To examine the costs of caring for community-dwelling patients with Alzheimer’s disease (AD) dementia in relation to the time to institutionalisation.GERAS was a prospective, non-interventional cohort study in community-dwelling patients with AD dementia and their caregivers in three European countries. Using identified factors associated with time to institutionalisation, models were developed to estimate the time to institutionalisation for all patients. Estimates of monthly total societal costs, patient healthcare costs and total patient costs  prior to institutionalisation were developed as a function of the time to institutionalisation.p < 0.001). Having a non-spousal informal caregiver was associated with a faster time to institutionalisation , as was each one-point worsening in baseline score of MMSE, instrumental activities of daily living and behavioural disturbance . Total societal costs, total patient costs and, to a lesser extent, patient healthcare-only costs were associated with time to institutionalisation. In the 5 years pre-institutionalisation, monthly total societal costs increased by more than £1000  from £1900 to £3160 and monthly total patient costs almost doubled from £770 to £1529.Of the 1495 patients assessed at baseline, 307 (20.5%) were institutionalised over 36 months. Disease severity at baseline [based on Mini-Mental State Examination (MMSE) scores] was associated with risk of being institutionalised during follow up  contains supplementary material, which is available to authorized users. Cost-of-illness (COI) studies have shown that the two most important cost drivers in dementia care from a societal perspective are the costs of institutional care and the costs of informal care for patients who are cared for at home , 2.Time to institutionalisation  is often used as an outcome measure in Alzheimer’s disease (AD) dementia trials. The median time to institutionalisation in dementia studies has been estimated to be between 30 and 40 months from study entry, but this depends on the severity of the patient’s disease at the time of study entry . This isEconomic models of AD assessing long-term costs and outcomes generally use modelling techniques to extrapolate from short-term data because the follow-up periods in clinical trials are usually too short to measure parameters such as institutionalisation and death. To better estimate these models, detailed knowledge of the factors associated with patient care and societal costs is necessary. Time to institutionalisation could potentially be a significant determinant of societal costs in patients with AD dementia. However, it has only been used in a few model-based economic evaluations in AD dementia , 11. TheProspective observational studies such as the GERAS study, of the costs and resource use associated with community-dwelling patients with AD dementia and their caregivers , may proThe aims of the current study were to estimate the healthcare and societal costs of caring for community-dwelling patients with AD dementia in relation to the time to institutionalisation. We present a proof-of-concept methodological approach for modelling disease progression and costs in patients with AD dementia for use in a cost-effectiveness model, using the GERAS population as an example. By applying the results of the GERAS study, we first assessed the cumulative incidence of institutionalisation over 36 months of follow up and identified factors associated with time to institutionalisation. Using these factors, we developed parametric models to estimate time to institutionalisation for all patients and then estimated healthcare and societal costs as a function of time to institutionalisation from the parametric models.GERAS was an 18-month prospective observational study of costs associated with the care of community-dwelling patients with AD dementia and their caregivers in three European countries  . The stuThe study enrolled community-dwelling patients aged at least 55 years, meeting the National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer’s Disease and Related Disorders Association criteria for probable AD , with a Data were collected at baseline and during routine care visits at 6, 12 and 18 months in all three countries and at 24, 30 and 36 months in France and Germany. Information collected for patients and caregivers included sociodemographics, comorbidities, medications, and health-related quality of life (HRQoL).Patient cognitive function was assessed using the MMSE, with lower scores indicating reduced cognitive functioning. