High-throughput sequencing (HTS) technologies completed by the bioinformatic analysis of the generated data are becoming an important detection technique for virus diagnostics. They have the potential to replace or complement the current PCR-based methods thanks to their improved inclusivity and analytical sensitivity, as well as their overall good repeatability and reproducibility. Cross-contamination is a well-known phenomenon in molecular diagnostics and corresponds to the exchange of genetic material between samples. Cross-contamination management was a key drawback during the development of PCR-based detection and is now adequately monitored in routine diagnostics. HTS technologies are facing similar difficulties due to their very high analytical sensitivity. As a single viral read could be detected in millions of sequencing reads, it is mandatory to fix a detection threshold that will be informed by estimated cross-contamination. Cross-contamination monitoring should therefore be a priority when detecting viruses by HTS technologies.We present Cont-ID, a bioinformatic tool designed to check for cross-contamination by analysing the relative abundance of virus sequencing reads identified in sequence metagenomic datasets and their duplication between samples. It can be applied when the samples in a sequencing batch have been processed in parallel in the laboratory and with at least one specific external control called Alien control. Using 273 real datasets, including 68 virus species from different hosts  and several library preparation protocols , we demonstrated that Cont-ID classifies with high accuracy (91%) viral species detection into (true) infection or (cross) contamination. This classification raises confidence in the detection and facilitates the downstream interpretation and confirmation of the results by prioritising the virus detections that should be confirmed.Cross-contamination between samples when detecting viruses using HTS (Illumina technology) can be monitored and highlighted by Cont-ID . Cont-ID is based on a flexible methodology relying on the output of bioinformatics analyses of the sequencing reads and considering the contamination pattern specific to each batch of samples. The Cont-ID method is adaptable so that each laboratory can optimise it before its validation and routine use.The online version contains supplementary material available at 10.1186/s12915-023-01708-w. The advent of high-throughput sequencing (HTS) technologies coupled with the development of powerful bioinformatics approaches has improved our ability to detect viruses in a non-targeted way from any sample collected from diverse sources. Noteworthy, detecting viruses by HTS technologies relies on many steps in the laboratory: sampling, transport and storage, nucleic acid extraction, library preparation and sequencing . CompareThe analytical sensitivity, e.g. the ability to detect viral species at very low concentration in a sample, has been demonstrated to be similar to or even better than RT-PCR for animals  or plantThe broader application of HTS technologies for virus detection, with the simultaneous analysis of tens to hundreds of samples, is raising a significant challenge that needs to be addressed: the management of cross-contamination between samples. Scientists and diagnosticians already faced such challenges decades ago during the development of PCR-based techniques for detecting plants , 12 or aThe consequences of erroneous detection due to cross-contamination between samples can be catastrophic, as described for tuberculosis prior to HTS  but alsoSo, even if the cross-contamination issue of HTS, and particularly with the widely adopted Illumina technology, is long known and discussed in the scientific community, proper methodologies and dedicated algorithms to address it are still missing. Until now, the burden of detection confirmation relied on the virologist’s expertise and the use of laboratory tests to independently confirm the presence of the virus in the sample, which is a fastidious, costly, and time-consuming task. To minimise the confirmation burden, arbitrary thresholds (like 5 or 10 reads) , 18 haveTo handle cross-contamination, several laboratory protocol improvements have been implemented over time: laboratory or reagent decontamination, alternate dual indexes, inter-run washing , 21 or, Many generalist bioinformatic tools, such as Kraken  or BLASTAccording to EDAM ontology , tools thttps://github.com/johrollin/viral_contamination.To solve this issue, we present Cont-ID, a method designed to check sample cross-contamination for viruses previously identified in metagenomic datasets. It relies on a simple requirement: every sample in a sequencing batch should have been processed at the same time and followed the same steps in the laboratory with at least one alien control as external control. Cont-ID uses a voting system to classify every species prediction on each sample of the sequencing batch into (true) infection (true presence of the virus in the sample) or (cross) contamination. This tool will help the virologist to distinguish virus presence and virus cross-contamination in HTS data generated by Illumina technology, improving the reliability of viral detection and the efficiency of downstream confirmation and characterisation analyses. It can also help to improve feedback on upstream steps that might be linked to cross-contamination events. Cont-ID is an open-source python (v3)-based script method freely available here: In viral metagenomics, detecting multiple viral species in the same sample is frequent, and a virus species can be seen with different confidence levels in several samples of the same sequencing batch. Therefore, Cont-ID aims to determine whether a given detected virus in a sample is likely to be a contaminant or not by comparing it to the results from the other samples of the same sequencing batch, e.g. samples processed in parallel and following the same laboratory steps.Cont-ID does not require any development or maintenance of database as it only relies on data generated by usual bioinformatics tools for Illumina dataset analyses and, most specifically, two elements: (i) the normalised abundance estimation  and (ii) the number of identical reads among pairs of samples (deduplication ratio). These input metrics are easy to obtain as the abundance estimation can be calculated by using any mapping tool like BWA  or a reaComputing the two elements mentioned above into three different metrics for the alien virus and each detected virus, Cont-ID can predict through three rules if a given viral species detection is likely a cross-contaminant or not in the sequencing batch, as described in Fig. The three rules classify as contamination or infection each element according to the pattern of reads number observed among the samples and the alien control for the alien virus and the considered viral species. Rules one and two both use the  reads abundance estimation, while rule three uses the assessment of unique  reads. Rules are calculated after normalising the number of reads per sample and are described more precisely in Fig. The first rule uses the mapping ratio of each virus in each sample (corresponding to an element): the number of reads of each element is divided by the maximum number of reads of the corresponding virus in one of the samples. This first rule compares this mapping ratio for the element with the control 1 metrics calculated for the alien virus and corresponds to the average number of reads mapped on the alien virus in the samples for which the alien is a cross-contaminant, plus three times the standard deviation of this average. The second rule relies on the number of reads of the element in the sample. The rule compares it with control 2, corresponding to the number of alien reads identified in the alien control. The third rule is based on each element’s deduplication ratio, which is compared with control 3. The average deduplication ratio of the alien virus reads between each tested sample and the alien control.We aimed to find the most reliable formula for threshold calculation on each rule while allowing a part of adaptability according to the biological system used. As the system variability can come from the laboratory using HTS, the host and type of sample , the type of virus (integrated or non) or the extraction protocol used , each rule includes a third number  that is called adaptability metrics see Fig. . The X wDefault values of the three adaptability metrics have been provided in this publication after their optimisation on the banana datasets and their evaluation of other datasets. Nevertheless, users can independently modify them during the evaluation or validation of Cont-ID applied to their datasets. A careful evaluation of the adaptability metrics by the user is recommended to evaluate their impact on the diagnostic performance of the test. In addition, several sets of adaptability metrics can be run in parallel for further improvements in diagnostics performance. The value given to the adaptability metrics and controls resulting is always recorded in an additional log file  concentration of the alien virus, so reads from that viral species are more prone to be detected in other samples when cross-contamination occurs from the alien control to the other samples. Similarly, if another virus is found in the alien control sample, that is also an indication of potential contamination . The alien control is bioinformatically processed exactly as the samples of interest to generate the alien metrics for each sample (in a separate tabulated file). In the absence of external alien control, it is still possible to analyse sequencing batches if they include samples from different host species and some detected viruses, preferably at high abundance, are known to infect only some of the host species. In such a case, the alien file should be filled with the selected virus as if it were an alien . Nevertheless, the threshold set-up and the results will be less accurate and include fewer samples (the samples corresponding to species that can be hosts for the virus could not be considered). In addition, a high degree of confidence is needed regarding the actual infection of the sample selected as alien control by the virus selected as an alien virus. Cont-ID always requires at least one (cross) contamination in the alien file to be reported; otherwise, the threshold calculation will fail; in that case, the tool will state it.The second application condition is related to the processing of the samples and the alien control. The alien control and all the other samples in a given batch should have been processed together in parallel for all the laboratory steps  and bioinformatics . This is a good diagnostic practice, but it is even more important here as the goal is to observe cross-contamination levels. The assumption is that the level seen with the alien represents what could have happened in samples of interest. Therefore, this assumption depends on processing all samples and control in parallel.The third condition is that, once the user has fixed the adaptability metrics, the analysis should be carried out batch per batch. The calculation of sample and alien metrics is dynamically done for each batch as cross-contamination patterns can strongly vary between batches, as recently shown for banana samples .Musa genus)  to correct [relax]) for three of them .There is, therefore, a slight improvement with relaxed mapping parameters, and we set these parameters by default to generate the input files. Indeed, with the relaxed parameters, the number of reads for each element  increases along the rise of the number of elements in the batch. This means that we change the rule’s value of the threshold see Fig. , in a waAs we did not know the divergence of the virus genomes between different samples and the reference genomes, it seemed more logical to use relaxed mapping parameters by default. According to the virus system the user is working on and the ICTV demarcation criteria that go with it, these parameters should or could be adapted.To highlight the potential impact of the virus biology on the results of Cont-ID, the analysis of banana batches was split between integrated and non-integrated viruses. Indeed, several species of BSV are integrated into its host genome, which complicates the reliable detection of BSV infection from sequencing datasets. Consequently, it has been recommended to confirm any detection of BSV reads by an independent PCR test combined with immunocapture of viral particles .Table Musa genome, were excluded from the calculation of performance criteria for the banana datasets. BSCAV was also excluded (despite not being integrated) because of its similarity with other BSV species.The global accuracy is lower for BSV species (79–92%) compared to the other viruses (88–100%), even if the maximum accuracy obtained with batch C (92%) was high. In addition, the proportion of inconclusive results should also be considered, and this proportion was much higher for BSV (23–45%) than for the other viruses (4–16%). So, the overall performance of Cont-ID is