Bangladesh struggles with undernutrition in women and young children. Nutrition‐sensitive agriculture programmes can help address rural undernutrition. However, questions remain on the costs of multisectoral programmes. This study estimates the economic costs of the Targeting and Re‐aligning Agriculture to Improve Nutrition (TRAIN) programme, which integrated nutrition behaviour change and agricultural extension with a credit platform to support women's income generation. We used the Strengthening Economic Evaluation for Multisectoral Strategies for Nutrition (SEEMS‐Nutrition) approach. The approach aligns costs with a multisectoral nutrition typology, identifying inputs and costs along programme impact pathways. We measure and allocate costs for activities and inputs, combining expenditures and micro‐costing. Quantitative and qualitative data were collected retrospectively from implementers and beneficiaries. Expenditure data and economic costs were combined to calculate incremental economic costs. The intervention was designed around a randomised control trial. Incremental costs are presented by treatment arm. The total incremental cost was $795,040.34 for a 3.5‐year period. The annual incremental costs per household were US$65.37 (Arm 2), USD$114.15 (Arm 3) and $157.11 (Arm 4). Total costs were led by nutrition counselling (37%), agriculture extension (12%), supervision (12%), training (12%), monitoring and evaluation (9%) and community events (5%). Total input costs were led by personnel (68%), travel (12%) and supplies (7%). This study presents the total incremental costs of an agriculture‐nutrition intervention implemented through a microcredit platform. Costs per household compare favourably with similar interventions. Our results illustrate the value of a standardised costing approach for comparison with other multisectoral nutrition interventions.  Nutrition‐sensitive agriculture programmes can improve rural undernutrition but lack information on costs.We use a standardised approach to estimate the total incremental costs of an integrated nutrition intervention in Bangladesh to improve maternal and child undernutrition.Costs per household compare favourably with similar interventions.This study provides evidence on the costs of integration to support the design and implementation of multisectoral nutrition programmes.Our results illustrate the value of standardising costing to facilitate comparisons with other multisectoral nutrition interventions. The prevalence of stunting in Bangladesh is 31% for children under 5, with 9% severely stunted and 2% severely wasted. Further, children in rural areas of the country are more likely to be stunted than their counterparts in urban areas. In Bangladesh, 24% of ever‐married women between the ages of 15−19 years of age are undernourished and rural women are more likely to suffer from undernourishment than urban women. At the same time, the proportion of overweight women has increased to 32%, highlighting the problem of both under and overnutrition in the country  programmes, in particular, have the potential to accelerate progress in addressing child malnutrition  project was a multisectoral nutrition‐sensitive intervention implemented in rural Bangladesh by the nonprofit Building Resources Across Communities (BRAC). The intervention was designed to be evaluated by a randomised controlled trial (RCT). The RCT baseline survey was implemented in 2016−2017 by the International Food Policy Research Institute (IFPRI).This study presents the incremental costs of implementing an integrated agriculture‐nutrition intervention through an existing BRAC microcredit platform.Financial costs represent the implementing partner's actual expenditures on goods and services purchased to deploy the intervention. Economic costs, on the other hand, are defined as the opportunity cost of all of the resources used to produce something; and can include the value of resources that may not have been paid for, such as volunteer frontline worker time or programme participant time. Once data becomes available from an ongoing impact evaluation, these costs will be combined with programme benefits for a full economic evaluation including cost‐benefit and cost‐effectiveness analyses.Our findings serve many purposes. A robust understanding of the costs of multisectoral nutrition strategies is critical for priority‐setting and for motivating donors. This study is useful for governments and development partners to target investments in multisectoral nutrition programmes. Standardised unit cost data provides a cost benchmark for governments, donors and non‐profit organisations on how to design, budget and measure the resource requirements of interventions. Lastly, this analysis contributes to an effort to build an evidence base on the costs of multisectoral nutrition programmes across different settings and platforms. This study was conducted by the Strengthening Economic Evaluation for Multisectoral Strategies for Nutrition (SEEMS‐Nutrition) consortium, led by the University of Washington in partnership with IFPRI. SEEMS‐Nutrition develops standardised approaches and tools to assess the costs and benefits of multisectoral nutrition programmes.1.1TRAIN was a 3.5‐year project (2016−2020) designed to address evidence gaps on agriculture‐based interventions to improve maternal and child nutrition. BRAC started the programme in 2016 in the Dhaka, Khulna and Rangpur divisions. A total of 5040 households were selected from 144 unions of 36 Subdistricts from 10 districts. TRAIN incorporated a BCC strategy for maternal and child health and nutrition into a female‐focused microcredit programme promoting production diversity and income generation.BRAC has led microcredit programmes in Bangladesh since 1974. Dabi, a credit platform lending only to women, has coverage through 2146 local branches with more than 3.5 million borrowers. Dabi provides loans to women to increase income and production in agriculture and to promote empowerment. Dabi disburses $1.8 billion in loans annually, the majority for agriculture (60%).The TRAIN programme had an additive design with three different treatment arms and a control group. Each arm built an additional component onto the control arm (the existing Dabi microfinance platform) . This paper presents the cost analysis from 40 months (3.5 years) of programme implementation (October 2016−January 2020). Given that the last 3 months of implementation were disrupted by COVID‐19 and extended until October 2020, our study only covers the pre‐COVID period.2This study used a novel standardised costing approach that contributes to gaps in the literature on the costs of multisectoral nutrition programmes  chain interventions that (1) increase the supply of nutrient‐rich foods, (2) increase the demand for nutrient‐rich foods, and (3) promote the enabling environment for nutrition. Step 2 maps the programme impact pathways to clearly articulate the linkages from activities to outputs and outcomes. Step 3 identifies all activities, inputs and costs along the impact pathway. Step 4 identifies outputs and outcomes for each activity to define the components of total and unit costs. The four‐step framework estimates the direct intervention costs and opportunity costs associated with all programme activities.Multisectoral nutrition approaches may include one or more of the intervention typologies with different components, services and outputs. Therefore, it is important to clearly define the unit cost for one or more outputs. Supporting Information: Appendix Table 2.1The cost analysis captures the total costs of the TRAIN intervention incremental to the existing microcredit programme. Primary and secondary cost data were collected for the period of October 2016−January 2020, which included 6 months of start‐up and 3 years of full implementation. The SEEMS mixed‐methods approach combines financial expenditure data with micro‐costing methods to identify and value resources and allocate costs to activities and inputs. The Activity‐Based‐Costing‐Ingredients (ABC‐I) method  and DMs (n = 1), and IDIs with BRAC staff (n = 3) and PKs (n = 3). In the second round, 7 additional FGDs were conducted at BRAC regional offices in Rangpur (n = 4), Dhaka (n = 2), and Khulna (n = 4) divisions with PKs for a total of 24 participants. One FGD was conducted with FOs (n = 2) and one FGD was conducted with DMs (n = 5). Additional IDIs were conducted with BRAC head office staff (n = 4). A local research collaborator facilitated and translated interviews in the local language. Supporting Information: Appendix Table Primary cost data were collected in two rounds (May 2019 and February 2020) through semistructured in‐depth interviews (IDIs) and focus group discussions (FGDs). IDIs and FGDs gathered data on interviewees' opportunity costs and out‐of‐pocket (OPP) expenses. In the first round of data collection, we organised IDIs and FGDs at a centralised location. FGDs were conducted with FOs . We then mapped line‐item expenditures to standardised input and activity codes using the template. Third, we used Excel to summarise and analyse micro‐costing data from the qualitative interviews and focus group discussion.Most line‐item expenditures were easily mapped to standardised SEEMS activity and input codes. However, there were some exceptions: (1) BRAC personnel who contributed to multiple activities; and (2) shared capital and supply costs. For the first, we developed allocation rules for each activity using data from KIIs and FGDs. For example, using qualitative interviews we found PKs spent 80% of their time on nutrition counselling, 10% on training, 5% on planning and 5% on coordination meetings. PK salaries were allocated accordingly to those activities. Shared inputs or capital costs were allocated proportionally across the related activities. Shared costs are described in greater detail below.2.2.1First, all nonshared financial and economic personnel costs were allocated across programme activities. We combined expenditure and time allocation data from BRAC staff and frontline workers (IDIs and FGDs). This information was used to allocate personnel costs to programme activities (Supporting Information: Appendix Table 2.2.2Estimates of frontline worker (PK) costs include personnel costs from BRAC expenditures combined with estimates of OOP costs and the valuation of time above the contracted 36‐h week. Economic costs such as OOPs and overtime hours/travel were not reimbursed by BRAC. PK personnel costs, gleaned from both financial expenditure and economic cost data were allocated based on time spent on programme activities.2.2.3Beneficiaries participated in programme activities such as household counselling and community events. We estimated the opportunity costs of participation in TRAIN activities from process evaluation data. Opportunity costs were based on information on beneficiaries’ OOP expenses and the average time per year spent on programme activities. To value beneficiaries' time accurately, we used daily wage rates for agricultural labour for men and women from a 2017 IFPRI survey in implementation villages. We used mean daily wage rates for male and female agricultural workers to value beneficiaries' time. Once we obtained all personnel and beneficiary costs aligned to programme activities, we mapped them onto the standardised SEEMS activity categories . We estimate annual cost per beneficiary and the annual cost per household by treatment arm. The cost profile is the share of the disaggregated cost over the total programme costs for the 3.5‐year period.2.2.6https://www.oracle.com/applications/crystalball). This technique produces estimates of thousands of possible scenarios, generating ranges of potential outcomes and identifying inputs that are impactful on the model. Distributions were assigned to the following parameters: (i) variation in the amount of frontline worker  overtime, (ii) variation in frontline worker (PK) OOP costs and (iii) variation in beneficiary opportunity costs (Table 1.Overtime and OOP expenses for frontline workers (PKs): The estimate of overtime includes labour and travel time that was not reimbursed during the salaried work week. PK overtime varied by geographical location and by which treatment arm they supported with their activities. Variation in overtime by treatment arm was due to the additive design of the intervention. For example, Arm 4 involves more programme activities than Arm 2 or 3. The geographical spread of intervention households and the frontline workers' ability to afford transportation costs also affected their time investments. Variations in frontline worker overtime and OOP expenses were simulated using the means and standard deviations from actual data collected from the sample of PKs converted into shape and scale parameters for the gamma distribution.2.Beneficiary opportunity costs: Beneficiary participation in the programme was estimated using process evaluation data on the amount of time spent on TRAIN activities. The baseline survey provided local wage rates for men and women. The sensitivity analysis varied opportunity costs separately for men and women using means and standard deviations from the process evaluation and assumes a gamma distribution Table .A Monte Carlo probabilistic sensitivity analysis was performed using Oracle's