The proposed biosensor is very convenient for point-of-care applications.Aptasensors based on surface-enhanced Raman spectroscopy (SERS) are of high interest due to the superior specificity and low limit of detection. It is possible to produce stable and cheap SERS-active substrates and portable equipment meeting the requirements of point-of-care devices. Here we combine the membrane filtration and SERS-active substrate in the one pot. This approach allows efficient adsorption of the viruses from the solution onto aptamer-covered silver nanoparticles. Specific determination of the viruses was provided by the aptamer to influenza A virus labeled with the Raman-active label. The SERS-signal from the label was decreased with a descending concentration of the target virus. Even several virus particles in the sample provided an increase in SERS-spectra intensity, requiring only a few minutes for the interaction between the aptamer and the virus. The limit of detection of the aptasensor was as low as 10 viral particles per mL (VP/mL) of influenza A virus or 2 VP/mL per probe. This value overcomes the limit of detection of PCR techniques (∼10 Surface-enhanced Raman spectroscopy (SERS) is a spectroscopic method, which implies multiple amplification of Raman scattering intensity by nanostructured interfaces of metals and dielectrics. Raman spectra are characteristic for a chemical structure of compounds; thus, they are used for identification purposes. It is possible to decode complex mixtures consisting of several different compounds; this feature allows simultaneous identification of several analytes in the one pot . Besidesper se are rather complex for specific determination as thousands of peptides and proteins are composed of the same set of amino acids. Raman spectroscopy of biomacromolecules does not allow sequence determination while revealing composition of some surface epitopes only. The specific determination can be provided using recognizing molecules such as antibodies or nucleic acid aptamers labeled with Raman-active labels, tags or fluorophores  and Sigma-Aldrich . The following modification of DNA aptamer RHA0385 was synthesized by Synthol : (SH-dT)-5′-TTGGGTTATTTTGGGAGGGC-GGGGGTT-3′-Су3. The solutions were prepared in ultrapure water with resistivity of 18.2 MΩ.via the addition of 0.05% (v/v) glutaric aldehyde and preserved via the addition of 0.03% (w/v) NaN3, and stored at +4°C. The viral-dependent agglutination of red blood cells was not impaired by the inactivation procedure; thus, the inactivation procedure did not disrupt the conformation of the hemagglutinin protein, a target of DNA aptamer RHA0385. Previously, we have shown that aptamer RHA0385 inhibits viral-dependent agglutination of red blood cells due to binding of influenza A hemagglutinin as a part of the inactivated influenza A viruses (Influenza A virus from H7N1 subtype (A/chicken/Rostock/45/1934) and influenza B virus (B/Victoria/2/1987) were propagated in the allantoic cavity of 10-day-old embryonated specific pathogen-free chicken eggs. Eggs were incubated at 37°C, cooled at 4°C for 48 h post-infection, and harvested 16 h later. The study design was approved by the Ethics Committee of the Chumakov Institute of Poliomyelitis and Viral Encephalitides, Moscow, Russia . Viruses were inactivated  viruses . These dViral titer was determined using a hemagglutination assay. A measure of 50 µl of solutions of the viruses diluted two times step-by-step in a phosphate-buffered saline was placed in a V-shaped 96-well microtiter plate in a volume of 50 µl. Then, 50 µl of 0.5% chicken red blood cells in the phosphate buffered saline was added to the well. The plate was kept in the refrigerator at 4°C for 1 h. Then, the hemagglutination titer was estimated as the maximal dilution of the virus that did not cause the precipitate of red blood cells; this well contained 1 HAU of the virus in the probe.® PMX420-QUATT instrument , while the data were analyzed by ZetaView NTA software. Operating instructions of the manufacturer were followed before calibrating the instrument with a known concentration of 100 nm polystyrene nanoparticles . The standards were suspended in particle-free water, whereas the investigated samples were diluted 1:100 with phosphate buffered saline (PBS). Particles were counted and size-distributed at 10 cycles of 11 frames per cycle under sensitivity of 65 and a shutter value of 100.Nanoparticle Tracking Analysis (NTA) was conducted using a ZetaView8 cm−2 were produced according to previous work .Polyethylene terephthalate track-etched membrane with average pore diameter of 360 nm and pore density of 2.6·10ous work . The nan−1 line from the test substance on the membrane and on the substrate, an absolute Q-factor value was calculated.Raman XY-topography of the surface of track membranes was performed with a step of 200 microns on an area of 2 × 2 mm. Thus, 100 spectra of the test substance (4-aminobenzenethiol) were recorded on each of the five measured membranes and five commercial substrates EnSpectr SERS. As a result of comparing the averaged intensities of the 1,142 cm9 -times in PBS buffer. 200 µL of the sample was filtered through the membrane using Microspin FV-2400  for 2 min. SERS spectra were recorded for 400 ms with averaging 20 repeats using Raman spectrometer EnSpectr SERS R532  with laser wavelength of 532 nm. The diameter of the beam was 2 mm. Each experiment was repeated 3 times; the normalized SERS or SEF intensities were averaged. The limit of detection was calculated from the linear regression of SERS (SEF) intensities plotted versus logarithm of influenza concentration (the ascending part of the curve was used). The limit of detection was estimated as the concentration of the virus which increased the SERS (SEF) intensity by three standard deviations over the intensity in the blank sample. The limit of quantification was estimated as the concentration of the virus which increased the SERS (SEF) intensity by ten standard deviations over the intensity in the blank sample.The aptamer was folded into the active conformation by diluting in PBS buffer in concentration of 2 µМ with subsequent heating at 95°C for 5 min and cooling at room temperature. The aptamer solution was diluted in PBS buffer to concentration of 200 nM. The membrane was incubated in 200 nM aptamer solution for 15 min. A round fragment of the membrane with a diameter of 3 mm was placed onto the filter block of Corning Costar Spin-X centrifuge tube with cellulose acetate membrane filters, pore size 0.45 µm . Virus-containing allantois fluids were diluted 20–10Scanning electron microscopy (SEM) of the membrane samples was performed using electron microscope SU 8020 equipped with cold field cathode . The resolution and contrast of the images were improved by evaporating a layer of gold-palladium alloy with a thickness of 5 nm.–5 g/mL was applied to a membrane with a silver nanostructure layer. This substance contains an SH-group and therefore remains on the surface of the membrane and does not leak through the pores, thereby making it possible to compare signals from the membrane and from a commercial substrate EnSpectr SERS  with a known Q-factor, namely, Blue & Green Substrate  with the enhancement factor of 2.2·106. Comparing SERS signals from the test molecules under the same conditions of excitation and signal registration, it is possible to calculate the coefficient of membrane amplification. The Q-factor calculated in this way was 7·105  were tested. The highest SEF intensity was achieved for the membrane functionalization with the aptamer solution in PBS.The change of aptamer concentration from 20 nM to 2 µM increased the surface-enhanced fluorescence (SEF) and surface-enhanced Raman spectroscopy (SERS) of the reporter monotonously . SimilarFiltration through the membrane also affects intensities of SEF and SERS spectra of the reporter. The intensities of both SEF and SERS spectra were decreased substantially after the filtering of 200 µl of PBS . The decAdditionally, the performance of the sensor was estimated for 20 nM, 200 nM and 2 µM concentration of the aptamer. The treatment with influenza A virus changed the SERS and SEF intensities non-monotonously. The membrane treated with the aptamer concentration of 200 nM provided the increase in SERS and SEF in the presence of the virus. Whereas the membrane treated with the aptamer concentration of 2 µM provided the decrease in the spectrum intensity . IncubatA possible explanation of the SERS and SEF signal dependencies is connected with the orientation of the reporter relative to the surface . A modifThe additional experiment was performed to ensure that the concept can be translated to other viruses. The concentration dependence of SERS intensity of TAMRA label conjugated with thiolated aptamer to respiratory syncytial virus (RSV) is nearly the same for the samples with filtration of PBS buffer and respiratory syncytial virus strain A2 . Namely,Aptamers provide high affinity and selectivity. However, immobilization and modification can affect both affinity and specificity. This effect was shown for different aptamers, including aptamers to S-protein of SARS-CoV-2, thrombin, epidermal factor growth receptor . The strHere we tested an alternative setup where the 3′-end-labeled aptamer is adsorbed on the SERS-active surface due to thiol-modification at the 5′-end of the aptamer. The adsorbed aptamer provided SERS and SEF signals of Cyanine-3. The design of the aptamer (i.e. sites for incorporation of thiol and Cyanine-3) provided the increase in surface-enhanced Raman and fluorescence spectra intensities in the presence of the analyte  contrary9 VP/mL with a mean diameter of 160 ± 30 nm.There are many ways to estimate the quantity of influenza A virus. The most popular units are copies of viral genome determined by PCR, hemagglutination units (HAU) determined by the interaction with red blood cells, and plaque forming units (pfu) determined by a cell viability assay. These values can be recalculated into viral particles using several published correlations; however, this procedure provides a rough estimation. Here we performed a nanoparticle tracking analysis to estimate the