lower when applied to BSV and did not solve the issues of appropriate detection in sequencing data of viral infection from viruses integrated into the plant genome. Consequently, BSGFV, BSIMV, BSMYV and BSOLV, which correspond to different but closely related species of Banana streak virus (BSV) integrated into the The performance of Cont-ID using the two cases was further evaluated while diversifying the hosts  and the sequencing protocols .Table For most of the datasets, case 1 performed worse than case 2, probably due to the design of the case metrics  is available in Additional file Despite significant efforts to limit cross-contamination , this still represents a concern and the appropriate distinction between low-level infection, and cross-sample contamination is crucial for the large-scale development of HTS technologies as a diagnostic test. Furthermore, it should be adequately managed because identifying and monitoring the cross-contaminations improves the detection results’ reliability. In other words, it can help to find the source of contamination in the laboratory, take appropriate measures to minimise it, and raise confidence in the detected viruses.This publication improved a preliminary work on determining an adaptative contamination threshold for the detection of plant viruses  which usCont-ID discriminated virus infection and cross-contamination between samples within a sequencing batch with a global accuracy of 91% (median = 95%) on the diverse range of datasets included in its evaluation. The diversity of situations included viral species belonging to diverse viral families with cellular hosts belonging to plant or animal kingdoms and three different library preparation protocols. Importantly, the default values of adaptability metrics determined from banana dataset predicted cross-contamination with high accuracy  and remain high even on human datasets (94%). Noteworthy, these additional datasets were carefully selected to check that they fit the Cont-ID requirements . To further help the user in the analysis, we provide the detailed vote prediction in the result file  read is contained in another (larger) read; having that option active or not will significantly impact the deduplication ratio. For example, the inclusion of PCR amplification step in the library preparation protocol, the very high abundance of viral genomes in the sample or the low complexity of the library can impact this deduplication ratio and should be taken into account when setting up the parameters for Cont-ID application.As shown in the results, mismatch parameters are very impactful for the mapping. Considerations like ICTV demarcation criteria or what parameters the biologist would use to reconstruct the whole viral genome are helpful in deciding the ones to use for Cont-ID input. In that regard, testing and expertise in bioinformatics analysis are heavily beneficial. Here, the 20% mismatch parameter performed well; it might be different in other datasets  configurations or when working with databases containing many reference genomes. Indeed, independently of the mismatch parameters used, using more genome references for each expected species could also improve the ability to detect sequences from distant isolates by better covering the genetic diversity of the virus.The biology of the virus should also be considered, as shown by the results obtained with viruses with functionally integrated genomes in the host, like BSV species. Our conclusion is that they should be considered independently from the non-integrated viruses. It was challenging to extract a reliable metric for BSV as the differentiation between reads from integrated genomes and reads from viral particles is impossible. Indeed, the biology of viruses integrated into the host genome differs from non-integrated viruses, as viral genome transcription can happen without viral particle production. We have not tested our method on species with different biological behaviour like viroids or phages. But optimisation of the adaptative metrics might likely be required in order to use Cont-ID with high accuracy. Viroid genomes are generally smaller than viruses, while the phage genomes tend to be much larger and have specific biological features. For example, a different level of identical reads and abundance  could be obtained between the different scales of genome size.For these reasons, Cont-ID allows the evaluation of other values for adaptability metrics  by each user to adapt the tool and optimise its diagnostic performance depending on the biological matrix, the protocol and the purpose of the test. Independently, the user can also adapt the metrics to reach the appropriate balance between FN and FP by deciding if, for the purpose of the test and the available resource for confirming detection, it is preferable to be overpredicting contamination to be confident that all the virus detection remaining are true infection or the opposite (overpredicting infection to be sure not to miss any).In our tests, the analysis of the wrong predictions showed that none of the proposed rules  allowed us to reach satisfactory accuracy with a proper balance between FN and FP -PCR can also be wrongly positive if the contamination occurred at an early stage and the (RT)-PCR was carried out on the same nucleic acids extract). Other information about the virus-plant interaction should be considered, like virus-species-cultivar compatibility or geographical virus distribution (see investigation on unexpected viruses ).Cont-ID also presents some limitations that need to be discussed. First, the number of identical reads estimation comes from the deduplication procedure, which is an approximation, and that can be a problem because it can consider the non-specific reads  as identical to the probable source of contamination by mistake. Indeed, this can be the case if, for example, two samples are infected by the same virus isolate at very different concentrations. The presence of duplicated reads might suggest contamination instead of a low-level infection. The risk of such an extreme situation is limited using two other rules, although interpreting the data will require good expertise in virus genomic variability and detailed information on the sample origins and virus prevalence and diversity. External source of contamination  are not considered by Cont-ID and therefore can also be source of error if they involve the same virus species as the one of interest.Also, identification up to species subtype (like expected for Influenza A virus) was not considered during the testing phase. When considered relevant, such subspecies distinction could be used with care, ensuring the unequivocal assignation of the reads to its corresponding subspecies. In other words, the genomes of the subspecies need to be different enough and the mapping parameters adapted to avoid unspecific mapping of reads between subtypes. In such cases, thresholds used should be monitored carefully to unsure the integrity and reliability of the analysis. In addition, the duplication metric assumes that contamination (if any) comes from the sample with the highest number of reads. This theory seems logical since the more reads in a sample, the higher the probability of detecting a few reads from it contaminating other samples . Nevertheless, it can create a bias when a virus is highly abundant in two (or more) samples and detected with a low frequency in others. In that case, it is difficult to determine the true origin of cross-contamination. Such a case could be a fundamental limit of our current method. If several samples with a very high abundance of reads are present in a batch, as developed here, Cont-ID should be applied as many times as the number of highly abundant samples. Ideally, Cont-ID should include the read duplication comparison of each sample to all other samples for a virus, but this can raise additional issues , and, at this stage, it was not implemented.We must also keep in mind that the relative quantity of genetic material between samples might change because the biologist normalises the quantity of DNA/RNA at two steps of the process: before starting library preparation and during the pooling of the prepared libraries, meaning that the differential in genomic material concentration  is resettled. If cross-contamination happens before that step, it can lower Cont-ID prediction accuracy. This bias in the estimation of abundance is another limitation of our method.Using an  control helps to know the expected level of contamination but is also impacted by the limit of detection inherent to the standard bioinformatic procedures. Indeed, working with very few reads for some viruses makes some analyses impossible when below their detection limit. For example, the calculation of the duplication rate below a minimal number of reads  of a virus did not make sense. The limit of calculation of the input metrics is another limitation of Cont-ID.Cont-ID accuracy was high, but additional improvements can probably be explored, for example, by exploiting the ability of other metrics generated during bio-informatic analyses  to help detect contamination. In fact, some of these metrics with several thresholds were tested for Cont-ID before selecting the three rules described in Fig. Nevertheless, adding more metrics will also complexify the decision system. If more metrics are considered for cross-contamination prediction, other implementations  might be envisioned to replace the current voting system. On the other hand, the detection in the alien control of sequencing reads of other viruses detected in the tested samples is also the consequence of contamination from one of the tested samples toward the alien control. This information is not used now but could also be considered for future improvements as it requires less complex modifications to implement. In addition, it might allow refinement of Cont-ID, potentially introducing an adaptation of threshold per virus instead of a single threshold for all samples from the sequencing batch. The idea is that two viruses present in the same batch may have different relative abundance behaviour in the samples, so setting up a limit that can adapt for each virus should improve the tool’s ability to distinguish real infection from cross-contamination. Finally, working with the combination of all/some viruses profile instead of each individually for contamination check (similarly to what is used in metabarcoding of bacteria) can also be considered. Indeed, when a sample contaminates another, it is expected that all the viruses (highly frequent) from the contaminating sample can be found in the contaminated samples. Monitoring the virus detection profile of samples can provide additional information for cross-contamination (and ease the quest for contamination origin). Even if there is still improvement to be made, Cont-ID has already delivered an excellent ability to consider the level of contamination genuinely present in a batch.In conclusion, detection of cross-contamination is complex; in the age of sequencing, the issue of cross-contamination across samples is increasingly important; therefore, Cont-ID will facilitate the interpretation of results by the virologist/diagnostician and reduces the confirmation burden. We demonstrated that simple metrics like relative abundance estimation and redundancies of genetic material (reads duplicates) could help monitor contamination occurring in the laboratory. The method accurately distinguished cross-contamination from infection in very diverse HTS viral datasets generated by short reads Illumina technology. Our standard parameters allowed very good accuracy (median = 95%); in addition, Cont-ID has several levels of flexibility and can be adapted by each user to take into account the specificities of the detection test . We believe this is the first significant step toward increasing the monitoring and management of sample cross-contamination when using HTS technologies for virus detection.Project name: Cont-ID.https://github.com/johrollin/Cont_IDProject home page: Operating system(s): Platform independent.Programming language: Python (v3.7).Other requirements: pandas; NumPy.License: GNU GPL-3.0Any restrictions to use by non-academics: none.Additional file 1: Table S1. Log from ContID. Table S2. Summary of metric. Table S3. Pre-compute summary. Table S4. Compute test for target sample. Table S5. compute test for alien. Table S6. Manual prediction (no BSV and > 5reads).Additional file 2: Dataset A. Index for dataset A. Dataset B. Index for dataset B. Dataset C. Index for dataset C. Dataset D. Index for dataset D. Dataset E. Index for dataset E. Dataset F. Index for dataset F. Dataset G. Index for dataset G. Dataset H. Index for dataset H. Dataset I. Index for dataset I. Dataset J. Index for dataset J.Additional file 3: Table S1. Number of vote for mapping. Table S2. Input control batch C. Table S3. Input sample batch C. Table S4. Result sample batch C. Table S5. Input BSV batch C. Table S6. Result BSV batch C. Table S7. Analysis sample batch C. Table S8. Analysis BSV batch C. Table S9. sRNA predictions. Table S10. Confusion matrix.


