Although adefovir dipivoxil, stavudine, and cidofovir are virtually discontinued for clinical use, tenofovir disoproxil fumarate and tenofovir alafenamide remain the most important antivirals against HIV and HBV infections worldwide. Overall, the broad-spectrum antiviral potential of nucleos(t)ide analogues supports their development to treat or prevent current and emerging infectious diseases worldwide.Nucleoside and nucleotide analogues are essential antivirals in the treatment of infectious diseases such as human immunodeficiency virus (HIV), hepatitis B virus (HBV), hepatitis C virus (HCV), herpes simplex virus (HSV), varicella-zoster virus (VZV), and human cytomegalovirus (HCMV). To celebrate the 80th birthday of Prof. Dr. Erik De Clercq on 28 March 2021, this review provides an overview of his contributions to eight approved nucleos(t)ide drugs: (i) three adenosine nucleotide analogues, namely tenofovir disoproxil fumarate (Viread N-conjugation, halogenation) have been proposed to design novel nucleoside or nucleotide analogues, such as the acyclic fleximer analogues -9H-purin-9-yl)ethoxy)methyl] phosphonoyl) di-L-alaninate) [Despite its antiviral activity, PMEG was the most cytotoxic compound among phosphonylmethoxyalkyl derivatives, because its diphosphates could be incorporated into host DNA and block cellular DNA polymerase delta and epsilon . A subseor model . To incraninate) . In fiveaninate) . Despite®) for treating HCMV retinitis in AIDS patients, (ii) zalcitabine  for HIV treatment, (iii) emtricitabine (Emtriva®) for HIV treatment, and (iv) lamivudine (Epivir®) for HIV and HBV treatment ide analogues have been approved for clinical use, including (i) cidofovir (Vistidereatment . This seCidofovir is an acyclic nucleoside phosphonate b licenseAmong the list of eight nucleos(t)ide analogues  co-contr®)—a monophosphoramidate prodrug of an adenosine analogue ide analogues. As a popular example of broad-spectrum antiviral activities, remdesivir inhibits many RNA viruses such as ebolavirus and respiratory pathogens including Middle East respiratory syndrome coronavirus, severe acute respiratory syndrome coronavirus (SARS-CoV), and SARS-CoV-2 . In regaanalogue e that acIn addition to the discovery of broad-spectrum antivirals with better potency, it is important to develop highly selective nucleos(t)ide analogues with minimal toxicity because antiviral nucleos(t)ides may exert toxicity through the disruption of natural nucleoside triphosphate pools and interfere with activities of host polymerases such as human mitochondrial DNA/RNA polymerases  . Future


The estimation of farm-specific time windows for the introduction of highly-pathogenic avian influenza (HPAI) virus can be used to increase the efficiency of disease control measures such as contact tracing and may help to identify risk factors for virus introduction. The aims of this research are to (1) develop and test an accurate approach for estimating farm-specific virus introduction windows and (2) evaluate this approach by applying it to 11 outbreaks of HPAI (H5N8) on Dutch commercial poultry farms during the years 2014 and 2016. We used a stochastic simulation model with susceptible, infectious and recovered/removed disease stages to generate distributions for the period from virus introduction to detection. The model was parameterized using data from the literature, except for the within-flock transmission rate, which was estimated from disease-induced mortality data using two newly developed methods that describe HPAI outbreaks using either a deterministic model (A) or a stochastic approach (B). Model testing using simulated outbreaks showed that both method A and B performed well. Application to field data showed that method A could be successfully applied to 8 out of 11 HPAI H5N8 outbreaks and is the most generally applicable one, when data on disease-induced mortality is scarce. Wild waterfowl is the main reservoir for low pathogenic avian influenza (LPAI) viruses4, which may evolve into highly pathogenic strains after introduction of LPAI subtypes H5 and H7 into poultry flocks6. Since the beginning of the 21th century, evidence has emerged that an endemic situation of HPAI virus strains in poultry flocks in South East Asia has created the opportunity for spill over into wild bird populations and migratory waterfowl is thought to be responsible for the spread of different HPAI viruses of subtype H5Nx to parts of the world where there is no endemic HPAI situation in commercial poultry4. For example, in the Netherlands recent outbreaks of H5N8 (in 2014 and 2016) and H5N6 (2017) in commercial poultry are thought to have been caused by virus that is brought by migratory waterfowl infected with HPAI to areas in the neighbourhood of poultry farms9. It is therefore important to be prepared for new HPAI outbreaks and have plans in place to control the spread of HPAI.Highly pathogenic avian influenza (HPAI) virus may cause severe clinical signs in poultry species, resulting in very high mortality rates10. For this purpose (amongst others), many countries have put systems in place to record the location of farms and movement of poultry11. Having accurate estimates of the time of virus introduction will improve the efficiency and efficacy of contact tracing by focusing efforts on the time window that an outbreak farm was infectious. Secondly, the time from the introduction of a virus on a farm until its detection is a sensitive parameter in simulation models that try to estimate the size and duration of epidemics given various control measures13. Thirdly, it may also help to identify and assess the importance of potential risk factors for the introduction of a disease on a farm. For example, movements of people, vehicles or materials or ecological/environmental changes in the direct surroundings of the poultry house are more likely to be associated with virus introduction onto a farm if they happen close to the estimated introduction time14. And having accurate estimates of the time of disease introduction makes it possible to account for the timing of introduction through the year (e.g. the month) in the analysis of risk-factors for disease introductions.Estimating a time window for the introduction of a virus on a farm is important for the prevention and control of disease outbreaks for several reasons. Firstly, contact-tracing may be an important control measure that aims to prevent or reduce the size of an epidemic15 or from data on egg production16. The study on HPAI describes a method to estimate the virus introduction time from disease-induced mortality data17. In the latter study, a stochastic simulation model was derived to describe the spread of HPAI virus within flocks of chickens. Using this model, a window for the introduction of HPAI was derived by tracking the time that was needed for the predicted disease-induced mortality to match the observed mortality at the time of detection.Surprisingly, literature on the estimation of the introduction time of avian influenza into farms from outbreak data is scarce. To our knowledge there are three studies estimating the introduction time of avian influenza virus, two for LPAI and one for HPAI. The studies on LPAI derive the virus introduction time by fitting a deterministic simulation model to data on either the prevalence of infected and seroconverted animals19. To our knowledge, there is only one published method to estimate a farm-specific between-bird transmission rate of HPAI virus from daily disease-induced mortality data21  uses more accurate estimates of the farm-specific transmission rate and (2) can be applied to cases for which daily mortality is scarce. To show how this approach can be used in practice, we will apply it to HPAI-outbreaks on commercial poultry farms in the Netherlands during the years 2014 and 2016.We analysed mortality data from poultry farms in the Netherlands with outbreaks of HPAI virus clade 2.3.4.4 of subtype H5N8 during the years 2014 and 2016. The mortality of poultry in each house on these farms was recorded daily by the farmer on a production chart until detection of the disease and subsequent culling. Production charts with daily mortality were gathered by the Netherlands Food and Consumer Product Safety Authority (NVWA) during a standardized-investigation interview of the farmer after an outbreak was confirmed by laboratory diagnostics. In 2014 there were outbreaks on four chicken farms and one meat duck farm. In 2016 there were outbreaks on four chicken farms, four meat duck farms and on the premises of one backyard bird wholesale company. Chicken farms were either layers or broiler breeders. The outbreaks on one chicken farm and one duck farm in 2016 were excluded from our analysis, because disease-induced mortality could not be estimated for a period of at least 2 days (see below). The outbreak on the premises of the backyard bird wholesale company was also excluded, since mortality data were not available by species. Therefore, we analysed data from 11 out of the 14 HPAI H5N8 outbreaks in 2014 and 2016: 7 outbreaks on chicken farms and 4 outbreaks on duck farms. The virus introduction windows for these farms were estimated using mortality data for one specific poultry house per farm, where clinical disease signs were observed. The virus infections on most farms were also limited to one poultry house on the farm premises. For one outbreak on a duck farm in 2014 (farm E), enhanced mortality was observed in both an old and a young flock (kept in separate houses). We only used the data on the young flock, since the old flock was already slaughtered at the time of detection. For another outbreak on a duck farm in 2016 (farm H), enhanced mortality was detected in two houses and both houses were culled on the same day. However, only one of these houses was included in our analysis (house 1), because disease-induced mortality in the other house could not be estimated for a period of at least two days. For the two breeder farms, we combined mortality data for cocks and hens. Table Determine the time interval prior to detection during which the recorded mortality can be considered to