quantity of nanoparticles in the sample. This technique physically calculates nanoparticles in the sample with a simultaneous estimation of their size. The distribution is shown in 3 VP/mL (−1 over the control without influenza A virus. The limit of detection for the SERS sensor was as little as 10 VP/mL (2 VP per probe) of influenza A virus; whereas the limit of detection for the SEF sensor 25 VP/mL of influenza A virus (5 VP per probe). These results are the best among published works (see the Discussion part for the detailed comparison). The limit of quantification was estimated as 33 VP/mL and 80 VP/mL for SERS and SEF dependencies, respectively. The quantification is rather complicated as the dependence is non-linear.Normalized SERS and SEF intensities were used to estimate performance of the membrane-based aptasensor for influenza A virus. The intensities of the samples with influenza A virus were divided by the intensities in the control experiments without the virus. The closest counterpart of influenza A virus, influenza B virus, was used to estimate specificity of the sensor. Normalized SERS and SEF intensities were >1 for a wide concentration range of influenza A virus . Whereas3 VP/mL . Further3 VP/mL . ObviousThe development of SERS substrates is ongoing aiming to combine high enhancement factors with simplicity of fabrication of the substrate and its application. A huge variety of solid substrates obtained with a lithography , multilaHere we used membranes decorated with silver nanoparticles. The polyethylene terephthalate track-etched membranes can be obtained with a pore diameter from 10 nm to 1 µ providing a wide range of applications. In particular, we performed concentrating of the influenza A viruses and their purification from any molecules and macromolecules . SERS-acvia a thiol group. The RHA0385 aptamer for influenza A virus was modified by thiol at the 5′-end and Cyanine-3 dye at the 3′-end. Adsorption of the labelled aptamer onto the SERS-substrate provided a SERS spectrum due to proximity of the label to the nanostructured surface. Initially, we used this aptamer to study the efficiency of adsorption of the aptamer onto the SERS-active substrate. Unexpectedly, we have explored that the aptamer could provide analyte-dependent changes of surface-enhanced Raman and fluorescence spectra. Although the detailed mechanism of the process is not clear; we suppose that influenza A viral particles orients the aptamers on the surface approaching the reporter to the surface  (6–4·108 VP/mL) and even PCR with reverse transcription (LoD = 3·102–1.2·103 VP/mL) (Specificity of the sensor was demonstrated by the absence of the increase of SERS and SEF in the presence of off-target influenza B virus. The limit of detection (LoD) was 10 VP/mL of influenza A virus for SERS spectrometry; SEF spectrometry provided 2.5-fold higher LoD, 25 VP/mL. The detailed comparison with LODs of re-ported SERS-based sensors for virus determination is provided in 50) . The LoD3 VP/mL) . The LoD3 VP/mL) . ConsideA novel type of a biosensor for virus identification has been proposed. The biosensor has several benefits over the standard types of sensors. For instance, it has a low cost (approximately 0.5 USD) and a high sensitivity (the LoD is 10–100 times lower than the LoD of RT PCR) requiring only a minimal number of steps. If we consider the market of rapid tests, then the main advantages of our sensor are a low price and excellent sensitivity. The main disadvantage in point-of-care application is requirement of Raman spectrometer. In our study, we used a portable autonomous Raman device. It could be made as cheap as $1,000 by using a simplified optical filtering and a detection scheme without registering a full spectrum a single reporter by rather focusing on a set of unique Raman lines.


Nicotiana tabacum L. cv. Xanthi nc and N. glutionosa L. with defined virus concentrations were first performed with half-leaf randomization in a Latin square design. Subsequently, models were implemented using Python software and fitted to the number of local lesions. All models could fit to the data for quantifying ToBRFV based on local lesions, among which the modified Poisson model had the best prediction of virus concentration in spike samples based on local lesions, although data of individual indicator plants showed variations. More accurate modeling was obtained from the test plant N. glutinosa than from N. tabacum cv. Xanthi nc. The position of the half-leaves on the test plants had no significant effect on the number of local lesions.Considering the availability of serological and molecular biological methods, the bioassay has been paled into insignificance, although it is the only experimental method that can be used to demonstrate the infectivity of a virus. We compared goodness-of-fit and predictability power of five models for the quantification of tomato brown rugose fruit virus (ToBRFV) based on local lesion assays: the Kleczkowski model, Furumoto and Mickey models I and II, the Gokhale and Bald model (growth curve model), and the modified Poisson model. For this purpose, mechanical inoculations onto  Tomato brown rugose fruit virus (ToBRFV), a member of the virus genus Tobamovirus, in the family Virgaviridae, is of utmost concern for tomato production globally [Solanum lycopersicum L.) [Capsicum annuum L.) [Nicotiana spp. It has been reported that ToBRFV causes local lesions on Nicotiana tabacum L. cv. Xanthi nc and N. glutionosa L., as well as systemic infection in N. benthamiana Domin and N. clevelandii A. Gray [Chenopodium murale L. and Solanum nigrum L. around infected tomato crops [It is currently argued that globally . The virglobally . ToBRFV icum L.) ,17,18,19nuum L.) ,21,22,23nuum L.)  in 35 conuum L.) , and hasnuum L.) ,27,28,29nuum L.)  and is onuum L.) . It has nuum L.) . The virnuum L.) ,32. In ato crops .Tobamoviruses including ToBRFV could be dispersed by the transport of contaminated seeds over a long distance. Moreover, they are stable viruses  and remaVirus quantification involves counting virus particles or viral genome fragment(s) in a known volume to determine their concentration. Applications are in diagnostics and in research, for example, in virus–host interaction studies. Several methods have been developed to determine the viral load in a given sample, based on chemical or physical principles. These methods include electron microscopy, serological assays such as ELISA (enzyme-linked immunosorbent assay), and q(RT)-PCR (quantitative reverse-transcribed polymerase chain reaction). None of these methods can show or confirm infectivity of the virus in plant sap or virus suspensions. Plant viruses are not viable themselves and depend on a living host. Therefore, only a bioassay can show infectivity of the virus and, if necessary, quantify infectious virus particles. However, molecular methods are more sensitive than bioassay. In 1929, Holmes established a local lesion assay and showed that the number of local lesions on the inoculated host depends on the virus titer . Since tA biological assay is based on the viability or infectivity of a pathogen. In the case of plant viruses, a bioassay using a susceptible host plant is the only available method to verify pathogenicity. So far, the evaluation is mostly qualitative, i.e., the pathogen is infectious or non-infectious. A quantitative evaluation requires a reproducible relationship/regression to be established between the number of local lesions induced by the virus after artificial inoculation and the virus concentration in the inoculum.In this study, we report (i) the accuracy of the different models for quantifying ToBRFV in virus suspensions and plant homogenates with a known virus concentration, and (ii) the suitability of the models for the prediction of virus concentrations in a plant homogenate with an unknown virus concentration.N. benthamiana plants at the 4–5 well-developed leaf stage with ToBRFV PV-1236 were systemically infected and showed stunting and severe leaf deformation in the newly emerged two top leaves 8–14 days post-inoculation (dpi). A few of the inoculated plants were kept for another 2 weeks, giving them more time to grow. These plants, however, stopped growing  observed on different leaves after inoculation with the same virus preparation deviate greatly from a normal distribution, and the standard errors of x values depend on their magnitude, these data should be transformed [Inoculation of nsformed .2X error . The application of a spike sample with a defined virus concentration enables the validation of the models in the prediction of the correct virus concentration in an unknown sample. Our results show that the host plant used in local lesion assays also has a significant effect on accuracy of the models to determine correct virus concentrations in virus inoculum.The established fitted models were used to predict the concentration of ToBRFV in a spike sample using the estimated parameters shown for each local lesion host . The virectively . The besN. tabacum cv. Xanthi nc and N. glutinosa   , regardlAlthough bioassays play a key role in several fields of plant virology, such as host range determination ,58,59, sN. tabacum cv. Xanthi nc and N. glutinosa)  . Howeverntensity ,73. The ncreases . TherefoThe numbers of local lesions produced by different leaves when inoculated with the same virus preparation deviate significantly from a normal distribution, and the standard errors of x values depend on their magnitude. Therefore, before fitting the models, data should be appropriately transformed . The auterred to .Models for the correlation of the local lesion number to the virus concentration were developed as our understanding of virus–plant interactions increased. The Bald 1937 model is based on the Poisson distribution and it is assumed that a single virus particle can cause infection. Moreover, it is assumed that the leaf surface of the local lesion host can be infected with the same probability. Therefore, all the susceptible regions or entry points of a leaf have the same probability of infection with regard to the distribution of particles inside the regions, which follows the Poisson distribution. This