Pork is one of the most important types of sources of protein in the human diet. Swine diseases cause major economic losses to the global pig industry. Early diagnosis and prevention of diseases are fundamental factors for maintaining the health of animals on pig farms. The present article is the first report on the successful isolation of the swinepox virus in Russia. The results of a laboratory experiment on animals revealed a low pathogenicity of the Russian isolate for pigs. We suggest that this isolate can be used as a viral vector for the development of vaccines for animals. In addition, we have demonstrated for the first time that nasal and oral swabs can be used for PCR diagnosis of the disease and for swinepox virus isolation.Suipoxvirus genus of the Poxviridae family and is an etiologic agent of a worldwide disease specific for domestic and wild pigs. SWPV outbreaks are sporadically recorded in different regions of Russia. In 2013, an outbreak of the disease causing skin lesions was registered on a pig farm in Russia. The presence of SWPV in the scab samples was assessed by in-house real-time PCR, reference PCR amplification, and nucleotide sequencing of the viral late transcription factor-3 (VLTF-3) gene and was then confirmed by virus isolation. Thus, the in-house real-time PCR proposed in this study could serve as a useful tool for the rapid specific detection of the swinepox virus. In the study, it has been demonstrated for the first time that nasal and oral swabs can be used for PCR diagnosis of the disease and for swinepox virus isolation. Phylogenetic analysis revealed that the isolated virus was closely related to SWPV isolates registered in Germany, USA, and Brazil, and slightly differed from the Indian isolates. During experimental infection of pigs, a low pathogenicity of the Russian isolate was observed. Our data provides the first report on the isolation and characterization of swinepox virus in Russia.Swinepox virus (SWPV) is the only member of the  Haematopinus suis) and domestic flies (Musca domestica) serve as the mechanical vectors of infection. Infection in sows can be asymptomatic but can often lead to vertical transmission (mother-to-child), resulting in miscarriages and stillbirths [Swinepox is a contagious general infection of pigs, characterized by the appearance of pox lesions on the skin of animals similar to smallpox lesions in humans . At varillbirths ,7,10.Suipoxvirus of the Poxviridae family. This virus contains a linear double-stranded DNA genome with a length of 146 kbp and is the etiological agent of eruptive dermatitis in pigs. Viral DNA contains around 150 putative genes [The causative agent of swinepox is the SWPV virus of the genus ve genes . The penSwine pox can be diagnosed by the appearance of typical clinical signs and lesions such as papules and pustules on the abdominal skin. Several molecular methods have also been developed to detect swine pox ,18. ReceOutbreaks of SWPV disease are recorded sporadically in various regions of Russia. Since 2007, the virus has been detected in animals from 7 farms in four regions of Russia  [The present article is the first report on the successful isolation of the swinepox virus in Russia. In this study, we also present the phylogenetic analysis of the virus that caused swinepox in the Belgorod region of the Russian Federation. It has been demonstrated for the first time that nasal and oral swabs can be used for PCR diagnosis of the disease and for virus isolation.In 2013, in the Belgorod region of Russia, on the territory of a pig-breeding complex, a disease associated with skin lesions was revealed among pigs. Swine pox was preliminarily diagnosed by clinical signs. Scab-like clinical specimens collected from the infected animals were transported to the Federal Research Center for Virology and Microbiology (FRCVM) for virus isolation and molecular detection of SWPV. The skin samples were stored at −20 °C.2. The complete medium was changed every 24 h for 4–6 days until the formation of a monolayer of cells.Porcine kidney (PK-15) cells  were obtained from the American Type Culture Collection . The primary cell culture of porcine testicles was generated as described in . Large wFor virus isolation, 70–80% confluent monolayers of cells (PK-15 and a primary cell culture of porcine testicles) in a T25 flask were inoculated with 1 mL of a clarified 10% skin scab suspension. After 60 min of adsorption, the viral material was removed, and 6 mL of fresh MEM was added. Cells were incubated for 7 days and observed daily under a microscope for the presence of a cytopathic effect (CPE). After incubation, the cells were frozen and thawed three times. The resulting clarified cell suspension was used for further passage of the virus and/or titration of virus. Five blind passages were performed in the cell culture to isolate the virus from the clinical samples.To adapt the virus to the PK-15 cells, the 5th blind passage of the infected primary culture of porcine testes was used. The 70–80% confluent monolayers of the PK-15 cells in a T25 flask were inoculated with 1 mL of infected primary cell suspension. After 60 min of adsorption, viral material was removed and 5 mL of fresh MEM containing 2% fetal bovine serum was added. Cells were incubated for 7 days and observed daily under a microscope for the presence of CPE.The virus was titrated by visualization of the CPE in the primary cell culture of porcine testicles. Titers were expressed as the tissue culture median infectious dose (TCID50), according to the Reed–Muench method .DNA was isolated from clinical specimens (skin scab) and from an infected porcine testicular cell culture using the DNeasy blood and tissue kit  according to the manufacturer’s instructions. In-house real-time PCR was performed using a qPCRmix-HS kit  with primers specific to the SPV032 gene . Cycling conditions were initial denaturation at 95 °C for 120 s, followed by 40 cycles of denaturation at 95 °C for 10 s, primer annealing, and finally extension at 60 °C for 20 s. PCR amplification of the viral late transcription factor-3 (VLTF-3) gene was performed as described by Medaglia et al. . PCR wasPCR amplification of the SPV119, SVP120, and SPV133 genes was conducted as previously described by Riyesh et al. [PCR products were analyzed by electrophoresis in 2% agarose gel in TAE buffer.PCR products were purified using a Cleanup Standard kit , and DNA was sequenced by the Sanger method on a 3130xl Genetic Analyzer , and then further analyzed using Sequence Analyzer software . The resulting viral genes were then aligned in MEGA-X using the ClustalW algorithm . Phyloge6 TCID50 per piglet (0.25 × 106 TCID50 per site). Pigs from group 3 were not infected and were kept as controls in the challenge experiment. A total of 15 large white pigs, 3 months old, and free from specific pathogens, were randomly divided into three groups (n = 5 per group). Each group was housed in the isolated rooms of the FRCVM animal facility for an acclimatization period of one week prior to infection as well as throughout the experiment (30 dpi). Pigs in group 1 were infected subcutaneously in 4 different areas of the neck and ears with a clarified 10% suspension of the skin crust, 1 mL per pig (0.25 mL per site). The pigs in group 2 were infected subcutaneously with the SWPV isolate adapted to PK-15 cells, 1 × 10After infection, all animals were examined daily for skin lesions and general clinical signs of SWP, and their rectal temperatures were also recorded. Clinical assessment of SPWV-infected animals was performed in 8 different categories  . Blood sIn order to isolate the virus, porcine kidney cells (PK-15) and a primary culture of pig testicles were inoculated with 1 mL of clarified 10% skin scab suspension. Unfortunately, it was not possible to isolate the virus from the clinical samples by culturing them on PK-15 cells, as CPEs were not observed in these cells during five consecutive blind passages. At the same time, the virus was successfully isolated using a primary culture of porcine testicular cells. During the first two passages, no visible changes in the cell culture were observed, and at the third blind passage, CPEs were characterized by the rounding and detachment of cells in certain sites of the cell monolayer. At the fifth blind passage, 3–4 days after infection (dpi), CPEs were observed, characterized by the massive rounding of cells, their detachment, and