Crystal Ball software , 48% of which came from hired labour. The remaining 20% of personnel costs represent beneficiary opportunity costs. This category was followed by travel/per diems (12%), supplies (7%), contracted services (6%) and overhead (6%).Programme activities were then mapped to a NSV chain typology Figure . Results3.2Total incremental costs per household per year ranged from a minimum of USD$36.62  to a maximum of USD$87.50 for arm 4, which included all programme components . Arm 3 included two components—nutrition and nutrition‐sensitive agriculture, and fell in between at USD$65.18. As many activities occurred at the household level, the programme was designed to target more than one household member. Considering both the index  female and male in the household, the cost per household drops to USD$19.61, USD$34.25 and USD$47.13 by arm, respectively  and/or programme complexity (number of components), the unit cost per household increases. These increases are driven by personnel costs for additional activities such as agriculture extension or gender sessions (Arms 3 and 4). Similarly, beneficiary opportunity costs increased with programme complexity. Opportunity costs increased because beneficiaries spent more time in community events; home visits are longer with additional counselling components. Our results showed the index beneficiary women had higher opportunity costs across all treatment arms in all three scenarios; nearly double the costs of their husbands. This is likely because household visits targeted women. However, if programme impacts do not differ significantly by treatment arm, additional complexity may not be worth the cost.We also examine the costs of incorporating women's empowerment activities. Women's empowerment activities accounted for 30% of programme costs, as part of facilitating the enabling environment for nutrition. As noted, the unit cost per household increased with programme complexity. Thus, Arm 4, which has the most activities, including gender forums and men's sensitisation, had the highest unit costs per beneficiary. Women spent almost twice the amount of time than men; our sensitivity analysis emphasises the range of higher opportunity costs for women Table ; but do Examining costs by activity, the main driver was household nutrition counselling. Coupled with the agricultural household visits, training, and community events, beneficiary outreach accounted for over half of the costs. This finding reflects the intensive nature of activities to reach beneficiaries. Examining costs by input, the main cost driver was personnel. Personnel costs were driven by frontline workers, accounting for over two‐thirds of total personnel costs. Salary data were gathered from BRAC expenditures. We also estimated frontline workers economic costs in overtime work and OOP costs. Our analysis found variations in opportunity costs that have important implications for scale‐up and sustainability. Among frontline workers, PKs spent a substantial amount of their time and resources conducting their work. These costs could limit the ability of PKs to conduct their activities in the long run. BRAC addressed this constraint by increasing PK travel stipends over time.We then mapped costs to the NSV chain typology. We found similar results to the first programme costed with the SEEMS approach: a nutrition‐sensitive agriculture programme, with agriculture and nutrition BCC delivered through community‐based preschools in Malawi  production and consumption in Kenya were USD$155 per pregnant woman or USD$110 per beneficiary when mothers and infant children were included (C. E. Levin et al., Incorporating frontline worker opportunity costs in the TRAIN cost analysis highlights the programme's sustainability concerns. Despite the travel stipend provided in the second year, frontline workers shouldered additional OOP costs given the intensive nature of household‐level interventions. SEEMS‐Nutrition is underlining such questions in building evidence on the costs and cost‐effectiveness of nutrition‐sensitive interventions. This study presents full programme costs in the pre‐COVID period and is an important step toward a comprehensive economic evaluation. As noted above, a recent systematic review has found that nutrition‐sensitive agricultural interventions have a significant positive impact on dietary diversity among children 6−60 months old (Margolies et al. 5This study presents the financial and economic incremental costs of implementing an integrated agriculture‐nutrition intervention through a micro‐credit platform in Bangladesh. Cost‐per beneficiary estimates compare favourably with multisectoral nutrition‐sensitive interventions implemented through different platforms. These results demonstrate that a standardised approach for measuring the costs of multisectoral nutrition strategies enhances comparability and transparency, increasing the application of cost data for assessing affordability for use in evaluation, planning and policymaking.Giang Thai led data collection and cost analyses and contributed to the manuscript. Amy Margolies contributed to the cost study methodology, conducted cost analyses and led drafting of the manuscript. Aulo Gelli contributed to the cost study methodology and to conception and design, provided technical advice, drafted and reviewed manuscripts. Nasrin Sultana co‐led data collection and reviewed the manuscript. Esther Choo contributed to the data analysis and reviewed the manuscript. Neha Kumar reviewed the manuscript. Carol Levin contributed to the cost study methodology and to conception and design, provided technical advice, participated in data collection, edited the manuscript and reviewed manuscripts.The authors declare no conflict of interest.Supplementary information.Click here for additional data file.


Until the month of April 2022, cases of monkeypox virus infection in humans were hardly documented outside of the endemic African regions. There are cases now throughout the world. Infected exotic pets have taken the monkeypox virus out of Africa. Following the universal eradication of smallpox in the 1970s, occurrences of monkeypox have attracted attention on a global scale. The western hemisphere and European nations are seeing the majority of the monkeypox infections linked to the 2022 epidemic. Numerous groups are working on contact-tracing initiatives, but it is still unclear what started this outbreak. The precise cause of monkeypox is uncertain, as the virus's origins have been linked to a number of rodents and small animals. Testing for monkeypox DNA from a patient using polymerase chain reaction (PCR) or viral culture isolation material can both be used to confirm monkeypox infection. Monkeypox is from the family: Poxviridae, subfamily: chordopoxvirinae, genus: orthopoxvirus and species: Monkeypox virus. The DNA virus monkeypox virus (MPXV), which causes the zoonotic illness MPX, or monkeypox, is divided into two genetic clades: The Congo Basin (CB) and the west Africa (WA) clades. Monkeypox's true impact on public health is uncertain. A zoonotic orthopoxvirus called monkeypox (MPX) inadvertently produces smallpox-like illness in humans, but with far lesser fatality. Poxviruses are brick-shaped and have a linear double-stranded DNA genome enclosed in a lipoprotein sheath. The fact that this virus is indigenous to widespread outbreaks across the western hemisphere, as well as western and central Africa which can be connected to the trade of exotic pets and traveling abroad, makes it therapeutically significant . When moPathogenesis and causesAnimals can contract the viral illness known as "monkeypox", primarily some primates, including humans, rodents, and monkeys, but has milder symptoms than smallpox. Smallpox and cowpox viruses are in the same family as the monkeypox virus. Humans can contract the monkeypox virus from sick animals by getting bitten by them or directly coming into contact with their bodily fluids. Extended close contact, typically between family members, can also cause person-to-person transmission. Exposure to respiratory droplets may also be a means of transmission . The genThe west African clade's open reading frames had deletions and fragmentations that reduced its pathogenicity. In human cells made from patients who had previously contracted monkeypox, central African monkeypox suppresses T-cell receptor-mediated T-cell activation, preventing the generation of inflammatory cytokines ,12. The After viral penetration, the monkeypox virus reproduces at the entry site by any channel , then spreads to nearby lymph nodes, causing seeding of other organs with viruses and viral dissemination which are caused by viremia. This is the incubation stage that can last up to 21 days and normally lasts 7 to 14 days -18. BefoHistory to diagnose monkeypoxA differential diagnosis can be made using historical markers of monkeypox infection, such as recently traveling to areas where it is endemic, contact with native wild creatures that have been imported, and caring for an infected animal or human, but clinical symptoms are crucial. Close contact with respiratory secretions and infected person's skin sores or contaminated clothing and bedding can spread diseases from one person to other . VeterinClinical featuresThe symptoms of MPX, which have 4-21 days of incubation, with symptoms ranging from fever to respiratory distress, are identical to those of smallpox with the exception of lymphadenopathy. Exhibiting symptoms for two to four weeks, monkeypox is a self-limiting illness (4-14 days) . InitialTreatment modalitiesFortunately, monkeypox infections often have a clinical course that is modest and self-limiting. Consequently, it rarely needs specialized therapy, and instead, treatment is supportive. There are currently no known cures for monkeypox, although smallpox research shows that the treatment options for monkeypox include the vaccinia vaccine, cidofovir, tecovirimat, and vaccinia immune globulin (VIG), however, there is little evidence to back this up . The WorPrognosis of the diseaseMost cases of monkeypox are self-limiting, exhibiting symptoms for two to four weeks. However, some patients could have serious illnesses. Serious cases of monkeypox are more common in children, which are correlated with the patient's health state, the degree of viral exposure, and the kind of consequences ,28. The Modalities to prevent monkeypox diseaseIn order to prevent the transmission of monkeypox in locations where it is prevalent, according to the CDC direct sexual contact oral, anal, and vaginal sex or touching the genitals of a person and direct contact with blood with monkeypox need to be inhibited. For the public to become more informed, massive health education activities are needed, provide instructions on how to handle possible animal reservoir species , and warn people to stay away from afflicted people. The prevention of human-to-human transmission in healthcare depends on infection control techniques. Improved isolation procedures and nursing techniques call for training as well as sufficient resources in the form of staff and facilities. Health professionals and anybody treating or coming in contact with monkeypox patients should get immunized. According to estimates, receiving a smallpox immunization offers 85% cross-protection against monkeypox infection . Living Differential diagnosisThe most typical alternative medical diagnosis is chickenpox. In contrast to chickenpox, which has a brief prodromal time, centrifugal rash distribution, no lymphadenopathy, and a rapid rate of rash spread, monkeypox has a long prodromal phase, centrifugal rash distribution, and lymphadenopathy . Other dThe monkeypox virus causes the zoonosis known as human monkeypox. Monkeypox virus is an orthopoxvirus related to smallpox, first noted in 1970 in central Africa. Humans have a fever, headache, general malaise and weariness, enlarged lymph nodes, and other symptoms two weeks after infection. A rash of raised pimples develops on the face and torso a few days later. The sickness gradually takes its course in two to four weeks and eventually crusts and peels off. Transmission may occur through direct contact with bodily fluids, skin lesions, respiratory droplets of infected animals, or indirect contact with contaminated fomites. Contact with an infected person's skin sores or respiratory secretions, or human-to-human transmission can also occur as a result of contaminated clothing and bedding. However, for transmission, prolonged face-to-face contact is frequently required via respiratory droplet particles increasing the vulnerability of medical professionals, family members, and any close associates of active cases. Given alleged flaws in disease reporting and confirmation, precise prevalence and incidence are challenging to establish. However, after the end of the regular smallpox immunization program, these measures have grown. Only symptoms are addressed during treatment. By separating patients and maintaining good cleanliness around them, outbreaks are controlled. It is crucial that local and international authorities raise awareness and take action to prevent the virus spread in the community. Careful collaboration between public health authorities, doctors, and the community will be necessary to contain this expanding international pandemic.