reflect disease-induced mortalityEstimate the farm-specific transmission parameter Enter the above-mentioned parameter values into a stochastic model and repeatedly simulate the disease outbreak on a farm until the calculated cumulative disease-induced mortality exceeds the observed mortality throughout the time interval determined in step 1. Each simulation is started with one latent individual. The length of the simulated period is recorded for every simulation and used to construct a distribution for the virus introduction time window.Perform a sensitivity analysis to uncertain parameters.To back-calculate from mortality data the probable time window of virus introduction into an individual outbreak farm, we used a SEIR (susceptible-exposed-infectious-recovered) modelling approach consisting of the following steps:Below, these four steps are described in more detail. For step 2, we developed two new methods (A and B). Method A estimates 22. In short, this approach identifies the first instance when, for two consecutive days, the mortality is significantly higher than the daily mortality during the preceding week-long period. The number of consecutive time points To estimate for each outbreak farm from which day onwards the observed mortality reflected HPAI virus-induced mortality instead of background mortality due to other causes, we used a moving weekly average approachIn order to estimate the farm-specific transmission rate Model parameters describing the distributions of the latent and infectious periods and the percentage of infected birds dying were assumed to be the same for farms housing the same species and taken from the literature. Below we present two alternative methods to estimate a farm-specific transmission parameter Having obtained an estimate of transmission parameter mentclass2pt{minimThe distribution for the time of virus introduction for a given value of parameter 23. Further details about the methodology used for the literature review and the estimation of these parameters are described in Supplementary Methods The length of the exposed stage, the infectious stages, the shape parameter of the gamma distribution for these stages and the fraction of animals dying from HPAI subtype H5N8 in 2014 were estimated from the literature for both chicken and duck farms. For the HPAI H5N8 outbreaks in 2016, these parameter values were estimated from a transmission experimentβ using a maximum likelihood approach. The likelihood-ratio test was used to obtain lower and upper 95% confidence bounds.We used and compared two methods for the estimation of the transmission parameter This method estimates the value of parameter Although this method requires the estimation of the time of virus introduction .A detailed description of the deterministic model is given in Supplementary Methods 21 and Bos et al.20. Below, we first describe the method developed in these two studies and then explain why and how we adjusted it to obtain Method B. The original method uses a product-binomial likelihood function to account for the stochastic variation in the early phase of an outbreak when there are relatively low numbers of infected birds and the deterministic approximation of method A may be unsatisfactory. Analogous to the estimation of β from small-scale transmission experiments3, the method calculates the probability that susceptible individuals become infected or escape infection during one-day time steps. Denoting the number of time steps for which the probability on a certain number of new cases can be calculated as Method B is building on the work by Tiensin et al.21 and as summarized in Supplementary Methods Here 21, the infection probability is approximated as According to a standard approximationThe value of growth rate Here we use the total population size at the start of an outbreak . In addition, from a practical point of view, often there are only 2 or 3 data points available for the estimation of the transmission rate. The second requirement avoids estimates of the transmission rate without an indication of the uncertainty. The third requirement ensures a minimum quality of the fit and is a strict criterion, since we are dealing with biological data and therefore complex and stochastic processes. The value of coefficient All analyses were coded and performed using R version 3.4.0 .22. Notably, for 8 of the 11 farms, the estimated period with virus-induced mortality was two or three days long. For the other three outbreak farms this period was between 4 to 7 days long.In Table The values for these parameters and their sources as derived from literature are summarized in Table We tested the methods A and B as well as the literature method by applying them to simulated HPAI outbreaks on a chicken farm for values of transmission parameter Estimates for ms Table . Only foentclass1pt{minimaentclass1pt{minimaThe estimated values of ms Table . MaximumThe mean time of virus introduction for outbreaks within a single outbreak season also varied considerably for both chicken and duck farms Table . For chiβ and a lower maximum likelihood estimate of parameter β  as well as in later outbreak phases (methods A and B). Application to simulated outbreaks with a known transmission rate showed that both methods A and B provided much more accurate estimates than the method most often used in the literature. To demonstrate our approach for estimating the virus introduction window, we applied it to outbreaks of HPAI virus subtype H5N8 in duck and chicken farms in the Netherlands during the years 2014 and 2016.We explored two different methods for estimating the transmission rate of HPAI virus within flocks. Method A estimates the transmission rate directly from the disease-induced mortality data. In contrast to method B, it assumes a continuous exponential increase in the number of infectious individuals during the initial phase of an outbreak. The fact that it could be successfully applied to 8 out of the 11 outbreaks shows that most outbreaks had already entered the exponential or a later outbreak phase when detected. The exception was the outbreak on one duck farm (farm E in 2014) for which the daily mortality fluctuated strongly in time shortly prior to detection. One must realize that we rely on self-reported mortality data recorded by poultry farmers and that the accuracy of these records will vary between poultry farmers during a clinical episode with high potential economic and emotional impact. The two remaining farms to which method A could not be successfully applied did show exponential growth, but the likelihood functions were too flat to reliably estimate the transmission rate. This is because method A requires the estimation of a second model parameter in addition to the transmission rate.21. Method B therefore seems better suited for studies at the population rather than at the individual farm level.The second method (B) could only be applied successfully to 2 out of the 11 outbreak farms. This is because this method does not estimate the transmission rate directly from the disease-induced mortality data, but indirectly from the back-calculated number of infectious individuals in time and the number of new cases produced by these individuals during one-day time steps. The number of time steps for which the back-calculation procedure can predict both the number of infectious individuals and the number of new cases depends on two factors: the number of days for which mortality was assumed to be disease-induced and the length of the latent period. This is because the back-calculation procedure first predicts the daily number of new cases from the mortality data and then adds the latent period to predict when these new cases become infectious. For most of the H5N8 outbreaks analysed in this study, disease-induced mortality data was scarce (because poultry farmers reacted fast on a suspicious disease situation). In combination with a latent period of 1 or 2 days, the back-calculation procedure could not provide enough time steps for these outbreaks with information on both the number of infectious individuals and the number of new cases in order to estimate the transmission rate of HPAI virus. The studies in which this back-calculation procedure was introduced for estimating the transmission rate of HPAI did not estimate farm-specific transmission rates, but instead used the back-calculated information from all farms to estimate a farm-independent, i.e. average transmission rateFor the two outbreaks in this study where both methods A and B could be applied, the estimated transmission rates were very similar, but the confidence intervals were smaller for method B than method A. This is probably because method A involves the estimation of an additional model parameter from the mortality data.Most of the outbreaks for which disease-induced mortality data was scarce involved poultry flocks that were detected after discovery of the primary case flock in the Netherlands in 2014 and 2016. The three outbreak farms with 4 to 7 days of data on virus-induced mortality were either primary case herds or the first infected farm of a certain type. This scarcity of data on disease-induced mortality may therefore be explained by a higher level of alertness following detection of the primary cases.30 and from 0.66 to 19.9 in the field21. It can be concluded that the farm-specific estimates of the transmission rate of HPAI H5N8 virus in domestic chickens are close to or within the range of transmission rates found for other HPAI subtypes. For domestic ducks, the transmission rate was estimated to be 1.22 for HPAI H5N8 virus and 1.6 for HPAI H5N1 virus in transmission experiments31. Estimates of the transmission rate in our study were very similar for two out of the three outbreaks on duck farms (0.9–1.6), but much higher for the remaining farm (11.8). It should be noted that the testing of methods A and B using simulated outbreaks with a known transmission rate showed that they may slightly underestimate the value of the transmission rate, but were much more accurate than the method described in the literature21 above to estimate the transmission rate from