death, which is indicative an efficient viral replication in these cells. At the fifth passage, the virus accumulation was 6.00 ± 0.25 lg TCID50/mL. To adapt the SWPV isolate to laboratory cell cultures, the virus suspension from 5th passage was used to infect the PK-15 cells. Then, the virus was cultured in the PK-15 cell line for another 3–5 blind passages. Typical SWPV-induced CPEs  were observed in the PK-15 cell at 6–7 dpi. Infectious activity was estimated at 6.5 ± 0.25 TCID50/mL, indicating effective virus replication in this cell line. The use of the continuous cell line PK-15 was deemed to be more convenient for the accumulation of large volumes of the virus, the production of recombinant SWPV strains, and the expression of foreign proteins in the case of using SWPV as a viral vector.Two PCR assays were used to identify and confirm SWPV. First, an in-house real-time PCR was performed to amplify the SPV032 gene encoding a PKR inhibitor. All skin scab samples assessed by real-time PCR were SWPV-positive. To confirm these results, the viral late transcription factor-3 (VLTF-3) gene was amplified as described previously . PCR ampFor phylogenetic analysis of the Russian SWPV isolate, the sequences of the VLTF-3, SPV119, SVP120, and SPV133 genes were used. Amplified DNA fragments containing the indicated genes of the Russian SWPV isolate Alekseevskii were sequenced. The sequences of the VLTF-3, SPV119, SVP120, and SPV133 genes were deposited in GenBank under the accession numbers MW892733, MF045845.1, MF045846.1, and MF045847.1, respectively. Then, the gene sequences were aligned with the other known poxviruses sequences from GenBank. Based on the sequences of the VLTF-3 gene, a phylogenetic tree was constructed using the maximum likelihood method . As showPhylogenetic analysis based on VLTF-3 gene sequences revealed that the Russian isolate was grouped with SWPV isolates from the USA, Brazil, and Germany. Based on the identity matrix generated using the SDT 1.2 software, the Alekseevskii isolate showed a 100% identity with isolates 17077-99 (USA), Holambra (Brazil), SWPV/wildboar/GER/2019 (Germany), and SWPV/domestic/GER/2019 (Germany), and 98% identity with Spv/As-IND/01/14 (India) and Spv/As-IND/02/14 (India), respectively . At the For further genetic characterization of the Russian isolate, the amino acid sequences of the SPV119, SVP120, and SPV133 genes were compared with the sequences of the SWPV isolates from different geographic regions . It turned out that the Russian isolate Alekseevskii was 100% identical to isolates 17077-99 (USA), SWPV/wildboar/GER/2019 (Germany), and SWPV/domestic/GER/2019 (Germany) in the sequences of all three genes. However, comparison of the amino acid sequences of the SPV119, SPV120, and SPV133 genes of the Russian and Indian isolates revealed substitutions of individual amino acid residues. For these isolates, the amino acid sequence identity of SPV119, SPV120 and SPV133 was 96.75%, 97.05%, and 98.88%, respectively.6 TCID50/mL, which matched the expected titer. The material used to the infect the animals of group 1 (clarified 10% skin scab suspension) was not titrated. Five pigs from group 3 (control group) were inoculated with sterile PBS (1 mL per pig) to mimic infection. Two groups of pigs were infected subcutaneously in four different areas of the neck and ears. The first group was infected with a purified 10% suspension of skin scab, and the second with a suspension of the SWPV isolate adapted to PK-15 cells. Back titration of the material used to infect the animals of group 2 showed that the pigs were inoculated with material containing 1 × 10Five days after infection, all animals in group 1 developed a vesicular skin lesion, which then turned into pustules B. AnimalBlood, along with oral and nasal swabs were collected daily from 3 to 14 days after infection (dpi), and then at 3-day intervals to detect the virus and SWPV genome. Real-time PCR analysis showed that all blood samples did not contain the viral DNA, indicating the absence of viremia in the infected animals. However, the presence of viral DNA was confirmed with PCR in oral and nasal swabs taken in the range of 8–11 to 30 dpi. Some samples with a positive PCR signal, collected on the 11th and 27th day after infection, were used for virus isolation in the primary culture of porcine testicles. The virus was successfully isolated from all selected samples after the third blind passage, in which the development of characteristic CPEs were observed. These results indicated the presence of an infectious virus in oral and nasal swab samples, and that the virus could be released into the environment over a sufficiently long period of time.Information on the animal experiment is presented in Streptococcus suis, Staphylococcus spp., Actinomyces pyogenes, Fusobacterium necrophorum, and Porcine circovirus Type 2 (PCV2) [In 2013, in the Belgorod region of Russia, a disease associated with damage to the skin of pigs was detected on the territory of a pig breeding complex. Based on clinical signs, pathology, virus isolation, and molecular studies, a diagnosis of SWPV was made. Based on the negative PCR results, other viral pathogens were excluded as the cause of the disease. Unfortunately, there are no epidemiological data on which to estimate the overall morbidity, cumulative mortality, and mortality  of this condition. The lack of these data made it impossible to state that adult pigs had also been sick during the outbreak on the farm to the same extent as the piglets. It is possible that the observed clinical signs that were detected only in piglets correlated with the results of other researchers who reported a higher morbidity and mortality of piglets due to the smallpox presence in pigsties ,13,26,27To diagnose viral infections, it is customary to isolate the virus in cell cultures with subsequent molecular identification . IsolatiIn the present study, the isolated SWPV was successfully adapted to the PK-15 cell line. Although there are positive examples of the direct isolation of swinepox virus in the PK-15 cell culture from clinical samples ,33, we fRecombinant poxviruses, including swinepox virus, are strong candidates as live viral vectors for use in humans and animals ,35,36,37Knowledge of the genetic diversity of strains, wildlife reservoirs, and the evolutionary origins of SWPV is limited. The sequences of the VLTF-3, SPV119, SVP120, and SPV133 genes were used for the phylogenetic analysis of the Russian isolate and the other SWPV isolates. The VLTF-3 gene encodes a viral late transcription factor and is often used in phylogenetic studies of SWPV. Two other genes, SPV119 and SPV120, encode extracellular envelope glycoproteins. It has been shown that the last gene, CPV133, encodes a protein belonging to the A52R-like family . The A52The last outbreak of swinepox registered in Russia occurred in 2019 in the Kaluga region. This region borders the Belgorod region, which may indicate the persistence of the virus in this area. Unfortunately, we did not receive material from the Kaluga region for the analysis of this isolate. This information could help in further studying the epidemiology of swine pox in Russia, and in determining whether new outbreaks are caused by isolates circulating in the country or isolates imported from abroad. Further studies of genome-wide Russian SPWV isolates are essential to determine their sequence diversity and draw conclusions about the transmission routes and possible reservoirs of the virus.In conclusion, this is the first study describing the successful isolation and characterization of SWPV in Russia. The presence of SWPV in the scab samples was evaluated by in-house real-time PCR, reference PCR amplification, and nucleotide sequencing of the viral late transcription factor-3 (VLTF-3) gene and was then confirmed by virus isolation. Phylogenetic analysis based on VLTF-3 gene sequences revealed that the Russian isolate was grouped with SWPV isolates from the USA, Brazil, and Germany. Due to the low pathogenicity of the Russian isolate during the experimental infection of pigs, we assume that this isolate can be used following genetic modification in the future as a viral vector for the expression of foreign proteins or recombinant vaccine development. In addition, it has been demonstrated that the real-time PCR proposed in this study can serve as a useful tool for the rapid specific detection of the swinepox virus.