Since 1Microbiota refers to the range of microbial species that live inside the human body and interact with it . The gas2One of the largest animal reservoirs for the virus is rodents . They arThe monkeypox virus can incubate for five to three weeks, and symptoms can appear between two and five weeks after incubation . Fever, Fecal transplant therapies have raised concerns amongst the Food and Drug Administration (FDA) that they may expose patients to monkeypox. A recent study has found monkeypox viral DNA in rectal swabs and feces samples from infected people . Moreove3MPVX can enter the body via the oropharynx, nasopharynx, or intradermal pathways . The mon4Since the number of cases of monkeypox virus has been drastically amplifying, several studies have found monkeypox virus DNA in rectal swabs and identified gastrointestinal symptoms in infected subjects ,9 raisinAs FMT is potentially therapeutic in a wide range of conditions, including but not limited to refractory Clostridioides difficile infection, inflammatory bowel diseases, HIV infection, etc., . It is nIn addition, the donor-screening process should be updated in the context of these recent transmission incidents and attempts should be made to screen for emerging infectious diseases including SARS-CoV-2 and the monkeypox virus.In conclusion, additional clinical studies are necessary to validate the relationship between monkeypox and FMT and a highly efficacious and reliable test should be designed to detect monkeypox viral DNA in fecal matter.No ethical approval was required for this paper.No funding was acquired for this paper.No consent was required for this paper.This study did not involve any volunteers or patients; hence no consent was needed.Rabia Owais: Concept of the study, writing the paper, and final approval of the manuscript. Maham Iqbal: writing the paper and final approval of the manuscript.1.Name of the registry: Not applicable2.Unique Identifying number or registration ID: Not applicable3.Hyperlink to your specific registration (must be publicly accessible and will be checked): Not applicableRabia Owais, Maham Iqbal.None.


Surveillance programs are essential for the prevention and control of mosquito-borne arboviruses that cause serious human and animal diseases. Viral metatranscriptomic sequencing can enhance surveillance by enabling untargeted, high-throughput arbovirus detection. We used metatranscriptomic sequencing to screen field-collected mosquitoes for arboviruses to better understand how metatranscriptomics can be utilised in routine surveillance. Following a significant flood event in 2016, more than 56,000 mosquitoes were collected over seven weeks from field traps set up in Victoria, Australia. The traps were split into samples of 1000 mosquitoes or less and sequenced on the Illumina HiSeq. Five arboviruses relevant to public health  were detected a total of 33 times in the metatranscriptomic data, with 94% confirmed using reverse transcription quantitative PCR (RT-qPCR). Analysis of metatranscriptomic cytochrome oxidase I (COI) sequences enabled the detection of 12 mosquito and two biting midge species. Screening of the same traps by an established public health arbovirus surveillance program corroborated the metatranscriptomic arbovirus and mosquito species detections. Assembly of genome sequences from the metatranscriptomic data also led to the detection of 51 insect-specific viruses, both known and previously undescribed, and allowed phylogenetic comparison to past strains. We have demonstrated how metatranscriptomics can enhance surveillance by enabling untargeted arbovirus detection, providing genomic epidemiological data, and simultaneously identifying vector species from large, unsorted mosquito traps. Arthropod-borne viruses (arboviruses) are distributed worldwide and, in recent years, have caused epidemics such as dengue, chikungunya, and Zika fever . Dengue a priori knowledge of the virus sequence, which limits the detection of divergent strains and restricts the discovery of unexpected novel viruses. Virus-specific PCRs also limit the number of targets and can decrease in sensitivity over time due to genomic drift in rapidly evolving viruses [As the primary vector of arboviruses, mosquito populations are monitored by surveillance programs in order to detect and control arboviral activity. A common approach is to trap mosquitoes and test them for the presence of arboviruses using cell culture. This involves morphologically identifying the mosquitoes to species level and inoculating a homogenised subsample onto a range of suitable cell lines, which are then screened for arboviral presence using an appropriate immunological staining method or by observing for cytopathic effect ,8. In re viruses ,13.Metatranscriptomics  is an untargeted approach to virus detection that, unlike PCR, can generate whole genome sequences for all RNA viruses present in a sample. Focusing on RNA viruses is suited to arbovirus surveillance as all known arboviruses have an RNA genome, with the exception of African swine fever virus (ASFV), a double-stranded DNA virus that is transmitted by soft ticks . The phyOne of the challenges in implementing metatranscriptomics as a surveillance tool is the bioinformatics analysis involved in handling the sequencing data. A typical approach to metatranscriptomic virus detection involves comparing the sequencing reads or assembled contiguous sequences (contigs) to a reference database containing previously described virus sequences. Using a large, public database such as those offered by the National Center for Biotechnology Information (NCBI) provides comprehensive virus identification. However, these databases are littered with misannotated sequences, which can confound results . AlternaDecreases in sequencing costs have led to a rise in metatranscriptomic studies, with mosquito pools often sequenced to characterise viromes and discover new viruses ,22, inveThe goal of this study is to assess the utility of metatranscriptomics in arbovirus surveillance. Using traps collected in 2016 following a significant flooding event in regional Victoria, Australia, we screened over 56,000 mosquitoes for arboviruses using metatranscriptomic sequencing. The results were confirmed using quantitative RT-PCR (RT-qPCR) and used to assess previously established positive detection criteria. The metatranscriptomic data was further utilised for mosquito and biting midge identification and detection of insect-specific viruses, both known and previously undescribed. We also compared the metatranscriptomic results to existing surveillance program data for the same traps to examine the different outcomes of established methods and novel approaches to arbovirus surveillance.The mosquitoes used in this study were collected as part of the Department of Health Victorian Arbovirus Disease Control Program (VADCP). Mosquito collection was performed on a weekly basis using carbon dioxide-baited encephalitis virus surveillance (EVS) light traps  overnighA subsample of mosquitoes was taken from each trap for arbovirus screening by the VADCP . These mEach mosquito trap was sorted into different-sized subsamples. The first subsample for each trap consisted of 100 mosquitoes, which were counted and weighed. The remainder of each trap was then sorted into subsamples consisting of 1000 mosquitoes based on the weight of the initial 100 mosquitoes. This sample size was chosen based on previous findings indicating metatranscriptomic sequencing can detect an arbovirus from a single infected mosquito in a pool of 1000 . Any remg, and 140 μL of supernatant was removed from each subsample. Nucleic acid was extracted from the supernatant using the QIAamp Viral RNA Mini Kit  according to the manufacturer’s instructions, excluding the addition of carrier RNA. A double elution was performed using 2 × 40 μL of Buffer AVE and stored at −80 °C until library preparation. An extraction negative control was included in each batch of extractions and consisted of 140 μL of Buffer AVL as input.To homogenise the mosquitoes for nucleic acid extraction, 3 mm glass beads  and Buffer AVL  scaled according to mosquito number  were addLibraries were prepared for metatranscriptomic sequencing using the NuGEN Ovation Universal RNA-Seq System with custom rRNA depletion. Briefly, 2 μL of undiluted RNA (68–200 ng) was DNase treated, converted into cDNA, and sheared into 200–400 bp fragments using a Covaris S220 Focused-ultrasonicator. A library negative control was included in each batch of library preparation and consisted of 2 μL of UltraPure water  as input. Additionally, 2 μL of RNA from a pool of 1000 mosquitoes containing a single Ross River virus (RRV) infected mosquito  was usedde novo assembly, performed using Trinity v2.4.0 [−5. Abundance was measured by mapping trimmed, interleaved reads back to the contigs using BWA-MEM v0.7.17-r1188 [−5 were removed [The reads were demultiplexed into subsamples and used for y v2.4.0  with reay v2.4.0  were taxy v2.4.0  with the17-r1188  and obta17-r1188 . Taxonom17-r1188  for anal17-r1188  and ggpl17-r1188  packages removed . Non-metCulex pipiens mosquito species complex that cannot be differentiated by COI had their names conglomerated so that they would be counted as one [https://doi.org/10.6084/m9.figshare.10246826.v3 (accessed on 31 March 2020). The BLASTn was performed with an e-value threshold of 10−5, and the results were filtered for contigs with >300 bp alignment length and >95% identical match to COI sequences. Read counts for these contigs were acquired from the previously performed idxstats analysis, summed per species and per trap, and used to plot species abundance with ggplot2.The mosquito (Diptera: Culicidae) and biting midge (Diptera: Ceratopogonidae) species in each trap were determined by using BLASTn v2.9.0+  to compad as one . The dathttps://doi.org/10.6084/m9.figshare.12055830 (accessed on 31 March 2020). The screening was performed by mapping the subsample contigs and reads to the arbovirus database with BWA-MEM and using BBMap pileup [The metatranscriptomic data generated from the trap subsamples were screened for arboviruses of public health interest using a targeted custom database based on those listed in Mackenzie et al.  and Vasip pileup  to measup pileup . The perRT-qPCR was used to test all 86 subsamples for any arboviruses detected during the targeted arbovirus screen, with a total of five assays performed: Ross River virus (RRV), Sindbis virus (SINV), Trubanaman virus (TRUV), Umatilla virus (UMAV) and Wongorr virus (WGRV). The mosquito subsample RNA was diluted 1:5 with UltraPure water  for use in the RT-qPCR assays. The RRV , SINV 4, TRUV 2 and UMAVIn order to perform phylogenetic analysis, consensus sequences were derived from the read-based alignments that had >90% coverage of reference sequences from the targeted arbovirus screen. SAMtools mpileup and BCFtools consensus were used to generate the consensus sequences, and then MAFFT v7.429  was usedde novo assembly classified as viral by DIAMOND BLASTx were used to investigate the broader virome present in the trap subsamples. The contigs were compared to the NCBI nucleotide (nt) database (acquired 28 October 2019) using BLASTn with an e-value threshold of 10−5 and filtered to remove contigs with non-viral matches. The remaining viral contigs were further filtered for matches to RNA viruses by comparing them to all viral RNA-dependent RNA polymerase (RdRp) protein sequences on RefSeq using DIAMOND BLASTx with an e-value threshold of 10−5.Contigs from the initial −5 [Abundance estimates were determined by mapping reads back to the RNA virus contigs and measuring read counts, as previously described, and summing the reads per trap. The RNA virus contig abundances were imported into phyloseq along with COI contig abundances (as previously identified via the COI BLASTn search). The read counts were normalised and filtered to remove taxa with a mean abundance below 10−5 . The difThe RNA virus contig RdRp BLASTx results and associated abundance information were plotted in RStudio using ggplot. For the sake of brevity, only RNA virus contigs greater than 1000 bp in length , with atBased on weight, a total of 62,218 mosquitoes were trapped in Gannawarra, Mildura and Wellington over a seven-week period in 2016  and two nematode species (Wuchereria bancrofti and Brugia timori). However, further investigation indicated that these reads were derived from mosquito rRNA, and so they were re-classified as arthropod for counting. Archaea and bacteria were represented by a mean of 1% of reads (range: 0.0–17%) and were not characterised as part of this study. The percentage of viral reads varied among the subsamples (1–82%), with certain traps having more viral reads than others. For instance, in Gannawarra, the Week 45 and 46 traps had a mean of 55% viral reads, whereas the Week 47–51 traps had a mean of 8% viral reads.For the 86 mosquito subsamples, a mean of 10,575,201 paired reads was generated per subsample . When the reads were mapped to taxonomically classified contigs , a mean The three extraction negative controls had a low number of sequencing reads compared to the subsamples, with a mean of 8112 paired reads per sample . Three out of the four library negative controls also had a low number of sequencing reads . However, there was one library negative control with 11,926,882 paired reads. Of the taxonomically classified reads for this library negative control, 80% were plant and 16% were bacterial, suggesting contamination. These plant and bacterial species were also present in some other samples from the same sequencing pool as the contaminated library negative control but not in samples that were processed with it during library preparation, indicating that contamination occurred during sequencing. When comparing all taxa, both the extraction and library negative controls were distinct from the trap subsamples . The taxAedes camptorhynchus .The COI-based analysis of the metatranscriptomic data from all 86 subsamples identified 12 mosquito and two biting midge species in the 21 traps used in this study. The 12 mosquito species were detected over the seven-week period, with the two inland locations, Gannawarra and Mildura, sharing similar mosquito species in contrast to the coastal location, Wellington, which primarily had rhynchus A. The moAedes theobaldi and Tripteroides atripes, and biting midge species Culicoides marksi, not detected. Analysis using only the 100-mosquito subsamples also resulted in the taxonomic dropout of other species, such as Anopheles annulipes, which was absent in seven of 19 traps positive for this species. In total, there were 26 taxonomic dropouts in 16 of the 21 traps when using only the 100-mosquito subsample data, compared to when all of the trap subsamples were used.When only the 100-mosquito subsamples were used for COI-based metatranscriptomic analysis, abundance estimates for prevalent species were often comparable to those generated with all of the trap subsamples B. HoweveRelative abundance estimates based on morphological identification of initial trap subsamples by the VADCP in 2016 were similar to COI-based metatranscriptomic estimates for the most prevalent species, despite representing two separate mosquito subsamples C. HoweveThree criteria were assessed for arbovirus detection during the targeted screen of the metatranscriptomic data: Fold Coverage by Reads ratio (FCR-r), Percent Coverage by Contigs ratio (%CC-r), and Percent Coverage by Reads ratio (%CR-r), with values ≥ 2 considered positive . CountinUsing the targeted database with the %CR-r criterion to screen the metatranscriptomic data resulted in the detection of five arboviruses: RRV, SINV, TRUV, UMAV and WGRV A. Out of2 = 0.9, Of the 33 metatranscriptomic subsample detections, 31 were confirmed using RT-qPCR . The twoDespite representing a separate subsample of mosquitoes in each trap, the initial cell culture screening performed by the VADCP detected four out of the five arboviruses detected via metatranscriptomics C. UMAV wOut of the 33 metatranscriptomic arbovirus detections made in the subsamples, only 10 provided the coverage required to generate a consensus sequence for use in phylogenetic analysis: three for RRV ; three for SINV ; three for TRUV ; and one for UMAV .All three RRV detections grouped within the recently described Genotype 4 (G4) lineage A, to whiCulex annulirostris mosquitoes in 1975, and Chinese strain YN_222, which was isolated from a midge in 2013 [Phylogenetic analysis revealed that all three SINV detections belonged to the SINV-II genotype B, along  in 2013 . SINV fr in 2013 , shared Anopheles annulipes mosquitoes. The TRUV detections from Gannawarra in Week 46 and Mildura in Week 51 were similar (99.47%) and shared > 98% nucleotide identity with strains SW27572 and Murrumbidgee 934. The TRUV from Mildura in Week 47 was 5% different to the other two detections, sharing most nucleotide identity with strain MRM3630 (97.3%).When compared with Segment M sequences from orthobunyaviruses in the Mapputta group, the three TRUV detections clustered with other TRUV strains C, forminCulex quinquefasciatus mosquitoes in 2015, also in Victoria [Of the three UMAV detections from Mildura, only the one from Week 50 had enough coverage of a genome segment to allow phylogenetic analysis D. ComparVictoria . The twoVictoria , forminghttps://doi.org/10.6084/m9.figshare.17071970, accessed on 20 July 2022).In addition to the five arboviruses detected using the targeted database, contigs matching 51 other viruses were assembled from the trap subsample reads, ranging in size from 1–20 kbp . Of the Based on the read number per positive trap, the most abundant viruses were those matching Hubei arthropod virus 1 , Ngewotan virus , Culex-associated Tombus-like virus , and Yongsan picorna-like virus 2 . Certain viruses fluctuated in abundance over time, such as Ngewotan virus , which iAedes camptorhynchus mosquitoes in traps from that location , it detected others that are difficult to identify morphologically, such as Culex palpalis, which is almost indistinguishable from the closely related Culex annulirostris, an important Australian vector species [Culicoides marksi, a vector of animal arboviruses [The metatranscriptomic COI analysis resulted in the detection of 12 mosquito species and two biting midge species in the traps A. Whilst species . Furtheroviruses , metatraoviruses  , Minnal virus (MINV), Netivot virus (NETV), and Llano Seco virus (LLSV) [https://doi.org/10.6084/m9.figshare.12055830, accessed on 31 March 2020) and insufficient coverage (<30%) to perform phylogenetic analysis. In an attempt to acquire more of the WGRV genome, the assembled contigs were screened for sequences similar to those publicly available for WGRV, but none were found (unpublished data). The search was confounded by the segmented nature of the WGRV genome and the presence of other orbiviruses in the same traps . Phylogenetic analysis of the NS2 protein (Segment 7) from UMAV showed that it was most similar to another UMAV sequence from Victoria  and KHV s (LLSV) . These vs (LLSV) ,70. The me traps . Evidentme traps . FurtherThe majority of metatranscriptomic arbovirus detections were confirmed by RT-qPCR, with only two subsample detections missed by the SINV assay . The fouApart from SINV detection, screening with RT-qPCR offered highly sensitive results, with an additional four arbovirus detections made at a trap level: 1 RRV, 2 TRUV and 1 WGRV. The RRV detection had a Ct value of 39.82, indicating RRV was present at a very low concentration and at the limit of detection . These rThe metatranscriptomic results were supported by the initial screening performed by the VADCP during the 2016–2017 surveillance season, which recorded similar mosquito species and arbovirus detections, despite using a separate subsample of mosquitoes in each trap C. The usThe subsampling employed by the VADCP in response to surges in mosquito numbers during the 2016–2017 surveillance season  assumes Metatranscriptomic arbovirus detection was determined by the percent coverage of the arbovirus genome by reads ratio (%CR-r), which was shown to be the most effective criterion for detection from the field traps . This coFurther analysis of the metatranscriptomic trap data revealed a broad viral diversity, with assembled contigs matching 51 viruses specific to or originating from arthropods . This isWhile the exploration of broader viral diversity in the field traps enabled the detection of both known and novel viruses, it was also a complex and time-consuming process that would challenge routine surveillance activities. Unlike the targeted arbovirus screening of the metatranscriptomic data, there are no established criteria for the positive detection of previously undescribed viruses. While there are standards for reporting metatranscriptomic virus genomes , it is dBy processing unsorted, bulk mosquito traps for untargeted, high-throughput arbovirus detection and vector species identification, we have demonstrated metatranscriptomics as a high-value resource for arbovirus surveillance programs. The methods and resources presented here, including the curated reference sequence databases and refined positive detection criteria, can help facilitate the incorporation of metatranscriptomics into routine surveillance activities. Future efforts should focus on standardising operating procedures, further refining limits of detection to diagnostic standards, optimising the protocol to lower assay cost, developing user-friendly data analysis software, and expanding reference sequence databases . The imp


Colocasia esculenta) plants with and without symptoms of ABVC disease were sampled from two locations in Papua New Guinea and examined for viruses using high-throughput sequencing (HTS). Similar to previous reports, isolates of CBDaV were present only in symptomatic plants, further supporting its role in the disease. The only other viruses consistently present in symptomatic plants were badnaviruses: taro bacilliform virus (TaBV) and/or taro bacilliform CH virus (TaBCHV). If ABVC requires co-infection by multiple viruses, CBDaV and badnavirus infection appears to be the most likely combination. The complete genomes of two isolates of CBDaV and TaBCHV, and single isolates of TaBV and dasheen mosaic virus, were obtained in this study, furthering our knowledge of the genetic diversity of these relatively understudied taro viruses. HTS data also provided evidence for an agent similar to umbra-like viruses that we are tentatively designating it as Colocasia umbra-like virus (CULV).Alomae-bobone virus complex (ABVC) is a lethal but still understudied disease that is limited to the Solomon Islands and Papua New Guinea. The only virus clearly associated to ABVC is Colocasia bobone disease-associated virus (CBDaV). Taro ( Colocasia esculenta [L.] Schott) is a major staple crop grown in tropical regions around the world. In many Pacific Island countries, taro has both economic and cultural importance. Taro is primarily propagated by vegetative cuttings, making it prone to the accumulation of viruses and the negative impacts resulting from their infection. A severe disease of taro associated with viral infection has only been reported in the Solomon Islands [Taro  [Alphanucleorhabdovirus colocasiae, and Colocasia bobone disease-associated virus (CBDaV) [Cytorhabdovirus colocasiae; two bacilliform-shaped badnaviruses, taro bacilliform virus (TaBV) [Taro bacilliform virus and Taro bacilliform CH virus, respectively; and one flexuous rod-shaped potyvirus, dasheen mosaic virus (DsMV) [Dasheen mosaic virus. Revill and collaborators [ABVC has been associated with co-infection by multiple viruses ,2,4. Aftus spp.) . Virusesus spp.) , a membe (CBDaV) , a membes (TaBV)  and taros (TaBV) , memberss (DsMV) , member borators  used PCRborators ,14.In understudied viral pathosystems where new species or genetic variants are likely, high-throughput sequencing (HTS) has emerged as a powerful tool for virome investigation ,15, incl® , and transported to the University of Hawaii under USDA permit P526P-17-03915. Upon arrival, the samples were stored at −20 °C until processed.In June 2019, three leaf samples were collected from taro plants growing in Popondetta  and Kokoda , PNG, that were displaying typical ABVC symptoms A. Two adC. esculenta (PRJNA587719) using Geneious mapper plug-in implemented in Geneious v. 10.1.3 [de novo-assembled contiguous sequences (contigs) from the non-taro sequence reads. These contigs were annotated using BLASTX searches [ftp://ftp.ncbi.nih.gov/genomes/Viruses/all.fna.tar.gz) last accessed on 20 March 2020. Contigs with similarities to virus sequences were then used as references for an iterative mapping approach [Double-stranded RNAs (dsRNAs) are an optimal and cost-effective starting material for HTS with the highest potential for detection and characterization of plant viruses and viroids . Therefo. 10.1.3  to enric. 10.1.3  producedsearches  against approach  using thE. coli Poly (A) Polymerase  to determine both 5′ and 3′ ends. All amplicons were cloned into pGEM-T Easy  and three to five clones were sequenced per amplicon.For the viruses detected, a group of overlapping primer sets  were desTNAs were extracted from ~0.1 g of leaf tissue from each sample using the CTAB method . TNAs unEcoRI and SacI  and analyzed by gel electrophoresis in 0.8% agarose gels in TBE buffer. Both restriction endonucleases were predicted to cut at least once in the TaBV and TaBCHV isolate sequences available at GenBank that were analyzed.TNAs (not digested with DNase) were subjected to rolling circle amplification (RCA) using TempliPhi 100 Amplification Kit . Amplification was performed at 36 °C for 18 h, then the reaction was terminated by incubation at 65 °C for 10 min. RCA-amplified DNA was independently digested using Genomic and proteomic analyses were performed in silico as detailed previously . BrieflyPhylogenetic relationships between the taro viruses characterized in this study and their homologs were inferred using the nucleotide or amino acid sequences of whole genomes or multiple protein domains conserved among some or all of these viruses using the best model of evolution as determined in MEGA 7.0.26 . MultiplThe three taro plants from Popondetta and Kokoda with symptoms of ABVC were severely stunted A. Their C. esculenta reference genome underwent de novo assembly, producing 663 contigs. Of these, 188 contigs were determined to be of viral origin (HTS generated ~14.5 M paired-end reads (300 nt) from a cDNA library using dsRNA as a template which was extracted from pooled tissue sampled from the three ABVC-symptomatic plants A. Of thel origin . In addiPCR-based assays were used to determine the virus status of the samples collected from the three taro plants affected by ABVC as well as the two plants with interveinal chlorosis but no ABVC-associated symptoms . SequencCytorhabdovirus caricae. Similar to other cytorhabdoviruses, the CBDaV isolates had enough genetic divergence that different strains could be distinguished (PNG and SI)  and one from Kokoda . CBDaV-PNG-P and CBDaV-PNG-K were 12,205 nt in length  , shared  and SI) A.Two badnaviruses, TaBV and TaBCHV, were identified in taro samples collected from PNG. The complete genomes of one isolate of TaBV and two isolates of TaBCHV were obtained using HTS data, RT-PCR, and RCA. TaBV-PNG-K (GenBank ON086737) was sequenced from a sample from Kokoda and found to be 7825 nt in length  . It had The complete genome of a DsMV isolate was recovered from a taro sample from Kokoda. Excluding the 3′ poly(A) tail, DsMV-PNG-K (GenBank ON086743) was 10,066 nt in length   and possA contig with 2.3 kb in length  was identified in the HTS dataset that putatively possessed a partial ORF encoding a protein product with moderate homology to the RNA-dependent RNA polymerase (RdRp) of umbra-like viruses . EffortsOryzavirus (Reoviridae family) [The full genome of TaRV identified in two samples from Popondetta, has been reported elsewhere . Briefly family) .Despite being a severe disease of taro, ABVC remains understudied. Fundamentally, critical knowledge gaps on its etiology, host-pathogen interactions, and transmission remain to be studied. Most examinations of the disease were performed by electron microscopy, which has a limited ability to identify the agents present. In the last three decades, several viruses infecting taro have been molecularly characterized and named. But work aimed at identifying the virus(es) associated with ABVC has had limitations, especially if ABVC symptoms are caused by multiple viruses.In a survey of taro viruses in Pacific Island countries using PCR-based diagnostic assays, Revill and collaborators  found CBThe question still remains whether ABVC is indeed a “virus complex” resulting in a synergistic effect of virus co-infection. Viral synergisms in plants cause more severe symptoms than infections caused by either virus alone. These are described in several well-studied diseases including sweet potato virus disease and maize lethal necrosis ,35. Earl® prevented our ability to conduct such experiments for our study. Such assays could involve the use of vector-mediated transmission using planthoppers for CBDaV [The involvement of badnaviruses in synergistic diseases is well-documented , so the or CBDaV  and mealor CBDaV . Alternaor CBDaV . Followior CBDaV  and the or CBDaV , an infePrior to this study, genomic sequence information was only available for one isolate of CBDaV. By sequencing two additional isolates, we have documented some of the genetic diversity of this virus, which is essential for the development of reliable molecular detection assays. These sequence data also confirmed the presence of a small ORF between those encoding the G and L proteins reported in a recent study on a newly characterized strawberry cytorhabdovirus . In addiUsing these data, we were able to develop new molecular detection assays for each virus that may help reduce type II errors  during diagnostic testing. Given the genetic variation of viruses infecting taro: CBDaV up to 17%, this study, TaBV up to 14% , TaBCHV


This cross-sectional study evaluates trends in out-of-pocket costs for and characteristics of pharmacy-dispensed buprenorphine medications for opioid use disorder treatment by type of payer from 2015 to 2020. How do out-of-pocket costs and prescription characteristics for buprenorphine prescriptions approved for opioid use disorder treatment vary by payer?Cross-sectional analysis was used to calculate daily out-of-pocket costs and prescription characteristics for retail pharmacy–dispensed buprenorphine from 2015 to 2020. Mean daily out-of-pocket costs decreased overall from $4.79 in 2015 (7 375 508 prescriptions) to $1.91 in 2020 (13 486 822 prescriptions), but out-of-pocket costs continued to vary by payer in 2020.These findings suggest that financial barriers to accessing and maintaining buprenorphine treatment for opioid use disorder may exist and differ by type of and access to prescription coverage. Buprenorphine has been approved for opioid use disorder treatment, yet remains underutilized. Cost may present a barrier; little is known about how out-of-pocket costs vary.To determine if out-of-pocket costs and prescription characteristics for buprenorphine varied by type of payer.This cross-sectional study used all-payer data on retail pharmacy–dispensed buprenorphine prescriptions from January 1, 2015, through December 31, 2020, for adults (aged ≥18 years) in the US, excluding formulations primarily used to treat pain. Data were analyzed from July 2021 to June 2022.Type of payer  for dispensed prescription.All outcomes are prescription-level. Mean and median daily out-of-pocket costs were calculated overall and by payer type. Prescription characteristics  were examined by payer type.Although mean daily out-of-pocket costs decreased overall from $4.79  in 2015 (7 375 508 prescriptions) to $1.91  in 2020 (13 486 822 prescriptions), out-of-pocket costs continued to vary by payer in 2020. Medicaid had the lowest mean daily out-of-pocket cost across all years—$0.18  in 2015, and $0.10  in 2020. Private and commercial paid prescriptions fell from $4.80  per day in 2015 to $1.82  in 2020. Self-pay and assistance categories had the highest mean daily out-of-pocket costs across study years . Medicaid paid prescriptions had a mean supply of 15.59 days  and the lowest percentage of generic prescriptions . Out-of-pocket cost varied by prescriber location and patient characteristics; mean costs were highest for prescriptions written in the South , metropolitan counties , and for individuals aged 35 to 44 years .This cross-sectional study found that mean daily out-of-pocket costs for buprenorphine were lower in 2020 than in 2015, but variation by payer existed in all study years. Financial barriers to accessing and maintaining buprenorphine for opioid use disorder treatment may exist and differ by type of prescription coverage. Future research could monitor costs and identify potential barriers that may impact access and retention in care. This study examines how daily out-of-pocket costs and characteristics of buprenorphine MOUD prescriptions dispensed via retail pharmacies varied by payer from 2015 through 2020.Buprenorphine remains underutilized, possibly due to barriers including clinician availability, wait times, and cost.STROBE) reporting guideline reporting guideline. The US Centers for Disease Control and Prevention determined human participant regulations, including informed participant consent, and institutional review board review was not applicable because deidentified secondary data were used in accordance with 45 CFR §46.Data are from the IQVIA Longitudinal Prescription (LRx) database, capturing 92% of retail pharmacy prescriptions in the US. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (Buprenorphine prescriptions dispensed to adults (aged ≥18 years) from January 1, 2015, through December 31, 2020, were identified. Buprenorphine formulations primarily used to treat pain (Butrans and Belbuca) were not included in the sample. Prescriptions missing cost (2 917 421 prescriptions) and approximately 0.1% of the sample with negative or exceedingly high cost values (67 631 prescriptions) were excluded. The final sample was 67 592 683 prescriptions from 2015 to 2020.Prescription payers captured in the LRx data were combined into 6 categories: private and commercial, self-pay, Medicaid, Medicare, assistance, and unknown. Private and commercial included several forms of employer-sponsored health insurance, plans purchased through health insurance exchanges, and those administered by pharmacy benefit managers. Self-pay indicates a prescription was paid for entirely with cash. Medicaid includes Medicaid managed care or fee-for-service Medicaid. Medicare indicates prescriptions paid by Medicare Part D. The assistance category indicates payment using a discount card (including non-Medicare senior discount cards), a coupon, or a voucher. The unknown category was composed of prescriptions that were missing payer type or had “unspecified third party” or “unknown” for payer type . Nonmetropolitan counties were those designated as micropolitan statistical areas or noncore counties. Costs were adjusted to 2020 dollars using the Average Consumer Price Index Research Series for 2020.13 This descriptive study did not involve statistical tests. Stata version 14.2 (StataCorp) was used for all analyses. The analysis was conducted from July 2021 to June 2022.Analysis was cross-sectional and all outcomes were prescription-level. Daily out-of-pocket costs were calculated to facilitate comparisons across prescriptions of differing duration. This analysis