field data.To our knowledge, this study is the first to publish estimates of the transmission rate of HPAI H5N8 from field data for domestic chickens as well as domestic ducks. Estimates of the transmission rate of H5N8 for chickens in 2014 and 2016 varied from 4.4 to 34.4 (median 8.5). Published transmission rates of HPAI in domestic chickens for other HPAI subtypes  varied from 0.73 to 33 in transmission experiments30 and is probably caused by the effect of the latent period on the time interval from the moment an individual becomes infected until the first secondary case (generation time). Assuming that the length of the infectious period and the transmission rate remain constant, an increase in the latent period will decrease the growth rate of the number of infectious individuals, since the same amount of secondary infections is produced over a longer period. To achieve a good fit to the observed growth of the number of disease-induced deaths , the longer latent period will be compensated for by increasing the value of the transmission rate.The sensitivity analysis showed that the estimated transmission rates were most sensitive to changes in the latent period. An increase of the latent period by 2 days could increase the transmission rate up to 14.1 times. This sensitivity to changes in the latent period was also observed in other studies32. Flock age did significantly influence the transmission rate of HPAI virus in chicken flocks with lower transmission in older flocks20. This age-effect was however not found for turkey flocks32. Differences in the density of chickens may also cause variation in the transmission rate, but the birds on all farms included in our analysis were housed indoors and densities in these houses are approximately similar in the Netherlands. In addition, we assumed frequency-dependent transmission in our SEIR model framework with a fixed contact rate independent of the bird density. Finally, the presence of co-morbidities due to other diseases may also cause variation in the within-flock transmission rate. The number of outbreaks included in this study was too small to determine the effect of the different explanatory variables on the transmission rate.As mentioned above, the estimated transmission rate could vary considerably between farms of the same type that were infected in the same avian influenza season, e.g. from 4.4 to 34.4 for outbreaks on layer farms in 2014 and from 1.22 to 11.8 on meat duck farms in 2016. Several studies have explored the effect of flock age and flock size on the transmission rate of HPAI in poultry flocks. Flock size did not significantly influence the transmission rate of HPAI virus in chicken and turkey farms33. Our study shows that the most likely time of HPAI introduction was ≤ 14.8 days prior to disease detection for outbreaks on chicken farms and ≤ 18.8 days prior to disease detection for outbreaks on duck farms. The sensitivity analysis showed that these estimates are robust to changes in the values of epidemiological parameters The approach that we developed in this study for the estimation of the introduction window of HPAI onto poultry farms can account for differences in the epidemiology between HPAI subtypes and poultry species by adjusting the values of epidemiological parameters in the SEIR framework. The approach can be extended to other livestock diseases by including other clinical signs than mortality and by including seroprevalence data, as long as these can be predicted using the SEIR framework.34. Some models have been developed that account for both direct and environmental transmission in flocks36, but the relative contribution of both transmission pathways was not estimated in these studies by fitting the model to data. Experimental studies that quantify both direct and environmental transmission of HPAI in poultry flocks have not yet been conducted to our knowledge. To separate the direct and environmental transmission pathways in a flock, we would therefore need data on disease-induced mortality as well as the viral load in the environment. Data on the environmental HPAI load are not collected by farmers. Therefore, we were unable to separate direct and environmental transmission in our models.A limitation of our modelling framework is that it does not account for environmental transmission of HPAI. Infectious birds will shed virus contaminating e.g. bedding material and drinking water and it is known that highly pathogenic avian influenza can survive for longer periods of time in the environment37 as well as Bayesian approaches such as Approximate Bayesian Computation38, Data Augmentation Markov-Chain Monte Carlo39, and particle Markov-Chain Monte Carlo40.A possible improvement to our method would be to determine both the transmission rate and the virus introduction window by fitting the stochastic SEIR model directly to the disease-induced mortality data. This would reduce the number of steps in our method, since the farm-specific transmission rate does not have to be estimated in a separate step. Methods to fit stochastic simulation models to data include both frequentist approaches such as iterated filteringA possible disadvantage of fitting a stochastic model directly to the mortality data is the computational burden, which is likely to be higher than when fitting a deterministic model to data. Since the back-tracing of sources during an epidemic requires fast methods to estimate the virus introduction window, the computational burden is important when developing a method for estimating virus introduction windows. We aim to explore the possibility and usefulness of estimating the transmission rate as well as virus introduction window in one step by fitting the stochastic SEIR model directly to the data in future studies.To conclude, we developed an approach for estimating the introduction window of HPAI virus into individual poultry farms using farm-specific estimates of the transmission rate. We successfully applied this method to 8 out of 11 outbreaks of HPAI virus subtype H5N8 into chicken and duck farms in the Netherlands in 2014 and 2016. The results of our analysis can be used to improve the efficiency and efficacy of contact-tracing efforts by focusing on the time window that a farm was infectious. To our knowledge this study reports the first estimates of the transmission rate of HPAI virus subtype H5N8 in flocks of domestic chickens and ducks.Supplementary information


This powerful approach has spurred new investigation of the viral metagenome (virome). The rich virome datasets accumulated illuminate important ecological phenomena such as virus spread among host reservoirs (wild and domestic), effects of ecosystem simplification caused by human activities (and agriculture) on the biodiversity and the emergence of new viruses in crops. To be effective, however, HTS-based virome studies must successfully navigate challenges and pitfalls at each procedural step, from plant sampling to library preparation and bioinformatic analyses. This review summarizes major advances in plant virus ecology associated with technological developments, and then presents important considerations and best practices for HTS use in virome studies.The ecology of plant viruses began to be explored at the end of the 19th century. Since then, major advances have revealed mechanisms of virus-host-vector interactions in various environments. These advances have been accelerated by new technlogies for virus detection and characterization, most recently including high throughput sequencing (HTS). HTS allows investigators, for the first time, to characterize all or nearly all viruses in a sample without  The field of plant virus ecology examines complex interactions among plant-associated viruses, their hosts and their vectors, and the environment, effectively extending the perspective of plant virus epidemiology . TraditiAs a field, plant virus ecology draws on diverse disciplines including virology, ecology, epidemiology, plant biology and entomology . The stuWe describe here the evolution of the techniques used in plant virus ecology and their respective impact in this field, emphasizing new high-throughput sequencing-based approaches. Several reviews have already been published on virus ecology and HTS  but as oEupatorium plants in Japan in AD 752, as highlighted by The first plant virus infection phenotypes were likely recorded more than 1,200 years ago in Since the discovery of TMV, scientists have sought to study the interactions between plants and viruses, but also to decipher how viruses are transmitted between plants. Hence, the first half of the 20th century saw the building of the foundations for plant virus ecology, thanks to the use of host symptomatology, sap transmission, indicator plants, biochemistry and microscopy , but also by beetles, mites, nematodes, fungi, and protists. It was also revealed that plant viruses can be vertically transmitted Major advances in understanding plant-virus co-evolution were obtained too, with the confirmation that virus infections could commonly be found in asymptomatic wild plants . This waStudies of plant-virus interactions at the interface between managed and natural vegetation have been conducted for nearly a century  PCR detection protocols , in partTM sequencing) or native long-reads  can potentially represent the full or nearly full genome of a virus.Bioinformatic advances leverage the power of HTS technologies and provide further insight about viral genomes. Given sufficient sequencing depth or target availability, contigs assembled from short reads , at periods of greatest viral diversity, or after a certain period of virus spread, etc. For virome diversity studies, the lack of information makes it very complicated (perhaps impossible) to determine the optimal sampling period for novel viruses. In that case, it may be best to use repeated sampling.Several factors influence the multiplication rate of viruses and their accumulation within plant hosts: the time of sampling, i.e., days post-infection (DPI); environmental conditions ; phenoloThere is generally no optimal sampling, but virus titers are typically