The sperm interacts with seminal plasma proteins during its transport through the female reproductive tract to reach the oocyte. Seminal plasma proteins have been associated as biomarkers of fertility in bovine males, while two-dimensional electrophoresis in polyacrylamide gels under denaturing conditions (2D-PAGE) is a useful technique for their separation, allowing their subsequent analysis with the aid of specialised software. Brahman bulls are known for their tolerance to tropical conditions such as low-quality pastures, high temperatures, and relative humidity as well as moderate resistance to infestations by parasites and insects. The present study describes the two-dimensional electrophoretic profiles of the seminal plasma proteins in the rainy and dry seasons, associating them with the fertility of Brahman bulls in the Colombian Orinoquía in a 90-days breeding season and a single-sire mating system (1 bull per 50 Brahman cows) with 60 consecutive days of rest. The fertility-related seminal plasma protein spots increased in the dry season. Likewise, a meaningful relationship was found between the protein spots that possibly coincide with the Binder of Sperm Proteins. It was also found that bulls with the highest percentages of pregnancy also had similarities in their 2D seminal plasma maps. We conclude that the seminal plasma protein profile of Brahman bulls raised in the Colombian low tropic changes between rainy and dry seasons, and such changes may influence the reproductive performance of those animals. This is the first description of seminal plasma proteins profile of adult Brahman bulls raised in Colombian low tropics.Seminal plasma protein profiles change between rainy and dry seasons in the Colombian low tropic, with some proteins more expressed during the rainy season and other more expressed during the dry season.Seminal plasma protein profiles vary from high and low fertility Brahman bulls, as shown by the association between the seminal plasma proteins and the pregnancy rates of bulls.Bull fertility is one of the factors that affects the livestock production. The fertility of bulls is responsible for almost 80% of the reproductive capacity of the beef cattle herds . Thus, oIn this context, the study of the proteomics of seminal plasma constituted a diagnostic method with a sensitive basis to evaluate the fertility of a seminal sample (Bos taurus indicus) have been developed to withstand higher temperatures than Bos taurus taurus bulls . The historical pluviometry data from January 2001 to December 2015 were also obtained by the IDEAM to estimate the monthly averages of rain fall and define the rainy and dry seasons of the studied region .The temperature-humidity index (THI) was estimated by the following equation described by Where, T° = Air temperature (°Celsius), and Rh = Relative humidity of the air (%).Nine adult (5–8 years old) Brahman bulls, in good health and reproductively sound with known reproductive history and presenting a scrotal perimeter higher than 36 cm, were used in the present study. Bulls were extensively managed and fed mainly with Brachiarias with no supplementation.Bulls were subjected to a 90-day breeding season that took place during the most rainy months (from April to June 2016) in Orinoquia, followed by a 60-day resting period (July and August 2016). Afterwards, a new 90-day breeding season took place from September to November 2016, followed by an additional 60-day resting period from December 2016 to January 2017. ® ElectroJac®6, Neogen Animal Safety, Lexington, KY), and the sperm analysis was performed in field conditions according to the Brazilian College of Animal Reproduction (®) by measuring the transmittance at 620 nm of semen samples previously diluted 1:100 with 2.9% sodium citrate. The remaining semen samples were used to obtain the seminal plasma for the study of proteins, as described ahead.The breeding soundness evaluation of the bulls was performed at the beginning and the end of each breeding season . Data from the of the pregnancy detection in the females were used to estimate the pregnancy rates (PR) for each bull according to the following formula):Based on their respective pregnancy percentages, bulls were classified as low fertility (≤ 70%), normal fertility (71%–79%), and high fertility (≥ 80%)  was transferred to new tubes. A volume of 1.0 μL/mL of a 5 μM Phenyl Methyl Sulfonyl Fluoride (PMSF) solution was added to the supernatant to avoid proteolysis. Then, samples were transported in liquid nitrogen to the Proteomics area from the Genetics and Molecular Biology laboratory in Agrosavia , where these samples again underwent centrifugation under the same conditions, filtered employing 0.22 μm filtering devices and stored at −80ºC until further analysis.Semen samples collected for the BSE were also used to obtain seminal plasma for the study of seminal plasma proteins. Briefly, the semen samples were centrifuged for 5 min at 5,400 xProtein concentration in seminal plasma samples was quantified by the colorimetric method described by , employi® Tetra Cell chamber. Equilibrated strips were carefully placed on the top of 15% polyacrylamide gels, and separation was carried out at 85 V for 3 h. Gels were stained with a Coomassie Brilliant Blue solution and distained with acetic acid-methanol (1:3) solution. Images were acquired in a Gel-Doc XR analyser , and the spots pattern was analysed by the PD Quest software  was performed according to the model of THI and other parameters such as the scrotal perimeter, seminal quality parameters, and pregnancy rates were compared in the two different climate periods (rainy and dry) using an ANOVA and a post-hoc mean comparison test (Tukey). In addition, a Pearson correlation test was used to establish associations between the temperature-humidity index, scrotal perimeter, seminal quality parameters, pregnancy percentages, and the relative quantity of each protein spot. Also, the intensities of seminal plasma proteins were compared in high and low fetility bulls with Tukey’s test.p > 0.05). On the other hand, p < 0.05), thus, the general pregnancy rates, in this study, it was 64.0 ± 19.9%. The scrotal circumferences, seminal traits, and the pregnancy rates did not differ among breeding soundness evaluations  and 3103 (16.0kDa –pl 4.9) . Spots number refers to those depicted in r = − 0.73; p < 0.05).The analysis of the gels showed a total of 128 protein spots with molecular weights between 13.4 kDa and 68.2 kDa and isoelectric points (pIs) between 3.5 and 9.7. Not all spots were evidenced in all animals. Finally, those bulls with high and low fertility were chosen to establish the association between the SPP and the pregnancy percentage. To the best of our knowledge, this is an important study associating seminal plasma proteins with andrological parameters, pregnancy rates of Brahman bulls and climate in Colombian field conditions.Different researchers have described the risk of moderate to severe heat stress when the temperature-humidity index (THI) is ≥ 76 for bovines , they seAlthough bulls in this experiment were similar in the breeding soundness evaluation, they showed different pregnancy rates . This reBos taurus indicus) in reproductive breeding and resting periods were obtained in the department of Meta. The definitions that are indicated in this study as SPP are similar in molecular weight and isoelectric point to that reported by some researchers as seminal plasma proteins  and 3103 (16.0kDa – pI 4.9), spots that coincide with BSP1–BSP3 . These sWhen comparing the results regarding the fertility ranks of the bulls, we observed that Protein Spots 2006 and 5102, whose molecular weights and isoelectric points (13 to 16 kDa/pI 5.5 to 6.7) are in agreement with those of Spermadhesin Z13, were more concentrated in the less fertile bulls seminal plasma, which would coincide with that mentioned by Likewise, Protein Spots 8304 and 8305 (26kDa/pI 6.2), whose molecular weights and pI agree with those of Prostaglandin D synthase type lipocalin (PGDS), showed higher concentrations in the bulls that showed higher pregnancy percentages. This finding coincides with that reported by In this research, Bulls 3 and 9 were those with the highest fertility, while Bulls 7 and 8 were those with the lowest fertility. Another finding of this investigation, which is related to bulls of lower fertility (Bulls 7 and 8), is that they showed high concentrations of protein Spots 5401, 402 and 505 (28 to 30kDa/pI 3.6 to 5.2), spots whose molecular weights and pI coincide with BSP5, an acid glycoprotein involved in sperm capacitation, in conjunction with glycosaminoglycans, modulating cholesterol output, which is dependent on time and concentration , which could be albumin, a protein that is produced in the testis, epididymis, and prostate, in addition to the albumin that comes from blood circulation, and which is associated with the alteration of the lipid composition of the plasma membrane, intervening in the training or decapacitation of the sperm. Albumin has also been negatively related to progressive motility and sperm viability  was found in the lower fertility bulls, a spot that, as mentioned above, may be BSP5. This protein is not found in the most fertile bulls in this climate season. During the dry season, the seminal plasma of the less fertile bulls, presented Spots 2006 (14.8 kDa/pI 5.4), 5102 (15.6 kDa/pI 6.4), 0002 (15.5 kDa/pI 6.6), 5105 (15.7 kDa/pI 4.9) and 1104 (15.7 kDa/pI 5.5), whose molecular weights and pIs coincide with BSP1/BSP3.Although the breeding soundness evaluation was conducted before the mating periods, and received a satisfactory qualification, bulls showed differences in pregnancy rates and seminal plasma protein profiles. That suggested a potential association between SPP and the fertility of Brahman bulls raised in different situations in Colombia. In this sense, those bulls with better fertility had higher concentrations of Spots 8304 and 8305. Conversely, Spots 2006, 5102, 5401, 402, 505, 3802 and 3804 were more expressed in those bulls with low fertility. We also conclude that the climatic variations through the year in the Colombian low tropics did not affect reproductive efficiency of Brahman bulls, indicating high adaptability in those animals. These results could serve as a reference to continue conducting studies that allow the selection of the potential sires, using the bovine seminal plasma proteins aiming at a higher reproductive performance for Bos indicus bulls raised under Colombian tropical conditions.


