and protocols for emerging infectious disease epidemics.

Influenza During the 2010-2020 Decade in the United States: Seasonal Outbreaks and Vaccine Interventions

The 10 years between the last influenza pandemic and start of the severe acute respiratory syndrome coronavirus 2 pandemic have been marked by great advances in our ability to follow influenza occurrence and determine vaccine effectiveness (VE), largely based on widespread use of the polymerase chain reaction assay. We examine the results, focusing mainly on data from the United States and inactivated vaccines. Surveillance has expanded, resulting in increased ability to characterize circulating viruses and their impact. The surveillance has often confirmed previous observations on timing of outbreaks and age groups affected, which can now be examined in greater detail. Selection of strains for vaccines is now based on enhanced viral characterization using immunologic, virologic, and computational techniques not previously available. Vaccine coverage has been largely stable, but VE has remained modest and, in some years, very low. We discuss ways to improve VE based on existing technology while we work toward supraseasonal vaccines.

An overview of advancement in aptasensors for influenza detection

INTRODUCTION

The platforms for early identification of infectious diseases such as influenza has seen a surge in recent years as delayed diagnosis of such infections can lead to dreadful effects causing large numbers of deaths. The time taken in detection of an infectious disease may vary from a few days to a few weeks depending upon the choice of the techniques. So, there is an urgent need for advanced methodologies for early diagnosis of the influenza.

AREAS COVERED

The emergence of "Aptasensor" synergistically with biosensors for diagnosis has opened a new era for sensitive, selective and early detection approaches. This review described various conventional as well as advanced methods based on artificial immunogenic nucleotide sequences complementing a part of the virus, i.e., aptamers based aptasensors for influenza diagnosis and the challenges faced in their commercialization.

EXPERT OPINION

Although numerous traditional methods are available for influenza detection but mostly associated with low sensitivity, specificity, high cost, trained personnel, and animals required for virus culture/ antibody raising as the major drawbacks. Aptamers can be manufactured invitro as 'chemical antibodies' at commercial level, no animal required. Following these advantages, aptamers can pave the way for an efficient diagnostic technique as compared to other existing conventional methods..

Comparing the Cytokine Storms of COVID-19 and Pandemic Influenza

Emerging respiratory viruses are major health threats due to their potential to cause massive outbreaks. Over the past 2 years, the coronavirus disease 2019 (COVID-19) pandemic has caused millions of cases of severe infection and deaths worldwide. Although natural and vaccine-induced protective immune mechanisms against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been increasingly identified, the factors that determine morbimortality are less clear. Comparing the immune signatures of COVID-19 and other severe respiratory infections such as the pandemic influenza might help dissipate current controversies about the origin of their severe manifestations. As such, identifying homologies in the immunopathology of both diseases could provide targets for immunotherapy directed to block shared pathogenic mechanisms. Meanwhile, finding unique characteristics that differentiate each infection could shed light on specific immune alterations exploitable for diagnostic and individualized therapeutics for each case. In this study, we summarize immunopathological aspects of COVID-19 and pandemic influenza from the perspective of cytokine storms as the driving force underlying morbidity. Thereby, we analyze similarities and differences in the cytokine profiles of both infections, aiming to bring forward those molecules more attractive for translational medicine and drug development.

Tollip Inhibits IL-33 Release and Inflammation in Influenza A Virus-Infected Mouse Airways

Respiratory influenza A virus (IAV) infection continues to pose significant challenges in healthcare of human diseases including asthma. IAV infection in mice was shown to increase IL-33, a key cytokine in driving airway inflammation in asthma, but how IL-33 is regulated during viral infection remains unclear. We previously found that a genetic mutation in Toll-interacting protein (Tollip) was linked to less airway epithelial Tollip expression, increased neutrophil chemokines, and lower lung function in asthma patients. As Tollip is involved in maintaining mitochondrial function, and mitochondrial stress may contribute to extracellular ATP release and IL-33 secretion, we hypothesized that Tollip downregulates IL-33 secretion via inhibiting ATP release during IAV infection. Wild-type and Tollip knockout (KO) mice were infected with IAV and treated with either an ATP converter apyrase or an IL-33 decoy receptor soluble ST2 (sST2). KO mice significantly lost more body weight and had increased extracellular ATP, IL-33 release, and neutrophilic inflammation. Apyrase treatment reduced extracellular ATP levels, IL-33 release, and neutrophilic inflammation in Tollip KO mice. Excessive lung neutrophilic inflammation in IAV-infected Tollip KO mice was reduced by sST2, which was coupled with less IL-33 release. Our data suggest that Tollip inhibits IAV infection, potentially by inhibiting extracellular ATP release and reducing IL-33 activation and lung inflammation. In addition, sST2 may serve as a potential therapeutic approach to mitigate respiratory viral infection in human subjects with Tollip deficiency.