higher in actively-growing, green vegetation than in senescent plant tissues. In-depth knowledge of biology of the known viruses within the plant community is therefore a bonus and should guide the choice of sampling time(s). For instance, temperate climate pathosystems involving aphids may be preferentially sampled in spring and autumn, when plants are growing actively and vector populations are expanding and actively disseminating . This woThe broad application of HTS technologies for plant virus detection has driven a paradigm change: co-infections are now recognized to be much more frequent than single infections  have high titer. The presence of several strains or several viruses sharing regions of homology can also hamper the proper reconstruction of viral genomes. Indeed, around the homologous region (for which a single contig will be generated), several 5′ and 3′ flanking reads and contigs can be assembled, which can protocol .infection prevalence – the proportion of individual plants that have detectable virus infection; (ii) to characterize the species (or variant) composition of a viral community or population; and (iii) to evaluate patterns of viral diversity, including viral taxa richness – the number of taxonomic units, such as species, genera, or families within a community. There are both general and aim-specific sampling considerations.The protocol used for collection of samples from the field is critical but often poorly documented in virological reports. To support robust data analysis and interpretation, it is essential to consider the relationships among the question to be investigated, the overall study design, and the sampling strategy. Three common aims of field sampling for HTS studies are (i) to quantify 1. Sampling insect communities raises additional considerations beyond the scope of this article. The interested reader is directed to specific literature on this question, in particular rticular . QuestioIn heterogeneous landscapes, planning may further involve discernment of plant community types within a larger sampling unit and development of a stratified sampling scheme in which sampling effort is allocated to each community type as a function of its relative area. If a larger unit is 40% dry meadow and 60% wet meadow, for example, sampling effort might be allocated proportionately.Once sampling compartments are identified, protocols must be developed for determining (i) at which points material will be collected, and (ii) how much of each plant(s) will be collected at each point. Decisions about sampling locations are best made prior to entering the field to remove user bias. For example, pre-determined points can be selected in the lab with a Geographic Information System (GIS) and then located with a Global Positioning System (GPS) in the field. These points might be located completely randomly or be selected in a grid for systematic sampling . If a grOnce sampling points are chosen, it is also essential to develop a protocol guiding the selection of individual samples at each point. The sampling unit might be an individual plant, several plants, or a quadrat from which all or some of the plants are collected . A samplStudies aimed at quantifying infection prevalence – a common objective in agricultural research – focus on determining the proportion of plants carrying infection. In this context, virus infection can be considered as a plant characteristic, and plants can be sampled using standard plant ecology methods. Individual plants may be barcoded separately and sequenced. Alternatively, pooled plants may be sequenced and then viruses of interest targeted in individual plants with specific (RT-)PCR or ELISA methods. A critical consideration is to ensure that individuals are sampled without regard to symptoms and that all age/size classes of interest are evaluated.A more complex aim is to characterize the virus community (“virome”) within a given area, plant community, or plant population. Typically, the objective is to identify all or most of the viral taxa within the study unit and perhaps also to compare virus community composition across host species, locations, or treatments. Important questions include, (i) Will the sampling protocol return a representative sample of the study unit? (ii) Can the desired comparisons across study units be fairly made?To characterize the virome infecting a given plant community, the sampling effort may be structured to reflect the relative representation of the plant community’s members, as evident in their relative abundance, relative cover, or relative biomass. A virome reflecting the relative abundance of individual plant species could be achieved, for example, by complete random sampling of individual plants, given sufficient sampling effort. Alternatively, sampling that is representative of plant species’ relative abundances might also be achieved by first evaluating plant community composition and then conducting stratified sampling in which each plant species is sampled in proportion to its relative abundance. A third approach is to harvest all of the plants within numerous randomly-placed quadrats  . This apTaxon accumulation curves (collector’s curves) illustrate the gain in species numbers detected with increasing sampling effort, but do not predict species identity. They are sometimes used, however, to roughly assess the sampling effort at which species number begins to saturate  or the number of different taxonomic units within a community sample. A related measure is taxa density, the number of taxonomic units per unit area. More complex indices, such as the Shannon-Wiener diversity index (H’) and Simpson’s diversity index (D1), incorporate information about both taxa richness and their relative abundance or evenness; the choice of index – of which there are many – may strongly influence the interpretation of results  in each of two communities and this number represents 7 taxa in community A and 25 in community B, then viral taxa richness is much greater in community B, all else being equal. . In this example, comparisons between the two communities are straightforward because the same number of viruses has been enumerated in each. If the number differed, then rarefaction curves could be used to estimate how many taxa would be represented if the larger sample were subsampled to match the size of the smaller one . AlternaOne must recall that “individual” in this context refers to an individual viral unit, however defined, not to an individual plant, and that “sample-based” assessments include assessments of individual or multiple plants. So with individual-based assessments, for example, one evaluates the number of distinct taxa represented by a given number of virus individuals and then might compare this richness value across treatments. For instance, 100 virus individuals might represent seven taxa in one treatment, and twenty-five in another, giving the latter greater virus richness. One advantage of working with richness values is that taxa accumulation curves and corresponding rarefaction curves may be more directly useful in permitting comparisons. Rarefaction determines how many taxa would likely be present in smaller subsamples of the total sampled population . Thus, aAnother crucial parameter is the strategy for pooling individuals or samples for sequencing , which can be necessary to reduce costs. Individual plants might be pooled, for instance, by species (a subsample of each species present) or by sampling location . The optimal pool size depends on striking a sometime difficult balance between the number of pools to be sequenced and the number of individual plants per pool. Increasing pool size is likely to exacerbate competition among viruses for sequencing reads; the larger the pool, the higher the probability that some low prevalence or low concentration viruses may be missed if other viruses are present in high abundance (dilution effect). An example is the current practice of inoculating a mild strain of pepino mosaic virus in tomato production to prevent the infection by severe strains. This mild strain is present in high concentration in the plants . If such plants are included in a pool, the majority of viral reads might come from the PepMV mild strain, hampering the detection of other viruses at low abundance.This issue is arguably most important in studies that seek to characterize the complete viral community, and perhaps less important for relative comparisons among sampling groups. Nested sample pooling is one approach for determining optimal pooling strategy. In this approach, a series of pools with increasing number of pooled plants from the same community or from an increasing number of communities is collected and sequenced. The results will give a preliminary overview of the geographic heterogeneity of the sampled population and thus of the sample size required for good representation. In all cases, it is advisable to collect and store individual plants for downstream confirmation of HTS results.Pooling strategies should also consider that some plant species may contain inhibitors  that can negatively affect the amplification and sequencing steps in some protocols . A preliThe selection of the target nucleic acids population is critical as it defines the types of viral sequences that will be detected, including total RNA/DNA, RNA-Seq, Virion-Associated Nucleic Acids (VANA), dsRNA, small RNA, circular ssDNA or amplification of targeted PCR products using generic primers. While HTS technologies have the theoretical capacity to target any viral nucleic acid in any host plant or vector, the available protocols present distinct advantages and limitations as reviewed previously  and summa priori evaluation of the viruses and viroids potentially infecting the plants in the study area. However, neither the viruses, nor their (genomic) characteristics are a priori all known. Hence protocol selection may be influenced most strongly by information about which viruses are common in the study area or are most interesting given the question(s) addressed. The research question and the objectives of the study will thus be crucial