examined daily out-of-pocket costs by calculating mean costs with 95% CIs and median costs with IQRs, overall and by payer type. Mean days supply with 95% CI and percentage of generic (vs name brand) prescriptions with 95% CI were also calculated. Mean daily out-of-pocket costs by prescription characteristics were examined, including age and sex of patient in receipt of the prescription and US census regionThere were 7 375 508 buprenorphine MOUD prescriptions dispensed in 2015 and 13 486 822 in 2020 . In 2015Overall mean daily out-of-pocket costs decreased from $4.79  in 2015 to $1.91  in 2020. Medicaid had the lowest mean out-of-pocket cost in all years—$0.18  in 2015, $0.10  in 2020—and $0.00 median out-of-pocket cost from 2016 to 2020. Private and commercial mean cost fell from $4.80  in 2015 to $1.82  in 2020, but was still higher than Medicaid  or Medicare  in 2020. Self-pay and assistance had the highest mean out-of-pocket costs; self-pay was $9.76  in 2015, $10.60  in 2016, and $8.44  in 2020; the assistance category was $8.72  in 2015, $10.41  in 2017, and $6.31  in 2020.Among prescriptions dispensed in 2020, private and commercial , Medicare , and unknown  had longer mean durations, while those paid by self-pay had the shortest  . MedicaiAcross payers, daily mean out-of-pocket costs were highest in the South at $2.91  and lowest in the Northeast at $1.04  . More thThis study found that out-of-pocket costs for buprenorphine prescriptions have decreased since 2015 and costs continued to vary by payer in 2020. In 2020, an individual would face daily mean out-of-pocket costs of $0.10 for a prescription paid by Medicaid or $0.46 for one paid by Medicare, versus $1.82 for a prescription paid by private and commercial. Furthermore, for the approximately 1-in-5 prescriptions dispensed in 2020 that were paid by assistance or self-pay, mean daily out-of-pocket costs were $6.31 or $8.44, respectively. These daily costs can add to up substantial costs for patients retained on long-term therapy or receiving longer duration prescriptions, such as for a 30-days supply. Variation in mean out-of-pocket costs also exists by clinician location and patient characteristics, suggesting that financial barriers for individuals accessing and maintaining MOUD treatment may depend on how one pays for prescriptions and factors influencing access to prescription coverage.14 Future studies might examine the reason for the shorter duration in the Medicaid category and its potential implication for retention in MOUD treatment, as well as if there are differences in outcomes for patients using different types of buprenorphine, including generic vs name-brand buprenorphine formulations.Medicaid paid for the largest number of prescriptions and increased most from 2015 to 2020. Prescriptions covered by Medicaid differed from other payers, with a shorter mean duration and a greater proportion of nongeneric prescriptions, potentially due to state Medicaid requirements that afford preferred status to certain forms of buprenorphine for Medicaid-covered prescriptions.9 used data on individuals with insurance coverage from large self-insured employers and found that among those retained on buprenorphine treatment for less than 180 days, the 2016 to 2017 mean daily cost was $2.63. Dunphy et al also found a $1 increase in daily out-of-pocket buprenorphine cost was associated with a 12–14% decrease in the odds of MOUD treatment retention, highlighting the importance of this study’s findings. Greater financial barriers to treatment retention might exist according to payer, particularly for self-pay patients and those using assistance.Study findings on daily out-of-pocket costs for private and commercial paid prescriptions are consistent with prior research. Dunphy et alStudy limitations include that the IQVIA data do not allow differentiation between prescriptions received for MOUD vs those diverted to other uses, such as off-label use for pain. The IQVIA LRx data are not weighted to be geographically representative. Prescription coverage may be lower in certain regions, such as those with higher numbers of health maintenance organizations, which may dispense prescriptions through their own pharmacies. Information on patient race and ethnicity was not available in the data. Any inaccuracies in clinician address have the potential to bias estimates at the county level. Additionally, this analysis only included retail pharmacy–dispensed buprenorphine and did not examine other MOUD types, such as methadone or extended-release naltrexone.In this cross-sectional study of all-payer pharmaceutical data, findings showed that while mean daily out-of-pocket costs for buprenorphine decreased between 2015 and 2020, costs continued to vary by payer and remain high for some in 2020. Public health and insurer strategies aimed at reducing higher patient out-of-pocket costs may address financial barriers and improve buprenorphine treatment retention. Future research might continue to examine how cost-sharing may impact MOUD treatment initiation and retention under different payers.


Lassa fever (LF) and yellow fever (YF) belong to a group of viral hemorrhagic fevers (VHFs). These viruses have common features and damages the organs and blood vessels; they also impair the body’s homeostasis. Some VHFs cause mild disease, while some cause severe disease and death such as in the case of Ebola or Marburg. LF virus and YF virus are two of the most recent emerging viruses in Africa, resulting in severe hemorrhagic fever in humans. Lassa fever virus is continuously on the rise both in Nigeria and neighboring countries in West Africa, with an estimate of over 500,000 cases of LF, and 5000 deaths, annually. YF virus is endemic in temperate climate regions of Africa, Central America , and South America  with an annual estimated cases of 200,000 and 30,000 deaths globally. This review examines the impact of the COVID-19 pandemic on the trend in epidemiology of these two VHFs to delineate responses that are associated with protective or pathogenic outcomes. Arenaviridae and Flaviviridae genera, respectively. They also share similar zoological features and are often known as emerging tropical–viral diseases. Their negative impact on public health is partly due to their high virulence and human-to-human transmission (Lassa fever) and vector-to-human transmission (Yellow fever). LF and YF also share common pathogenesis in terms of their primary cell targets being myeloid cells and in terms of the organs that are affected. However, the type of immune responses that are induced by LF and YF infection and their roles in protection or pathogenesis differ significantly. These differences may play a role in understanding disease, and the differences in disease epidemiological trends during the pandemic period. This review article describes the human immune responses to LF and YF viruses and illustrates the differences between the viral spreads during the COVID-19 era. Comprehensive understanding of innate immunity to these zoonotic viruses may guide the development of effective medications that will boost human protective immune responses.Lassa fever virus and yellow fever viruses belong to viral genera; the The World Health Organization (WHO) declared the coronavirus infection COVID-19) a global pandemic on the month of March 2020. It has become one of the deadliest pandemics the world has experienced in decades. SARS-CoV-2, a virus that causes COVID-19, was first detected in Wuhan, China, in December 2019 [9 a globaAedes aegypti) and rodents (multimammate rat) as vectors, respectively [Since early 2020, the morbidity and mortality rates of COVID-19 have increased in Nigeria [ectively ,4. The sInfections with COVID-19, YF, and LF symptoms are challenging to differentiate, particularly at the early stages, due to similarities with their clinical signs. Additionally, coinfections with arboviruses or flaviviruses are not thoroughly studied in Nigeria. The country’s resource-limited healthcare system faces the risk of becoming overwhelmed with multiple socio-economic crises. A limited number of virology labs and expert virologists make it difficult to respond timely to infectious disease outbreaks and to prevent infection spread.Lassa fever virus is an enveloped, single-stranded RNA virus, a causative agent of LF first recorded in 1969 during an outbreak in the city of Jos in Plateau state, Nigeria and was named after a city called Lassa in Borno state Nigeria . LF is kMastomys natalensis; a.k.a “African rat”) or via consumption of the rats, as a common practice in some endemic regions, and inhalation of virus-laden particles. Human-to-human transmission has also been seen in hospital settings [LF is contracted through a bite from an infected rodent  for macrophages and DCs to increase the activities of co-stimulatory molecules that are required for antigen presentation and for enhancing T-cell responses. The importance of these natural immune responses in viral attack is supported by several mechanisms that are encoded by vertebrate viruses to elude these responses [The main target cells of the LF virus are the myeloid cells, which include macrophages and dendritic cells. The LF virus arrests dendritic cells (DC) in an immature, inactive state despite DC migration to the lymph nodes, and the greater disruption in the function of antigen-presenting cells (APCs) by LF viruses compared to SARS-CoV-2 might correlate with the general lack of adaptive immune responses in fatal LF. It is also important to note that the scientific findings regarding the interactions between LF and SAR-CoV-2 viruses and myeloid cells were made using human cells extracted and derived in cell culture, which does not accurately depict all features of infection and cell maturation esponses , for exaesponses . Additioesponses ,19. Suchesponses ,21. Simiesponses . These cesponses a–c.On the other hand, sRNA derived from SARS-CoV-2 genomic can be detected by RIG-1 and MDA5 intracellularly. SARS-CoV-2 has evolved several evasion strategies to counteract the human innate immune response  by decreasing the levels of IFNs; patients with mild and moderate COVID-19 have low levels of Type 1 and Type 3 IFNs in their blood serum . EvidentManagement of patients diagnosed with LF could be challenging considering the disease’s insidious onset and non-specific clinical symptoms. However, supportive and symptomatic treatment with intravenous fluid resuscitation and electrolyte balance is beneficial in LF patients. An antiviral drug (Ribavirin) has proven successful in the early treatment of patients diagnosed with Lassa fever LF.Aedes and Haemogogus species [Aedes and Haemogogus species. Humans become infected when they tour or work in the rainforest and are bitten by infected mosquitoes [Sylvan YF: also known as “jungle” yellow fever infestation, this occurs in the temperate climate regions  where the primary carriers of the virus are monkeys following bites by wild mosquitoes of the squitoes .Intermediate YF: here, the semi-domestic mosquitoes (wild and household bred) infect monkeys and humans. Increased contact between people and infected mosquitoes leads to increased transmission, especially in densely populated regions. this type of transmission is the most typical type of outbreak in Africa .Aedes aegypti mosquitoes and where people with no immunity due to lack of vaccination live. In this condition, infected mosquitoes transmit the virus from person to person [Urban YF: this is slightly like the intermediate type, as transmission occurs when the infected group of people spread the YF virus in heavily populated regions with several breeding grounds for o person .Yellow fever (YF) is an acute viral hemorrhagic infectious disease caused by the YF virus, an RNA virus from the Flavivirus genus, and transmitted by bites from mosquitoes belonging to the  species . The thrYellow fever is distributed in the west, central, and