What to Expect from COVID-19 and from COVID-19 Vaccine for Expecting or Lactating Women

Recent studies identified pregnancy as a high-risk condition for the development of maternal-fetal complications in the case of the SARS-CoV-2 infection. Therefore, the scientific community is now considering pregnant women a "fragile" category that should be vaccinated with high priority. The number of pregnant women undergoing hospitalization since summer 2021, including Intensive Care Unit admission, is growing, as well as the risk of preterm birth. Evidence from both animals and humans suggest that, similarly to other vaccines routinely administered in pregnancy, COVID-19 vaccines are not crossing the placenta, do not increase the risk of miscarriage, preterm birth, stillbirth, the birth of small gestational age neonates, as well as the risk of congenital abnormalities. To date, the World Health Organization and scientific literature are promoting and encouraging the vaccination of all pregnant and lactating women. The aim of our narrative review is to present the available literature regarding this issue with the aim to provide appropriate answers to the most frequent requests, doubts, and fears that have led many expecting and lactating women not to become vaccinated during this pandemic period.

Mutation in Hemagglutinin Antigenic Sites in Influenza A pH1N1 Viruses from 2015–2019 in the United States Mountain West, Europe, and the Northern Hemisphere

H1N1 influenza A virus is a respiratory pathogen that undergoes antigenic shift and antigenic drift to improve viral fitness. Tracking the evolutionary trends of H1N1 aids with the current detection and the future response to new viral strains as they emerge. Here, we characterize antigenic drift events observed in the hemagglutinin (HA) sequence of the pandemic H1N1 lineage from 2015–2019. We observed the substitutions S200P, K147N, and P154S, together with other mutations in structural, functional, and/or epitope regions in 2015–2019 HA protein sequences from the Mountain West region of the United States, the larger United States, Europe, and other Northern Hemisphere countries. We reconstructed multiple phylogenetic trees to track the relationships and spread of these mutations and tested for evidence of selection pressure on HA. We found that the prevalence of amino acid substitutions at positions 147, 154, 159, 200, and 233 significantly changed throughout the studied geographical regions between 2015 and 2019. We also found evidence of coevolution among a subset of these amino acid substitutions. The results from this study could be relevant for future epidemiological tracking and vaccine prediction efforts. Similar analyses in the future could identify additional sequence changes that could affect the pathogenicity and/or infectivity of this virus in its human host.

The Continued Threat of Influenza A Viruses

Influenza A virus (IAV) is a major cause of respiratory infections worldwide, with the most severe cases occurring in the very young and in elderly individuals [...]

Resilience as a measure of preparedness for pandemic influenza outbreaks

The global crisis generated by COVID-19 has heightened awareness of pandemic outbreaks. From a public health preparedness standpoint, it is essential to assess the impact of a pandemic and also the resilience of the affected communities, which is the ability to withstand and recover quickly after a pandemic outbreak. The infection attack rate has been the common metric to assess community response to a pandemic outbreak, while it focuses on the number of infected it does not capture other dimensions such as the recovery time. The aim of this research is to develop community resilience measures and demonstrate their estimation using a simulated pandemic outbreak in a region in the USA. Three scenarios are analysed with different combinations of virus transmissibility rates and non-pharmaceutical interventions. I The inclusion of the resilience framework in the pandemics outbreak analysis will enable decision makers to capture the multi dimensional nature of community response.

Genome-wide CRISPR-Cas9 screening identifies the CYTH2 host gene as a potential therapeutic target of influenza viral infection

Host genes critical for viral infection are effective antiviral drug targets with tremendous potential due to their universal characteristics against different subtypes of viruses and minimization of drug resistance. Accordingly, we execute a genome-wide CRISPR-Cas9 screen with multiple rounds of survival selection. Enriched in this screen are several genes critical for host sialic acid biosynthesis and transportation, including the cytohesin 2 (CYTH2), tetratricopeptide repeat protein 24 (TTC24), and N-acetylneuraminate synthase (NANS), which we confirm are responsible for efficient influenza viral infection. Moreover, we reveal that CYTH2 is required for the early stage of influenza virus infection by mediating endosomal trafficking. Furthermore, CYTH2 antagonist SecinH3 blunts influenza virus infection in vivo. In summary, these data suggest that CYTH2 is an attractive target for developing host-directed antiviral drugs and therapeutics against influenza virus infection.

