State-based Medicaid programs have begun using All Patient Refined–Diagnosis-Related Groups (APR-DRGs) to determine hospital reimbursement rates. Medicaid provides coverage for 45% of childhood cancer admissions. This study aimed to examine how well APR-DRGs reflect admission costs for childhood cancer chemotherapy to inform clinicians, hospitals, and policymakers in the wake of policy changes.We identified 25,613 chemotherapy admissions in the 2009 Kids’ Inpatient Database. To determine how well APR-DRGs explain costs, we applied a hierarchic linear regression model of hospital costs, allowing for a variety of patient, hospital, and geographic confounders.P < .001), with costs increasing by DRG severity code. Diagnosis, age, and hospital characteristics also predicted costs above and beyond those explained by APR-DRGs. Compared with admissions for patients with acute lymphoblastic leukemia, costs of admissions for patients with acute myelomonocytic leukemia were 82% higher; non-Hodgkin lymphoma, 20% higher; Hodgkin lymphoma, 25% lower; and CNS tumors, 27% lower. Admissions for children who were 10 years of age or older cost 26% to 35% more than admissions for infants. Admissions to children’s hospitals cost 46% more than admissions to other hospital types.APR-DRGs proved to be the most important predictors of admission costs (APR-DRGs developed for adults are applicable to childhood cancer chemotherapy but should be refined to account for cancer diagnosis and patient age. Possible policy and clinical management changes merit further study to address factors not captured by APR-DRGs. State-based Medicaid systems historically used combinations of cost-based and managed care capitation to pay for hospital care. In 2013, states began shifting Medicaid inpatient reimbursement to prospective strategies using All Patient Refined–DRGs (APR-DRGs).7 APR-DRGs further classify admissions within a DRG into one of four severity codes after considering factors such as severity of the primary illness, comorbidities and secondary diagnoses, and services used during the admission. A reimbursement rate is assigned to each DRG plus severity code. Of course, some patients will actually have higher costs than expected; costs will be lower for others. The differences cancel one another out as long as the mix of patients within each DRG is random, and DRGs accurately reflect clinical resource utilization.Diagnosis-related groups (DRGs) are designed to classify clinically similar groups of patients with similar resource requirements.9 Almost half of childhood cancer admissions are for chemotherapy.8 Because Medicaid provides medical coverage for 45% of pediatric cancer admissions,10 the frequency and high resource utilization of chemotherapy admissions makes this an area of potential fiscal risk if APR-DRGs and actual utilization are not aligned.Most DRGs are the same for children and adults, with the exception of acute leukemia (DRG 690) and groups for neonatal conditions. To date, little examination of the adequacy of APR-DRGs to predict resource utilization in childhood admissions has occurred. Childhood cancer admissions cost approximately $1.9 billion nationally in 2009, nearly 11% of all inpatient costs for non-newborn children younger than age 21 years.Our overall goal is to provide guidance for clinicians, hospitals, and policymakers in view of policy to reimburse Medicaid admissions using APR-DRGs. In this report, we analyze the ability of APR-DRGs to predict inpatient costs among children with cancer admitted for chemotherapy by examining national samples of hospital admissions for 2009. The analysis controlled for diagnosis-, patient-, and hospital-level factors, and we estimated the effect each set of factors had on admission costs.11 The Institutional Review Board of Baylor College of Medicine considered this research exempt from review.A cancer cohort was identified from the 2009 Kids’ Inpatient Database (KID), compiled by the Healthcare Cost and Utilization Project, and sponsored by the US Agency for Healthcare Research and Quality.12 and up to 25 discharge diagnoses and 15 procedures coded according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). KID also includes patient age, sex, health care payer, and a measure of the patient’s income status by zip code. States optionally provide race/ethnicity, income status, and hospital characteristics; any systematically omitted data were coded as missing.KID samples approximately 80% of pediatric discharges from community hospitals; the 2009 sampling included 3.4 million deidentified admission records for patients younger than the age of 21 years from 5,128 hospitals in 44 states. For each record, the database contains APR-DRGs as assigned by the 3M grouper version  in use at discharge9 Admissions to deliver chemotherapy were identified as admissions with a chemotherapy or immunotherapy procedure on or before hospital day 2 or with chemotherapy as the primary diagnostic code.9 Each admission was assigned to one of seven cancer diagnoses by ICD-9-CM codes or categorized as dual if it had more than one cancer diagnosis: acute lymphoblastic leukemia (ALL), acute myelomonocytic leukemia (AML), non-Hodgkin lymphomas, Hodgkin lymphomas, CNS tumors, bone and joint tumors, and soft tissue tumors. Discharge codes used to assign diagnoses are listed in Appendix Cancer-related admissions were identified from KID by an ICD-9-CM code for cancer in any discharge diagnosis.13 KID contains hospital characteristics including a hospital’s designation by the Children’s Hospitals of America, teaching status, location , and state. For this analysis, admissions at children’s specialty hospitals were excluded because they accounted for less than 1% of all admissions. KID categorizes hospitals as small, medium, or large by considering number of beds, urban or rural location, and state. This hospital-size category caused unacceptable multicollinearity with other hospital descriptors. Instead, the number of chemotherapy admissions for each hospital was used as a descriptor of cancer volume, with categorization into five groups using cut point selection.14 States were grouped into nine divisions defined by the US Census Bureau.15Total admission charges in KID were converted to costs using cost-to-charge ratios.The most frequent APR-DRGs were identified from the chemotherapy admissions. APR-DRGs, including severity codes that individually represented more than 1% of admissions, were included as individual explanatory variables. APR-DRGs below this threshold were combined into a single “other” category.16 Postestimation Wald tests were used to compare factors within categorical variables.The analytic purpose was to determine how well APR-DRGs explain the total cost of an individual admission relative to other patient or hospital characteristics. We estimated linear hierarchic regression models, recognizing that admissions (level one) are clustered within hospitals (level two). Four linear models with random hospital effects were estimated: only APR-DRG variables, only patient characteristics, only hospital characteristics, and all characteristics of interest.15 The sensitivity of the model to extreme costs was assessed by excluding admissions costing more than $85,000 (three standard deviations above the mean cost) or less than $1,200. Another analysis examined the sensitivity of the model when limited to only DRG 693. Finally, the robustness of the model was tested after identifying admissions associated with an infection,9 a possible quality indicator.We tested the robustness of our models under alternative scenarios. Admissions with systematically omitted race/ethnicity or hospital characteristics were removed from all four models. The effect of state-level variation was evaluated by first including an indicator for individual states and then grouping states into four regions.We performed exploratory analyses on two descriptors that significantly affected costs after controlling for APR-DRGs in the full model: diagnosis and age. To explore the variation in the distribution of diagnoses and admission costs among hospitals, we first identified all hospitals with more than 25 chemotherapy admissions, with 65% to 75% of these admissions classified as APR-DRG 693-2 (n = 60). For each hospital, we calculated the ratio of combined ALL, bone or joint tumor, or soft tissue tumor admissions to AML admissions within this APR-DRG. We then calculated the mean and range of costs of AML admissions within APR-DRG 693-2 from the four hospitals (of the 60) with more than 10 AML admissions. To explore the effects of age on costs, we limited the full model to each of the three most common diagnoses: ALL, bone and joint tumors, and soft tissue tumors. We tested each limited model with and without interactions between age and APR-DRG.The characteristics of 23,846 admissions for chemotherapy are listed in 11 but each accounted for fewer than 100 childhood chemotherapy admissions. Sixteen (59%) of the 27 freestanding children’s hospitals accounted for more than 200 chemotherapy admissions each; however, only five (19%) were considered large. Characteristics of the remaining 44 hospitals were omitted; 36 (82%) of these included fewer than 100 chemotherapy admissions each.The sample included admissions from 369 hospitals. Most hospitals were identified as not a children’s hospital  or a children’s unit in a general hospital . The majority of these hospitals  were considered large hospitals by the KID definition ,r2 = 0.162) of the variation in costs—more than the variation explained by patient characteristics only (13%) or by hospital characteristics only (9%). The full model that combined all of the variables had much higher explanatory power (33%). Most of the coefficients that proved significant (P < .001) in the three partial models remained so in the full model, although they were somewhat smaller, as would be expected. This implies that these significant variables were individually capturing important information explaining variation in costs among patients.Focusing on the full model estimates, the allocated APR-DRGs were the explanatory variables with the greatest coefficients. Costs for those allocated to DRG 693 increased by severity code. Compared with those allocated to 693.1, admissions allocated to 693.2 were 15% more costly; those allocated to 693.3 were 61% more costly, and those allocated to 693.4 were 163% more costly. Admissions allocated to APR-DRG 690.2 were 135% more costly than 693.1 admissions.Costs varied for admissions according to cancer diagnosis, over and above the APR-DRG allocation of the admission. Average costs were similar for admissions for ALL, bone or joint tumors, or soft tissue tumors, each comprising one-quarter of all admissions. Compared with admission costs for ALL, costs were higher for AML (81%) and non-Hodgkin lymphoma (20%) admissions but lower for Hodgkin lymphoma (−25%) and CNS (−27%) admissions. The size