for protocol selection, along with a botanical inventory. For example, there are currently no viroids known to infect Poaceae. So, the VANA method – which concentrates DNA and RNA associated with virions but not viroids – could be a good choice when studying grassland samples, as long as it is understood that the analysis would likely miss any novel Poaceae-infecting viroids.These limitations warrant a careful There are additional important considerations in sample and library preparation. For example, many current metagenomics-based protocols include a random PCR amplification step prior to sequencing, which can create chimeric reads and introduce bias in the relative proportion of sequences. This, among others, has so far limited the ability to evaluate the relative frequencies of viral sequences in a sample. Moreover, HTS-based approaches have already proven to be as susceptible to between-samples contamination as PCR-based assays , thus reTM platforms range from 75 to 300 nt for single or paired reads, with error rates increasing with longer reads (TM or Oxford NanoporeTM technologies can generate much longer reads (and therefore a better reconstruction of genomes and isolates) but with lower sequencing accuracy  complicates sequence assembly and annotation, and makes genome reconstruction and strain identification more difficult , 2019. Mer reads . Pacificaccuracy .Sequencing depth is directly correlated with an improved ability to detect viruses present at low abundance/concentration, as shown for small RNA sequencing . On the Bioinformatic analyses may be performed to examine viral reads and assign them to virus taxonomic units, in order to estimate virus(es) genetic structure and diversity (richness and evenness), or even to characterize novel virus species. However, different population-based or taxonomy-based terms are often used in these analyses, such as virus species, isolates, strains, operational taxonomic unit (OTU) or quasispecies, while their definition has been discussed by virologists  and thesMastrevirus tree  of any newly identified virus can be challenging too. Indeed, the identification of a putative new virus alone will be of limited usefulness in a range of ecological studies. A characterization framework has recently been published, proposing a scaled approach to progressively and efficiently characterize the biology of a newly identified virus and the associated risks for the wild and domesticated plants .taxa richness (the number of taxa present within a sample or environment) and taxa diversity  are two key ecological metrics  and then performing a sequence clustering. Other proxys have been used to estimate viral richness, for example the number of viral families or genera, or OTUs defined using other approaches but these other proxys tend to have even more limitations than the use of OTUs based on a clustering approach. A very good description of this approach for virus classification was presented by Viral populations can evolve quickly by mutation and/or recombination at each replication cycle. The resulting variants may bear large genome rearrangements as well as single-nucleotide polymorphism (SNP) mutations. Accessing viral intra-specific diversity through HTS has value for specific scientific aims , such asTo genotype and haplotype SNPs remains a challenging bioinformatic task for a single virus in a single sample. It becomes obviously more complex with samples containing multiple viruses from multiple hosts. In the case of mixtures of isolates, reconstruction of genomic sequences may be particularly complex, if not altogether impossible, especially when multiple closely related isolates are involved or for quasi-species with high variability. Moreover, the differentiation between SNP and sequencing errors is intrinsically difficult for low frequency variants. It can be impacted by the quality of the HTS sequences produced, by read length, but also by the algorithms used for quality control, contig assembly, and SNP identification. The statistical approach selected for the haplotyping analysis is another key factor to consider; its performance is determined by mutation rate and was demonstrated to decrease with genetic diversity of the sample see .The confirmation of SNPs represents another challenge: targeted real-time PCR could be used but the need to design and produce two probes per SNP remains costly. Another potential way to reinforce the significance of the analysis is to sequence duplicates of each sample or to combine the use of different HTS approaches that have different distribution of sequencing errors. While costly, these approaches have the potential to distinguish true SNPs from sequencing errors. Running duplicates is a strategy that is often performed for HTS-based barcoding  and coulThe study of plant virus ecology, which began at the end of the 19th century, has been accelerated by the evolution of new methods used to detect plant viruses in various environments and in many plants simultaneously: the HTS technologies offer huge opportunities to improve virome characterization and address novel questions such as the examination of virus spread among host reservoirs (wild and domestic), the effects of ecosystem simplification caused by human activities (and agriculture) on the biodiversity, and the emergence of new viruses in crops. Recently, conceptual shifts in the understanding of the origins and diversification of the plant virome were achieved, leading to the design of the first comprehensive virus megataxonomy. Nevertheless, the use of HTS in ecological studies is associated with a series of challenges and pitfalls described in detail in this review for each procedural step in the field, in the laboratory and for bioinformatic analyses. The rapid evolution of the sequencing platforms and bioinformatic pipelines promises numerous findings in viral metagenomic studies over the coming years. These novel approaches developed in plant virology will directly be transferable to other models for a better understanding of host-virus interactions and ecological virology in general.FM and SM conceived and wrote the manuscript. FM created the figures and prepared the final version of the manuscript and all authors approved it. In particular, PR and CM edited the viromics advances and field sampling sections, respectively. TC, CM, PR, RV, and DF edited the bioinformatics sections. All authors collectively revised and improved the manuscript and made useful suggestions on all sections of the review article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To investigate the clinical and genetic characteristics of patients with glycogen storage disease type Ib (GSD Ib).SLC37A4 gene were detected, of which c. 572C > T was the common variant and c. 680G > A was a novel variant. The 3 cases of GSD Ib were mainly affected by liver enlargement, growth retardation, etc., and all had a history of repeated infections. At the onset, patients mainly manifested as mildly elevated alanine-aminotransferase (ALT), accompanied by decreased absolute neutrophil count (ANC), hypertriglyceridemia, and metabolic disorders . After long-term treatment by oral uncooked cornstarch, the abnormal liver enzymes gradually returned to normal, and metabolic abnormalities were basically controlled most of the time. With increasing age, ANC of 2 patients decreased progressively, whereas the times of infections was reduced.This report retrospectively analyzed the clinical data of 3 patients with GSD Ib admitted into our hospital, and summarized their onset characteristics, clinical manifestations, related examinations and treatment as well as mutational spectrum. After gene sequencing, the diagnosis of GSD Ib was confirmed in all 3 patients. Five variants of SLC37A4 variant. The possibility of GSD type Ib should be kept on alert when a patient suffers recurrent infections, accompanied by hepatomegaly, elevated liver enzymes, hypoglycemia, dyslipidemia, and metabolic disorders.We reported 3 cases with GSD Ib and a novel  G6PC gene (OMIM #613742) or SLC37A4 gene (OMIM # 602671), and the incidence is about 1:100,000 [G6PC gene variant causes the deficiency of glucose-6-phosphatase alpha (G6Pase-α) activity, which leads to GSD type Ia (OMIM#232200), accounting for about 80% of GSDI patients, whereas the SLC37A4 gene variant causes the deficiency of glucose-6-phosphate transporter protein (G6PT), which underlies GSD type Ib (OMIM# 232220), accounting for about 20% of GSDI cases [Glycogen storage disease type I (GSDI) is a group of autosomal recessive inherited metabolic disorders with varying clinical severity caused by variants in the e OMIM #63742 or Se OMIM #63742 or SSLC37A4 gene is highly expressed in hematopoietic progenitor cells, its defect has a significant effect on myeloid progenitor cells [Both type Ia and Ib are characterized by hepatomegaly and metabolic abnormalities such as hypoglycemia, hyperlipemia, lactic acidosis, and hyperuricemia. The or cells , 5.SLC37A4 variant, one of which had novel variant.We report 3 additional cases of GSD Ib with The clinical features of the 3 patients were summarized in Tables This female patient was admitted to hospital because of “abdominal distension for over 1 year” at the age of 15 months. At admission, her height SDS was − 2.26 and weight was 9 kg (10th–25th centile). There were abnormalities in absolute neutrophil count (ANC 1.07 × 10^9/L), liver function , blood lipid (TG 8.84 mmol/L), fasting glucose (3.7 mmol/L), lactic acid (8.78 mmol/L), and blood gases (BE − 8.5). The liver ultrasound suggested hepatomegaly. She underwent liver biopsy and showed glycogen storage as confirmed by periodic acid-Schiff staining (PAS). She was also tested for glucose response to epinephrine stimulation after overnight fasting and the result was positive. Combined with the above results, she was clinically diagnosed as GSD. She was given oral uncooked cornstarch four daily doses of 1.0–2.0 g/kg (3am–9am–3pm–9pm). According to current guidelines, follow-up is recommended every 3–6 months (adjusted by disease changes and their ages). However, the patient did not take oral uncooked cornstarch