SARS-CoV-2 and Influenza Viruses: Strategies to Cope with Co-infection and Bioinformatics Perspective

Almost a century after the devastating pandemic of the Spanish flu, humankind is facing the relatively comparable global outbreak of COVID‐19. COVID‐19 is an infectious disease caused by SARS‐CoV‐2 with an unprecedented transmission pattern. In the face of the recent repercussions of COVID‐19, many have argued that the clinical experience with influenza through the last century may have tremendous implications in the containment of this newly emerged viral disease. During the last 2 years, from the emergence of COVID‐19, tremendous advances have been made in diagnosing and treating coinfections. Several approved vaccines are available now for the primary prevention of COVID‐19 and specific treatments exist to alleviate symptoms. The present review article aims to discuss the pathophysiology, diagnosis, and treatment of SARS‐CoV‐2 and influenza A virus coinfection while delivering a bioinformatics‐based insight into this subject matter.

Anti-COVID-19 Nanomaterials: Directions to Improve Prevention, Diagnosis, and Treatment

Following the announcement of the outbreak of COVID-19 by the World Health Organization, unprecedented efforts were made by researchers around the world to combat the disease. So far, various methods have been developed to combat this "virus" nano enemy, in close collaboration with the clinical and scientific communities. Nanotechnology based on modifiable engineering materials and useful physicochemical properties has demonstrated several methods in the fight against SARS-CoV-2. Here, based on what has been clarified so far from the life cycle of SARS-CoV-2, through an interdisciplinary perspective based on computational science, engineering, pharmacology, medicine, biology, and virology, the role of nano-tools in the trio of prevention, diagnosis, and treatment is highlighted. The special properties of different nanomaterials have led to their widespread use in the development of personal protective equipment, anti-viral nano-coats, and disinfectants in the fight against SARS-CoV-2 out-body. The development of nano-based vaccines acts as a strong shield in-body. In addition, fast detection with high efficiency of SARS-CoV-2 by nanomaterial-based point-of-care devices is another nanotechnology capability. Finally, nanotechnology can play an effective role as an agents carrier, such as agents for blocking angiotensin-converting enzyme 2 (ACE2) receptors, gene editing agents, and therapeutic agents. As a general conclusion, it can be said that nanoparticles can be widely used in disinfection applications outside in vivo. However, in in vivo applications, although it has provided promising results, it still needs to be evaluated for possible unintended immunotoxicity. Reviews like these can be important documents for future unwanted pandemics.

mRNA Vaccine Development for Emerging Animal and Zoonotic Diseases

In the prevention and treatment of infectious diseases, mRNA vaccines hold great promise because of their low risk of insertional mutagenesis, high potency, accelerated development cycles, and potential for low-cost manufacture. In past years, several mRNA vaccines have entered clinical trials and have shown promise for offering solutions to combat emerging and re-emerging infectious diseases such as rabies, Zika, and influenza. Recently, the successful application of mRNA vaccines against COVID-19 has further validated the platform and opened the floodgates to mRNA vaccine's potential in infectious disease prevention, especially in the veterinary field. In this review, we describe our current understanding of the mRNA vaccines and the technologies used for mRNA vaccine development. We also provide an overview of mRNA vaccines developed for animal infectious diseases and discuss directions and challenges for the future applications of this promising vaccine platform in the veterinary field.

Mortality Associated With Influenza and Respiratory Syncytial Virus in the US, 1999-2018

IMPORTANCE

Respiratory syncytial virus (RSV) mortality estimates have not been updated since 2009, and no study has assessed changes in influenza mortality after the 2009 pandemic. Updated burden estimates are needed to characterize long-term changes in the epidemiology of these viruses.

OBJECTIVE

To evaluate excess mortality from RSV and influenza in the US from 1999 to 2018.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used data from 50.3 million US death certificates from 1999 to 2018 to create age-specific linear regression models and assess weekly mortality fluctuations above a seasonal baseline associated with RSV and influenza. Statistical analysis was performed for 1043 weeks from January 3, 1999, to December 29, 2018.