model has two parameters, including the maximum number of local lesions observed in the assay and an unknown parameter which may account for the relative infectivity of any virus sample . This mok in number, available outside a cell have a Poisson distribution with mean, α, proportional to concentration; (ii) the number of virus particles entering the cell with a fixed probability x of penetration has a binomial distribution with parameters k and x; and (iii) x has α, β distribution with parameters α and β and they arrive at the following model: Furumoto and Mickey ,53 also  by Bald  with a d by Bald  can be aThe Kleczkowski model  is basedGokhale and Bald  derived Bald et al. 1990 considered along the growth curve model a modified Poisson model resembling the Furumoto and Mickey model II. This modified Poisson model uses the Furumoto and Mickey model II to estimate C values for each observation, which in turn are used to estimate V and N. This model needs to be optimized. To this end, for each i, When the developments of the models are compared, they share or differ in a few key basic assumptions which could be summarized as follows: (a) infection can start from a single virus particle that was introduced into a susceptible region of the host tissue; (b) each epidermal cell is a susceptible region; (c) cells differ in the probability of penetration by a virus particle; (d) all susceptible regions are identical; (e) a minimum dose of virus particles is needed to start an infection in a susceptible region; (f) the susceptibility of the regions varies in such a way that the logarithms of minimal infective doses are normally distributed. The Bald 1936 model follows assumptions a, b, and d; the Furumoto and Mickey models are based on assumptions a, b, and c; and the Kleczkowski model is based on assumptions e and f. In contrast, in the growth curve model, there were no clear explicit physical assumptions (e and f). It is a mainly data-based model and has been used accordingly for modeling disease development or for describing bacterial growth curves. The modified Poisson model is a modified version of the Poisson model to which, depending on the slope of the infection–dilution curve, it could be adopted. It is worth mentioning that the general performance of the presented models for ToBRFV is in accordance with the previously published data for TMV to which the models have been originally established . This inN. glutinosa in comparison to N. tabacum cv. Xanthi nc. Based on the data presented in The application of a spike sample with a defined virus concentration enables the validation of the models in the prediction of the correct virus concentration in an unknown sample. Our results show that the host plant used in local lesion assays also has a significant effect on the accuracy of the models to determine correct virus concentrations in virus inocula. Since the underlying assumptions of these models about the susceptible regions are different, it is hard to infer which physical interpretation, such as susceptibility/tolerance of host or physical characteristics of the leaf surface, is the main reason for the higher accuracy of the model in Like other quantification methods such as qRT-PCR, for each new bioassay trial, a new dilution curve needs to be derived, because the slope of the dilution curve might not be constant among all individual experiments. As the average number of local lesions of each virus concentration in different trials might vary, this can affect the slope of the applied model. This observation was already made when the original models for TMV were developed .It should be mentioned that the minimum number of virus particles that could cause a local lesion is still unknown. However, all the models can predict the minimum load of the virus needed for the production of a lesion. However, the interpretation of the minimum load depends on the assumption of the models. In the modified Poisson model and Furumoto and Mickey models I and II, it corresponds to the concentration of a virus suspension in which one particle can infect one cell (one-particle-one-region assumption). In the case of the Kleczkowski model, it corresponds to the distribution of a minimal dose of virus for the production of a lesion, and the growth curve model has no physical interpretation.The inherent limitation of bioassay-based models is the need to observe at least one local lesion in any half-leaf unit to estimate the virus concentration. This means for any concentration that does not cause local lesions, models might underestimate the concentration as a negative control. This could be remedied by increasing the number of repetitions and decreasing the dilution factor from 10 or 5 to 2.N. glutinosa in this study, based on the Kleczkowski model, they were calculated as 0.00321 mg/mL and 8.37 × 10−6 mg/mL for items a and b, respectively. It should be mentioned that real-time RT-PCR could detect a lower concentration of the virus, and it is more sensitive than a bioassay; therefore, based on plant health regulations, RT-PCR and real-time RT-PCR are recommended for the detection of the virus.Therefore, in our experiment, we calculated two different virus concentrations: (a) the average of one lesion on all inoculated half-leaf units and (b) the observation of only one lesion in a total of 24 half-leaf units. In the case of ToBRFV on We showed the capability of the presented models based on bioassays in the estimation of the virus concentration in an unknown sample after fitting the models on serially diluted virus particles with a defined concentration . Thus, there is opportunity to quantify the virus concentration. In the case of disinfection treatments or any physical treatment (such as thermotherapy), the reduction in the virus titer can be measured, and subsequently, the efficacy of the treatment can be calculated. Based on our knowledge, it is the first report of the quantification of ToBRFV based on bioassay and it offers new insights through the assessment of viral load. In addition, the presented analysis and assessment of bioassay data could also be used in other fields of application where quantitative evaluation is required but chemicals/ingredients hamper RT-PCR, such as screening for antiviral agents, polymers, and virucides. For example, quantitative changes in virus titers during disinfection treatments on seeds and even on different surfaces in greenhouses can be determined. Thus, the efficiency of those treatments can be ascertained in detail, and weak points caused by the process or the user can be identified and used to develop new control strategies.N. benthamiana as a systemic host when they had 4–5 and 7–8 well-developed leaves. In both cases, the systemic leaves were collected 10–14 dpi and were kept at ∗70 °C until they were subjected to virus purification. As a second systemic host, N. clevelandii was used as a source of inoculum for bioassays. Systemically infected N. clevelandii leaves were inoculated when they had 7–8 well-developed leaves to obtain more infected plant material for downstream inoculation assays. Theses leaves were collected four weeks after inoculation of N. clevelandii, mixed and homogenized with mortar and pestle with the help of liquid nitrogen. The ground leaves were immediately stored at −70 °C until they were used as a source of inoculum in some serial dilution–infection experiments.ToBRFV isolate PV-1236 was obtained from DSMZ  and was propagated on N. clevelandii leaves, (ii) purified particles, and (iii) purified particles mixed with sap of a non-infected N. clevelandii. The latter, a so-called spike sample, contained a defined number of ToBRFV particles and was used to determine the accuracy of the models in calculating the virus concentration.In general, three types of inocula were used in bioassays: (i) infected N. tabacum cv. Xanthi nc and N. glutinosa [Nicotiana spp. plants were grown in pots filled with bedding substrate  at 25 °C, and a photoperiod of 16 h light and 8 h dark.Bioassays were comparatively carried out with two local lesion hosts recommended for tobamoviruses: lutinosa ,75. The lutinosa . The Nicw/v). To prepare spike samples, N. clevelandii leaves ground in the presence of distilled water in a 1:5 ratio and purified virus particles were added to the first dilution (1:5) to a final concentration of 0.4 mg/mL. The number of local lesions was recorded 5–7 dpi. This spike sample was used as a treatment in the same experiments with dilution series.The purified ToBRFV particles were serially diluted in distilled water with dilution factors of 5 or 10. Then, 100 µL of each dilution was inoculated on each half-leaf. Just before inoculation, Celite was added to all dilutions to a final concentration of 3%  containing 0.1% (w/v) Na-DIECA and 0.1% (v/v) thioglycolate with a Waring blender and squeezed through two layers of cheesecloth. Then, 150 mL chloroform was added to the filtrate and mixed on a stirrer for 10 min. The resulting emulsion was clarified by low-speed centrifugation for 20 min at 5000× g in a swinging bucket rotor. The supernatant was concentrated by ultracentrifugation for 90 min at 90,000× g with a fixed-angle rotor using steel beakers. The supernatant was discarded and the resulting pellets were resuspended in 5 mL of phosphate buffer (0.1 M pH = 8) containing 0.01 M ascorbic acid and centrifuged at 5000× g at 4 °C for 10 min. The supernatant was ultracentrifuged through 20 mL of a 30% sucrose cushion per polycarbonate bottle for 2.5 h at 90,000× g in a Beckman L7.55 ultracentrifuge and a Ti 45 titanium fixed-angle rotor. The sediments were resuspended in 2 mL of 0.1 M phosphate buffer per tube and homogenized with a glass potter. The resulting homogenate was centrifuged at 12,000× g for 15 min. The supernatant was centrifuged in CsCl solution (1.325 g/cm3) at 90,000× g overnight in a vertical rotor. The ToBRFV bands were removed with a syringe, diluted with two volumes of phosphate buffer (0.01 M pH = 8), and submitted to a second CsCl density gradient to obtain a pure virus suspension. The subtracted ToBRFV bands were mixed with 0.1 M phosphate buffer pH 7.0 and again centrifuged for 4 h in the fixed-angle rotor at 90,000× g. The sediment of purified virus particles was resuspended in 0.1 M phosphate buffer (pH = 7) containing 0.01 M Na-EDTA and kept at 4 °C for further usage.A total of 100 g of ToBRFV infected 260 nm 0.1% = 3.18 determined for TMV (Zaitlin 2000) was used for calculation of the virus concentration of the purified ToBRFV particles.The virus concentration in the virus suspension was measured via spectrophotometry with two different devices: BioPhotometer 6131  using a macro cuvette (1 mL volume) and -UV/VIS-Spectralphotometer NanoDrop™ One/OneC . Each sample was measured at least 5 times at 260 nm, and the mean value was used to calculate the virus concentration based on the Lambert–Beers equation. A specific extinction coefficient of EData transformation was applied using the equation abscissa . The Pytabscissa ,53, and abscissa  models wDue to uncertainty in the experiments, we assume that the standard deviation of errors in Y data is p < 0.05. Statistical analyses were performed with SPSS version 25 .The Kolmogorov–Smirnov test was used to check the normality of the data. Analysis of variance (ANOVA) was used to determine the statistical effect of plant species and leaf position in observed local lesions. Differences between means were assessed using the Tukey HSD test at To confirm the ToBRFV origin of symptoms observed on systemic and local lesion hosts, appropriate leaves were subjected to DAS-ELISA (double antibody sandwich ELISA). The serological tests were applied using the ToBRFV-specific antibody and conjugate RT-1236 following the supplier’s instructions . To perform RT-PCR, total RNA was extracted from leaves using the Spectrum™ Plant Total RNA Kit , and 1.5 µg of the isolated RNA was subjected to cDNA synthesis in a 20 µL reaction volume containing 100 pmol random hexamers, 1× transcriptase buffer, 2 mM dNTPs, 20 units of RNasin, and 100 units of Maxima H minus reverse transcriptase , following the thermal profile recommended by the manufacturer (Thermo Scientific™). The Phusion™ High-Fidelity DNA Polymerase kit (Thermo Scientific™) was used for a 20 µL PCR reaction containing 1 µL of synthesized cDNA as a template, and the primer pair of ToBRFV-1482-s (5′-TAATCAGCAAGTTTAGTTTG-3′)/ToBRFV-1677-as (5′-TCAGTCACTAATCTATCGTG-3′) following the supplier’s instructions. The primers were designed in this study with the help of Primer Express™ Software v3 . The RT-PCR products were sent for direct sequencing from both directions with PCR product-specific primers .


Dengue is a mosquito-borne viral infection, found in tropical and sub-tropical climates worldwide, mostly in urban and semi-urban areas. Countries like Pakistan receive heavy rains annually resulting in floods in urban cities due to poor drainage systems. Currently, different cities of Pakistan are at high risk of dengue outbreaks, as multiple dengue cases have been reported due to poor flood control and drainage systems. After heavy rain in urban areas, mosquitoes are provided with a favorable environment for their breeding and transmission through stagnant water due to poor maintenance of the drainage system. The history of the dengue virus in Pakistan shows that there is a closed relationship between dengue outbreaks and a rainfall. There is no specific treatment for dengue; however, the outbreak can be controlled through internet of medical things (IoMT). In this paper, we propose a novel privacy-preserved IoMT model to control dengue virus outbreaks by tracking dengue virus-infected patients based on bedding location extracted using call data record analysis (CDRA). Once the bedding location of the patient is identified, then the actual infected spot can be easily located by using geographic information system mapping. Once the targeted spots are identified, then it is very easy to eliminate the dengue by spraying the affected areas with the help of unmanned aerial vehicles (UAVs). The proposed model identifies the targeted spots up to 100%, based on the bedding location of the patient using CDRA. The history of the dengue virus in the human population is not known because the dengue virus is asymptomatic, and the diagnosis of such diseases is difficult . In 992 perature . Dengue perature ,8. At thperature . In counperature . As a reperature . Until tperature .Other factors are densely populated cities, unhygienic drinking water, and refugee camps, which enhance dengue virus spread in different regions of the country. Dengue virus was first set up in Pakistan by importing tires with eggs of the infected mosquitoes in them at the seaport of Karachi . In 1994This paper proposes a novel geographical position-based model to control dengue virus outbreaks by tracking and extracting the bedding location of dengue virus-infected patients through CDRA while keeping the privacy of the patient intact. After the extraction of bedding locations, these bedding locations are mapped or spotted on GIS mapping using IoMT-based cloud computing. Then an algorithm initiates to find the number or percentage of reported cases in these bedding location spots. This algorithm sets a threshold if the number of reported cases exceeds the threshold, then that location is marked red on the GIS map. Information about these red-marked locations is shared from the cloud to the concerning health department, and then the department is responsible for sending spray drones to red-marked locations.Health records in developing countries need to be digitalized. Hospitals record data of reportable diseases using electronic health records (EHR). Hospitals use different EHR systems; they need to have standard EHR systems installed to record and maintain cases of reported diseases. Public health informatics contribute their work in the local area, reporting cases using techniques that are not of standard form ,20,21. TSeveral factors of dengue outbreaks and their epidemiology have been explored in literature ,38. TherTherefore, dengue virus transmission and its relationship with climatic factors are not simple and need further research on the dynamics of dengue in the region of the population with high rates of dengue infections to understand dengue transmission globally. To manage infectious diseases such as, dengue spatial analysis using geographic information system (GIS) has gained its worth in many studies ,56,57. IIn Pakistan, dengue cases and their outbreaks put an enormous burden on the government annually ,76. The Applications of drones have continuously increased in recent years and remain successful in gaining researchers attention. The main reason that they remain the center of interest for researchers is their properties, such as delivery service, in which human direct contact is eliminated (non-human contact delivery). Similarly, other properties are surveillance and spray drones. ‘Drone’ is the commercial name of a UAV . A UAV is an aircraft capable of flying without a human pilot and is used to move products of different types from one place to another place. Basic and advanced are the two subcategories of UAV; the first category drones are monitored and controlled remotely by humans, while the second category drones are controlled and monitored by sensors and LiDAR. In the recent COVID-19 pandemic, it was observed that drone usage in the health department was widely increased. During the COVID-19 pandemic, government health departments used these drones for three major tasks: drone delivery service (for the delivery of vaccines and medicines), surveillance (to keep an eye on the people who are violating quarantine rules), and spray drones (used for spraying the infected area for disinfection).As researchers have concluded that the life of a COVID-19 virus is sufficiently long on different surfaces to remain infectious, it is very much important to disinfect the infected locations as well. For this purpose, spray drones were manufactured. Spray drones are special drones developed for spraying purposes to disinfect infected areas. These drones were manufactured during COVID-19 pandemic . SeveralTo locate dengue virus infected patients, an internet-of-medical-things (IoMT) based model is required to overcome the spread of dengue virus. The mentioned model can be achieved by using the history of a patient’s call record. This spread can be reduced by tracking and extracting the bedding location of the dengue virus infected patient through call data record analysis (CDRA). The privacy of the infected patient is preserved, as the information will be only available to the relevant and authorized personnel only.When the suspected person has symptoms of dengue virus, and he or she is tested positive for dengue virus, then he or she will fill the proposed performa, in which he or she must fill five locations addresses where the patient has visited or been for the last 7 days. These provided bedding locations will be extracted through the call data record (CDR). After the extraction of bedding locations, these locations will be spotted on the map through GIS mapping. Then, our proposed model will compare the number of cases in these mapped locations with the threshold set in our model in a radius of 500 m. Now, the identification of actual or infected locations will be achieved by comparing the number of cases with the threshold, and those extracted locations will be marked red on the map where the number of cases exceeds the threshold. The government authority will be informed about these red marked locations to send spray drones to the infected areas. Dengue virus outbreaks can be controlled or completely stopped by implementing our proposed novel model. The working mechanism of the proposed model is shown in the form of a flowchart as shown in To locate dengue-infected patients, a geographical position-based model is needed to control or minimize the spread of the dengue virus. To achieve the required model, the patient location can be tracked by using his or her call history and the base station from where the patient mobile receives and send signals. Once the patient location is tracked, the dengue virus spread can be controlled or minimized by spraying the infected area with the help of UAVs, isolating, and monitoring the infected dengue virus patient. If this