and significance of the estimates for the effects of each diagnosis changed between the model including patient characteristics only and the full model, implying that the APR-DRGs were capturing some, but not all, of the effects of diagnosis on admission costs.Age was another important predictor of costs. Admissions for children 10 to 14 years of age had 25% higher costs than admissions for infants; those for children older than 15 years had 35% higher costs. No other patient characteristic significantly explained costs.Of the hospital characteristics, the only significant factor was whether the admission was to a freestanding children’s hospital. Such admissions had costs that were, on average, 46% higher than those for other hospital types. The number of chemotherapy admissions had a modest but significant effect on admission costs in the full model but not in the model including hospital characteristics only. Stepwise removal of patient descriptors demonstrated this variable was affected by collinearity with both age and diagnosis.Effects of APR-DRGs on costs were unchanged after removing admissions with systematically missing race/ethnicity, economic status, or hospital information and when geography was grouped by state or region. The effects of the severity codes within DRG 693 were also unchanged when the sample was restricted to admissions allocated to this DRG. Approximately 3% of all chemotherapy admissions had extremely high or low costs. Removal of these admissions decreased the effects of APR-DRG 693.4 to 1.378, APR-DRG other to 0.710, and AML to 0.689, although all three remained significant. These three admission descriptors were over-represented  in the extremely high–cost admissions. We identified 5,824 admissions with one or more discharge diagnosis codes for infection. Infection status was associated with 32% higher costs, with its inclusion reducing the size of the coefficients associated with the APR-DRG by 10% to 20%.Because AML admissions were infrequent but had a large effect on admission costs, the burden of the higher costs may be unevenly distributed among hospitals. Of 60 hospitals with 65% to 75% of admissions classified as APR-DRG 693.2, 13 (22%) had no AML admissions. For the remaining hospitals, ratios ranged from 97:1 to 3:1 ALL, bone or joint tumor, or soft tissue tumor admissions for every AML admission, suggesting some hospitals admit a larger proportion of patients with AML than others. Older age was associated with higher-cost admissions. Age also factors into risk stratification and treatment decisions in many diagnoses, so we ran the full model limited to each diagnosis group. When the model included only ALL admissions, admissions for infants and children age older than 15 years were significantly more costly than those of other age groups. Costs increased steadily by age for soft tissue tumor admissions but not significantly for bone or joint tumor admissions. Infants, our reference group, represented less than 1% of bone or joint tumor admissions. When we applied postestimation Wald testing, each bone or joint tumor age category differed significantly from one another, with the exception of those age 1 to younger than 5 compared with age 5 to younger than 10 years. There were no significant interactions between APR-DRG and age categories.19 In light of this policy change, it will be crucial to identify which hospitals are experiencing the most significant drops in reimbursement and whether these drops are associated with reductions in treatment or worst outcomes.We analyzed the ability of APR-DRGs to predict the costs of chemotherapy admissions for childhood cancers from a nationally representative data set. In this model, most admissions were classified as DRG 693. Increasing APR-DRG severity codes within this DRG were associated with increasing costs, suggesting that severity codes capture increasing intensity of chemotherapy admissions. We also identified factors potentially predictive of the admission costs currently not adequately captured by DRGs, notably cancer diagnosis and age. Medicaid systems have adopted prospective reimbursement schemes, in part, to drive efficiency. Historically, such adoptions have also been associated with hospitals or providers decreasing services because of underpayment.20 found that congenital cardiac disease was systematically misclassified by APR-DRGs in the United States, with potential effects on hospital-level mortality metrics. In a single-institution study, DRGs underidentified trauma patients, with resulting financial misappropriation.21 The EuroDRG research group classified 10 encounter types by 11 DRG algorithms and found significant variation across countries.25 Continued analysis of how well DRGs predict actual patient care is required to ensure that DRGs adequately reflect patient characteristics and treatments in both pediatric and adult admissions, particularly in light of recent incorporation of ICD-10-CM codes. Although the Centers for Medicare and Medicaid Services predicts minimal impact on overall payments as DRG algorithms change from ICD-9-CM to ICD-10-CM codes, there may be a varying impact at the hospital level.26A small but growing body of literature is identifying limitations of DRG algorithms in capturing important clinical factors. Parnell et al30 Childhood AML treatment has been associated with high toxicities32 and prolonged admissions.33 APR-DRG reimbursement schemes will likely encourage clinicians and researchers to reconsider some components of AML treatment, but significant changes require time to ensure that patient outcomes and safety are maintained. If cancer diagnoses within APR-DRGs are not adequately accounted for in reimbursement policies, hospitals with more AML admissions may be at higher fiscal risk.Our model suggests that costs associated with cancer diagnosis are not completely captured by the APR-DRGs. Diagnosis determines the chemotherapy regimen and, in turn, toxicity and supportive measures.34 than children of other ages. This clinical risk stratification may underlie the higher costs for infants and children with ALL who are older than 15 years. Alternatively, chemotherapy is dosed according to weight, which naturally increases with age during childhood. For higher-cost pharmaceuticals, the difference between infants and teenagers for the same agent may be substantial.27 The drivers of this cost difference could be studied further in data sets that allow identification of possible cost drivers.We performed age and diagnosis subanalyses that demonstrated how difficult it is to make specific policy or practice recommendations with respect to age. Age affected costs differently when we limited our samples to a single diagnosis, suggesting that diagnosis- and risk-specific treatments may play a role. For example, infants and children older than 10 years of age with ALL are considered at higher risk for recurrence and treated with more-intense chemotherapy35 sickle cell disease,36 and sinusitis.37 Direct comparison of our results with these studies is difficult because they used hospital charges rather than costs in their analyses. Romley et al38 linked higher costs to better outcomes for congenital heart disease surgery in this same hospital cohort. We cannot make such a direct link, because cancer outcomes are measured at a time quite distant from chemotherapy delivery and are influenced by many factors. In this data set, freestanding children’s hospitals each cared for a large volume of patients but were overall small compared with other hospital types. The average freestanding children’s hospital has approximately 280 beds , with specialized care focused on a limited population and an emphasis on medically complex cases.39 This has the potential to put freestanding children’s hospitals at a disadvantage in both economies of scale and economies of scope. Hospitals treating adults, already subject to DRG reimbursement, might have found cost-saving measures in cross-hospital services that lowered the overall cost for pediatric admissions. Only 27 of the 43 freestanding children’s hospitals in the United States were identified in this model, and this subset may differ from those excluded because their states do not participate in KID. The hospital characteristics included in KID are general descriptors; future studies including more detailed hospital information will be important for hospital administrators and policymakers desiring to maintain the critical role these institutions play in medical care for children.We found that freestanding children’s hospitals had significantly higher costs on average than other types of hospitals. Previous literature suggests this difference may not be limited to chemotherapy, because similar effects have been identified in cardiac transplantation,6 These limitations can be addressed in other smaller data sets and were offset by the broad geographic and hospital sampling of KID. Finally, KID only provides a total charge for the admission, a markup of costs determined by each hospital.40 We used cost-to-charge ratios that represented markup practices aggregated across the entire hospital; this may not accurately reflect the costs of individual departments. However, past studies have suggested that the difference is generally less than 10%41 and that similar admissions, such as those within a DRG group, are likely to use similar resources across organizations, further decreasing between-hospital variation.42Several limitations arise from the use of the KID data set. First, a lack of pharmaceutical data prevented investigation of the chemotherapy delivered. Second, the data set lacks patient identification. A child may be admitted multiple times during the course of his or her cancer treatment, but repeat admissions could not be tracked. Therefore, the influences of heavier users of medical care could not be accounted for outside of the sociodemographic characteristics we included. However, because reimbursement occurs by admission rather than the complete course of treatment, this methodologic limitation actually reflects payment arrangements. We were unable to determine actual reimbursement arrangements for each hospital. Each state determines its own reimbursement weights for APR-DRG and severity-level admissions, adjustments for local wages or participation in medical education, and provisions for admissions with extremely high costs or prolonged hospitalizations.In conclusion, APR-DRGs reflected much of the differences in costs of childhood cancer chemotherapy admissions. However, this analysis suggests that other factors are also important in predicting costs, most notably diagnosis and patient age. These should be considered in future design of APR-DRGs to make them more resource homogeneous. As health care resources become increasingly constrained, understanding where and how care and reimbursement currently misalign can identify target areas for future study and refinement.