MAIN OUTCOMES AND MEASURES

Excess mortality associated with RSV and influenza estimated from the difference between observed and expected underlying respiratory mortality each season.

RESULTS

There were 50.3 million death certificates (50.1% women and 49.9% men; mean [SD] age at death, 72.7 [18.6] years) included in this analysis, 1.0% for children younger than 1 year and 73.4% for adults aged 65 years or older. A mean of 6549 (95% CI, 6140-6958) underlying respiratory deaths were associated with RSV annually, including 96 (95% CI, 92-99) deaths among children younger than 1 year. For influenza, there were 10 171 (95% CI, 9652-10 691) underlying respiratory deaths per year, with 23 deaths (95% CI, 19-27) among children younger than 1 year. The highest mean mortality rate per 100 000 population for both viruses was among adults aged 65 years or older at 14.7 (95% CI, 13.8-15.5) for RSV and 20.5 (95% CI, 19.4-21.5) for influenza. A lower proportion of influenza deaths occurred among those aged 65 years or older compared with earlier estimates (75.1% [95% CI, 67.4%-82.8%]). Influenza mortality was highest among those aged 65 years or older in seasons when A/H3N2 predominated (18 739 [95% CI, 16 616-21 336] deaths in 2017-2018) and among those aged 5 to 49 years when A/H1N1pdm2009 predominated (1683 [95% CI, 1583-1787] deaths in 2013-2014). Results were sensitive to the choice of mortality outcome and method, with the broadest outcome associated with annual means of 23 352 (95% CI, 21 814-24 891) excess deaths for RSV and 27 171 (95% CI, 25 142-29 199) for influenza.

CONCLUSIONS AND RELEVANCE

This study suggests that RSV poses a greater risk than influenza to infants, while both are associated with substantial mortality among elderly individuals. Influenza has large interannual variability, affecting different age groups depending on the circulating virus. The emergence of the influenza A/H1N1pdm2009 pandemic virus in 2009 shifted mortality toward middle-aged adults, a trend still observed to date. This study's estimates provide a benchmark to evaluate the mortality benefits associated with interventions against respiratory viruses, including new or improved immunization strategies.

Designer DNA nanostructures for viral inhibition

Emerging viral diseases can substantially threaten national and global public health. Central to our ability to successfully tackle these diseases is the need to quickly detect the causative virus and neutralize it efficiently. Here we present the rational design of DNA nanostructures to inhibit dengue virus infection. The designer DNA nanostructure (DDN) can bind to complementary epitopes on antigens dispersed across the surface of a viral particle. Since these antigens are arranged in a defined geometric pattern that is unique to each virus, the structure of the DDN is designed to mirror the spatial arrangement of antigens on the viral particle, providing very high viral binding avidity. We describe how available structural data can be used to identify unique spatial patterns of antigens on the surface of a viral particle. We then present a procedure for synthesizing DDNs using a combination of in silico design principles, self-assembly, and characterization using gel electrophoresis, atomic force microscopy and surface plasmon resonance spectroscopy. Finally, we evaluate the efficacy of a DDN in inhibiting dengue virus infection via plaque-forming assays. We expect this protocol to take 2-3 d to complete virus antigen pattern identification from existing cryogenic electron microscopy data, ~2 weeks for DDN design, synthesis, and virus binding characterization, and ~2 weeks for DDN cytotoxicity and antiviral efficacy assays.

ICU Resource Limitations During Peak Seasonal Influenza: Results of a 2018 National Feasibility Study

OBJECTIVES

Demonstrate the feasibility of weekly data collection and analysis of public health emergency (PHE) data. Assess fluctuations in, and challenges of, resource matching and potential effect on patient care for influenza in ICUs.

DESIGN

Multicenter prospective noninterventional study testing effectiveness of leveraging the Discovery Critical Care Research Network Program for Resilience and Emergency Preparedness (Discovery-PREP) in performing PHE research. A 20-question internet survey was developed to prospectively assess ICU influenza-related resource stress. An informatics tool was designed to track responses; data were analyzed within 24 hours of weekly survey completion by the team biostatistician for timely reporting.

PARTICIPANTS

Critical care and Emergency Medicine Discovery-PREP network investigators self-selected to participate in the voluntary query.

SETTING

ICUs of 13 hospitals throughout the United States, 12 academic, and one community.

INTERVENTIONS

ICU physicians were electronically surveyed weekly over 17 weeks during the influenza season (January 2018-April 2018). Responses were collected for 48 hours after each email query.

MEASUREMENTS AND MAIN RESULTS

The average weekly response among the sites was 79% (range, 65-100%). Significant stress, defined as alterations in ICU staffing and/or resource allocation, occurred in up to 41% of sites during the national peak of influenza activity. These alterations included changes in staffing, not accepting external patient transfers, and canceling elective surgery. During this same period, up to 17% of the sites indicated that these changes might not have been sufficient to prevent potentially avoidable patient harm.

CONCLUSIONS

This novel approach to querying ICU operational stress indicated that almost half of participating sites experienced critical care resource limitations during peak influenza season and required process and/or staffing changes to better balance resources with patient care demands. This weekly national reporting infrastructure could be adapted and expanded to better inform providers, hospital emergency management teams, and government leaders during PHEs.

Primer of COVID-19 Vaccines for the Perioperative Physician

Dr. Emanuel Papper, the founding chairman of the Department of Anesthesiology at Columbia University, was passionate about research, training, and innovation. At an event held in his honor on March 20, 2021, experts came together to discuss the coronavirus disease 2019 (COVID-19) pandemic and its myriad challenges. Dr. Wellington Sun, MD, of Vaxcellerant LLC, an expert in infectious disease and vaccine research and development, presented a "Primer of COVID-19 vaccines for the perioperative physician." Operation Warp Speed was successful in producing multiple efficacious and safe vaccines for use in the United States and around the globe. Their development and authorization for emergency use occurred in an unprecedented timespan of <1 year. Technology such as V-SAFE has helped to accrue extensive postdevelopment safety data that will be utilized for licensure of these vaccines. The COVID-19 vaccine success is tempered by the knowledge that severe acute respiratory syndrome coronavirus 2 continues its natural selection of variants that threaten the efficacy of vaccines. Important questions remain regarding the future of the COVID-19 pandemic, severe acute respiratory syndrome coronavirus 2 variants, successful vaccination strategies, and preparedness for future pandemics.

MicroRNAs affect GPCR and Ion channel genes needed for influenza replication

Influenza virus causes seasonal epidemics and sporadic pandemics resulting in morbidity, mortality, and economic losses worldwide. Understanding how to regulate influenza virus replication is important for developing vaccine and therapeutic strategies. Identifying microRNAs (miRs) that affect host genes used by influenza virus for replication can support an antiviral strategy. In this study, G-protein coupled receptor (GPCR) and ion channel (IC) host genes in human alveolar epithelial (A549) cells used by influenza virus for replication (Orr-Burks et al., 2021) were examined as miR target genes following A/CA/04/09- or B/Yamagata/16/1988 replication. Thirty-three miRs were predicted to target GPCR or IC genes and their miR mimics were evaluated for their ability to decrease influenza virus replication. Paired miR inhibitors were used as an ancillary measure to confirm or not the antiviral effects of a miR mimic. Fifteen miRs lowered influenza virus replication and four miRs were found to reduce replication irrespective of virus strain and type differences. These findings provide evidence for novel miR disease intervention strategies for influenza viruses.

Predictors of heart failure symptoms in hereditary hemorrhagic telangiectasia patients with hepatic arteriovenous malformations

BACKGROUND

Hepatic arteriovenous malformations (AVMs) in hereditary hemorrhagic telangiectasia (HHT) patients are most commonly hepatic artery to hepatic venous shunts which can result in high-output heart failure. This condition can be debilitating and is a leading cause of liver transplantation in HHT patients. However, it is not known what characteristics can discriminate between asymptomatic patients and those who will develop heart failure symptoms.

RESULTS

176 patients with HHT were evaluated with computed tomography angiography (CTA) between April 2004 and February 2019 at our HHT Center of Excellence. 63/176 (35.8%) patients were found to have hepatic AVMs on CTA. 18 of these patients were excluded because of the presence of another condition which could confound evaluation of heart failure symptoms. In the remaining 45 patients included in our cohort, 25/45 (55.6%) patients were classified as asymptomatic and 20/45 (44.4%) were classified as symptomatic, and these groups were compared. In symptomatic patients, mean common hepatic artery (CHA) diameter was significantly higher (11.1 versus 8.4 mm) and mean hemoglobin levels were significantly lower (10.7 vs 12.6 g/dL). A stepwise multiple logistic regression analysis demonstrated that both CHA diameter and hemoglobin level were independent predictors of heart failure symptoms with ORs of 2.554 (95% CI 1.372-4.754) and 0.489 (95% CI 0.299-0.799), respectively. The receiver operator characteristic (ROC) curve of our analysis demonstrated an AUC of 0.906 (95% CI 0.816-0.996), sensitivity 80.0% (95% CI 55.7-93.4%), and specificity 75.0% (95% CI 52.9-89.4%).

CONCLUSIONS

CTA is an effective and easily reproducible method to evaluate hepatic involvement of HHT. Utilizing CTA, clinical, and laboratory data we determined CHA diameter and hemoglobin level were independent predictors of heart failure symptoms.

Household Preparedness and Perceptions of Workforce Preparedness During Pandemics: A Health Care Employee Survey at the US Department of Veterans Affairs

OBJECTIVE

To examine the effects of household preparedness on perceptions of workplace preparedness during a pandemic among all employees at the US Department of Veterans Affairs (VA) medical facilities.

METHODS

The VA Preparedness Survey (October-December 2018, Los Angeles, CA) used a stratified simple random, web-based survey. Multivariate statistical analyses examined the effect of household preparedness on perceptions of workforce preparedness during a pandemic: institutional readiness; desire for additional training; and understanding their role and its importance.

RESULTS

VA employees totaling 4026 participated. For a pandemic, 55% were confident in their VA medical facility's ability to respond, 63% would like additional training, 49% understood their role during a response, and 68% reported their role as important. Only 23% reported being "well prepared" at home during major disasters. After controlling for study-relevant factors, household preparedness was positively associated with perceptions of workforce preparedness during a pandemic.

CONCLUSIONS

Efforts to increase household preparedness for health care employees could bolster workforce preparedness during pandemics. Organizations should consider robust policies and strategies, such as flexible work arrangements, in order to mitigate factors that may serve as barriers to household preparedness.

Fluid Dynamics of Respiratory Infectious Diseases

The host-to-host transmission of respiratory infectious diseases is fundamentally enabled by the interaction of pathogens with a variety of fluids (gas or liquid) that shape pathogen encapsulation and emission, transport and persistence in the environment, and new host invasion and infection. Deciphering the mechanisms and fluid properties that govern and promote these steps of pathogen transmission will enable better risk assessment and infection control strategies, and may reveal previously underappreciated ways in which the pathogens might actually adapt to or manipulate the physical and chemical characteristics of these carrier fluids to benefit their own transmission. In this article, I review our current understanding of the mechanisms shaping the fluid dynamics of respiratory infectious diseases.

Characteristics of patients with ST-segment elevated myocardial infarction (STEMI) at the initial stage of the COVID-19 pandemic: a systematic review and meta-analysis

BACKGROUND

The COVID-19 pandemic has become a new challenge to the medical system in various countries. The patients with ST-segment elevated myocardial infarction (STEMI) were also affected.

METHODS

We used a random-effects mode to analyze the differences of the baseline characteristics and therapeutic features between STEMI patients admitted before and after the start of the COVID-19 pandemic.

RESULTS

Thirty eight studies involving 79,753 patients were included in this analysis. The number of hospitalized STEMI patients decreased by 26% after the start of the COVID-19 pandemic. There were no differences in age, sex, prevalence of diabetes, hypertension, dyslipidemia or percutaneous coronary intervention rate between the STEMI patients before and after the start of the COVID-19 pandemic. However, the STEMI patients admitted after the start of the COVID-19 pandemic had a significantly increased time from symptom onset to first medical contact (standard mean difference: 0.51, 95% confidence interval: 0.24-0.78, p < .001) and an increased in-hospital mortality (odds ratio: 1.70, 95% confidence interval:1.14-2.56, p < .001); The in-hospital mortality of the STEMI patients with COVID-19 was 24% (95% confidence interval: 0.15-0.33); The in-hospital mortality of the STEMI patients with COVID-19 was significantly higher than that of the STEMI patients without COVID-19 at the initial stage of the COVID-19 pandemic (odds ratio: 7.28, 95% confidence interval: 2.75-19.28, p < .001).

CONCLUSION

The number of admitted STEMI patients was reduced while the in-hospital mortality and the time from symptom onset to first medical contact were increased during the COVID-19 pandemic.