The price of prescription drugs is such a large problem that it has gained bipartisan support. While the Trump Administration made strides to improve transparency in pricing through the federal “Transparency in Coverage” rule which requires insurers to disclose list and net prices for medications while also giving patients personalized cost-sharing estimates, enacted with much industry opposition, this action is not a direct measure towards reducing costs. A system that allows lifesaving medication such as epinephrine to cost patients anywhere from $150 to $700 dollars is one that could benefit from prescription drug price control measures H.R. 3 is an important check on the inflated market for prescription drugs. Approximately 25% of Americans find it difficult to afford necessary prescriptions due to the high costs While the Congressional Budget Office (CBO) concludes that there would be approximately 8 fewer drugs introduced into the United States’ market over the next decade and 30 fewer over the subsequent decade, this assumes that pharmaceutical companies would not reduce their costs in other places to allow for more money to invest in research There are no restrictions that are keeping pharmaceutical companies from changing their allocation of revenue to ensure that there continues to be research conducted for new drugs. The fear that “price controls imposed today will result in less research tomorrow” The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Cucurbits are economically important crops that are widely cultivated in many parts of the world, including the southern US. In recent years, higher temperatures have favored the rapid build-up of whiteflies in the fall-grown cucurbits in this region. As a result, whitefly-transmitted viruses (WTVs) have severely impacted the marketable yield of cucurbits. In this review, we discuss three major groups of WTVs negatively impacting cucurbit cultivation in the southern US, including begomoviruses, criniviruses, and ipomoviruses. Here, we discuss the available information on the biology, epidemiology and advances made toward detecting and managing these viruses, including sources of resistance and cultural practices.  Cucurbitaceae and comprise 965 species in 95 genera [Cucumis melo var. cantalupensis Naudin), cucumber (C. sativus L.), honeydew (C. melo L. (Inodorus Group) ‘Honey Dew’), muskmelon (C. melo), pumpkin (Cucurbita spp.), yellow squash (C. pepo), watermelon (Citrullus lanatus), and zucchini (Cucurbita pepo L.) [Cucurbits are important crops that are consumed as fruits or vegetables. They belong to the family 5 genera . They ar5 genera . Cucurbi5 genera . The US 5 genera . Economipepo L.) .Bemisia tabaci) (Hemiptera: Aleyrodidae) and the viruses that they transmit. In the states of Georgia and Florida, cucurbit production is heavily affected by these viruses and there are sporadic reports of incidence from other states in the Southern US. During the fall of 2015-2017, whitefly-transmitted diseases were responsible for 35% of crop losses in squash in Georgia [B. tabaci that was complemented with virus incidence [Cucurbits can be infected by many pathogens including at least 59 different species of plant viruses from different genera [ Georgia . ExtensiBegomovirus within the family Geminiviridae [Cucurbit leaf crumple virus (CuLCrV) is the most prominent DNA virus infecting cucurbits in the Southeastern US. CuLCrV was discovered in California during the fall of 1998 on volunteer watermelon in the Imperial Valley . Symptomiviridae ,12. Geneiviridae . Cucurbita pepo L.) in Florida in 2009 [In the southeastern US, CuLCrV was first reported on yellow straightneck and zucchini squash  in a persistent and circulative manner [B. tabaci, with low frequency in each case. However, the rate of transovarial transmission was demonstrated to be low at only 3.93% in nymphs and 3.09% in adults [CuLCrV is transmitted by the whitefly e manner ,17. CuLCn adults . Addition adults . Seed trn adults ,19,20, bSquash leaf curl virus (SLCV) was the first begomovirus infecting cucurbits identified in the USA. It was recorded on squash exhibiting leaf curl and stunted growth symptoms in the early 1980s from California . CharactNicotiana benthamiana and tomato [WmCSV is an Old World begomovirus initially reported from Yemen infecting watermelon plants in 1986, , and it d tomato . A few yd tomato . The ided tomato .Crinivirus in the family Closteroviridae [B. tabaci, MEAM1 and Mediterranean  [Criniviruses belong to the genus oviridae  and poseoviridae . Crinivioviridae . In the merly Q) ,32,33. Tmerly Q) .Cucumis melo in Texas [C. pepo L.) [CYSDV was discovered in the United Arab Emirates in 1982 on melons  and is nin Texas , followein Texas , and it in Texas . Soon thpepo L.) . In Georpepo L.) . The sympepo L.) . Yellowipepo L.)   . In a supepo L.) . CYSDV ipepo L.) . CYSDV wpepo L.)  and commpepo L.) .Cucumis melo), cucumber (C. sativus), and watermelon (Citrullus lanatus) [Luffa cylindrica [Beta vulgaris (beet), Chenopodium amaranticolor, C. quinoa, Datura stramonium (datura), Spinacia oleracea (spinach), Lactuca sativa (lettuce), and Nicotiana benthamiana [CCYV was first reported in 2004 from Japan where it caused heavy losses in melon production . CCYV islanatus) . Under elindrica  and non-thamiana . Mixed ithamiana . SymptomIpomovirus and the name Squash vein yellowing virus (SqVYV) was proposed [Ipomoviruses are whitefly-transmitted plant viruses in the family Potyviridae and possess filamentous flexuous virions [B. tabaci MEAM1 in a semi-persistent manner [In 2003, a previously unknown virus was detected in squash in Florida . The hosproposed . Ipomovi virions ,65. The  virions . Unlike t manner . In 2005t manner . The symt manner ,68. Momordica balsamina) and smellmelon (Cucumis melo) under natural conditions and creeping cucumber (Melothria pendula) under experimental conditions [Coccinia grandis) [Under experimental conditions, SqVYV infects a wide variety of cucurbits such as pumpkin, tropical pumpkin, squash, and luffa. The virus is devastating mainly due to its ability to cause vine decline and fruit rot in watermelons. On other cucurbits, such as pumpkin, squashes, and in luffa, SqVYV develops vein yellowing in inoculated and systemic leaves, while in cantaloupe and cucumber vein yellowing was transient in systemic leaves immediately above the inoculated leaves. SqVYV also infects cucurbit weeds such as balsam apple (nditions . Squash grandis) . SqVYV fgrandis) . In balsgrandis) . SqVYV igrandis) , and hasgrandis) . SqVYV wgrandis)  but not B. tabaci (sweet potato whitefly) and Trialeurodes vaporariorum Westwood (greenhouse whitefly) [B. tabaci is a major threat to vegetable production in the southern US. Bemisia tabaci (MEAM1) was initially introduced in the US around 1985 and has since rapidly spread across the southern US [Globally, whiteflies (Hemiptera:Aleyrodidae) have long been economically important agricultural pests. Whiteflies transmit more than 300 known plant pathogenic viruses to more than 1000 known plant species across the world ,73. The hitefly) . These tthern US ,76. Follthern US ,78,79.The development of high-throughput sequencing (HTS) and bioinformatics has accelerated the discovery of novel viruses. The use of high-throughput sequencing facilitated in the discovery of other plant viruses infections recently in Georgia. Because very little information is available about these viruses, they are mentioned in this article.Phenuiviridae in the order Bunyavirales [Phenuiviridae) [Watermelon crinkle leaf-associated virus 1 (WCLaV-1) and watermelon crinkle leaf-associated virus 2 (WCLaV-2) are rather recently discovered viruses and not much is yet known about them. Both viruses were discovered in China by HTS on watermelon displaying virus-like symptoms, including leaf crinkling, mosaic, and bunchy top in samples collected in 2015 and 2016 during a field survey . WCLaV-1avirales . Howeveravirales . Based oviridae) .In 2021, these viruses were reported from Texas on watermelons displaying symptoms consisting of mild leaf crinkling and yellow mosaic patterns . ThereafWCLaV-1 infected watermelon plants displayed mosaic, crinkling, and bunching symptoms in Georgia, as described previously. The virus was detected in five counties within south Georgia, indicating its widespread prevalence in the state . WCLaV-2Cucumis melo endornavirus (CmEV), C. melo amalgavirus (CmAV1), and C. melo cryptic virus (CmCV) were recently reported in Georgia in the spring of 2021. CmAV1 and CmEV were detetcted on watermelon and cantaloupe. CmCV was identified by HTS on watermelon; however, the virus could not be detected by RT-PCR in any of the field samples.Unlike acute viruses, persistent viruses do not cause symptoms in infected plants. Hence, these viruses were previously called “cryptic” viruses . For theEndornavirus and the family Endornaviridae while CmCV belongs to Deltapartitivirus of the family Partitivirdae, and CmAV1 is a virus member of the genus Amalgavirus of the family Amalgaviridae. Persistent plant viruses are not known to be transmitted horizontally by vectors [CmEV belongs to the genus  vectors . However vectors . Persist vectors .Mixed infection refers to the existence of more than one virus at a given time in a host. Mixed infections have the potential to result in higher yield loss  and breaThe accumulation of criniviruses, CCYV and CYSDV was significantly decreased compared to single virus-infected plants under greenhouse  and fielPhaseolus vulgaris (snap beans) in Florida [CuLCrV virus has a wide host range which includes squash, watermelon, cantaloupe, and many gourds, whereas honeydew melon, crenshaw melons, and casaba melons were least susceptible . Heavy i Florida  and Geor Florida . Not mucLactuca sativa (lettuce), snap bean, Medicago sativa , and some weed species such as Solanum elaeagnifolium (silverleaf nightshade), Malva neglecta , Sisymbrium irio (London rocket), Physalis wrightii (Wright’s groundcherry), and Sida hederacea  were found to harbor CYSDV [Amaranthus spp.) is a potential reservoir for CYSDV in Florida [Raphanus raphanistrum L.) is potential reservoir for CCYV in Georgia [Momordica charantia is a potential reservoir host of SqVYV [CYSDV infects both cucurbits as well as non-cucurbit crops. The main cucurbit hosts in the Southeast includes cucumber, cantaloupe, squash, and watermelon. In California, some non-cucurbit crops such as or CYSDV . These c Florida  while wi Georgia . Momordiof SqVYV . Common of SqVYV . Unlike crop hosts, weeds can survive even during non-crop seasons. Weeds grown during the summer support the multiplication of whiteflies while winter weeds support continuity of whitefly population. Whitefly