These limitations should be considered when counseling pregnant women about the risks and benefits of testing for Zika virus infection during pregnancy. This updated guidance emphasizes a shared decision-making model for testing and screening pregnant women, one in which patients and providers work together to make decisions about testing and care plans based on patient preferences and values, clinical judgment, and a balanced assessment of risks and expected outcomes.CDC has updated the interim guidance for U.S. health care providers caring for pregnant women with possible Zika virus exposure in response to 1) declining prevalence of Zika virus disease in the World Health Organization’s Region of the Americas (Americas) and 2) emerging evidence indicating prolonged detection of Zika virus immunoglobulin M (IgM) antibodies. Zika virus cases were first reported in the Americas during 2015–2016; however, the incidence of Zika virus disease has since declined. As the prevalence of Zika virus disease declines, the likelihood of false-positive test results increases. In addition, emerging epidemiologic and laboratory data indicate that, as is the case with other flaviviruses, Zika virus IgM antibodies can persist beyond 12 weeks after infection. Therefore, IgM test results cannot always reliably distinguish between an infection that occurred the definition of possible Zika virus exposure has not changed and includes travel to, or residence in an area with risk for mosquito-borne Zika virus transmission or sex with a partner who has traveled to or resides in an area with risk for mosquito-borne Zika virus transmission. These areas can be found on the CDC “Zika Travel Information” webpage.*For these recommendations, Key recommendations include the following:before and during the current pregnancy, at every prenatal care visit.1) All pregnant women in the United States and U.S. territories should be asked about possible Zika virus exposure  CDC recommends that pregnant women not travel to any area with risk for Zika virus transmission. It is also recommended that pregnant women with a sex partner who has traveled to or lives in an area with risk for Zika virus transmission use condoms or abstain from sex for the duration of the pregnancy.2) Pregnant women with recent possible Zika virus exposure and symptomsof Zika virus disease should be tested to diagnose the cause of their symptoms. The updated recommendations include concurrent Zika virus nucleic acid test (NAT) and serologic testing as soon as possible through 12 weeks after symptom onset.with ongoing possible Zika virus exposureAsymptomatic pregnant women should be offered Zika virus NAT testing three times during pregnancy. IgM testing is no longer routinely recommended because IgM can persist for months after infection; therefore, IgM results cannot reliably determine whether an infection occurred during the current pregnancy. The optimal timing and frequency of testing of asymptomatic pregnant women with NAT alone is unknown. For pregnant women who have received a diagnosis of laboratory-confirmed Zika virus infection  ≥10 and dengue virus PRNT <10 results]) any time before or during the current pregnancy, additional Zika virus testing is not recommended. For pregnant women without a prior laboratory-confirmed diagnosis of Zika virus, NAT testing should be offered at the initiation of prenatal care, and if Zika virus RNA is not detected on clinical specimens, two additional tests should be offered during the course of the pregnancy coinciding with prenatal visits.3) 4) Asymptomatic pregnant women who have recentwithout ongoing possible exposure are not routinely recommended to have Zika virus testing.possible Zika virus exposure  but  Testing should be considered using a shared patient-provider decision-making model, one in which patients and providers work together to make decisions about testing and care plans based on patient preferences and values, clinical judgment, a balanced assessment of risks and expected outcomes, and the jurisdiction’s recommendations. Based on the epidemiology of Zika virus transmission and other epidemiologic considerations , jurisdictions might recommend testing of asymptomatic pregnant women, either for clinical care or as part of Zika virus surveillance. With the decline in the prevalence of Zika virus disease, the updated recommendations for the evaluation and testing of pregnant women with recent possible Zika virus exposure but without ongoing possible exposure are now the same for all areas with any risk for Zika virus transmission.5) Pregnant women who have recent possible Zika virus exposure and who have a fetus with prenatal ultrasound findings consistent with congenital Zika virus syndrome should receive Zika virus testing to assist in establishing the etiology of the birth defects. Testing should include both NAT and IgM tests.6) The comprehensive approach to testing placental and fetal tissues has been updated. Testing placental and fetal tissue specimens can be performed for diagnostic purposes in certain scenarios . However, testing of placental tissues for Zika virus infection is not routinely recommended for asymptomatic pregnant women who have recent possible Zika virus exposure but without ongoing possible exposure and who have a live born infant without evidence of possible Zika virus–associated birth defects.with ongoing possible Zika virus exposure is not warranted7) Zika virus IgM testing as part of preconception counseling to establish baseline IgM results for nonpregnant women  because Zika virus IgM testing is no longer routinely recommended for asymptomatic pregnant women with ongoing possible Zika virus exposure.CDC continues to evaluate all available evidence and will update recommendations as new information becomes available.Zika virus is a mosquito-borne flavivirus that is closely related to dengue, West Nile, Japanese encephalitis, and yellow fever viruses  have been reclassified to indicate that Zika virus transmission has been interrupted . However, in light of the limitations of serologic testing , for pregnant women without a previous diagnosis of laboratory-confirmed Zika virus infection, including those with laboratory evidence of flavivirus infection or laboratory evidence of presumptive Zika virus or flavivirus infection  from the Food and Drug Administration (FDA) for use on nontissue clinical specimens.Several assays can be used to detect Zika virus IgM antibodies in serum or cerebrospinal fluid. Zika virus IgM tests can be difficult to interpret because of false-positives and cross-reactivity with other flaviviruses, especially in persons who were previously infected with or vaccinated against a related flavivirus  mosquito season after introduction of Zika virus, testing becomes more complex. Given the evolving situation and the many uncertainties, the updated testing algorithms for symptomatic and asymptomatic pregnant women  Figure  emphasizitations . To addritations  have alsBOXbefore and during the current pregnancy. Health care providers should ask about the presence of symptoms of Zika virus disease , and place, duration, and type of travel to assess a woman’s potential for exposure to Zika virus and other flaviviruses .Pregnant women with possible Zika virus exposure should be asked about their risk for exposure both before the current pregnancy and one that occurred during the current pregnancy.It is important to ascertain whether a woman had exposure to Zika virus before the current pregnancy because Zika virus immunoglobulin M (IgM) antibodies can be detected for months after an infection. A positive Zika virus IgM result could indicate antibodies from infection before the current pregnancy, thus limiting the ability to distinguish between an infection that occurred It is important to ascertain whether a woman had exposure to flaviviruses other than Zika virus before the current pregnancy because a positive IgM result might have been caused by cross-reactivity from a previous flavivirus exposure.Health care providers and counselors should provide appropriate pretest counseling to inform decisions on whether to test; Zika virus test results should be interpreted within the context of known limitations.A negative Zika virus IgM test result, if performed during the recommended time frame, in the setting of a negative Zika virus nucleic acid test (NAT) result, provides some reassurance of absence of Zika virus infection during the current pregnancy. However, a negative Zika virus IgM test result should be interpreted within the context of the limitations of the assay.When plaque reduction neutralization testing (PRNT) is indicated and performed during the recommended time frame, a negative PRNT result in the setting of a negative NAT result indicates that there is no laboratory evidence of Zika virus infection.before and during the current pregnancy. Health care providers should ask about presence of symptoms of Zika virus disease  and place, duration, and type of travel to assess a woman’s potential for Zika virus exposure. Data from other mosquito-borne illnesses indicate that intensity of transmission, duration of travel, and type of travel influence the likelihood of infection , specifically  results . Zika viPregnant women with recent possible Zika virus exposure and symptoms of Zika virus disease. Testing for Zika virus infection is still recommended for pregnant women with symptoms of Zika virus disease and possible Zika virus exposure, with the main goal of establishing a diagnosis that accounts for their symptoms, or ruling out Zika virus infection so that an alternative diagnosis can be considered. Negative test results should prompt evaluation for other causes, which might include dengue virus or chikungunya virus infection, depending on the symptoms and epidemiology of circulating viruses.Concurrent NAT (serum and urine) and serologic testing (serum) is recommended for pregnant women as soon as possible, through 12 weeks after symptom onset . ReportsFor women who seek care >12 weeks after symptom onset, Zika virus IgM testing might be considered; however, a negative result does not rule out an infection during pregnancy because IgM levels decline over time. A positive result should be interpreted within the context of the known limitations of serologic testing.with ongoing possible Zika virus exposure.Asymptomatic pregnant women  For asymptomatic pregnant women with ongoing exposure to Zika virus, testing for Zika virus infection should be offered as part of routine obstetric care because it might identify acute infection during pregnancy  IgM testing has diagnostic limitations; 2) Zika virus NAT testing of serum does not reflect persistence in other body fluids . The current understanding of Zika virus shedding in genital secretions is limited  but . For asymptomatic pregnant women with recent possible Zika virus exposure , but without ongoing possible exposure, testing for Zika virus infection is not routinely recommended. However, testing should be considered using a shared decision-making model, one in which patients and providers work together to make decisions about testing and care plans based on patient preferences and values, clinical judgment, a balanced assessment of risks and expected outcomes, and the jurisdiction’s recommendations. Health care providers should consider potential exposure risk factors when deciding whether to advise testing. These include symptoms, type and length of possible exposure, Zika virus transmission trends at location of possible exposure and the use of prevention measures . Jurisdictional recommendations may take into account the epidemiology of Zika virus transmission and other epidemiologic considerations  in areas with risk for Zika virus transmission and, therefore, might include a routine recommendation to test asymptomatic pregnant women either for clinical care or as part of Zika virus infection surveillance.without ongoing possible exposure to address the increased probability of false positive results in the setting of the declining prevalence of Zika virus disease  should be considered to assess fetal anatomy, particularly fetal neuroanatomy, and to monitor growth. A study of 17 pregnancies in symptomatic women with laboratory-confirmed Zika virus infection and adverse fetal outcomes in Colombia and a summary of eight published studies of 37 pregnancies reported a median of 18 weeks from symptom onset to prenatal diagnosis of microcephaly  and 119–238 days (mean = 163 days), respectively, from maternal symptom onset  to provide insight into the potential etiology of the fetal loss or infant death , which cwithout ongoing possible exposure, it is critical that pediatric health care providers inquire about possible maternal and congenital Zika virus exposure for every newborn. Infants born to mothers with possible Zika virus exposure during pregnancy but who did not receive testing, including asymptomatic pregnant women with recent possible Zika virus exposure but without ongoing possible exposure, should receive a comprehensive physical examination, including standardized measurement of head circumference and newborn hearing screen, as part of routine pediatric care. In addition, based on the level of possible Zika virus exposure , the provider should consider whether further evaluation of the newborn for possible congenital Zika virus infection is warranted, in which case, a head ultrasound, and ophthalmologic assessment should be considered. Based on results of the evaluation, testing of the infant for Zika virus infection could be considered.Interim guidance for the evaluation of infants with congenital Zika virus exposure has been previously published; infants who meet one or more of the published criteria for testing for congenital Zika virus infection should be tested and evaluated in accordance with the updated CDC interim guidance for the evaluation and management of infants with possible Zika virus infection . Recommendations for outpatient management during the first 12 months of life include monitoring of head circumference and development and are provided in the updated CDC interim guidance for the evaluation and management of infants with possible Zika virus infection (This guidance also applies to infants born to mothers with negative maternal testing in the setting of ongoing possible Zika virus exposure or a possible Zika virus exposure that occurred more than 12 weeks before maternal testing (CDC recommends that pregnant women avoid travel to any area with risk for Zika virus transmission. To prevent Zika virus infection during pregnancy, all pregnant women and their partners should receive counseling on prevention measures including strategies to prevent mosquito bites and sexual transmission of Zika virus (


Christian Althaus and Nicola Low reflect on the contribution of sexual transmission to the spread of Zika virus. Sexual transmission of Zika virus could be as important as transmission through bites from infected mosquitoes, if media attention is any indicator ,2. One aPublicly available information about Zika virus infection leaves room for confusion about the relative importance of sex as a route of transmission. The website of the US Centers for Disease Control and Prevention (CDC) lists infected mosquito bites as the primary route of Zika transmission. Transmission through sex is also listed but without any contextual information about the frequency of reported cases, unlike the entries for transmission through blood transfusion or laboratory exposure . The CDCR0), and it can be estimated from the observed incidence of reported cases if we have information on the generation time, which is the average time from infection of one individual to the next individual. In the same setting, estimates of R0 for Zika are remarkably similar to those for dengue, another mosquito-borne infection, but there is considerable variation among studies done in different countries and populations [R0 for Zika were published before the evidence of sexual transmission started to accumulate. The first modelling study to include this secondary transmission route estimated that sexual transmission contributed about 3.0% to the overall R0 [Mathematical modelling studies can put the data into context. A Zika-infected person infects on average about two to five additional people when mosquitoes are the vector for transmission . This quulations . The earrall R0 , but therall R0  and 30% rall R0 . This lorall R0 , for whiR0 for sexual transmission, equivalent to the product of the infectious duration and the sexual transmission rate, is greater than one. If the infectious duration of an STI is short , transmissibility during a sexual partnership typically needs to be around 50% or more [Zika is probably not capable of sustained transmission through sexual intercourse in a general population. A prerequisite for a pathogen to spread as a sexually transmitted infection (STI) is that  or more . Of the  or more , which iInvestigation with follow up of episodes of sexually transmitted Zika virus infection remains necessary for several reasons. First, Zika could undergo sexual transmission within small clusters involving particular groups with frequent change of sexual partners, such as female sex workers or men who have sex with men. Enhanced surveillance to identify and characterize outbreaks will allow both implementation of control measures and better definition of currently uncertain quantities, including transmission probability and infectious duration. Second, the relative contribution of sexually transmitted cases could increase for populations in which mosquito-borne infections decrease or have not been present at all. Third, viral persistence and sexual transmission could serve as a reservoir for Zika to persist through seasons with low abundance of mosquitoes and could then facilitate regional and international spread. Fourth, larger epidemiological and virological studies are needed to address the many unanswered questions about sexual transmission of Zika virus, such as the prevalence of virus in bodily fluids and the infectivity of persisting virus. A systematic review of studies about sexual transmission of Zika virus would clarify the evidence about all dimensions of the causal relationship, such as temporality, strength of association, and biological plausibility, as has been done for congenital and other neurological complications of Zika .In conclusion, individual case reports show evidence that Zika virus has been transmitted from person to person through sexual intercourse—so it is sexually transmissible. But sexual transmission is not the primary route of transmission. For a disease to be designated a sexually transmitted infection, sexual intercourse should account for a substantial proportion of infections. Information and advice for the public should give clear messages about the relative contributions of mosquito-borne, vertical, sexual, and bloodborne transmission so that people can make informed choices about the preventive measures that they should take.