The Mechanism behind Influenza Virus Cytokine Storm

Influenza viruses are still a serious threat to human health. Cytokines are essential for cell-to-cell communication and viral clearance in the immune system, but excessive cytokines can cause serious immune pathology. Deaths caused by severe influenza are usually related to cytokine storms. The recent literature has described the mechanism behind the cytokine–storm network and how it can exacerbate host pathological damage. Biological factors such as sex, age, and obesity may cause biological differences between different individuals, which affects cytokine storms induced by the influenza virus. In this review, we summarize the mechanism behind influenza virus cytokine storms and the differences in cytokine storms of different ages and sexes, and in obesity.

Clinical features and risk factors for severe influenza in children: A study from multiple hospitals in Shanghai

BACKGROUND

The incidence and mortality of influenza in children had risen, but data are limited on children with severe influenza virus infection in China.

METHODS

We conducted a retrospective case-control study and collected the patients' clinical data. Clinical data including demography, clinical presentation, laboratory findings, radiologic findings, treatment and outcomes were collected. Children were clinically confirmed to have virus infection in Shanghai in three hospitals from June 2014 to June 2019.

RESULTS

During the study, 36,047 children were enrolled. Among them, 118 met the criteria for severe flu. Clinical symptoms such as fever, cough, gastrointestinal symptoms, coma and epilepsy were higher in the severe group. Complications such as pneumorrhagia, heart failure, septic shock, acute renal failure and influenza-associated encephalitis were higher in the severe influenza group than the death group. The laboratory findings including decreased hemoglobin, high alanine aminotransferase, high urea nitrogen and high lactate levels were risk factors for death in children with influenza.

CONCLUSION

Influenza-associated encephalopathy (IAE), acute respiratory distress syndrome (ARDS) were the common clinical manifestations and complications for the severe influenza, and delayed use of oseltamivir was found to be associated with fatality.

The coronavirus disease 2019 vaccine in pregnancy: risks, benefits, and recommendations

The coronavirus disease 2019 has caused over 2 million deaths worldwide, with over 412,000 deaths reported in Unites States. To date, at least 57,786 pregnant women in the United States have been infected, and 71 pregnant women have died. Although pregnant women are at higher risk of severe coronavirus disease 2019-related illness, clinical trials for the available vaccines excluded pregnant and lactating women. The safety and efficacy of the vaccines for pregnant women, the fetus, and the newborn remain unknown. A review of maternal and neonatal coronavirus disease 2019 morbidity and mortality data along with perinatal vaccine safety considerations are presented to assist providers with shared decision-making regarding vaccine administration for this group, including the healthcare worker who is pregnant, lactating, or considering pregnancy. The coronavirus disease 2019 vaccine should be offered to pregnant women after discussing the lack of safety data, with preferential administration for those at highest risk of severe infection, until safety and efficacy of these novel vaccines are validated.

All-Inkjet-Printed 3D Alveolar Barrier Model with Physiologically Relevant Microarchitecture

With the outbreak of new respiratory viruses and high mortality rates of pulmonary diseases, physiologically relevant models of human respiratory system are urgently needed to study disease pathogenesis, drug efficacy, and pharmaceutics. In this paper, a 3D alveolar barrier model fabricated by printing four human alveolar cell lines, namely, type I and II alveolar cells (NCI-H1703 and NCI-H441), lung fibroblasts (MRC5), and lung microvascular endothelial cells (HULEC-5a) is presented. Automated high-resolution deposition of alveolar cells by drop-on-demand inkjet printing enables to fabricate a three-layered alveolar barrier model with an unprecedented thickness of ≈10 µm. The results show that the 3D structured model better recapitulate the structure, morphologies, and functions of the lung tissue, compared not only to a conventional 2D cell culture model, as expected, but also a 3D non-structured model of a homogeneous mixture of the alveolar cells and collagen. Finally, it is demonstrated that this thin multilayered model reproduce practical tissue-level responses to influenza infection. Drop-on-demand inkjet-printing is an enabling technology for customization, scalable manufacturing, and standardization of their size and growth, and it is believed that this 3D alveolar barrier model can be used as an alternative to traditional test models for pathological and pharmaceutical applications.