regularly as prescribed. At the age of 12 years, she was finally diagnosed with GSD type 1b through genetic testing. She had an infection frequency of about 0.36 times/year before the diagnosis of genetic classification and was hospitalized for “repeated vomiting and diarrhea” 3 times in the past year, and had secondary “inflammatory bowel disease (IBD) and pancreatitis” during the last hospitalization . She received G-CSF treatment irregularly. At the last visit, the liver enzymes gradually returned to normal, triglycerides and lactic acid were decreased than before, but did not fall to normal levels. Her pyruvic acid and uric acid increased, and fasting hypoglycemia and metabolic acidosis have not been significantly improved.At the age of 3 years and a half, this male patient was admitted to hospital because of “hepatomegaly for over 3 years, upper respiratory tract infection for 1 week”. At admission, his height SDS was − 2.97 and weight was 13 kg (10th–25th centile). There were abnormalities in ANC 0.95 × 10^9 /L, liver function , TG 5.53 mmol/L, fasting glucose (3.8 mmol/L), lactic acid (10.75 mmol/L), and blood gases (BE − 10.9). The liver ultrasound also suggested hepatomegaly. He also underwent liver biopsy and showed glycogen storage. He was tested for glucose response to epinephrine stimulation after overnight fasting and the result was positive, too. According to all the results, he was clinically diagnosed as GSD. He took oral uncooked cornstarch regularly. At 13 years old, he was diagnosed with GSD type 1b through genetic testing. At the last visit, the liver enzymes gradually returned to normal, triglycerides, lactic acid, pyruvic acid, and uric acid were decreased than before, but did not fall to normal levels. His fasting hypoglycemia and metabolic acidosis have been significantly improved. He had a history of oral ulcers. As the treatment time extended, the number of infections was significantly reduced, only presented with minor infections, such as upper respiratory tract infection or oral ulcers that did not require hospitalization.This female patient was admitted to hospital because of “short stature, hepatomegaly for 6 months” at the age of 3 years old. At admission, her height SDS was − 1.48 and weight was 15 kg (50th–75th centile). There were abnormalities in ANC (0.82 × 10^9 /L), TG (3.92 mmol/L), fasting glucose (3.48 mmol/L), lactic acid (9.61 mmol/L), and uric acid (519 μmol/L). The liver ultrasound suggested hepatomegaly. She also underwent liver biopsy and was tested for glucose response to epinephrine stimulation after overnight fasting and the results were positive. Therefore, she was clinically diagnosed as GSD. She was given oral uncooked cornstarch regularly. At the age of 10 years and a half, she was finally diagnosed with GSD type 1b through genetic testing. At the last visit, her triglycerides, lactic acid, pyruvic acid and uric acid were decreased than before. Her fasting hypoglycemia and metabolic acidosis have been significantly improved. She had a history of upper respiratory tract infections, about once a year.GYS1, GYS2, G6PC, SLC37A4, GAA, AGL, GBE1, PYGM, PYGL, PFKM, PHKA2, PHKB, PHKG2, PHKA1, PGAM2, LDHA, ALDOA, ENO3, PGM1, GYG1, PRKAG2) was used for GSDs. We fragmented the genomic DNA which extracted from the sample, and the DNA probes were designed to tile along the exon regions and exon–intron boundaries of the target genes. After enrichment of DNA fragments, Illumina HiSeq X ten sequencer was used for high-throughput sequencing of the captured exon region. Sanger sequencing was finally used to verify co-segregation in the family. Suspected candidate variants were screened by comprehensively considering the genetic pattern and the clinical characteristics of the disease. The pathogenicity of variants was predicted according to the 2015-ACMG Standards and Guidelines.After obtaining the written informed consents, gene sequencing (Beijing MyGenostics Inc.) was performed on the probands and their parents. Our genetic testing strategy is a GSD panel based on target gene capture technology , 20 GSD SLC37A4 gene were detected in 3 patients  Type Ib is a group of inherited metabolic disorders caused by variants in the d Serbia .SLC37A4 gene is located on chromosome 11q23, consists of 9 exons, spans approximately 5.3 kb of genomic DNA, and is expressed ubiquitously in liver, kidney, intestine, blood and skeletal muscle [SLC37A4 gene encodes G6PT, G6PT transports G6P from the cytoplasm to the lumen of the endoplasmic reticulum and delivers it to the catalytic site of G6Pase-α or G6Pase-β. G6Pase-α and G6Pase-β are G6P hydrolases in the endoplasmic reticulum membrane, which in turn hydrolyze G6P to glucose and inorganic phosphate [SLC37A4 gene can cause G6PT deficiency or dysfunction, failing to complete the transport of G6P and resulting in disturbed glucose homeostasis, and then leading to hyperlipidemia, lactic acidosis, hyperuricemia, and other metabolic abnormalities. Whereas G6Pase-β couples functionally with G6PT to maintain neutrophil function and homeostasis. Consequently, G6PT is essential to maintain both interprandial glucose homeostasis and myeloid cell energy homeostasis [The human hosphate . Among tSLC37A4 gene that have been identified, including missense variants, nonsense variants, frameshift variants, splice site variants, and deletion variants, etc. There are ethnic variability in variant types and proportions [SLC37A4 gene were detected in 3 patients. Among them, c.680G > A is a novel variant, the c.572C > T (p.Pro191Leu) variant is the most common and only reported in the Chinese population [So far, there are 115 pathogenic variants in the portions , 12. Preportions –16. In opulation –19. We sSLC37A4 gene variants is both a metabolic and an immune disorder [The typical clinical manifestations of GSD Ib are similar to those of type Ia, including impaired glucose homeostasis such as liver enlargement and growth retardation. By contrast, neutropenia and neutrophil dysfunction are major clinical phenotypes of patients with GSD Ib. GSD Ib patients are often accompanied by neutropenia and are prone to frequent infectious diseases, such as recurrent upper respiratory tract infections, oral ulcers, enterocolitis and inflammatory bowel disease (IBD). There are also reports of GSD Ib combined with Crohn's disease . The exadisorder .A decreased number of neutrophils in peripheral blood is an important feature that distinguishes GSD Ib from GSD Ia. It is worth mentioning that not all patients diagnosed with GSD Ib based on metabolic phenotypes and genetic testing develop neutropenia, which may be related to the residual transport activity of G6PT . AccordiThe current treatment of GSD Ib is mainly symptomatic. As a serious metabolic and immune multisystem disorder, if not actively treated, it may cause the patient to be fatal in adolescence. Clinically, diet therapy  can maintain the patient's glucose stability and reduce the early symptoms of the disease , 21. ThoThe 3 patients in our study all had typical clinical manifestations such as elevated liver enzymes, fasting hypoglycemia, hyperlipidemia, hyperlactacidemia, lactic acidosis, and decreased neutrophil count, accompanied by hepatomegaly, growth retardation, and repeated infections. With increasing age, ANC of the 3 patients decreased progressively. Among them, the condition of patient 1 was poorly controlled, G-CSF was used irregularly, and the effect was not good, with secondary IBD and frequent hospitalizations due to infection. The other 2 patients were effectively controlled with age, and the number of infections was significantly decreased. Therefore, the clinical manifestations of patients with GSD Ib have certain heterogeneity.SLC37A4 variant. When recurrent upper respiratory tract infections or digestive tract symptoms are accompanied by hypoglycemia, dyslipidemia, metabolic disorders, elevated liver enzymes and/or neutropenia clinically, the possibility of GSD Ib should be vigilant.In summary, GSD Ib patients have various gene variant types and different clinical symptoms. We reported 3 cases with GSD Ib and a novel


Aedes mosquitoes that can potentially cause microcephaly, Guillain–Barré Syndrome, and other birth defects. Effective vaccines for Zika have not yet been developed. There is a necessity to establish an easily deployable, high-throughput, low-cost, and disposable point-of-care (POC) diagnostic platform for ZIKV infections. We report here an automated magnetic actuation platform suitable for a POC microfluidic sandwich enzyme-linked immunosorbent assay (ELISA) using antibody-coated superparamagnetic beads. The smartphone integrated immunoassay is developed for colorimetric detection of ZIKV nonstructural protein 1 (NS1) antigen using disposable chips to accommodate the reactions inside the chip in microliter volumes. An in-house-built magnetic actuator platform automatically moves the magnetic beads through different aqueous phases. The assay requires a total of 9 min to automatically control the post-capture washing, horseradish peroxidase (HRP) conjugated secondary antibody probing, washing again, and, finally, color development. By measuring the saturation intensity of the developed color from the smartphone captured video, the presented assay provides high sensitivity with a detection limit of 62.5 ng/mL in whole plasma. These results advocate a great promise that the platform would be useful for the POC diagnosis of Zika virus infection in patients and can be used in resource-limited settings.The Zika virus (ZIKV) is an emerging flavivirus transmitted to humans by  Aedes species mosquito, but it can also be transferred through sexual contact and from a pregnant woman to their offspring [The Zika virus (ZIKV) is a flavivirus that is closely related to other flaviviruses such as Dengue, West Nile, and Japanese encephalitis . The virffspring . Althougffspring ,4. The Zffspring .The lack of detection of ZIKV in an area cannot be necessarily equated to low levels of transmission or low levels of prevalence. The means of testing in certain areas, especially in rural and third world countries can be lacking, which