In this paper, we propose an emotion separated method(SeTF·IDF) to assign the emotion labels of sentences with different values, which has a better visual effect compared with the values represented by TF·IDF in the visualization of a multi-label Chinese emotional corpus Ren_CECps. Inspired by the enormous improvement of the visualization map propelled by the changed distances among the sentences, we being the first group utilizes the Word Mover’s Distance(WMD) algorithm as a way of feature representation in Chinese text emotion classification. Our experiments show that both in 80% for training, 20% for testing and 50% for training, 50% for testing experiments of Ren_CECps, WMD features get the best f1 scores and have a greater increase compared with the same dimension feature vectors obtained by dimension reduction TF·IDF method. Compared experiments in English corpus also show the efficiency of WMD features in the cross-language field. Since the pace of modern life becomes faster and faster, people always work and live with high stress. From the report published by WHO, one in four people in the world will be affected by mental or neurological disorders at some point in their lives . Thus, iEmotions play an important role in successful and effective human-human communication . There iIn this paper, we propose an emotion separated method(SeTF·IDF) to assign the emotion labels of sentences with different values, which has a better visual effect compared with the values represented by TF·IDF in the visualization of a multi-label Chinese emotional corpus Ren_CECps. The separated method shows excellent ability of distinguishing sentences from multi-emotion labels, which can move data points away and avoid overlapping from each other. The moved multi-emotion points inspire us to be the first group to utilize the Word Mover’s Distance(WMD) algorithm as a way of feature representation in Chinese text emotion classification. Our experiments based on sentence level of Ren_CECps show that both in 80% for training, 20% for testing and 50% for training, 50% for testing experiments of Ren_CECps, WMD features get the best F1-scores of 0.318 and 0.31, where the baseline of TF·IDF are 0.196 and 0.204 respectively and the enhanced baseline of SeTF·IDF are 0.293 and 0.283. Compared with the same dimension feature vectors obtained by dimension reduction TF·IDF method, the WMD features get 3 times’ improvement based on F1-scores of 0.115 and 0.116. To speed up the calculation of WMD, we make changes of WMD algorithm, which gives a 16000 times decrease of time-consuming. For a better comparison, some experiments based on 20 newsgroup data set are also conducted. English corpus based experiments give an almost ten times promotion of F1-scores between WMD features(0.646) and dimension reduction TF·IDF method(0.076). Those above shows the efficiency of WMD features in classification of cross-language data sets and the WMD features have a strong ability in multi-emotion classification.The remainder of this paper is organized as follows: Section 2 presents some related works. Section 3 gives the description of SeTF·IDF and describes the visualization of Chinese emotional corpus Ren_CECps. Section 4 goes for a comprehensive explanation of Word Mover’s Distance and the feature representation method. Section 5 illustrates the experimental configurations of two language data sets and draws the results in tables and graphs. In section 6, some discussions will be given. Section 7 presents the conclusions and future works.In 1997, “Affective Computing” was provided by Picard , which iFor similarity computing using a metric between two distributions, the Earth Mover’s Distance(EMD)  is one oThe most commonly used algorithms to represent the documents for similarity computing are statistic based algorithms like TF·IDF , LDA 2222, or trLimited by the NLP and machine learning algorithms, the pioneering studies in emotion computing were based on lexicons –28. AfteEmotion computing in Chinese has attracted many researchers due to the development of microblogging and tweet. Some studies in sentiment analysis of Chinese documents  turn to For estimating emotion of words that not registered in the lexicon, EMD can be applied to vectors of words and get higher accuracy compared with using only word importance value . As the For a multi-class corpus, we can make a 2D or 3D scatter-plot to have a good view of the distribution of the data. But for multi-class data with multi-label, to do the visualization with the same different colored points will make the 2D or 3D graph unreadable. Thus, how to match the multi-label information into a 2D or 3D graph is the key target need to be covered. In this paper, for visualizing the multi-label emotional corpus Ren_CECps, we propose an emotion separated TF·IDF method(SeTF·IDF) to represent each emotional category independently with different values. And to make a better 2D visualization, we use one of the stat-of-art dimension reduction algorithm t-SNE  to measuhttp://a1-www.is.tokushima-u.ac.jp/member/ren/Ren-CECps1.0/DocumentforRen-CECps1.0.html) is an annotated emotional Chinese corpus using Chinese blog texts. The corpus was annotated in three levels: document level, paragraph level, sentence level . In which, tfidfej means the TF·IDF result of emotion keyword wordi in emotion category ej. Setfi means the term frequency of emotion keyword wordi in emotion category ej. dfe means the total number of sentences which contain emotion keyword wordi.The good news is that in Ren_CECps the emotion keywords of a sentence are annotated. Thus, for an annotated emotion keyword, we calculate its Following the algorithm below, the words without emotional labels annotated are calculated through formula , for theM2 and corresponding label list l, we can draw the 2D graph of Ren_CECps. The colors needed for every labels are listed as the sets:Using 2D data Anger, Anxiety, Expect, Hate, Joy, Love, neutral, Sorrow, Surprise);Label: Color: 1: forsentence ∈ Sdo2:  3:   calculate vectors by formulas  and 2);;2);M and corresponding label ∈ L into list l4:   add vector into matrix end for5:  M50—50 dimensions’ row matrix reduced from M6:  M2—2 dimensions’ row matrix reduced from M507:  returnM2, l8:  end function9: The elongated distances between every category of sentences and the distributions changed in SeTF·IDF indeed have a better visual result compared with TF·IDF.“Love” points have the similar distribution compared with “Anxiety” points;Most of the “Sorrow” points come with no other emotional points embedding into their group.Sentences may have completely opposite emotion categories. In some clusters, there are pairs like “Sorrow and Joy”, “Hate and Love”.Fuzziness emotion categories such as “Expect” have the most frequency to appear with other emotion categories.The http://a1-www.is.tokushima-u.ac.jp/data_all/.Based on those features, we can have a more clear vision of Ren_CECps. For a better view, we also make a 3D visualization result for the emotional corpus at The visualization graphs in Word Mover’s Distance The word mover’s distance(WMD) 3. for words in TDdo               4.  end for4. M ← TD,V          5.  for words in TD′ do5.  distance matrix end for                     6.   distance matrix M′ ← TD′,V6. loopdi, dj in corpus D              7.  end for7. return emd             8.  return emd8.  end loop                  9. end loop9. To verify the improvement, we make three groups of experiments. Each group is a ten times’ computing based on 10 pairs of sentences which are selected randomly from Ren_CECps. The comparison of time-consuming is illustrated in Parallelization Though the fast WMD algorithm has a 16000 times improvement of computation efficiency compared with WMD algorithm, it’s still too slow for our experiments, so we parallelize the fast WMD model using 10-12 processes in 8 servers. To be consist, the words of WMD written in the rest part of this paper are all in the meaning of the fast WMD algorithm without special explanation.In this paper, the evaluation is measured by F1-score:tp is the number of true positive, fp is the number of false positive and fn is the number of false negatives. In this paper, both of precision and recall are calculated in ‘macro’ model using metrics package in sklearn at http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics.In which, http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html for our classification experiments. All of the SVM programs are running upon the default configuration.The experiments are arranged based on 50321 sentences(split in categories) of Ren_CECps and 18846 documents of 20 newsgroup. We use a Linear Support Vector Machine library at 1v1: The experiments based on the 50% of Ren_CECps for training and the rest 50% of Ren_CECps for testing;4v1: The experiments based on the 80% of Ren_CECps for training and the rest 20% of Ren_CECps for testing;20 news: The experiments based on the training and testing data of 20 newsgroup data set;1800:TF·IDF A low dimensional feature representation method, using SVD at http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html to reduce TF·IDF feature vectors into 1800 dimensions;2000:TF·IDF The 2000 dimensions feature representation method reduced from TF·IDF feature vectors;Seed_TF·IDF: Using cosine function to calculate the similarity between target data and seed sub-corpus, in which the final similarities will be the feature vectors of training and testing data. In these experiments, all of the data are initialized by TF·IDF. We assume  as the TF·IDF represented sub-corpus, in which vi is the TF·IDF vector. tj means the TF·IDF vector of training and testing data. Thus, the final feature vectors of training and testing data can be calculated as this function: , ⋯, cos, ⋯, cos)TF·IDF_word2vec: The enhanced TF·IDF method which uses the word embeddings trained by word2vec as the weights for the corresponding words. The feature vectors used in this experiment are exported by the multiplication of TF·IDF and the embedding matrix.sent2vec: In this experiment, we use sentences embeddings trained by sent2vec tool at https://github.com/epfml/sent2vec as feature vectors. Every documents of 20 newsgroup data set are converted into one line files. The output dimension of sentence embeddings is 700.We will make some classification experiments among TF·IDF, SeTF·IDF, WMD and a sentence embedding method which is one of the stat-of-the-art methods trained by sent2vec . To makeResults pre-processing As the results computed by WMD contain the “NaN” data. In order to fit data into SVM, we convert all of the “NaN” data into integer of zero. In discussion section, we will explain the reason.1800 and 5% higher than similarity representation method of Seed_TF·IDF.In 2000 and gets almost 10 times promotion on F1-score.But in 2000 and TF·IDF1800.We can find the TF·IDF_word2vec method gets the F1-score of 0.2209, 0.235 and 0.582 respectively in Figs 1800 and TF·IDF2000: In 1v1 and 4v1 experiments, the F1-scores of TF·IDF1800 drop two times compared with TF·IDF, while in 20 newsgroup, F1-scores of TF·IDF2000 decline nine times compared with TF·IDF(0.68 to 0.07).In Figs Difficulty in SeTF·IDF Though SeTF·IDF can match sentences into different emotion dimensions, the method is based on priori knowledges annotated in corpus. This means we cannot use SeTF·IDF to match a new sentence or document into multi-emotion dimensions due to lack of no emotional keywords annotated manually. That’s why we use SeTF·IDF as an enhanced baseline method. It’s an idealized results. The importance is this visualization algorithm makes us having a clearer visual results, and changes our way of thinking in training multi-label data.