infected patient is ignored at the initial stage, it will result in the spread of the dengue virus drastically. The CDRA algorithm will be used to locate or track the infected patients by monitoring their call history to control the spread of dengue virus. This algorithm can be easily implemented. By using CDRA, after the patient location is tracked, a spray team will be informed to spray the infected location using UAVs. Our proposed model can minimize or control dengue virus outbreaks. A flowchart of our proposed model and the CDRA  framework graphical abstract is shown in Algorithm 1 Proposed model.Require:CDR, present address, permanent address, work place address, visiting address, and 1:Perform CDRA to extract bedding location2:All suspect locations will be mapped on GIS’3:if Number of cases then > 4:    Spray the infected location5:else6:    Ignore7:end ifEnsure:Sprayed all infected locationsCDRA provides information about the caller and called users in the form of metadata. Metadata comprise a set of data that give detailed information about other data, such as how the cellular system is used by users in various ways. These metadata carry the following information: the location of the caller and called parties, the date and time of call, duration of call, IMEI information, cost of call per minute, and active call location  from the connected base transceiver station (BTS). The overall algorithm of the proposed model is given is given in Algorithm 1.In this section, based on the hypothesis, a case study is investigated to realize the main targets of the proposed model. In this way, the assessment of the desired results from the proposed model can be verified.When the suspected patient is tested positive with dengue virus, then the first step is to fill the proposed performa as shown in After the extraction of bedding locations, these bedding locations are mapped or spotted on GIS mapping using IoMT-based cloud computing. Then an algorithm initiates to find the number or percentage of reported cases in these bedding location spots. This algorithm sets a threshold of 20 reported cases per 500 m and is labeled as Let us assume that an infected patient, named Mr. A, fills the performa by providing the attributes such that his current address, permanent address, temporary address, workplace address, and visited address all are the same location as shown in In this case, let us suppose that the patient named Mr. B fills the performa by providing the attributes in the following manner such that his current address, permanent address, and workplace address are the same, while his temporary address and visiting address are the same as shown in In our third case, let us suppose that patient Mr. C fills the performa, and his attributes show that his current and permanent addresses are the same and his temporary and visited addresses are the same, while his workplace address is different as shown in The fourth case shows that the patient, Mr. D, fills the performa, and his attributes show that his permanent and workplace addresses are the same, and his current and temporary addresses are the same, while his visiting address is different as shown in In the 5th case, let us assume that for patient Mr. E, his performa attributes show that his permanent and current addresses are the same, while his workplace, visited, and temporary addresses are different as shown in In the literature, for the tracking and tracing of infected individuals, different models were proposed, but they were unable to track viral infection spread due to dengue. A comparison of our proposed model with existing tracking models is summarized in Dengue has traditionally been categorized as a neglected tropical disease not frequently associated with Pakistan. However, recent dengue activity in Pakistan demonstrates that areas of the country are at risk of experiencing outbreaks. Practitioners in areas with dengue mosquito vector species should be aware of the risk of imported dengue, which could result in an outbreak. Pakistan is one of the underdeveloped countries where there are no proper drainage systems and there is no proper system to keep these drains clear and clean for proper drainage. The government authorities have thus far failed to control dengue virus outbreaks because of improper management in the flood control department. The lack of proper sanitation has provided favorable conditions for the breeding and transmission of mosquitoes, resulting in outbreaks of diseases such as dengue and malaria. Since there is no specific treatment for dengue, this paper has proposed a novel geographical position-based model to control dengue virus outbreaks by tracking and extracting the bedding location of dengue virus-infected patients through CDRA, while keeping the privacy of the patient in tact. After the extraction of bedding locations, these bedding locations are mapped or spotted on GIS mapping using IoMT-based cloud computing. Then, an algorithm is initiated to find the number or percentage of reported cases in these bedding location spots. This algorithm set a threshold of 20 reported cases per 500 m and labeled as


Diseases, particularly those caused by viruses, are a major cause of bee colony losses. However, little is known about the prevalence of the pathogens, particularly virus prevalence, of the honey bee in Egypt, one of the most important countries for beekeeping and agricultural production in Africa. To address this shortfall, we determined the prevalence of ten widespread bee viruses in honey bee colonies in Egypt between two major season, winter and summer, in relation to colony infestation by varroa mites.Varroa destructor (varroa) mites. Honey bee worker samples were collected from 18 geographical regions across Egypt during two seasons: winter and summer of 2021. Three apiaries were chosen in each region, and a pooled sample of 150 worker bees was collected from five colonies in each apiary then screened by qPCR for 10 viral targets: acute bee paralysis virus (ABPV), black queen cell virus (BQCV), chronic bee paralysis virus (CBPV), deformed wing virus (DWV) genotypes A (DWV-A), B (DWV-B) and D (Egyptian bee virus), Israeli acute paralysis virus (IAPV), Kashmir bee virus (KBV), sacbrood virus (SBV), and slow bee paralysis virus (SBPV). Our results revealed that DWV-A was the most prevalent virus, followed by BQCV and ABPV; the DWV genotype now spreading across the world, DWV-B, was not detected. There was no difference in varroa infestation rates as well as virus prevalence between winter and summer. However, colonies infected with BQCV had a significantly higher varroa count (adjusted p < 0.05) in the winter season, indicating that there is a seasonal association between the intensity of infestation by varroa and the presence of this virus. We provide data on the current virus prevalence in Egypt, which could assist in the protection of Egypt’s beekeeping industry. Moreover, our study aids in the systematic assessment of the global honey bee virome by filling a knowledge gap about the prevalence of honey bee viruses in Egypt.Honey bees are essential for crop and wild plant pollination. However, many countries have reported high annual colony losses caused by multiple possible stressors. Diseases, particularly those caused by viruses, are a major cause of colony losses. However, little is known about the prevalence of honey bee pathogens, particularly virus prevalence, in Egyptian honey bees. To address this shortfall, we determined the prevalence of widespread bee viruses in honey bee colonies in Egypt—whether it is affected by geography, the season, or infestation with  Varroa destructor) mites, which are themselves widely recognised as a major cause of colony death and which can act synergistically with other biotic and abiotic stressors, leading to the collapse of host colonies [Honey bees play an important role in crop and wild plant pollination ,2. Howevcolonies . Additiocolonies .Varroa destructor virus-1), slow bee paralysis virus (SBPV), and sacbrood virus (SBV). Several of these can be transmitted by varroa mites, including ABPV, DWV-A, DWV-B, IAPV, KBV, and SBPV [Over twenty-four viruses have been identified in honey bees , most ofand SBPV . Additioand SBPV . It has and SBPV  and is aand SBPV . According to the FAO database, Egypt experienced a 50% decrease in the number of honey bee colonies between 2005 and 2016 , a decliThe purpose of this study was to investigate the prevalence of ten common bee viruses in honey bee colonies across Egypt and between two major season, winter and summer, in relation to colony infestation by varroa mites. Honey bees were collected from 54 apiaries in 18 governorates at 2 time points—during the winter (January–February) and during the summer (June–August) of 2021. A selection of 3 apiaries separated by at least 10 km were chosen in each governorate , with a We calculated the infestation rate by varroa mites of each sample. To do so, soap was mixed with warm water in a beaker with ca. 100 bees per sample retrieved from the −80 °C freezer. The beaker was shaken and stirred for 10 min to improve the efficiency of dislodging varroa mites from the sample. The sample was then flushed with a large volume of warm water to separate the bees from the mites before pouring the fluid through a sieve  with a mesh size of 3–4 mm to remove larger debris. To collect the mites, all of which should be washed through the first sieve, a second sieve (aperture 0.3 mm) was placed beneath the first. The mites that remained on the second sieve were counted, as were the washed bees in the sample, and the number of mites per 100 bees was calculated ,24.Quantitative PCR (qPCR) was used to detect the presence or absence of ten common honey bee RNA virus targets: DWV-A DWV-B, BQCV, Chronic bee paralysis virus (CBPV), ABPV, SBV, SBPV, KBV, IAPV, and DWV-E  using the primers listed in A selection of 10 frozen bees from each pooled sample of honey bee workers (150 bees from 5 hives) per apiary were taken for total RNA extraction, crushed in plastic RNase-free mesh extraction universal bags  with 500 µL of RLT-buffer per bee using an automated bee grinder, from which we recovered 200 µL of