Clinical and Immunological Factors That Distinguish COVID-19 From Pandemic Influenza A(H1N1)

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), is a global health threat with the potential to cause severe disease manifestations in the lungs. Although COVID-19 has been extensively characterized clinically, the factors distinguishing SARS-CoV-2 from other respiratory viruses are unknown. Here, we compared the clinical, histopathological, and immunological characteristics of patients with COVID-19 and pandemic influenza A(H1N1). We observed a higher frequency of respiratory symptoms, increased tissue injury markers, and a histological pattern of alveolar pneumonia in pandemic influenza A(H1N1) patients. Conversely, dry cough, gastrointestinal symptoms and interstitial lung pathology were observed in COVID-19 cases. Pandemic influenza A(H1N1) was characterized by higher levels of IL-1RA, TNF-α, CCL3, G-CSF, APRIL, sTNF-R1, sTNF-R2, sCD30, and sCD163. Meanwhile, COVID-19 displayed an immune profile distinguished by increased Th1 (IL-12, IFN-γ) and Th2 (IL-4, IL-5, IL-10, IL-13) cytokine levels, along with IL-1β, IL-6, CCL11, VEGF, TWEAK, TSLP, MMP-1, and MMP-3. Our data suggest that SARS-CoV-2 induces a dysbalanced polyfunctional inflammatory response that is different from the immune response against pandemic influenza A(H1N1). Furthermore, we demonstrated the diagnostic potential of some clinical and immune factors to differentiate both diseases. These findings might be relevant for the ongoing and future influenza seasons in the Northern Hemisphere, which are historically unique due to their convergence with the COVID-19 pandemic.

G-Protein-Coupled Receptor and Ion Channel Genes Used by Influenza Virus for Replication

Influenza epidemics result in morbidity and mortality each year. Vaccines are the most effective preventive measure but require annual reformulation, since a mismatch of vaccine strains can result in vaccine failure.

Influenza virus causes epidemics and sporadic pandemics resulting in morbidity, mortality, and economic losses. Influenza viruses require host genes to replicate. RNA interference (RNAi) screens can identify host genes coopted by influenza virus for replication. Targeting these proinfluenza genes can provide therapeutic strategies to reduce virus replication. Nineteen proinfluenza G-protein-coupled receptor (GPCR) and 13 proinfluenza ion channel genes were identified in human lung (A549) cells by use of small interfering RNAs (siRNAs). These proinfluenza genes were authenticated by testing influenza virus A/WSN/33-, A/CA/04/09-, and B/Yamagata/16/1988-infected A549 cells, resulting in the validation of 16 proinfluenza GPCR and 5 proinfluenza ion channel genes. These findings showed that several GPCR and ion channel genes are needed for the production of infectious influenza virus. These data provide potential targets for the development of host-directed therapeutic strategies to impede the influenza virus productive cycle so as to limit infection.

IMPORTANCE Influenza epidemics result in morbidity and mortality each year. Vaccines are the most effective preventive measure but require annual reformulation, since a mismatch of vaccine strains can result in vaccine failure. Antiviral measures are desirable particularly when vaccines fail. In this study, we used RNAi screening to identify several GPCR and ion channel genes needed for influenza virus replication. Understanding the host genes usurped by influenza virus during viral replication can help identify host genes that can be targeted for drug repurposing or for the development of antiviral drugs. The targeting of host genes is refractory to drug resistance generated by viral mutations, as well as providing a platform for the development of broad-spectrum antiviral drugs.

Mitigating Future Respiratory Virus Pandemics: New Threats and Approaches to Consider

Despite many recent efforts to predict and control emerging infectious disease threats to humans, we failed to anticipate the zoonotic viruses which led to pandemics in 2009 and 2020. The morbidity, mortality, and economic costs of these pandemics have been staggering. We desperately need a more targeted, cost-efficient, and sustainable strategy to detect and mitigate future zoonotic respiratory virus threats. Evidence suggests that the transition from an animal virus to a human pathogen is incremental and requires a considerable number of spillover events and considerable time before a pandemic variant emerges. This evolutionary view argues for the refocusing of public health resources on novel respiratory virus surveillance at human-animal interfaces in geographical hotspots for emerging infectious diseases. Where human-animal interface surveillance is not possible, a secondary high-yield, cost-efficient strategy is to conduct novel respiratory virus surveillance among pneumonia patients in these same hotspots. When novel pathogens are discovered, they must be quickly assessed for their human risk and, if indicated, mitigation strategies initiated. In this review, we discuss the most common respiratory virus threats, current efforts at early emerging pathogen detection, and propose and defend new molecular pathogen discovery strategies with the goal of preempting future pandemics.