“NaN” conversion Both of the results of the Chinese and English corpus computed by WMD contain “NaN” data. This makes the data disable to train in SVM model. Parsing the sentence pairs which “NaN” data happened, we found the “NaN” data always appear in short sentences or documents, and the target data are the same with the seed data, like “好! (English: good)” and “不知(English: I don’t know.)” in Chinese corpus, “Thanks!” and “It’s there…” in English corpus. One special situation is the documents with a lot of messy script codes in 20 newsgroup data set, and these messy codes will result in “NaN” data.Having known the contents leaded to error, we make two ways to convert the “NaN” data. One is replacing the “NaN” data with “0”, and the other one is “1”. The reason for “0” conversion is that the pairs of sentences are the same, and in distance meanings of WMD, “0” is the most suitable and practical; But considering the similarity vector, “0” elements are useless, and may cause information loss, “1” conversion maybe better.To verify which one is suitable, we convert the “NaN” data to “0” and “1” independently in both 1v1 and 4v1 experiments. The “1” conversion data gets 0.291 and 0.305 in F1-scores in 1v1 and 4v1 experiments respectively, a bit lower than “0” conversion of 0.310 and 0.318. Thus, we choose “0” conversion finally in all of the experiments.The opposite results in Chinese and English data sets In 1v1 and 4v1 experiments, the WMD method gets the best results. On the contrary, in 20 news, the TF·IDF gets the best result, and Seed_TF·IDF gets the second, third is WMD. One reason is the reduction rates mentioned above of the two language corpus are different. The other reason maybe the word embeddings of English used in the experiments have more missing words than Chinese embeddings, this can explain why TF·IDF_word2vec gets higher results and indeed should be higher than TF·IDF in Chinese corpus, but gets fourth rank in English corpus and almost 10% lower than TF·IDF.Distance changed by the emotion separated method can get higher visual performance in multi-label emotional corpus;The WMD algorithm is indeed efficient for classification.Different language has different information density of words. Thus can influence the results of feature representation methods.In Chinese corpus, owing to the high information density of words, for a certain degree of feature representation reduction, it’s good for the classifier to training models and can help improve results; For English corpus, due to the lower information density of words, the same degree for reducing features may not good for model training and needs more experiments to find the best degree.The experiments show some enlightening conclusions based on the cross-language corpus.Though we had given a fast computing frame of the original WMD algorithm, the calculation process is still time-consuming, to generalize the model, a more fast computing improvement is needed, and this will be our future work. For the emotion classification experiments, the just over 30% F1-scores cannot supply the emotional recognition applications, we will continue to improve this field.


R2 = 0.8131). Microfluidic DNA biochips capable of detecting specific DNA sequences are useful in medical diagnostics, drug discovery, food safety monitoring and agriculture. They are used as miniaturized platforms for analysis of nucleic acids-based biomarkers. Binding kinetics between immobilized single stranded DNA on the surface and its complementary strand present in the sample are of interest. To achieve optimal sensitivity with minimum sample size and rapid hybridization, ability to predict the kinetics of hybridization based on the thermodynamic characteristics of the probe is crucial. In this study, a computer aided numerical model for the design and optimization of a flow-through biochip was developed using a finite element technique packaged software tool  to simulate the transport of DNA through a microfluidic chamber to the reaction surface. The model accounts for fluid flow, convection and diffusion in the channel and on the reaction surface. Concentration, association rate constant, dissociation rate constant, recirculation flow rate, and temperature were key parameters affecting the rate of hybridization. The model predicted the kinetic profile and signal intensities of eighteen 20-mer probes targeting vancomycin resistance genes (VRGs). Predicted signal intensities and hybridization kinetics strongly correlated with experimental data in the biochip ( Microfluidic biochips are widely used for high-throughput analysis of mutations, expression profiles, and in identification of microorganisms  and are In this work, a three-dimensional mathematical model for selective nucleic acid hybridization in a microfluidic biochip was developed followed by comparison of theoretical predictions with experimental data. The biochip used was developed by Xeotron, now owned by Invitrogen  and has been previously described . To modeA three-dimensional mathematical model was implemented to describe the hybridization of a DNA target to a surface-immobilized probe in a glass wafer microfluidic biochip . The miIn this study, to evaluate the hybridization kinetics, only a single hybridization well was considered . In physThe target DNA solution flows into the micro channel at a constant flow rate. The flow of an aqueous solution of DNA inside a small channel is assumed as uniform. The target DNA molecules are transported by convection along the flow direction where they are free to diffuse. Fluid flow is circulated through the chip multiple times, so the fluid flow rate evaluated in this study is, in fact, the recirculation flow rate. When the target DNA molecule reaches the hybridization well, the well-known association–dissociation reaction occurs between the target DNA and the immobilized probes. Both the association and dissociation events are modeled here using a differential kinetic equation. Only one of the many hybridization wells, connected with a microchannel, is considered in the three-dimensional model . The fluThe incoming flow has a small concentration of the target DNA which is described by a convection–diffusion equation of the form,onk represents the association rate constant, offk is the dissociation rate constant, tR is the total surface concentration of probes, and B is the surface concentration of bound targets at time, t. The association rate constant, onk, depends on the DNA sequence involved, the temperature, and the ionic strength of the medium but not to the point that it would drastically affect the order of magnitude. Based on the experimental results reported in literature [6 M−1s−1 for onk was used in these simulations. The dissociation rate constant, offk, was calculated using the thermodynamic model presented in literature [Once the target DNA reaches the bottom of the hybridization well, the association and dissociation kinetics are described by using an ordinary differential equation,terature ,24,25, iterature . In thisNumerical simulations of a single hybridization well were performed with COMSOL Multiphysics (FEMLAB), a finite element software package, which uses two geometries and three application modes to solve the formula. The first geometry is three-dimensional and represents the channel connecting the micro well. Within this geometry, the model used two application modes: incompressible Navier–Stokes, and convection and diffusion to model the transport of the target DNA. The second geometry is two-dimensional and simulates the reaction surface with the diffusion application mode. It is assumed that the hybridization reactions take place on a 2D surface (bottom surface of the micro-wells) whereas bulk flow of incoming target DNA is resolved on a 3D geometry. To correctly model the multi-scale physics, extrusion-coupling variables were defined. These variables couple the mass transport from 3D bulk flow using a surface boundary condition to the hybridization reactions on a 2D surface. In COMSOL Version 4.2 and later, instead of using extrusion-coupling variables, a Surface Reaction interface can be used with a single 3D geometry. This interface resolves surface coverage of the adsorbed species, with a mass source or sink automatically coupled to the surface boundary condition in the bulk mass transport equation. The meshing around the channel was not greater than 10 μm and around the hybridization well it was smaller than 0.2 μm. In all simulations, an “insulation” boundary condition was chosen for the non-reactive surfaces at the top and the bottom of the microchannel except where the hybridization occurred. The model is solved in two steps using different solvers. First, it solves the incompressible Navier–Stokes application mode with a non-linear solver followed by the time-dependent solver to simultaneously solve the convection and diffusion and the diffusion application modes. The model predicts the concentration of duplexes formed due to interactions between the target DNA and immobilized probes inside the hybridization well.d is log dye concentration, L is the upper limit of the dynamic range (set to 3.0). Preset values were taken from the study [In order to correlate the duplex concentration obtained through our model with the hybridization signal intensities, we used Equation (5) ,(5)I=L1he study . For our2PO4, and 1 mM EDTA pH 7.7), and 0.4% Triton X-100 (Sigma-Aldrich) [The mathematical model was validated using the real-time hybridization experiments performed on Xeotron hybridization station . Eighteen targets were designed to complement the 18 probes for the hybridization experiments . HoweverAldrich) . SeveralVanC (5′-GCCTTATGTATGAACAAATGGCT-3′) type gene  ranging from 0.1 pmol to 100 pmol. As shown in The quantitative prediction of the effect of operating parameters on the signal intensity of DNA hybridization is investigated by performing 28 different simulations. A variety of parameters have distinct influences on the hybridization kinetics of ype gene . The binociation a. In accociation b. Simulaociation c primariFlow rate is one of the key parameters that determines whether the kinetics of DNA hybridization in the microfluidic biochip is transport-limited or reaction-limited . Here, tFor each of these flow rates, dimensionless parameters including the Reynold’s number and Péclet number have been calculated .offk, was calculated for each gene using thermodynamic model [onk, was kept constant throughout the simulations. Other parameters were obtained from literature as described in previous section and were kept constant in the simulations. A comparison of the model predictions of signal intensities after 14 h of hybridization with the experimental signal intensities obtained on the same biochip under similar conditions showed that the model could predict the final signal intensities reasonably well (R2 = 0.8131)  probes . The preic model  whereas  0.8131) . It was The 14-h association curve and signal intensity was accurately predicted for six of the VRGs probes using the developed model a. The moWe also observed, in predicted results and in hybridization experiments, that the signal intensities of most of the selected vancomycin gene targets did not change much after 6 h of hybridization, therefore shorter time (ca. 6 h) could have been chosen for hybridization instead of 14 h. In this way, the model can be used to estimate the optimized time for hybridization experiments, thereby enhancing hybridization experiments and in some cases reducing the overall detection time. The developed three-dimensional mathematical model identified sample concentration, flow rate, binding constants, and temperature as crucial parameters affecting the rate of DNA hybridization for VRGs. The flow rate is the most important parameter that determines whether the kinetics of DNA hybridization in the microfluidic biochip is transport or reaction-limited. Based on comparison between the predicted and experimental results on the DNA hybridization for VRGs, it was observed that the model predicts hybridization of DNA in the biochips except for a few cases where it either overestimates or underestimates the results. It also predicted that shorter time could have been chosen for our DNA hybridization experiments. Overall, the model provided an understanding of complex DNA:DNA hybridization kinetics in microfluidic biochips. It could play a vital role in optimizing hybridization protocols for antibiotic resistance genes and the development of chip and chamber geometries for detecting them, which is very important in point-of-care testing where multiple targets need to be detected simultaneously using low sample concentrations.


We tested 210 dengue virus‒negative samples collected from febrile patients during a dengue virus type 4 outbreak in Rio de Janeiro in April 2013 and found 3 samples positive for Zika virus. Our findings support previously published entomological data suggesting Zika virus was introduced into Brazil during October 2012–May 2013. In 2016, Brasil et al.  outbreak to investigate whether co-circulation might have been occurring before reported cases.By August 2015, Zika virus infection had been confirmed in 13 states of Brazil , some of which were located >2,500 miles apart  with acute febrile syndrome who visited an acute healthcare facility in Tijuca, a middle-class district in the northern zone of Rio de Janeiro, Brazil, during a DENV-4 outbreak occurring March‒May 2013. All samples tested negative for DENV RNA by RT-PCR and DENV nonstructural protein 1 by Platelia Dengue NS1 Ag ELISA   according to the manufacturer’s instructions. We performed quantitative RT-PCR (qRT-PCR) with the QuantiNova Probe RT-PCR Kit (QIAGEN) in a Rotor-Gene Q Sequence Detection System (QIAGEN) using 25-µL reaction mixtures containing 5 µL of RNA template. We used primers, probes, and cycling conditions for Zika virus detection recommended by the Centers for Disease Control and Prevention . We confirmed that the other 3 samples (2 from men and 1 from a woman) were positive for Zika virus genome after repetition of RNA extraction and qRT-PCR.5/µL, and leukocyte counts 4.4‒19.8 × 103 cells/µL.Zika virus‒positive patients were young , lived in Tijuca, had low-grade fever (1‒2 days) during acute disease, and had no underlying conditions. Their travel histories were not available. All patients reported prostration, myalgia, arthralgia, headache, retro-orbital pain, and nausea . None reZika virus dissemination beyond Asia and Africa occurred after the 2007 epidemic in Micronesia (However, our findings suggest that Zika virus had already been circulating in Rio de Janeiro since April 2013, consistent with the report by Metsky et al. (


The World Health Organization has declared Zika virus an international public health emergency. Knowledge of Zika virus genomic epidemiology is currently limited due to challenges in obtaining and processing samples for sequencing. The ZiBRA project is a United Kingdom–Brazil collaboration that aims to improve this situation using new sequencing technologies. On 1 February 2016, the World Health Organization declared a Public Health Emergency of International Concern in response to the transmission of Zika virus (ZIKV) in the Americas and beyond. Although ZIKV was first isolated as early as 1947, only 13 naturally occurring cases were identified in the six decades following its discovery, in Nigeria, Malaysia, and Indonesia [The first suspected cases of ZIKV infection in the Americas were detected in Brazil in early March 2015 in Natal (Rio Grande do Norte state) . These tViral molecular sequences can play an important role in tackling emerging epidemics and were widely used to understand the 2009 H1N1 influenza pandemic and the spread of Ebola virus in West Africa in 2013–2016 . Samplehttps://zika.labkey.com) suggest that, in an animal model of infection, ZIKV viral load reaches a maximum only 1–2 days after virus infection. Furthermore, in humans, it seems that viral load peaks within a week of onset of clinical symptoms [http://andersen-lab.com/zika-virus-pilot). Most commonly this is undertaken by growth in cell culture, but this method is laborious and has the potential to introduce culture-specific genetic changes. An alternative approach is genome-tiling PCR, a technique used to good effect on Ebola virus but which requires labor-intensive protocol development and testing for good results.Why are so few ZIKV genomes available more than a year after the virus was discovered in the Americas? Data from the Zika Open Research Portal (http://zibraproject.github.io). The founding ZiBRA team comprised 16 members, 14 of whom are from three Brazilian institutes: the University of Sao Paulo, FioCruz Bahia, and the Instituto Evandro Chagas. Further support has been provided by the Ministry of Health in Brazil. The project officially started in June 2016 and aims to address the following four questions.In order to improve this situation we initiated a pilot project—the Zika in Brazil Real-time Analysis (ZiBRA) project—which aims to improve the molecular surveillance and sequencing of ZIKV in Brazil through collaboration and capacity building with a number of Brazilian research groups and institutions. ZiBRA aims to share data and results as soon as they are finished and updates are provided on the ZiBRA website  to the local Central Laboratory of Public Health (LACEN), usually located in the capital city of each federal state, from where they are sent to a reference laboratory for molecular confirmation by PCR. However, during the ZiBRA fieldtrip, we found that many samples sent by the LACENs were still awaiting testing. Backlogs were caused by the unprecedented scale of the Zika public health emergency and by the fact that the laboratories must also cope with cases of suspected chikungunya and dengue virus infection, viruses that co-circulate in the same regions. Fast turnaround of molecular diagnostic results is clearly needed; some LACENs reported waiting 9 months between the date of sample collection and delivery of diagnostic results.http://zibraproject.github.io). As part of our commitment to open science practices, both the protocol and materials are freely available through our website in advance of formal publication. This open analysis enables Zika research to move forward faster internationally: weeks after publishing our multiplex PCR protocol, Kristian Andersen’s laboratory at the Scripps Research Institute adopted it for sequencing of clinical samples from the United States. They rapidly modified it to support the Illumina platform and subsequently released their protocol openly for others to use (http://andersen-lab.com/miseq-protocol-zika-virus-sequencing/).The ZiBRA team, with the help of LACEN personnel, tested 1349 samples for ZIKV RNA across Rio Grande do Norte, Paraíba, Recife, Maceió, and Bahia states, using previously described protocols  and the Complete genomic characterization of the ZIKV RNA-positive samples identified by ZiBRA will provide a framework for reconstructing the epidemic trajectories taken by ZIKV in the Americas and for tracking its spread into other geographic regions. We hope that ZiBRA will illustrate the practicality and benefits of open science and real-time data sharing during a public health emergency. Through our partnership with the Brazilian Ministry of Health, we shared all PCR results with the LACENs within 48 hours of analysis. In the future, we and a growing network of Brazilian scientists aim to extend the pilot ZiBRA project to a national scale, in order to establish a Brazilian surveillance network capable of detecting and characterizing a broad range of arboviruses in real-time.