homogenate. This was mixed with 300 µL RLT-buffer containing 1% β-mercaptoethanol, then RNA was extracted from the homogenate using an RNeasy mini kit  following the manufacturer’s instructions in a QiaCube robot , as described in Tehel et al. . The cont-tests with Welch’s correction for unequal variances and Bonferroni correction for multiple testing . An adjusted alpha level of 0.05 was used for all statistical tests to define significance. All statistical analyses and data visualizations were performed in GraphPad Prism 7.0 . Normality of data was assessed by use of the Kolmogorov–Smirnov test and homogeneity of variance was determined with Levene’s test. The results were expressed as the number of varroa per 100 bees or the presence or absence of varroa mites, as well as viral presence/absence in each apiary per season. Chi-square tests were used to test for differences in proportions . The association between viral presence (yes/no) and varroa infestation (number of varroa per 100 bees) were compared using Student’s t-test, p > 0.05; Varroa mites were found in 87% of colonies, a proportion which was identical in winter and summer seasons in 2021  in the prevalence of DWV-A, BQCV, ABPV, and CBPV between seasons. DWV-A was the most prevalent virus among the ten honey bee viruses screened, followed by BQCV, ABPV, and CBPV. KBV and SBPV were detected only in the winter while IAPV and SBV were detected only in the summer . SurprisViral infection and co-occurrence in apiaries varied subtly between seasons  and locat-test, adjusted p < 0.05; To determine whether increased varroa infestation was associated with the presence of a virus, we compared the number of varroa mites per 100 bees in colonies either with or without each virus. We found that apiaries that were infected with BQCV in the winter season held significantly more varroa mites than those in which we did not detect this virus  [V. destructor [There are multiple genotypes of DWV, including the originally described and widely distributed genotype A , the rapvirus-1) ,32, the virus-1) ,33, and virus-1) . Either structor . In our structor  . The mastructor ,36,37,38V. destructor [Another intriguing finding was that DWV-B was not detected in any of our samples, despite the fact that DWV-A is seemingly being replaced by DWV-B, which is expanding its range worldwide ,32 and astructor , our intstructor , DWV-B rApis cerana in Korea, where its occurrence in adult bees varied across geographic regions [A. mellifera has also been detected in France [A. mellifera in Austria [A. cerana [The second most prevalent virus among Egyptian honey bees was BQCV, which was detected in 63% (in winter) to 69% (in summer) of tested apiaries, followed by ABPV , and CBPV  in 2021. BQCV has also been reported as a highly prevalent virus in  regions ,43. A hin France , China [n France , Germanyn France , and then France . In cont Austria , Denmark Austria , Jordan  Austria , and Bra Austria , as well. cerana . ABPV ha. cerana . It has,. cerana , Yugosla. cerana , France . cerana , and the. cerana  in the 1V. destructor; if it were to be vectored by varroa, it might speed up colony collapse by varroa mites vectoring BQCV to pupae and adults, where it can be highly virulent [Why we found the presence of BQCV in a colony to be statistically associated with the level of varroa infestation in only the winter season is not clear because others have not found an association between them . BQCV isvirulent . It is gvirulent . TherefoA. mellifera [The current study revealed multiple concurrent infections of honey bee viruses that varied subtly across location and season. Furthermore, some viruses found in bee colonies during the summer  were not found in those colonies during the winter, and vice versa . Our results are consistent with previous research that has also reported simultaneous multiple infections of honey bee viruses ,61,62 anellifera ,63,64. Tellifera . Additioellifera  could siThis is the first comprehensive study to determine the prevalence and distribution of ten common honey bee viral targets in Egyptian honey bee colonies. DWV-A and BQCV were widely distributed in Egyptian honey bee colonies in both winter and summer, 2021, whereas the DWV variant now spreading around the world, DWV-B, was not. Our data on current viral prevalence in honey bees could assist in the protection of Egypt’s beekeeping industry. Moreover, our study aids in the systematic assessment of the global honey bee virome by filling a knowledge gap about the prevalence of honey bee viruses in Egypt.


In nature, viral coinfection is as widespread as viral infection alone. Viral coinfections often cause altered viral pathogenicity, disrupted host defense, and mixed-up clinical symptoms, all of which result in more difficult diagnosis and treatment of a disease. There are three major virus–virus interactions in coinfection cases: viral interference, viral synergy, and viral noninterference. We analyzed virus–virus interactions in both aspects of viruses and hosts and elucidated their possible mechanisms. Finally, we summarized the protocol of viral coinfection studies and key points in the process of virus separation and purification. In nature, it is common for multiple pathogens  to infect the same host simultaneously or successively. This phenomenon is defined as coinfection . TypicalA virus–virus interaction can be observed in five patterns: interference, synergy, noninterference, dependence assistance, and host–parasite relation. The most common virus–virus interaction in coinfection is interference, in which one virus competes to suppress the replication of another . SARS-CoIn contrast to interference, coinfection with certain viruses may enhance the replication of other viruses , which wIf coinfection has no effect on virus replication, it is defined as non-interference ,12,13. NDependence assistance and host–parasite relations are two specific viral relationships. Viruses with an incomplete genome, such as adeno-associated virus, with defective interfering particles, cannot complicate a replication cycle by themselves; instead, they require the assistance of a “helper virus”, such as adeno virus, herpes virus, or another intact virus, in order to finish their life cycle. These represent dependence assistance in viral interactions ,15,16.A host–parasite relation exists between virophages and giant viruses . VirophaInterference, synergy, and noninterference interactions are commonly identified in clinical viral coinfection cases. In this review, we focus on these three viral interactions and summarize their outcomes, mechanisms, and relative studies.Interactions in viral coinfections are primarily caused by changes in virus replication cycles (virus factors) and the replication environment (host factors), as shown in The causes of viral interference can be divided into two categories: interferon-mediated and non-interferon-mediated .Interferon (IFN)-mediated innate immunity is the most common reason for viral interference ,19. In vIFN induces multiple interferon-stimulating genes (ISGs) and activates multiple innate immunity signaling pathways ,31,32,33Non-interferon-mediated viral interference, also known as intrinsic interference, is the resistance of viral-infected cells to subsequent viral infections. This is particularly noticeable in foot-and-mouth disease virus (FMDV) coinfection cases, in which the attenuated A24 Cruzeiro strain interferes with the proliferation of homologous and heterologous strains . AnotherThe intrinsic interference between unrelated viruses can be found in the case of Newcastle disease virus (NDV) coinfection. Rubella virus can induce an interference state in infected host cells to avoid infection of NDV . There iIn addition to the contest for host proteins, there are several other interference mediators, including defective interfering particles (DI particles) , RNA intVirus interference can occur at each step of the virus-replication process, including virus attachment ,76,77,78Drosophila melanogaster could be accomplished by regulating RNAi and reverse transcriptase activity [Viral interference is also often found in persistent infections. Unlike acute infections, in which virus particles are eventually cleared by the immune system or host, viruses stay in infected cells for a long time in persistent infections . Virusesactivity . FragmenThe causes of viral synergy can be divided into two categories: interferon-mediated and non-interferon-mediated  and reticuloendotheliosis virus (REV) increases the replication of both viruses in cells . FurtherThe effect of coinfection on virus replication cycles is more intuitive and effective. A study by Goto et al. found that hPIV2 infection enhances IAV replication  by promoNoninterference is usually found between viruses with different tissue tropisms. For example, influenza viruses mainly infect the upper respiratory tract and lower respiratory tract, and occasionally infect extrapulmonary tissues, such as the eyes and intestines . HPV infA host could be actively and continuously infected by multiple viruses without any obvious signs of disease. This is called viral accommodation. Viral accommodation is usually observed in arthropods  and shriThe outcomes of viral coinfection attributed to the host can be divided into two categories: effects on viral transmission and viral pathogenicity The application of multiplex reverse-transcription quantitative real-time PCR (MRT-qPCR) ,151,152,(ii)Application of digital droplet PCR (ddPCR) makes it possible to identify two highly similar viruses . This me(iii)The transmission electron microscopy detection method of a gold nanoparticle gene probe also has applications in coinfection detection . This me(iv)Fayyadh et al. used multicolor imaging with self-assembled quantum dot probes to image and successfully detect H1N1, H3N2, and H9N2 influenza viruses in coinfected cells . This me(v)Srisomwat et al. developed a point-of-care testing (POCT) device for HIV/HCV DNA detection . EnhanceAlthough the identification of co-infections has been improved, the ability to detect target pathogens remains limited. The application of a next-generation sequencing (NGS) platform has improved virus