populations gain a foothold when weather warms and they multiply quickly during the summer. Identifying the appropriate weeds that support whiteflies and the WTVs and their timely removal are essential to break the continued life cycle of both whiteflies and WTVs . The success of virus disease detection and management depends upon diagnostics methods and early detection of viruses. Characteristic symptoms induced by viruses on diseased plants is the preliminary step in diagnosis, but this is nearly impossible due to the mixed infections of more than one virus of the same or different genus on a diseased plant at a time. Serological diagnostic tools are not available for any of the whitefly-transmitted viruses present in the Southeast US. It would be much simpler and lower cost if such a technique were available to screen large number of samples such as those required for epidemiological studies. CuLCrV concentrations are higher on the upper (young) leaves while that of criniviruses, CCYV and CYSDV are higher on the lower (old) leaves. Hence, samples for the detection of CuLCrV should be taken from upper leaves while those for CCYV and CYSDV should be taken near crown of the plants. SqVYV is unevenly distributed in its hosts and often appears to have a low titer in many tested plants . Hence, A simple one-step multiplex RT-PCR system has recently been developed for the simultaneous detection of cucurbit leaf crumple virus, cucurbit yellow stunting disorder virus, squash vein yellowing virus, and cucurbit chlorotic yellows virus . This asLoop-mediated isothermal amplification (LAMP) is an isothermal technique that does not require sophisticated instruments like thermal cyclers. A LAMP assay was developed for simple, rapid and efficient detection of CuLCrV. The sensitivity assay demonstrated that the LAMP reaction was more sensitive than conventional PCR, but less sensitive than qPCR. However, it was simpler and faster than PCR and qPCR. Furthermore, this assay was able to detect CuLCrV in mixed virus infections . RecombiHTS combined with subsequent application of bioinformatics for the detection and identification of both known and novel plant viruses and other pathogens has been proven to be successful with different sequencing platforms using nucleic acid preparation as the starting material . MoreoveB. tabaci MEAM1 and whitefly-transmitted viruses were evaluated in Georgia. Among them, insect exclusion netting (IEN) significantly reduces whiteflies and virus incidence on squash seedlings in the greenhouse [No single management tactic is effective enough to suppress whiteflies and reduce the transmission of viruses in Georgia and other southeastern states. Several management tactics aimed at reducing the impacts of eenhouse . In the eenhouse . Growerseenhouse . Insectieenhouse ,110. Reseenhouse . In suchculcrv, controls the resistance in PI 313970, a C. melo accession and likely in the other resistant accessions [Using cultivars with resistance to either vectors or the viruses transmitted by them is an economical and effective way to avoid yield loss. Currently, no cucurbit cultivars with resistance to CuLCrV, CYSDV, or CCYV are available commercially, but efforts are underway to identify sources of resistance ,113. A mResistance to CCYV was reported in a snap melon (Momordica group) (accession: JP 138332), originating from India . This liCys [Regarding CYSDV, resistance to cucurbit yellow stunting disorder virus was first reported in the accessions TGR1551  and TGR-1937 (PI 482431)  and in SCys . Two QTLCys . SeveralCys . ResistaCys  and likeCys . ScreeniCys . SeveralCys . SeveralCys . PI 3922Cys . Over the years, the production of cucurbits in the US has experienced challenges due to whiteflies and whitefly-transmitted viruses. In recent years, along with a higher incidence of whiteflies there has been an increase in the incidence of plant virus diseases they transmit. Mixed infections of two or more viruses are common adding to severity of impact in many cases. Efforts are underway to mitigate the grower’s losses and to increase profitability and sustainability. Ongoing research on identifying the resistance sources of whiteflies and the viruses they transmit, identifying overwintering hosts, and developing integrated pest and disease management practices is promisingMolecular understanding of the aspects of host-virus interaction, synergism in cucurbits, whitefly-host-virus interactions etc., is also needed to develop novel strategies of disease management and crop protection.


Acanthamoeba spp. bait systems. While this method has allowed for the discovery of plenty novel viruses in the Nucleocytoviricota, environmental -omics-based analyses have shown that there is a wealth of diversity among this phylum, particularly in marine datasets. The prevalence of these viruses in metatranscriptomes points toward their ecological importance in nutrient turnover in our oceans and as such, in depth study into non-amoebal Nucleocytoviricota should be considered a focal point in viral ecology. In this review, we report on Kratosvirus quantuckense (née Aureococcus anophagefferens Virus), an algae-infecting virus of the Imitervirales. Current systems for study in the Nucleocytoviricota differ significantly from this virus and its relatives, and a litany of trade-offs within physiology, coding potential, and ecology compared to these other viruses reveal the importance of K. quantuckense. Herein, we review the research that has been performed on this virus as well as its potential as a model system for algal-virus interactions.Since the discovery of the first “giant virus,” particular attention has been paid toward isolating and culturing these large DNA viruses through  Evidencefferens  as has befferens .K. quantuckense, a combination of in vitro, in situ, and in silico analyses have been performed to elucidate the role it plays in modulating HAB dynamics. Significant progress has been made to improve assays used to characterize infection of A. anophagefferens and virus propagation  . Blooms  country . It has efferens . Since tefferens . More brto Maine . While ito Maine . We notef Mexico  and as ff Mexico : herein ca. 1997 , of which little of the molecular content is understood  likely permits continued growth or population maintenance during a bloom where a species exhibiting exclusively photoautotrophy may not persist. Specifically, light attenuation, and consequently the inability for an A. anophagefferens population to meet their requirement for photosynthetic carbon would be counteracted by heterotrophic assimilation , infection by large icosahedral viruses was observed in vivo, both before and after the dissolution of most cellular organelles  and TEM imaging used to confirm large viral particles were attaching to algal cells and viral particles were produced internally  (K. quantuckense was sequenced in 2014 (and re-sequenced in 2022) to better resolve functional capabilities of the virus by defining the genetic potential  , the genotential .K. quantuckense was found to phylogenetically fit into the phylum Nucleocytoviricota  generated from diverse environments, including aquatic, terrestrial, permafrost, and arthropod-associated derived datasets  and the major capsid protein (MCP)], most of which have phototrophic hosts  among all presently isolated viruses (Oceanusvirus kaneohense), Pyramimonas orientalis virus (Heliosvirus raunefjordenense), and the proposed Mesomimiviridae clade which includes the Organic Lake phycodnaviruses  also has two copies of this gene, though these have a much higher amino acid identity (> 99%), pointing to a more recent duplication event  . Althouga genome , of certa genome . Based orthologs .Nucleocytoviricota infecting unicellular eukaryotes, K. quantuckense has a relatively small genome, rivaled by the similarly sized PBCV-1 and the 180 kbp genomes of the Prasinoviruses   .K. quantuckense similar to those in other Nucleocytoviricota include machinery for DNA replication, transcription, and translation . The presence of these and similar eukaryote-like genes may highlight the evolutionary past of the virus with a history of horizontal gene transfer, as while they are conserved between other similar algal viruses, they are holistically different from translational machinery in other Nucleocytoviricota  of late stage infected A. anophagefferens cells has been noted  and low (30 μmol m–2 s–1) light  light  have bee1) light . The str1) light . Decreason cycle . As prevon cycle . As decron cycle , this reting PgV . Importating PgV . Still, nfection .A. anophagefferens growth dynamics, as shown through differential expression of algal genes under different phosphorus concentrations  virus were among the most abundant viruses in the early stages of the bloom , however, some interesting phenomena have yet to the fully characterized. This includes the role of the glycocalyx in infection, the algae’s transition to and metabolic capabilities in dormancy (al bloom  and its dormancy , and itsdormancy ]. How thA. anophagefferens blooms are not an isolated phenomenon and have appeared in several independent areas geographically, with evidence of the ubiquitous nature of A. anophagefferens in general. Although brown tide bloom formation is a complex phenomenon, and no model predicts their appearance at present, background populations of A. anophagefferens cannot be exclusively ruled out as potential bloom seeds in new areas globally. Given the diverse nature of harmful algal blooms and their potential regulators, we recommend similar analyses as the ones described here be conducted on other lineages of algae infecting Nucleocytoviricota. The introduction of more systems for study in this diverse and variable phylum will only lead to a more complete understanding of bloom-virus dynamics. A continued understanding of this algae, and this host-virus system, will surely be valuable for our future approach to harmful algal blooms.Finally, attention must be paid to any algae implicated in harmful algal blooms. Not only is this a current issue, but also one that will only increase in prevalence given the changing climate. AT: Conceptualization, Formal analysis, Writing – original draft, Writing – review and editing. EC: Conceptualization, Methodology, Writing – original draft, Writing – review and editing. EG: Data curation, Writing – review and editing. MM: Data curation, Writing – review and editing. BC: Data curation, Writing – review and editing. FA: Data curation, Writing – review and editing. CX: Data curation, Writing – review and editing. CG: Data curation, Writing – review and editing. SW: Conceptualization, Data curation, Funding acquisition, Project administration, Writing – review and editing.