can attribute to some of these findings. The nonstructural protein 1 (NS1) is targeted for detection due to its important role as a biomarker in flaviviruses. The protein is secreted by cells that contain the virus . As the IgG and IgM antibodies are produced in the human body at the later stage of the infection (four to seven days), this makes them inapt for early stage detection. In contrast, NS1 antigen with a similar structure has been considered for highly sensitive, specific early stage detection for different flaviviruses previously ,12,13,14In this paper, we have developed a microfluidic magnetic ELISA (M-ELISA) system that provides benefits over other POC testing and traditional testing through its low cost, time efficiency, and automation. Compared to conventional sandwich ELISA, the unique shape of the microfluidic chip that we have developed can decrease cost due to the use of less reagents and time by cutting the test from 12 hours to approximately 10 min. This can be beneficial in certain parts of the world where accessibility and cost are significant factors. The reduction in time is achieved through the increase of surface area with the magnetic beads . The dev2SO4 was used to stop the color development. The absorbance was measured at 450 nm using a SpectraMax Gemini™ XPS/EM Microplate Reader .To validate the Antigen- Antibody (Ag–Ab) reaction, sandwich ELISA assay was first performed by coating a microplate with 100 μL of 2 μg/mL capture antibody for Zika NS1  in carbonate/bicarbonate buffer (pH 9.6), and then incubating it overnight at 4 °C. After washing three times with phosphate buffered saline (PBS) (pH 7.4), each well was blocked by 200 μL of SuperBlock T20 (PBS) Blocking Buffer  for 90 min on a 15 rpm shaker at room temperature. It was rewashed three times carefully, followed by 90 min incubation of 100 μL of recombinant Zika NS1 antigen  with different concentrations spiked in PBS at room temperature. One hundred microliters of HRP-labeled anti-Zika NS1 antibody  with 1:1000 dilution buffer ) was added and incubated for 60 min on a 15 rpm shaker at room temperature followed by three washes with PBS. Blue color development was carried out using a 3,3′,5,5′-tetramethylbenzidine (TMB) substrate  by incubating for 10 min in a dark place, and 2M HWe fabricated a three-layer microchip using a cheaper non-lithographic method by laser cutting  polymethylmethacrylate (PMMA) and double-sided adhesive (DSA) materials using an optimized design previously published . The topThe capture antibody was first biotinylated using a type B fast biotin conjugation kit (ab201796) obtained from Abcam. One hundred microliters of Zika NS1 Mab  was modified by 10 µL of biotin-modified reagent and agitated very gently. The antibody mixer was added to the lyophilized biotin vial (100 µg), followed by 15 min incubation at room temperature. Ten microliters of Quencher reagent was added to stop the biotin reaction and was incubated for 4 min. Magnetic particles with an average diameter of 1 μm coated with neutravidin  show higher affinity surface functionalization for biotinylated antibodies. Four hundred microliters of neutravidin-coated magnetic beads with 3500–4500 picomole/mg binding capacity were washed with 4 mL of PBS in a 5 mL Eppendorf Protein LoBind tube . A magnetic stand was used for attracting all the magnetic beads creating a pellet on the magnetic stand side tube wall. The supernatant was discarded using a pipette and substituted by the same amount of PBS and mixed gently with a pipette to wash the magnetic beads. The process was repeated two times. For Zika NS1 capturing, 4 mL of 25 μg/mL biotinylated Zika NS1 Mab  was conjugated with the neutravidin-coated magnetic beads. To create a magnetic bead–biotin–capture antibody conjugation, the mixer was incubated overnight on a shaker (15 rpm) at 4 °C. After incubation; the magnetic bead conjugation was washed three times following the earlier mentioned steps to remove boundless Zika NS1 antibodies. One hour of blocking was done by using SuperBlock T20 (PBS) Blocking Buffer  to functionalize the magnetic beads at room temperature followed by washing twice using 4 mL PBS. After rinsing the magnetic beads, the conjugation was resuspended in 4 mL PBS and stored at 4 °C for future use.2SO4. Beads were isolated with the help of a magnetic separator, and the liquid was transferred to a new well and absorbance was measured at 450 nm by using a SpectraMax Gemini™ XPS/EM Microplate Reader .Before using the magnetic bead–antibody solution, the beads were vortexed for 2–3 s to make a homogeneous solution. Thirty microliters of (1 mg/mL) magnetic beads conjugated with capture antibody was added on a conical-bottom 96-well plate , and then all supernatant was isolated and discarded with the help of a 96-well-plate magnetic separator stand. One hundred microliters of Zika NS1 antigen spiked in plasma was added on to the wells and mixed gently to create a homogeneous solution followed by a 45 min incubation at room temperature on a shaker at 15 rpm speed. After that, the beads were aggregated by applying them onto the magnetic separator for 30–40 s to allow the magnetic beads to create a pellet on the sidewall of the wells. The liquid was discarded by a pipette and replaced with 200 µL of PBS to wash the beads. Beads were washed three times carefully to remove all the uncaptured antigen. One hundred microlites of HRP-labeled anti-Zika NS1 antibody  with 1:1000 dilution buffer  was added and mixed slowly and incubated for 15 min on a 15 rpm shaker at room temperature. The beads were again washed three times by following the procedure mentioned above. One hundred microliters of TMB substrate was added, mixed, and incubated for 90 s in a dark place to generate a blue color. Color development was stopped by using 100 μL of 2M HAn Arduino-controlled in-house-built magnetic actuation platform  was usedThe prefabricated three-layer microfluidic chip was first loaded with all the reagents. First, 40 µL of (1 mg/mL) magnetic beads conjugated with capture antibody were taken into a 0.5 mL protein LoBind PCR tube, and then the supernatant was isolated with the help of a magnet and discarded by a pipette. One hundred microliters of Zika NS1 antigen diluted 2000-62.5 ng/mL concentration was added to the PCR tube and mixed gently. Then the PCR tube was incubated for 10 minutes at room temperature on a 15 rpm shaker to allow the beads to capture the antigen. In the meantime, the other reagents were loaded into the microfluidic chip with the help of a pipette. The loading steps in brief were as follows , first bThe microfluidic chip was placed on a white paper on a flat surface instantly after the completion of the M-ELISA. The covering tape was removed as quickly as possible, and a 30 s video was recorded using iPhone Xs cellphone c. The ca2 ≥ 0.9929). In this study, we used Dengue 2 NS1 antigen as negative control and we also performed standard sandwich ELISA to further confirm that there was no cross reactivity/false-positive result shown by the anti-Zika NS1 monoclonal antibody  to validate the antibody and the target binding. Results clearly show that the anti-Zika NS1 monoclonal antibody was binding as low as 78 pg/mL of Zika NS1 when it was spiked in PBS a with a antibody .2 ≥ 0.9929). This result indicates that the M-ELISA is also consistent like conventional sandwich ELISA. Therefore, the measurement of the saturation MPI to calculate the concentration of Zika NS1 is also reliable.After validating the Ag–Ab reaction, the anti-Zika NS1 monoclonal antibody was conjugated with superparamagnetic beads using a commercially available biotin conjugation kit. To confirm the magnetic bead–biotin–antibody binding, we performed a zeta potential analysis. A change in the negative surface charge indicates that the beads were successfully conjugated with antibodies. To further reduce the required reagent and assay timing for performing the M-ELISA we used a previously reported  Arduino-To fabricate the three layers of microfluidic chip to accommodate different reagents, we chose 0.75 mm thick PMMA for the top and bottom layers and a 1.5 mm middle layer . The totAfter validating target Ag–Ab binding by conventional sandwich ELISA and M-ELISA a,b, the To the best of our evidence, this is the first reported automated sandwich ELISA technique for detecting Zika NS1 antigen. Our goal was to develop and confirm the sandwich ELISA with superparamagnetic beads on a microfluidic chip. The non-lithographic process of chip fabrication has been reported previously ,23,24,25Recently, very few studies have been published for developing detection assays targeting ZIKV NS1 antigen. A double antibody sandwich ELISA-based colorimetric assay was able to detect it as low as 120 ng/mL . AnotherMoreover, the developed assay is suitable for detecting the Zika NS1 antigen without the help of costly specialized instruments , which in turn reduces cost per assay to below $2 (USD) . The uniWe developed an automated microfluidic assay integrated with smartphone that can obtain results in ~10 minutes, which can be beneficial in resource-limited settings. There were some shortcomings and further areas for our device to expand on. The sensitivity of our device is much higher than that of other point-of-care devices with a detection limit of 62.5 ng/mL. Further improvement in this area can result in great potential for this device to be clinically used in areas around the world. However, the cost effectiveness, automation, and time effectiveness compared to traditional ELISA’s can be beneficial in regions that struggle with poverty and lack of resources. The utilization of smartphone video and the in-house software can be operated by low-skilled workers. This, in turn, can further the accessibility of the developed device and allow anyone to record and interpret results. Without the use of high-cost specialized instruments, the developed chip can be kept at a low cost. These results show potential that the platform would be useful for the POC diagnosis/screening of Zika virus infection patients and can be used in resource-limited settings.