diagnosis and the discovery of new viruses. NGS does not require prior sequence information about the target genome and can detect most potential genomes in clinical samples ,158,159.Viral purification is extremely difficult in viral coinfection. In bacterial coinfection, different bacteria could be rapidly purified from a mixed culture by colony purification. On the other hand, multiple viruses cannot be easily purified directly from clinical samples. The isolation methods of viral coinfection mainly include CPE , organic(i)The purification of viruses by CPE is a mainstream method for virus isolation in coinfection, but it requires the selection of suitable cell lines, where one virus can produce obvious CPE while the other virus does not produce obvious CPE. The disadvantage of this method is whether or not some traditional virus isolation cell lines are sensitive to another virus, and coinfection may affect the formation of CPE. At present, it is feasible to separate snakehead retrovirus (SnRV) from grouper nervous necrosis virus (GNNV) by SGF-1 ; FMDV fr(ii)An endpoint dilution assay is used to isolate two viruses with a highly similar host range/orientation but different replication rates. However, the separation success rate is usually low. It needs subsequent molecular-level detection and multi-generation blind passages for verification. Beperet et al. successfully isolated two different subtypes of alphabaculoviruses from coinfection samples by an endpoint dilution assay . Dormito(iii)The Ab neutralization method is suitable for different serotype viruses or two viruses with a distant genetic relationship. This method has a high success rate, but it needs to be verified by subsequent multi-generation blind passages. For the coinfection of multiple serotypes of the same virus, the serotype is generally determined first, and then the 2-dimensional microneutralization test (2D-MNT) corresponding to the serotype is carried out. Mahajan used 2D-MNT to isolate and purify multiple serotype viruses from coinfection samples of FMDV .For coinfection, corresponding antibodies should be used, such as neutralizing PPRV in the coinfection of FMDV and PPRV , neutral(iv)The organic solvent treatment method has certain limitations. Whether an organic solvent can kill one virus without affecting another virus needs to be verified. The choice of organic solvent is crucial. At present, it is feasible to remove PPRV with an organic solvent in coinfection of FMDV and PPRV . The use(v)Hemadsorption is suitable for virus isolation from non-hemagglutinating viruses. The integrity of this method for virus isolation is uncertain and the virus needs to be transferred to susceptible cell lines for amplification. At present, it is feasible to remove PPRV in coinfections of FMDV and PPRV . Hemadso(vi)Acid/alkali treatment is suitable for the separation of one PH-sensitive virus and another non-PH-sensitive virus. However, due to the difference in PH sensitivity of the isolated virus and the misdetection of molecular detection methods, this method has some notable limitations. Acidic environments (PH < 6.6) can effectively inhibit AIV replication . The opt(vii)Reverse genetic system rescues viruses. Some viruses have a mature reverse genetics system. We can isolate the complete genome fragments of the virus from the positive samples and then obtain complete or defective viruses. The disadvantage of this method is that constructing the system necessitates a considerable workload, and it is not suitable for the separation of two related viruses.The successful isolation of viruses also depends on the cells used for virus purification. Sometimes, a single type of cell is not enough to isolate the virus ,175,176.Viral coinfection is common but complicated. Studies of coinfection and virus–virus interactions represent an emerging field in virology. Due to coinfections, one virus infection could impact the outcome of another virus. A faster viral coinfection detection and virus separation system should be established for further study. In the future, viral coinfection studies will improve diagnoses, the development of vaccines, and antiviral therapy. To make it easier to study coinfections, we have outlined some of the literature on viral coinfections so that individuals can better select particular viruses of interest .


Phytochemicals extracted from plant sources have potential remedial effects to cure a broad range of acute to severe illnesses and ailments. Quercetin is a flavonoid isolated from different dietary sources such as vegetables and fruits, exhibiting strong anti-inflammatory, anti-oxidative and non-toxic effects on the biological system. However, the direct uptake or administration of quercetin results in loss of functionality, poor activity, and reduced shelf-life of the bioactive component. In this regard, to improve the uptake, potential, and efficiency of natural components with prolonged storage in the host’s body after administration, numerous polymer drug delivery systems have been created. In the current study, three-dimensional (3D) porous  bio-polymeric foaming gelatin–alginate (GA) beads were fabricated for the entrapment of quercetin as therapeutic drug molecules—gelatin–alginate–quercetin (GAQ). The GAQ beads showed a significant uptake of quercetin molecules resulting in a reduction of reduced porosity up to 64% and pore size 63 µm with a controlled release profile in the PBS medium, showing ~80% release within 24 h. Subsequently, the GAQ beads showed remarkable antioxidant effects, and 95% anti-inflammatory activities along with remarkable in vitro cell culture growth and the observed proliferation of seeded fibroblast cells. Thus, we can conclude that the consistent release of quercetin showed non-toxic effects on normal cell lines and the bioactive surface of the GAQ beads enhances cell adhesion, proliferation, and differentiation more effectively than control GA polymeric beads and tissue culture plates (TCP). In summary, these findings show that these GAQ beads act as a biocompatible 3D construct with enormous potential in medicinal administration and tissue regeneration for accelerated healing. Natural components, including biopolymers and bioactive compounds isolated from different natural resources, such as plants, animals, and microbes, have been widely used for biomedical applications ,2. A widThe poor solubility and degradation of the Quercetin molecule during the local administration inside the host body reduce its therapeutic potential and efficiency . The druDifferent polymers have been studied and used as carriers for quercetin to enhance its therapeutic activity. Micro-emulsions of quercetin were prepared for the local administration of the drug molecule to study their effects under in vitro and in vivo animal systems to prevent UV-induced skin damage ,17. FahlPhysical entrapment of bioactive molecules or drugs into the biopolymeric matrices provides a biocompatible, bio-accessible, controlled drug-delivery system. The loading or entrapment of biomolecules in polymeric matrices is usually done by mixing, immersion, and physical adsorption . Three-dAlginate is a polysaccharide isolated from seaweed, commonly used as a biopolymer, and has a more comprehensive application. Alginate has good mechanical and foam stability but poor surface functionality for cellular adherence, which limits its applicability . In receCorrespondingly, gelatin is hydrolyzed from collagen protein, having a short peptide Arg–Gly–Asp (arginine–glycine–aspartate) sequence, which helps in cell attachment and proliferation . It is bTherefore, gelatin and alginate are used as a polymeric matrix for the entrapment of drugs and bioactive components to improve their shelf life and enhance activity and efficiency under in situ conditions ,36,37. PIn this study, we fabricated a Quercetin-loaded biopolymeric foam scaffold to protect the therapeutic compound during passage through the body as a carrier and mediator/activator of controlled release. Here, we used alginate–gelatin foaming used for the entrapment of the quercetin as a polysaccharide–protein porous matrix for the drug-delivery system and a tissue-engineered scaffold for better cell signaling. Subsequently, the combination of polysaccharide and protein molecules enhances the drug encapsulation and release efficiency. However, quercetin, as a bio-active molecule, protects the healthier cell from oxidative damage and enhances cell growth, and the synergistic effect of polymeric components with quercetin could enhance the biocompatibility and anti-inflammatory and anti-oxidative effects.Alginate (300 kDa), gelatin (bovine skin), phosphate-buffered saline (PBS), quercetin, calcium chloride, and acetic acid were obtained from Hi-Media, India Limited. The samples were prepared and characterized in PBS and double-distilled water.2 solution by the drop-extrusion method. A stable foam of alginate and gelatin polymeric blend was generated by blending at high speed in the mechanical stirrer and subsequently cross-linking in the CaCl2 solution by dropping through the syringe  and control beads (GA) were examined under an FESEM (field emission scanning electron microscope) by using standard protocols . The druEach sample of 50 mg beads was weighed, crushed, and dispersed in 100 mL of 1XPBS at a neutral pH. Dissolved samples were kept on a shaker at 200 rpm for 2 h. After complete dispersions, samples were filtered and examined at 266 nm by a UV spectrophotometer. The following formula determines the encapsulation efficiency.Further, the total immersion method was used to study the release of the quercetin profile from the surface of the gelatin–alginate bead. Each sample absorbance was recorded using a UV-Vis spectrophotometer, and the calibration curve of quercetin (50 µg/mL) was prepared for monitoring the drug release profile. The GAQ beads were immersed in 150 mL of 1X PBS and placed in a shaking water bath at 37 °C. After 12 h of incubation, 2 mL of the sample was taken out at a regular time interval of 30 min, and the absorbance was taken at 412 nm. After measuring the absorbance, the withdrawal sample was reimbursed into the main flask to make a final constant