When healthcare interventions prolong life, people consume medical and non-medical goods during the years of life they gain. It has been argued that the costs for medical consumption should be included in cost-effectiveness analyses from both a healthcare and societal perspective, and the costs for non-medical consumption should additionally be included when a societal perspective is applied. Standardized estimates of these so-called future costs are available in only a few countries and the impact of inclusion of these costs is likely to differ between countries. In this paper we present and compare future costs for five European countries and estimate the impact of including these costs on the cost-effectiveness of life-prolonging interventions. As countries differ in the availability of data, we illustrate how both individual- and aggregate-level data sources can be used to construct standardized estimates of future costs. Results show a large variation in costs between countries. The medical costs for the Netherlands, Germany, and the United Kingdom are large compared to Spain and Greece. Non-medical costs are higher in Germany, Spain, and the United Kingdom than in Greece. The impact of including future costs on the ICER similarly varied between countries, ranging from €1000 to €35,000 per QALY gained. The variation between countries in impact on the ICER is largest when considering medical costs and indicate differences in both structure and level of healthcare financing in these countries. Case study analyses were performed in which we highlight the large impact of including future costs on ICER relative to willingness-to-pay thresholds.The online version contains supplementary material available at 10.1007/s10198-022-01501-6. Cost-effectiveness analyses (CEA) are used to support decision-makers with decisions on the adoption of new healthcare technologies. The results of CEA are often presented as an incremental cost-effectiveness ratio (ICER), a ratio of incremental cost to incremental quality-adjusted life-years (QALY) gained by an intervention compared to an appropriate alternative –5. EffecTotal future costs are the sum of future medical and future non-medical costs. Future medical costs are often divided into (i) medical costs related to the disease targeted by the intervention, related medical costs; and (ii) medical costs for all other medical consumption, unrelated medical costs . For exaThe estimation of future unrelated medical costs is not without difficulties. As early as 1997, David Meltzer (1997) suggested to utilize healthcare expenditure (HCE) data by age which contain medical spending on all types of diseases . Cost prPreviously, studies demonstrated the inclusion of future costs in economic evaluations in several countries, such as England and Wales, the Netherlands, and the United States (U.S.) , 16–20. In this paper, we estimate and compare future costs of five European countries: the Netherlands, Germany, the UK, Spain, and Greece. For the Netherlands, both unrelated medical costsIn the theoretical model of Meltzer, the incremental costs in an ICER are seen as the difference in consumption minus production between two interventions.Related medical costs and productivity costs (PC) as presented in the first part of the equation are typically already included in CEA under the societal perspective , 10, 18.i. It has been demonstrated that time-to-death is a stronger predictor of HCE than age, coined as the red herring theory [DC) and costs in other years (survivor costs denoted SC) in Eq. NMC for the life-years remaining:The methodology applied in this study was developed by Van Baal et al.  and builg theory , 30. ThiAlthough it could be argued that non-medical costs can be different for different health states, the evidence for this is, as yet, inconclusive ; we, theh is the probability of a household having more than one adult and hh equiv shows the annual non-medical consumption per household equivalent. In this equation, w is the consumption share of a household member: 0.5 for an adult and 0.3 for a child. The probability of households having more than one adult is estimated from the UK data and replicated for Germany, Greece, and Spain, due to the unavailability of the necessary data in these countries. While unrelated medical costs can be decomposed for sex, non-medical costs cannot, and hence are only presented by age.where We calculate the impact of future costs on the ICER of life-extending treatments by dividing the costs over the life-years gained, as seen in Eq. . In this study, we derive . 2011 [where Database . The cou [. 2011 . As coun [. 2011 , 36. No We demonstrate the impact of including future costs in the ICER in a case study. Unrelated medical and non-medical costs results are included in a cost-effectiveness model for the Sacubitril/Valsartan treatment for Heart Failure Patients in the Netherlands by van der Pol et al. 2021 . Van derAverage healthcare expenditures per capita and household expenditures decomposed by relevant demographic characteristics like age and sex were preferred. Where such data were unavailable, the approach for deriving costs was adjusted depending on the type of the data found; for example, Greek medical costs by age were derived from aggregate expenditure and hospital admission data. Specific descriptions of the input data found, and methods used to derive costs in each country are presented in Table Medical costs based on aggregate healthcare data or hospital expenditure data combined with admission frequency dataThe cost inputs for medical and non-medical expenditures are presented in Fig. Cost inputs were interpolated using cubic splines, assuming average midpoints of the age groups for interpolating the costs. Due to limited cost data in some countries, we limit the costs of medical and non-medical consumption data to the working ages of 20–80. Medical costs can be corrected for country-specific related medical costs. As we use medical costs as a substitute for unrelated medical costs, the results are unadjusted as disease-specific costs. The unrelated medical costs used in the case study are derived using country-specific heart failure related costs. Heart failure related mortality and QoL data, and effectiveness of the Sacubitril/Valsartan treatment were available in the CEA model . In the Figure The third row in Fig. The impact of adding future costs to the ICER of life-saving interventions  is shown in Fig. The upper graph of Fig. The future costs by age in Fig. The purpose of cost-effectiveness analysis is to provide decision-makers with information on the costs and benefits of the adoption of new health technologies. Often overlooked are costs related to increased survival, and a possible reason for their exclusion is likely the fact that publicly available estimates are scarce. This paper provides standardized estimates of unrelated medical and non-medical costs according to a framework for cost-effectiveness analysis first presented by Meltzer 1997) . Medical97 . MediThe case study analyses of the Sacubitril/Valsartan treatment performed in this study indicate the economic significance of including future costs on the ICER, as the magnitudes are relatively large. Bear in mind, the impact on ICER estimates, or ‘Partial ICERs’, exclude intervention costs, productivity costs, and other cost components. We can compare the magnitude of the ‘Partial ICERs’ to generally accepted WTP thresholds to assess the impact upon cost-effectiveness. The UK and Dutch impact of both future costs  form a large part or may even exceed generally accepted WTP thresholds as suggested by the individual countries’ HTA agencies  . Meanwhi. 2015 show Dutch and German healthcare spending  as roughly double the spending in Greece, similar to our estimates [While the impact in each country is relatively large in relation to WTP thresholds, the variation in magnitude between countries is considerable. In particular, the large variation in cost components in the medical costs data input is of concern for the cross-country comparisons. Differences in health care financing may partly explain the variation in unrelated medical costs, especially between Greece and the other countries. Public financing of healthcare is a key cost input in the unrelated medical costs in this study. In addition, it affects the health and life expectancy of populations, influencing population-level estimates of quality of life and mortality –60. Cylestimates . Howeverstimates . To thisstimates –64; privstimates . Similarstimates . This buContrary to the increasing impact of unrelated medical costs on ICERs by age, non-medical costs have relatively stable cross-country variation over the ages, with a variation of roughly €10,000. The variation in costs between may partially be explained by variation in GDP between countries, for example the GDP of the Netherlands and the UK, €53,000 and €50,000 PPP respectively, are roughly double the GDP of Greece, at €28,000 PPP. Likewise, the Dutch and British combined impact of unrelated medical and non-medical costs on ICER results are about twice the Greek result, at low ages. At higher ages, the difference in impact between these countries rises to about a threefold difference . In term. 2020 investigated the utilization of healthcare services decomposed into healthcareOne element we sought to investigate was the relationship of the costs with age and sex in different countries. Both unrelated medical and non-medical costs show age and sex patterns which are, in general, in line with other studies on future costs such as van Baal, Perry-Duxbury, and Kellerborg and colleagues –11, withRegarding the interpretation of the data, an important limitation is the direct comparison of the results between countries. Methodological differences stemming from the scarcity of data, such as limited cost components in unrelated medical costs and unavailability of individual-level data for non-medical costs in some countries, have factored into the results. Furthermore, while age- and sex-consumption patterns were captured in the input data for most of the countries, the Greek age and sex pattern was approximated based on self-reported hospital admission proportions by age and sex. We were able to adjust the Spanish costs to the cost level of SHA system but were unable to make any adjustments to the Dutch (RIVM-based estimates) nor UK (HES-based estimates). This means the Dutch estimates is likely somewhat higher compared to the SHA based estimates, as RIVM includes cost components like international care, while British estimates are likely lower , 12. DueIn this paper, we provided estimates of future unrelated medical and non-medical costs for the UK, Germany, Spain, and Greece based on the methodology of Van Baal and colleagues. Hence giving researchers and health economists access to standardized estimates which can be applied in cost-effectiveness analyses in different country settings, following the recommendation of Meltzer . The datSupplementary file1 (XLSX 11 KB)Below is the link to the electronic supplementary material.