east Africa, Central America, and South America . YF remaYF symptoms can take 3–6 days to develop and include fever, chills, headache, backache, and muscle aches. About 15% of patients diagnosed with YF will develop jaundice, conjunctival hemorrhage, epistaxis, hematochezia, coffee ground emesis, hemorrhagic shock, organ failure (most commonly liver and kidney), and death . Severe Aedes mosquitoes) were believed to have been introduced into Mexico by slaves from endemic countries in West Africa during the slave trade period. Additionally, around the time of the slave trade with the migration of slaves to the United States, an epidemic of YF was reported in several cities, with a 10% mortality rate from YF recorded in Philadelphia in the year 1793 [The disease is endemic to sub-Saharan African and South American countries with temperate climates. The first case of YF was not documented; however, the first incident of a presumed case of YF was eventually documented in a Mayan article and described as hematemesis in Yucatan, Mexico, in 1648 . The YF ear 1793 . The firear 1793 .YF cases have been on the increase over the past few years, with each year’s outbreak surpassing the previous year. However, the outbreak of COVID-19 in 2020 was different. Despite the introduction of the YF vaccine into the 2004 regular immunization schedule of Nigeria, the incident rates of YF have continuously been on the rise with suboptimal mass immunization and rising rates of urbanization within the neighboring African countries, particularly Nigeria ,31. As oIn Nigeria, YF surveillance is case-based weekly surveillance with documentation on the probable cases and confirmed cases reported to health authorities at the local government areas (LGAs), as well as at state and national levels .The E protein, a heavily mutated region of the entire genome plays a key role in viral entry into immune cells. The E protein serves as the main target for the YF-17D vaccine by causing a mutation in the E protein thus, altering the YF virus tropism and affecting its virulence ,34,35,36The human immune response also affects the development of viral coinfections. This can be further explained by the actions of the naïve T cells upon sensing a viral infection, which convert into activated T cells and later into memory T cells. The memory T cell responses, which are generated to act against a particular viral infection, could influence the amount and strength of the immune response to any future or unrelated viral coinfection, a process called heterologous immunity (HI). The HI can be seen between viruses of similar structure or genus, multiple variants of the same virus type, among different viruses, between virus and bacteria, or between virus and protozoa . TherefoIn week 1 to 53 of the year 2020 a,b, the Diagnosing yellow fever during an outbreak of a pandemic (COVID-19) can be very challenging as it is clinically difficult to distinguish yellow fever from other infectious diseases  and COVID-19 based on clinical symptoms, especially when presenting with mild or atypical symptoms . SerologThere is no definitive treatment for YF, only symptomatic management. Vaccination against the yellow fever virus provides life-long immunity.All infectious diseases require a specific set of health interventions in the reduction of their transmission, morbidity, and mortality and their impact on the health system at large. A study showed that the most effective measure for decreasing the spread of Lassa fever is an increase in the death rate of the rodents by mice culling which alternatively decreases the number of infected rodents (African rats), and even could drive the disease to complete extinction . An addiAedes mosquitoes and preventing them from growing from the egg to larva and adult, by educating the public on the proper use of long-lasting insecticidal nets (LLINs) and ensuring that there is no breech in the supply of LLINs in the endemic areas [In the case of YF, an emergency mass vaccination with the YF-17D vaccine and vector control are needed in the control of yellow fever as the mass vaccination will help in increasing the population immunity and survivability rates in the country . Communiic areas .Nigeria’s COVID-19 response represents a unique situation of effectively implementing population-wide infectious disease control measures such as mask-wearing, isolation of infected people, personal protection, practicing good hygiene, and immunization. Additionally, a strong knowledge of the determinants of viral coinfections and interactions between two viruses remain as an emerging field in virology research. It is important to note that laboratory testing of clinical specimens often produces false-positive results when we try to link such results with a single identified pathogen as a cause of a disease process. Frequently, additional pathogens that contribute to the worsening of a disease outcome are undetected. As a result of viral interference as described in the article, one virus may lead to either genetic recombination or genetic reassortment, which subsequently yields antigenic variants that can be missed by passive immunity (vaccine-induced immunity) and antiviral drugs as in the case of yellow fever outbreaks in the dry season seen in It is also imperative to note that a strong acquired immunity is crucial for the efficient control of pathogens involved in heterologous infections. So, memory T cells’ responses to an individual virus can significantly affect both the amount and quality of immune response available against subsequent viral infections or any type of infection. Additionally, it is important to note that the subsequent infections may either decrease the amount of existing immune memory cells or produce additional effects that could boost immune defenses.In summary, based on the LF and YF disease patterns during the COVID-19 era (2020) we can strongly agree that there was indeed a correlation in the trends of YF and LF during their individual peak seasons (dry seasons), and with the continuous infection of the COVID-19 pandemic, the outcomes of viral coinfections need to be thoroughly explored and clearly comprehended in addition to developing advanced methods for diagnostics, preventive vaccines, and antiviral medications.


The genome of influenza A virus consists of eight single-stranded viral RNA (vRNA) segments. The nonconserved noncoding regions (NCRs) at the 3′ and 5′ termini of each segment show extremely low divergence and mutation rate. They appear as segment specific among the eight segments and also subtype specific among different subtype-determinant hemagglutinin (HA) and neuraminidase (NA) segments. In order to acquire in-depth knowledge on the sequence requirements of the segment-specific or subtype-specific NCRs (ssNCRs), we, in the context of WSN (H1N1) reverse genetics, designed a virus random nucleotide selection assay (vRNSA) in which we generated pHW2000-HA plasmid libraries with random nucleotides in each grouped nucleotide positions in the 3′ and 5′ H1-ssNCRs, followed by virus rescue, serial passage, and deep sequencing. The resulting sequence logos present a visualized dynamic overview of the hierarchical sequence requirements of the 3′ and 5′ H1-ssNCRs. It showed that, in the process of continuous passage, the 3′ H1-ssNCR, in general, stabilized more quickly than the 5′ H1-ssNCR. The nucleotides close to the highly conserved 3′ and 5′ promoter regions showed higher sequence stringency than nucleotides away from the promoter regions. All stabilized sequences displayed a common feature of high A/U ratios. Especially with our mutational function analyses, we demonstrate that the 3′ promoter-proximal nucleotides could cooperatively exert a direct effect on the transcription and replication of the HA segment. Together, these results provide in-depth knowledge for understanding the NCRs of influenza A virus.IMPORTANCE The segment-specific and subtype-specific nonconserved noncoding regions (ssNCRs) at both 3′ and 5′ ends of viral RNA segments of influenza A virus are largely conserved among the same segments of different viruses. However, the function-related sequence requirements of these ssNCRs remain unclear. In this study, through a novel self-designed vRNSA approach, we present a visualized dynamic overview diagram directly reflecting the hierarchical sequence requirements within and between the 3′ and 5′ H1-ssNCRs. The in-depth functional mutagenesis analyses further revealed that specific nucleotides in the 3′ promoter-proximal region could cooperatively exert a direct effect on viral RNA transcription and replication. This work further advanced our knowledge in understanding the nonconserved noncoding regions of influenza A viruses. Orthomyxoviridae. Within the virions, each viral RNA segment is present in the form of viral ribonucleoprotein complexes (vRNPs) in which the RNA associates with nucleoprotein (NP) oligomers and a heterotrimeric RNA-dependent RNA polymerase formed by polymerase basic 1 (PB1), PB2, and polymerase acid (PA)  . The strcid (PA) . The inicid (PA) –7. The tid (PA) –, while tid (PA) –. When suid (PA) –, 11.The eight vRNA segments of the influenza genome, designated polymerase basic 2 (PB2), polymerase basic 1 (PB1), polymerase acid (PA), hemagglutinin (HA), nucleoprotein (NP), neuraminidase (NA), matrix (M), and nonstructural protein (NS), contain one or two major open reading frames (ORFs) in negative sense, which are flanked by noncoding regions (NCRs) at both ends. The NCRs of each segment consist of a highly conserved promoter region  followed by the nonconserved noncoding region that are segment specific among the eight segments and subtype-specific NCRs among the two subtype-determinant HA and NA segments 1316. It haIt has been demonstrated that segment-specific NCRs play multiple roles in the life cycle of influenza A viruses. The 3′- and 5′-segment-specific NCRs together with adjacent terminal coding regions have been reported for each segment acting as selective packaging signals –27. The –In this study, we aimed to characterize the sequence requirements of the ssNCRs in the context of the H1 segment of influenza A virus. We designed a novel virus random nucleotide selection assay (vRNSA) to explore the nucleotide selectivity of the H1-ssNCRs during virus passage. Through this assay and our in-depth functional studies, we have obtained in-depth knowledge in understanding the basic characteristics of H1-ssNCRs. An A19U mutant and the appearance of its recovery U23G mutation further highlight the essentiality of the 3′ promoter-proximal nucleotides in regulating viral RNA transcription and replication.To explore the sequence requirements of H1-ssNCRs for virus propagation, in the context of WSN (H1N1) reverse genetics, we designed a virus random nucleotide selection assay (vRNSA) in which we provided random nucleotides at each nucleotide position in the 3′ and 5′ H1-ssNCRs, followed by virus rescue passage 0 (P0); six generation passages in Madin-Darby canine kidney (MDCK) cells; and deep sequencing at P0, P2, P4, and P6 . ConsideWe then conducted the vRNSA with these plasmid libraries in two independent experiments. Considering that the untranslated regions of influenza viruses have an extremely low divergence and evolutionary speed , togetheSince the stabilized sequences in 3'G2, 3'G3, and 5'G1 showed different sequences from the wild type, we then went on to examine the effects of these non-wild-type sequences on virus replication efficiencies and their stability along serial passage in cell culture. With the reverse genetics, we generated five mutant viruses containing the stabilized non-wild-type sequences as shown in Next, we analyzed the nucleotide composition of these stabilized non-wild-type sequences. The results showed that the proportion of A and U bases to the total bases (A/U ratio) was above 4/6 in the stabilized mutant sequences in the 3′ and 5′ H1-ssNCRs, e.g., the 3'G3-1 group was entirely composed of A and U bases . In addiSince the wild-type sequence was selected with an extremely high A/U ratio in 3'G1 at P6 in the two independent replicates, together with the observation that 15U, 17U, and 19A in 3'G1 were the fastest stabilized in P2 and remained stable from P2 to P6 passages, we were interested in examining the effects of these As or Us in regulating virus replication. To avoid changing the A/U ratio mentioned earlier, we mutated nucleotides 15U, 17U, and 19A into 15A, 17A, and 19U, respectively.First, we examined the effects of these mutants on viral RNA synthesis and protein production in an RNP reconstitution system derived from the WSN virus . The wilNext, we examined the effect of these mutations on viral replication efficiency. The wild-type and mutant viruses were rescued with the WSN reverse genetics, and the titers of the rescued viruses (P0) were examined by plaque assay. The U15A and U17A mutant viruses showed similar titers to the wild-type virus, while the viral titer of A19U decreased approximately 10-fold compared with that of the wild type . To furtIn order to assess the effect of the point mutation U23G, we constructed the U23G and A19U+U23G mutations in the pPOLI-HA plasmid and examined the effects of these mutants on viral RNA synthesis and protein production in the RNP reconstitution system as described earlier. In general, the results were consistent with the results observed above; the point mutation A19U significantly attenuated the level of all three species of viral RNAs, while the A19U+U23G mutation significantly increased the HA vRNA and mRNA levels compared with A19U mutation  and B. OSince the A19U mutation was introduced in the 3′ H1-ssNCR of vRNA, it would be used as the template to conduct both viral RNA transcription (vRNA→mRNA) and replication (vRNA↔cRNA); next, we wanted to explore the effects of the A19U mutation on viral RNA transcription and replication, respectively. We first investigated the effect of the A19U mutation on transcription through an RNP reconstitution system which contains a replication-deficient polymerase (PA-C95A)  to avoidTo further assess the effect of the U23G mutation on WSN virus growth efficiency, we generated U23G and A19U+U23G recombinant viruses using the reverse genetics and examined the titers of the rescued viruses (P0) by plaque assay. Unlike the significant attenuation (1 log reduction) observed for the A19U mutant virus, the titer of the A19U+U23G mutant virus was similar to that of the wild-type virus . In ordeThe segment-specific and subtype-specific NCRs of influenza A virus are highly conserved within each segment but vary significantly in length and sequences between different segments. However, the biological significance of these variations and the sequence requirements remain poorly understood. In this study, we investigated the sequence requirements of the 3′ and 5′ H1-ssNCRs in the context of H1N1 WSN virus. By performing the self-designed vRNSA, it provides, for the first time, a visualized dynamic overview directly reflecting the hierarchical sequence requirements within and between the 3′ and 5′ H1-ssNCRs. The results showed that, between the 3′ and 5′ H1-ssNCRs, the sequence requirements of the 3′ H1-ssNCR in general was more stringent than that of the 5′ H1-ssNCR; within the 3′ and 5′ H1-ssNCRs, sequence stringency is declining with the locations of the nucleotides away from the promoter. Moreover, the stabilized sequences presented a common feature of high A/U ratios. More importantly, we reported that the identities of nucleotides close to the 3′ vRNA promoter cooperatively play a role in regulating viral RNA transcription and replication.It is widely accepted that the key packaging signal of each segment is composed by ssNCRs and its adjacent terminal coding sequences at both ends. In the studies of packaging signal identifications for each segment of WSN virus through serial truncation of the terminal coding sequences, the authors mentioned that the 3′-terminal coding region is more important than the 5′-terminal coding region toward the efficient packaging of PB2, HA, NA, and NS segments –27. Cons–in vivo functional analyses demonstrate that the nucleotides adjacent to the promoter region in the 3′ H1-ssNCR cooperatively regulate the HA vRNA synthesis, further highlighting the importance of 3′ promoter-proximal nucleotides in regulating virus replication efficiency. Moreover, we previously studied the functions of the 3′ H1-ssNCR with a series of truncation mutants, and we found that the truncation with these six nucleotides or less (3′ 15 to 20) left could work with its adjacent coding nucleotide toward regulating HA vRNA template preference in a multisegment environment  . We thenIn summary, by using the self-designed vRNSA, we generate, for the first time, the visualized dynamic sequence selection logos for the 3′ and 5′ H1-ssNCRs during virus rescue and serial passage. It clearly differentiates the importance of the 3′ H1-ssNCR versus the 5′ H1-ssNCR and also demonstrates hierarchical sequence requirements of the nucleotides within the 3′ and 5′ H1-ssNCRs. Our functional assays further pinpoint the essentiality of the promoter-proximal nucleotides in regulating influenza virus replication. These data will not only broaden our knowledge in understanding the nonconserved noncoding region of influenza A virus but also may provide basic information to design the antisense oligonucleotide (ASO) in targeting key viral RNA sequences.2. Influenza A/WSN/33 viruses were kindly provided by Ervin Fodor .Madin-Darby canine kidney (MDCK), human lung carcinoma cell line A549, and human embryonic kidney 293T (HEK-293T) cells were purchased from the American Type Culture Collection (ATCC)  and maintained in Dulbecco’s modified Eagle’s medium (DMEM) (Gibco) supplemented with 10% fetal bovine serum (FBS) (Gibco), 100 U/mL penicillin, and 100 μg/mL streptomycin at 37°C and 5% COThe eight plasmids of the influenza virus A/WSN/33 (H1N1) reverse genetic system  were described previously . The vRN6 293T and MDCK cells (at a ratio of 2:1) in a 6-well plate were transfected with 0.5 μg of each plasmid of the influenza virus A/WSN/33 (H1N1) reverse genetic system  using Lipofectamine 2000 and Opti-MEM according to the manufacturer’s instructions. At 24 h posttransfection, the medium was replaced with DMEM containing 0.5% FBS and 0.5 μg/mL tosylsulfonyl phenylalanyl chloromethyl ketone (TPCK)-treated trypsin (Sigma). The virus supernatant was harvested 48 h after the medium was changed, and the viruses were plaque purified. The purified viruses were passaged for two generations in MDCK cells and subjected to virus genome sequencing. The virus titers were then determined by a standard plaque assay.Virus rescue was conducted by the previously described protocol for the eight-plasmid rescue system . BrieflyEscherichia coli strain DH5α, which resulted in at least 46 (each 3′ ssNCR group) or 48 (each 5′ ssNCR group) independent clones per library. We varied six or eight nucleotides for each library given the transformation efficiency in plasmid construction and the transfection efficiency in virus rescue. Then we rescued mixed viruses of the six libraries by the reverse genetics. A total of 107 293T cells in 10-cm dishes was transfected with 1 μL (1 μg/μL) of each plasmid of the influenza virus A/WSN/33 (H1N1) reverse genetics system , -NA, -M, and -NS). At 48 h posttransfection, the virus-containing supernatants were harvested and the virus titers were determined by plaque assay. The mixed viruses of each library were propagated for six generations incubated with 107 MDCK cells in T175 flasks at a multiplicity of infection (MOI) of 0.1 for 1 h at room temperature because 106 virus particles far exceed the total number of likelihood in each library. MDCK cells were then washed with phosphate-buffered saline (PBS) twice and overlaid with DMEM containing 0.5% FBS and 0.5 μg/mL TPCK-treated trypsin. When the cytopathic effect (CPE) reached about 50% of the total cells, the supernatants were harvested.The HA 3′ and 5′ ssNCRs of the WSN virus were divided into three groups. Each group of the 3′ H1-ssNCR contained six random nucleotides , except that the 3'G3 group contains an unchanged -3U nucleotide. Each group of the 5′ H1-ssNCR contained eight random nucleotides . Nucleotides in each group were randomized by performing PCR with primers containing degenerate nucleotides . The PCR products were ligated into an empty pHW2000 plasmid by BsmBI sites and then propagated in http://weblogo.berkeley.edu/logo.cgi)  and reverse transcribed according to the following method of virus genome sequencing. The reverse transcription (RT) products were then amplified together by PCR with a high-fidelity enzyme using HA 3′ H1-ssNCR-specific primers with different barcode sequences. To avoid excessive cycles of PCR that may affect the actual proportion of bases at different positions of the 3′ and 5′ H1-ssNCRs, we performed quantitative PCR (qPCR) at first with RT products that were diluted 5 times as the template to choose a suitable number of cycles for PCR and make sure that the PCR products were in exponential phase. After performing DNA gel electrophoresis and confirming the locations of lines, we recycled the PCR products and subjected them to deep sequencing. The ratios of different nucleotide identities at respective sites of HA ssNCRs were calculated and analyzed. The ssNCR sequence logos for each library showing the nucleotide frequency were constructed by the online Web server WebLogo (ogo.cgi) .GAAAGCAGGGTTCAGACGTGTGCTCTTCCGATCTAGC-3′ and 5′-AAAAGCAGGGTTCAGACGTGTGCTCTTCCGATCTAGC-3′; the natural nucleotide variation in the RT primers is underlined). The RT products were then amplified by PCR using HA NCR-specific primers . Finally, the PCR products were sequenced.The total RNA of each virus stock was extracted with TRI LS reagent (Sigma-Aldrich) and reverse transcribed using the SuperScript III first-strand synthesis system (Invitrogen) with a universal influenza A virus reverse transcription (RT) primer: (a mixture of 5′-MDCK or A549 cells were infected with wild-type or mutant viruses at a multiplicity of infection (MOI) of 0.001. At time points 24, 36, 48, 60, and 72 h postinfection (p.i.), the supernatants were collected. The virus titers were determined by plaque assay in MDCK cells. The growth curves shown are the average results of three independent experiments.6 HEK-293T cells were transfected with 0.5 μg each of four protein expression plasmids  and pPOLI-HA (wild type or mutant) using Lipofectamine 2000 and Opti-MEM according to the manufacturer’s instructions. Cells were harvested 24 h posttransfection. For Western blot analysis, the cells were lysed with Cytobuster lysis buffer (Novagen), followed by Western blotting with a monoclonal anti-HA antibody, with anti-β-actin antibody as an internal control. Protein expression levels were visualized with an Odyssey infrared imaging system . The relative protein expression level was analyzed using the integrated software of the Odyssey system. For primer extension analysis, total RNA was extracted using TRIzol reagent (Invitrogen). The levels of mRNA, cRNA, and vRNA of the HA template were analyzed by primer extension with two 32P-labeled HA-specific primers, as follows: 5′-TACTCAACTGTCGCCAGTTC-3′ was used to detect HA vRNA and 5′-GTCACTGCCACATTCTTCTC-3′ was used to detect HA mRNA and cRNA. The primer used for detecting internal control 5S rRNA was 5′-TCCCAGGCGGTCTCCCATCC-3′. Primer extension products were analyzed by 6% denaturing PAGE with 7 M urea in Tris-borate-EDTA (TBE) buffer and visualized by phosphor-imaging on an FLA-5000 scanner (Fuji). ImageJ was used to analyze the 32P-derived signal  with Dunnett correction was used for two-variable comparisons, while one-way ANOVA with Dunnett correction was used for one-variable comparisons.