volume. The drug release percentage was measured by comparing it with the standard calibration curve until complete release occurred.The antioxidant activity of the fabricated polymeric beads was measured in vitro by the 2, 2-diphenyl-1-picrylhydrazyl (DPPH) assay method as described in the protocol . BrieflyAfterwards, the anti-inflammatory activity of the biogenic beads was estimated by the human red blood cell (HRBC) membrane sterilization method ,43. In b2 incubator to check the sterility before cell seeding. Incubated beads have a clear medium without any turbidity, indicating no contamination, and were selected for the next step of cell seeding. Subsequently, 10 µL of cell suspension (~1 × 103 cells) was seeded over the incubated beads matrix evenly and fresh cell culture nutrient medium was added to the well. Here, the TCP well containing only cells without any beads was used as a negative control, with GA beads as the positive control and GAQ beads of different concentrations as test samples. Further, the TCP-containing cells seeded with or without beads were incubated at 37 °C in a CO2 incubator and we measured the cell growth and viability by performing an MTT assay and checking the absorbance at a wavelength of 490 nm at the regulative time periods of 3, 7 and 14 incubation day by using a microplate reader. However, the cell growth and neo-ECM formation for the tissue regeneration were measured by histological analysis of the microscopic samples of the captured FESEM images by ImageJ software.L929 mouse fibroblast cells were used to determine the cytotoxic effect and the effect of the fabricated beads on cell proliferation by the SEM image analysis and MTT cell growth assay analysis . In brieLong standing foam constancy of the polymeric sample is an essential aspect of the intact 3D porous structure, which is simply formed by self-crosslinking of monomeric submits and generates stable foam. Alginate has suitable viscosity and foam stability due to its amphiphilic nature . HoweverSubsequently, quercetin-encapsulated foaming beads of average bead size 1.960 ± 46 mm were successfully fabricated from the natural polymeric gelatin–alginate without using surfactant . The intThe highly porous structure facilitates the cell invasion and diffusion of cells and nutrients for better tissue regeneration. FESEM images of fabricated beads at a higher magnification showed the interconnected pores with good porosity up to 98% (mean porosity 88.56 ± 2.03%) for significant cellular growth . In an e−1, 1641 cm−1, 1542 cm−1, and 664 cm−1 for NH and C–O stretching and bending, respectively. Besides that, CN stretching peaks between 1070 and 1240 cm−1, –CH2 stretching peak at 1455 cm−1, and C–H bending at 1395 cm−1 were observed. However, in the case of alginate, spectra peaks at 1032 cm−1, 1232 cm−1, 1638 cm−1, 2185 cm−1 and 3452 cm−1 indicate the glucuronic (G) and manuronic (M) acid functional groups, respectively. The peaks at 885 cm−1 and 817 cm−1 showed the β-glycosidic linkages between the G and M units of alginate after cross-linking. Similarly, the quercetin FTIR spectra indicate the presence of all essential functional groups’ peaks as reported in the literature [FTIR spectra of polymer, drug, and GA and GAQ beads showed the proper interactions between the bead constituents c. In theterature ,23,24. HThe polymeric beads showed 100% drug encapsulation on the polymeric matrix without any significant loss of drug (quercetin) occurring in the medium. The drug-loaded beads showed free radical stabilization properties for up to 60 min with no significant inflammatory response occurring for a long time. The anti-oxidative and non-immunogenic nature of all GAQ beads increased by increasing the concentration of quercetin. Subsequent studies showed that the release of quercetin from the surface of the GAQ beads has a cumulative release profile of quercetin . For theThe biopolymers of plant–animal origin are biocompatible and non-toxic sources widely used for food and clinical applications. Gelatin and alginate are dressing materials for wound healing, encapsulation, and control of the biological system’s release of biomolecules or drugs ,39,48. HQuercetin-loaded scaffolds using natural biogenic polymers and bioactive compounds were successfully synthesized without toxic cross-linking agents and surfactants. However, the previous studies on the foaming scaffold methodology were based on using toxic surfactants to stabilize the foam. Here, the natural polymer used for scaffold fabrication has structural resemblances with native tissue ECM for better cell growth and tissue regeneration. The quercetin-loaded beads (GAQ) act as an ideal scaffold enriched with natural flavonoid molecules, having potential anti-inflammatory, anti-oxidative and non-toxicity effects for better cell growth and protecting them from damage. Besides that, the concentration effect of quercetin loaded on the alginate–gelatin polymeric beads showed a significant difference to their biological activity. Therefore, based on the in vitro studies of the successful entrapment and controlled release of quercetin molecules through GA, the modified GAQ beads were evaluated as a novel drug delivery system that could be used to treat several acute to chronic infections with healthy tissue regeneration at the damaged site.


Herein, we present our findings of an early appearance of the Monkeypox virus in Prague, Czech Republic. A retrospective analysis of biological samples, carried out on the 28th of April, revealed a previously unrecognized case of Monkeypox virus (MPxV) infection. Subsequent data analysis confirmed that the virus strain belongs to the ongoing outbreak. Combined with clinical and epidemiological investigations, we extended the roots of the current outbreak at least back to 16th of April, 2022. Monkeypox is a zoonotic infectious disease, closely related to smallpox and other Orthopoxviruses, with a clinical presentation similar to smallpox . While eAccording to the surveillance summary released by European Centre for Disease Prevention and Control (ECDC) up to 2nd of August 2022, a total of 15,926 MPxV cases have been identified in 38 countries throughout the Europe ; moreoveHere, we present a detailed description of an early case, confirmed both clinically as well as by Whole-Genome Sequencing (WGS) data and analyses. This report illustrates one of the earliest well-documented cases of the current outbreak.In Europe, Monkeypox was recognized as an emerging threat in the first half of May 2022, when Monkeypox cases were reported in Portugal and the United Kingdom ,10; thesIn the Czech Republic, the first laboratory confirmed case was reported on 24th of May 2022 . The patSurprisingly, through standard epidemiological investigation, it turned out that this patient was in contact with a patient hospitalized in Prague who was displaying similar symptoms from the second half of April  see , which wFurthermore, we sequenced the genome of the sample using the Oxford Nanopore Technologies (ONT) GridION platform in order to either confirm or decline that it belongs to the current outbreak. Nevertheless, this task turned out to be non-trivial, especially considering that the former patient was dismissed roughly a month before the time of the analysis. Therefore, the amount and quality of biologically relevant material was limited.Firstly, we directly sequenced the original DNA sample . Despite quite deep sequencing, only a minor fraction of reads (up to ~0.03%) originated from the target  genome. Moreover, poor quality and comparatively short reads suggested the degradation and fragmentation of the viral genome within the originally isolated DNA sample. Therefore, the data were sufficient only for the confirmation of the presence of MPxV.In parallel, we applied an amplicon tiling strategy that could be compared to the COVID-19 sequencing protocol . HoweverFinally, we succeeded in the cultivation of the virus from the original swab sample, which allowed the isolation of a sufficient amount of viral DNA in the required quality for the ONT sequencing, i.e., high-molecular-weight DNA. As a result, we sequenced the whole genome of the virus with sufficient coverage (~350×).Nevertheless, it should be noted that passaging the viruses might result in their adaptation to such artificial environments. Such a behavior was described even for poxviruses, however, under very specific conditions . MoreovePhylogenetic analysis performed using the assembled genome of the sample confirmed its clear association with the current outbreak see . The genThe COVID-19 pandemic has represented an unprecedented crisis for modern mankind which has affected nearly all dimensions of society and our usual daily lives ,19,20. NWe identified and documented a new early case of Monkeypox virus that modifies the comprehension of the current outbreak and its timeline. Considering the epidemiological history of the patient, the first symptoms of the disease were manifested already on 24th of April 2022. Furthermore, he claimed to have had unprotected sexual contact with a male of unknown nationality, Caucasian type, on 16th of April in Lisbon, Portugal. This suggests that the virus has been circulating in Europe at least from the first half of April 2022 see .This study documented the possibility that before the Monkeypox outbreak was recognized, some early cases might still be awaiting the correct diagnosis and description. Therefore, we encourage for re-analysis of suspicious samples/cases where possible.This study highlighted the importance of the cooperation of clinicians and epidemiologists with scientific institutions not only for the timely discovery of emerging outbreaks, but most importantly for discovering their roots. While timely diagnostics might prevent the spread of an outbreak within a country, studying its roots might lead to a better understanding of its dynamics and mitigation.Based on combined evidence from clinical, epidemiological and molecular data, we report an early case of the Monkeypox virus from the Czech Republic. We evidenced that the virus responsible for the current outbreak was circulating within the European population before 16th of April 2022.