Andes virus is unique among hantaviruses because it can be transmitted from person to person. This mechanism was previously supported by epidemiologic data and genetic evidence based only on partial sequences. We used full-length virus sequencing to confirm person-to-person transmission of this virus in a cluster of 3 cases in Argentina in 2014. Hantaviridae and genus Orthohantavirus. These viruses are responsible for hantavirus pulmonary syndrome (HPS) in the Americas. In Argentina, HPS was first described in 1995 during an outbreak in the Andean region of Patagonia, leading to the characterization of Andes virus (ANDV)  segment encodes a nucleocapsid protein, the medium  a glycoprotein precursor, and the large  an RNA-dependent RNA polymerase  died . P1 and For our investigation, we included 2 unrelated HPS case-patients (NCR1 and NCR2) from the same area for comparison. We confirmed HPS in all 5 patients by using laboratory detection of ANDV-specific IgM and reverse transcription quantitative PCR, as described . Patient P1 had 100% nucleotide identity in the complete S and M segments with P2/P3 but had 2 nt changes in the L segment (99.95% nucleotide identity) ; both diOn the basis of accurate epidemiologic findings, the only source of infection for P3 was her contact with P2 at Hospital Area El Bolsón. Despite the nucleotide differences between their isolates, co-exposure of P1 and P2 should not be discarded as a source of infection because these persons lived in the same house where they shared the same room and bed. However, even if one considers that these nucleotide changes were 2 silent mutations in the whole viral genome, person-to-person transmission is still the most probable way of infection for P2. A previous study reported a high degree of sequence diversity for the L segment of Puumala virus (For further comparison, we obtained the complete sequences of 4 ANDV genomes circulating during a short period in the same area where this virus was first described . This fi–The open reading frames encoding the nucleoprotein, glycoprotein precursor, and RNA polymerase had the same size as sizes of published sequences of ANDV (Future studies are needed to obtain additional complete viral sequences from rodent populations co-circulating in the same geographic area. This information will enable definitive differentiation of person-to-person transmission from co-exposures in patients with similar activities of risk in disease-endemic regions, which was the case for P1 and P2, both of whom reported collecting firewood in a forested area.We characterized the complete genome of an ANDV strain involved in a person-to-person transmission chain by using target-specific whole-genome sequencing. Our study contributed useful data for clarifying properties involved in the unusual transmissibility of ANDV. These data are crucial for optimal management of HPS case-patients and control of future outbreaks of this lethal disease.


At the end of 2019 reports started to surface of a novel viral infection that has become known as SARS-CoV-2 (causing the infection COVID-19) and was classified as a pandemic by the World Health Organization (WHO) in March 2020. Countries around the world have utilised a range of measures to face this pandemic and although each country has their own unique approach, it has typically included periods of self-isolation or quarantine to help limit both the spread of the virus and the burden on health care systems. During these periods of self-isolation, many have been asking themselves what they can do to help. There have been some uplifting reports of everyone pulling together to do their part, whether that is breweries, distilleries or perfume manufacturers making hand sanitiser,  or collaBut what could a materials chemist do to help? Viruses are often thought of as being a problem to be resolved by biologists and biochemists, yet in reality, there are many crossovers with chemistry and materials, ready to be exploited .Viruses are intracellular parasites with a very simple structure. They lack their own metabolism and require a host to replicate, so are (debatably) not living . InsteadThe key characteristic behind why soap is so effective is linked directly to the fact that viruses are self-assembled structures. Their lipid membranes are comprised of fatty acids with hydrophobic interactions between chains, leading to a stable outer membrane. When you introduce soap into the mixture these hydrophobic interactions are disrupted and the lipid membrane is ‘dissolved’, destroying or deactivating the virus. Materials capable of destroying a virus in such a way are termed virucidal Fig.  and therOn the topic of treatment options for viruses, there are two main approaches to antivirals, intracellular and extracellular, both of which can benefit greatly from input from materials chemists.Intracellular antivirals are almost exclusively small-molecule drugs that are designed to inhibit the intracellular replication of viruses, for example some of the promising candidates for treating SARS-CoV-2: Remdesivir  initial attachment receptors on the surface of cells ; these ein vivo environment dilution occurs readily, leading to release of the intact virion [In principal, these sound like our best approach; however, the interaction between material and virus can be reversible (termed virustatic) and so with a simple change in dilution, pH or temperature the infective virion would be released. There have been many examples of broad-spectrum virustatic materials including polymers , 12, denWhat if we could take these non-toxic, broad-spectrum entry inhibitors, modify them and change their mode of action from virustatic to virucidal? This change would lead to a material that was able to destroy a wide range of viruses on contact (i.e. broad spectrum) while remaining non-toxic. This might sound too good to be true, but my colleagues and I recently discovered materials capable of doing just that , 7.By modifying a sugar macrocycle, called cyclodextrin, with alkyl sulfonates, we were able to show broad-spectrum virucidal effects against viruses that use cell-surface heperan sulfates as their initial attachment receptors Fig. . These iThrough the use of molecular dynamic simulations, we show that upon binding of the modified cyclodextrins to the attachment receptor of the HSV2 virus, the protein trimer is altered only in the presence of the virucidal cyclodextrin variant.Initial tests of these antivirals against SARS-CoV-2 has shown promising efficacy, but further tests are needed to confirm this finding. The key now is to try and develop these types of materials, not only for clinical use in the fight against SARS-CoV-2 (if needed), but also ultimately so that they are ready for the next inevitable viral outbreak.This represents only one example of a material-based approach that can be used in the fight against viral infections. There are many other avenues waiting to be explored  that offer materials chemists further opportiunities to apply their expertise to the virus problem.