Viruses have drawn much attention in recent years due to increased recognition of their important roles in virology, immunology, clinical diagnosis, and therapy. Because the biological and physical properties of viruses significantly impact their applications, quantitative detection of individual virus particles has become a critical issue. However, due to various inherent limitations of conventional enumeration techniques such as infectious titer assays, immunological assays, and electron microscopic observation, this issue remains challenging. Thanks to significant advances in nanotechnology, nanostructure-based electrical sensors have emerged as promising platforms for real-time, sensitive detection of numerous bioanalytes. In this paper, we review recent progress in nanopore-based electrical sensing, with particular emphasis on the application of this technique to the quantification of virus particles. Our aim is to provide insights into this novel nanosensor technology, and highlight its ability to enhance current understanding of a variety of viruses. Viruses are a major cause of infectious diseases. As such, they hold great significance in virological and immunological research and have extensive applications in clinical diagnosis and therapy. Determining both the dimensions and number of viruses is extremely important in many applications, such as the production of virus-based vaccines and therapeutic agents; hence, quantitative detection of viruses is becoming increasingly important. From a microbiological perspective, viruses are infectious agents that replicate only inside host cells. Single virus particles, also known as virions, generally consist of either double-stranded or single-stranded genetic molecules (DNA or RNA) surrounded by a protein shell called a capsid. In some cases, the capsid is enclosed within an outer lipid envelope. Several techniques are available for virus quantification . Nanoparticle-sensing technologies can be broadly classified into two categories: visualization-based and non-visualization-based techniques. Visualization-based techniques include transmission electron microscope (TEM)  miniaturized system to detect single virus particles must address the problems and limitations associated with the aforementioned technologies. One promising approach is the use of electrical detection techniques based on probing changes in resistance and/or capacitance using a nanoscale constriction. Over the last two decades, these techniques have demonstrated great capabilities of sensing a wide range of biomolecules , smaller sample volume (~40 μL), and less operating time (~10 min) than traditional sensing techniques. Consequently, PRS has been employed for detection of DNA, proteins, viruses, bacteria, particles for drug delivery system , extracellular vesicles, and inorganic and polymeric nanoparticles  make them unsuitable for virus detection  by comparing their pulse height to that of standard polystyrene latex beads. In addition, viruses were counted not only by comparing viruses and latex beads, but also by measuring the flow rates, which is related to the concentration. They reported achieving a lower practical count limit of 5 × 107 particles/mL. The same group subsequently compared measurements of other viruses  using light-scattering and electron microscopy to RPS measurements  and specific antibody using RPS as shown in Figure Paramecium bursaria chlorella virus and attempted to elucidate the kinetics of the antibody-virus interaction by simply detecting changes in the pulse amplitude after adding antibodies to the virus samples. Zhou and coworkers   using a variety of state-of-the-art nanofabrication techniques   is used. This is insufficient for detecting the majority of viruses with a size in the 10 s of nm or viruses with strongly anisotropic dimensions . There is thus a demand for further decreases in pore size to improve detection limits.In-plane naopores are compactly integrated into micro/nanofluidic devices, leading to enhanced portability and fluid control, lower sample consumption, ease of observing particle translocation optically, and improved mass transfer of analytes to the nanopore. Moreover, incorporating multiple pores in series or in parallel can increase the throughput and the device functionality (Fraikin et al., Harms and coworkers fabricated a nanochannel with two nanopores in series to detect HBV capsids as shown in Figure In conclusion, nanopore-based electrical sensing techniques have experienced significant growth as emerging yet promising platforms for nanoparticle detection, driven by dramatic advances in nanotechnology. Nanopore-based electrical sensing provides excellent capabilities of quantifying virus particles in a real-time, label-free, high-throughput, and particle-by-particle manner.However, current trends suggest that some challenges still remain and need to be overcome if the range of practical applications is to widen. First, improvements in pore fabrication and signal readout are needed. Reproducible fabrication and improved readout capabilities will enhance measurement reproducibility, increase sensitivity, and lower the detection limits. Introducing surface modifications or coatings within nanopores is necessary as well, as this can minimize non-specific adsorption and pore clogging. Second, even though research indicates that the accuracy of electrical sensing is comparable to that of DLS and TEM technologies, the combination with other non-electrical sensing techniques during measurements will be an interesting trend. For example, it was reported that simultaneous electrical and optical analysis can provide “double-checked” results (Liu et al., The most obvious advantage of nanopore-based electrical methods is the possibility of detecting unknown and new species of viruses. Unknown viruses are intrinsically difficult to detect using traditional methods because information regarding the genome or membrane proteins is necessary prior to the design of PCR primers or antibodies, respectively. Furthermore, host cells must be found for isolating and obtaining the source DNA/RNA or protein from the viruses. Nanopore-based assays can detect unknown viruses in the absence of such biochemical information, however, based simply on virus electrical properties. Further developments in in-plane sensor technology could lead to mobile and wearable devices for monitoring infectious viruses ubiquitously, which could enhance public safety and health. From a long-term prospective, we can believe that nanopore-based virus-sensing techniques will assume a more central role in the quantification of viruses.LY wrote the draft and TY revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Use of the oral cholera vaccine (OCV) is a vital short-term strategy to control cholera in endemic areas with poor water and sanitation infrastructure. Identifying, estimating, and categorizing the delivery costs of OCV campaigns are useful in analyzing cost-effectiveness, understanding vaccine affordability, and in planning and decision making by program managers and policy makers.To review and re-estimate oral cholera vaccination program costs and propose a new standardized categorization that can help in collation, analysis, and comparison of delivery costs across countries.Peer reviewed publications listed in PubMed database, Google Scholar and World Health Organization (WHO) websites and unpublished data from organizations involved in oral cholera vaccination.st, 2014.The publications and reports containing oral cholera vaccination delivery costs, conducted in low- and middle-income countries based on World Bank Classification. Limits are humans and publication date before December 31No participants are involved, only costs are collected.Oral cholera vaccination and cost estimation.A systematic review was conducted using pre-defined inclusion and exclusion criteria. Cost items were categorized into four main cost groups: vaccination program preparation, vaccine administration, adverse events following immunization and vaccine procurement; the first three groups constituting the vaccine delivery costs. The costs were re-estimated in 2014 US dollars (US$) and in international dollar (I$).Ten studies were identified and included in the analysis. The vaccine delivery costs ranged from US$0.36 to US$ 6.32 (in US$2014) which was equivalent to I$ 0.99 to I$ 16.81 (in I$2014). The vaccine procurement costs ranged from US$ 0.29 to US$ 29.70 (in US$2014), which was equivalent to I$ 0.72 to I$ 78.96 (in I$2014). The delivery costs in routine immunization systems were lowest from US$ 0.36 (in US$2014) equivalent to I$ 0.99 (in I$2014).The reported cost categories are not standardized at collection point and may lead to misclassification. Costs for some OCV campaigns are not available and analysis does not include direct and indirect costs to vaccine recipients.Vaccine delivery cost estimation is needed for budgeting and economic analysis of vaccination programs. The cost categorization methodology presented in this study is helpful in collecting OCV delivery costs in a standardized manner, comparing delivery costs, planning vaccination campaigns and informing decision-making. We reviewed and re-estimated oral cholera vaccine delivery costs in low and middle income countries standardizing cost categories. The cost categorization proposed here can help in collation, analysis, comparison and economic analysis of OCV delivery costs across countries. Vibrio cholerae bacterium and characterized by severe, potentially life-threatening diarrhea  OR "vaccination"[All Fields]) AND  OR "costs and cost analysis"[All Fields])) AND ("cholera"[MeSH Terms] OR "cholera"[All Fields]) AND (("0001/01/01"[PDAT]: "2014/12/31"[PDAT]) AND "humans"[MeSH Terms]). After initial screening on title and abstract, studies using Dukoral, ORC-Vax and Shanchol conducted in LMICs as per the World Bank’s classification  that quaWe categorized cost items into four groups with subcategories in each based on the chronological order of implementing OCV campaigns using standardized definitions . VaccinaAfter categorizing costs from each paper we summarized the results and presented overall vaccination program costs as the sum of all four cost categories. As vaccines are often donated to countries, we differentiated overall vaccination program costs and vaccine delivery costs by segregating vaccine procurement costs. We estimated program cost and vaccine delivery cost per person for complete vaccination using three methods: 1) in United States Dollars (US$) as reported in the literature for the campaign year, 2) in 2014 US$ after adjusting for country level inflation and current exchange rate, and 3) in 2014 international dollars (I$) after adjusting for country level inflation and current purchasing power parity. The year 2014 was selected as the base year for cost analysis.In Method 1, we presented costs as recorded by the investigators for the campaign year (campaign year cost) in US$. Costs in local currency units (LCU) were converted to US$ based on the World Bank exchange rate reference database for that year . In MethWe employed three methods of program cost estimation for two reasons. First, costs from different campaign years and different countries are not comparable and therefore need to be adjusted to the same base year in order to eliminate inflation effects . Second,We identified 83 papers on PubMed search, of which eight were included based on the inclusion-exclusion criteria and two papers were obtained from other sources and personal communications . The proThe number of fully vaccinated people per campaign ranged from 23,751 in India to 143,706 in Guinea. The reported price per dose of Shanchol procurement varied from US$ 1.00 to US$ 2.40 while the price of Dukoral ranged from US$ 0 (free) to US$ 5.00.The cost categorization and presentation was inconsistent across the studies conducted as shown in Of the four major cost categories, vaccine procurement was the costliest component in all OCV campaigns. Whereas, the vaccine administration was the costliest item under vaccine delivery costs . Staff sCosts re-estimated in US$2014 shows high variability of delivery costs across the sites , Table 2When there is a cholera outbreak or an impending outbreak, there are three main intervention options besides management of cases and public awareness: Do nothing, water and sanitation improvement, and cholera vaccination. The cost of inaction against cholera outbreak could be substantial. One study reported that the drop in exports alone results in substantial trade loss accounting up to 1% of the countries’ GDP . BesidesThe delivery costs of OCV through mass campaigns differed by country and even within the same country and same settings. The delivery cost of Shanchol in US$ 2014 varied from $1.14 in India to $3.05 in South Sudan per fully immunized person. The difference was higher in I$2014, ranging from I$4.08 in India to I$14.39 in South Sudan. Some of those differences could be because of the difference in provisions and activities during vaccination as discussed below, while other factors could be that the costs are collected and reported differently. However, the costs of OCV delivery in US$ 2014 in three different IDP settings in South Sudan ranged from $1.28 to $3.38 per fully immunized person. This cost difference could be partially attributed to the scale of vaccination, lowest costs of $1.28 was at IDP camp that vaccinated 38,200 people compared to the highest costs of $3.38 was at IDP camp that covered only 7,400 people. Once cost collection, categorization and presentations are standardized, the costs in US$ should help donors and financing bodies to decide the comparative resources required for vaccination in various settings. Whereas the costs in I$ will be helpful in comparing delivery costs across countries when in-country resources are used partially or completely as the case in many preemptive vaccinations in endemic settings.Vaccine delivery costs were generally higher for Dukoral than Shanchol, with the exception of Uganda. The higher cost of Dukoral delivery could be partially because of its buffer requirement which complicates vaccine administration process requiring more materials and supplies. A higher proportion of delivery costs are constituted by materials and supply as reported in Tanzania (43.9%), Uganda (37.0%) and Mozambique (28.3%) where Dukoral was used. In Tanzania the material and supply costs were high because it included domestic vaccine storage and transport costs. The delivery costs in Uganda was lower because it did not include costs for program preparation  and cold chain costs as the campaign used existing cold chain system for vaccine storage at operational headquarters (Entebbe) and did not use cold chain at field level (Adjumani). Also, vaccination coverage and acceptability survey, and AEFI data collections were not accounted for in the costs. In Uganda, Mozambique and Indonesia the vaccine was air-delivered to the site of vaccination due to difficulty in transport or security reasons or due to the fear of breaking the cold chain, which added substantially to the costs.The costs of Shanchol delivery were highest in 2013 campaign in South Sudan compared to the other three countries that deployed the vaccine. In South Sudan 2013 campaign, items such as soap, cup and water was provided to Shanchol recipients, resulting in increased proportion of material and supply costs (42.1%). This was higher than other sites such as Bangladesh (13.6%) where these provisions were a part of a research activity and in Guinea (14.3%) where such provisions were part of outbreak preventive measures. Costs for Shanchol delivery were next highest in Guinea because of the transport costs where vaccination teams were mobile on car or boat. The proportion of staff salary and allowances as a part of vaccine administration cost was high in India (87.8%) and Bangladesh (62.7%) compared to Guinea (23.6%) and South Sudan (25.3%). The proportion was high in India because it included staff training costs and in Bangladesh it included costs for pre-vaccination census and intensive house to house mop-up vaccination.The overall costs for administering ORC-Vax was relatively cheaper in Vietnam compared to other OCVs partly due to the fact that it was integrated into their routine immunization system . This suVaccine procurement accounts for the highest proportion of the total vaccination program costs, the majority of which is due to the cost of the vaccine itself. Even if a country receives donated OCVs, international transportation of the donated vaccines to its borders as well as the clearance of the vaccines at the point of entry accounts for a sizable proportion of the costs. Besides scaling up vaccine supply through the entry of multiple competitive manufacturers , a singlThere are several limitations in our analysis. First, the studies analyzed included only direct costs. The indirect costs—such as loss of income and transportation costs for those who spend time to visit a vaccination post  are not Understanding the costs of cholera vaccination campaigns is of paramount importance in the economic evaluation as well as in planning future vaccination programs. Currently, there is limited OCV delivery cost data, collected inconsistently and reported capriciously limiting the comparability of costs across settings. Categorizing the costs into easily differentiable categories is useful to the planning process and comparison between campaigns. We recommend that future OCV costing studies include both financial and economic costs and use the cost categories defined in this study for clearer collation, analysis, and comparison of campaign costs.S1 Checklist(DOC)Click here for additional data file.S1 Flowchart(TIF)Click here for additional data file.


As virus diseases cannot be controlled by traditional plant protection methods, the risk of their spread have to be minimized on vegetatively propagated plants, such as grapevine. Metagenomic approaches used for virus diagnostics offer a unique opportunity to reveal the presence of all viral pathogens in the investigated plant, which is why their application can reduce the risk of using infected material for a new plantation. Here we used a special branch, deep sequencing of virus-derived small RNAs, of this high-throughput method for virus diagnostics, and determined viromes of vineyards in Hungary. With NGS of virus-derived small RNAs we could detect not only the viruses tested routinely, but also new ones, which had never been described in Hungary before. Virus presence did not correlate with the age of the plantation, moreover phylogenetic analysis of the identified virus isolates suggests that infections are mostly caused by the use of infected propagating material. Our results, validated by other molecular methods, raised further questions to be answered before this method can be introduced as a routine, reliable test for grapevine virus diagnostics. Grapevine can host more than 60 viruses and viroids , older leaves (lower than the 3rd internode), tendrils, and inflorescenses by CTAB method  and deduplicated by the Picard MarkDuplicates tool (http://broadinstitute.github.io/picard). For virus detection we used two different pipelines in parallel: (A) Short reads were mapped to viral reference genomes  by the BWA-aln short read aligner  (according to the manufacturer's instructions). The cDNA generated was used as templates for PCR reactions using Phire Hot Start II DNA Polymerase  and published diagnostic primers or new ones , by capillary method using 20xSSC. Hybridization was carried out at 65°C in Church buffer  overnight with the appropriate radioactively labeled probe, washed for 5 min in 2 × SSC, 0.1% SDS and for 15 min in 0.5 × SSC, 0.1% SDS at the temperature of the hybridization and exposed to an X-ray film.32-labeled, DNA probes were prepared by using the DecaLabel DNA Labelling Kit . As a template we used the PCR-amplified and purified product of cloned region of viral genome. The virus piece cloned was a 1663-bp part amplified with GPGV5557F and GPGV7220R for GPGV, a 1324-bp product amplified by RBDV_RNA1F_4082 and RBDV_RNA1R_5406 for RBDV RNA1, and a 927-bp product amplified by GSVsatF72 GSVsatR999 for GSV.Virus-specific, PAs a survey to detect virus infections in Hungarian vineyards samples were collected directly from the field, in a random fashion, independently of any apparent symptom, in May 2014 or from sprouts of single bud cuttings. 14 vineyards differing in the variety grown and the age of the plantation from 9 wine-growing regions of the country were sampled  of the whole viral reference genome was also calculated. During this analysis virus-specific contigs were assembled with different k-mers , and the resulting contigs were also aligned to this set of reference genomes. In Supplementary Table In order to validate our deep sequencing results we synthetized cDNA from RNAs representing plantation pools and set up PCR reactions by published diagnostic primers or with primers designed according to the sequenced sRNA reads . Interestingly enough, all of the GCMV RNA2 sequences in GenBank originated from Hungarian accessions , but since that time—thanks to NGS surveys - it has been reported from Canada  and a Reference Genome (NC_034205) , which strongly suggests its origin from the plantation material. Although we couldn't find RBDV-specific RT-PCR product from the 13_BV plantation pool, we tested individuals at this plantation, because RBDV-specific sRNAs were present and coverage of both RNA1 and RNA2 was about 40%. An RT-PCR product with primers amplifying RNA1 at the expected size was found in BV2 plant , the youngest, 1-year-old plantation 11_SZHU contained 9 viral pathogens, which suggests that the infection is usually brought about by the use of infected propagation material. Moreover, the sanitary status of the MK plantation at Tokaj (14-18_MK), containing different varieties, differed from one variety to another. The least infected variety was the rootstock variety Teleki-Kober 125 (infected only with GPGV and slightly with 1 viroid), whereas the most infected varieties were the frequently used Furmint and Hárslevelu clones (infected with eight viruses). This significant difference in viral status between the rows of the same plantation reduces the possibility of an on-site infection from the neighborhood by viral vectors.Combination of next generation sequencing, bioinformatics, and molecular biology techniques provide us a powerful new high-throughput diagnostic tool to monitor grapevine plantations and get a deep insight into the virus infection status of Hungarian vineyards.During our comprehensive survey, beside routinely tested viruses, presence of viruses never described in Hungary before, was revealed. Phylogenetic analysis of partial sequence of the virus isolates suggests that infection usually happened through the infected propagation material, highlighting the importance of virus diagnostic surveys by more sensitive methods than the routinely used ones.Traditional diagnostic methods can only answer the question if a particular pathogen is present in the sample or not. As a contrast sRNA NGS, as a metagenomics technique, seems to be a powerful new high-throughput diagnostic tool. Using this approach, presence of grapevine-infecting regulated viruses can be reliable determined. Randomly collected, pooled samples, instead of samples from selected symptomatic plants, offer an alternative, unbiased way to reveal the presence of viral pathogens in the vineyards, but uneven presence of viruses in the sampled plants could lower the concentration of the virus in the pooled library, which could lead to false negative result. However our results showed that false negative results originating from the presence of SNPs in the genome of the isolates, at the place of the genome where diagnostic primers were designed, can be avoided.Surveys by high-throughput methods, like sRNA NGS, will continuously provide us information about the presence and importance of different viral pathogens and can support the development of new sensitive tests for their routine diagnostics. Moreover, NGS has shown to be superior, both in reliability and speed, to biological indexing in grapevine (Al Rwahnih et al., NC: sRNA library preparation, molecular biology work, evaluation of data; JM, ES, and LeK: Bioinformatics analysis; ED: Molecular biology work; IB: sRNA library preparation; GS, LaK, GB, and GT: Evaluation of data, manuscript preparation; TD and JB: Experimental design, evaluation of data, manuscript preparation; EV: Experimental design, evaluation of data, writing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.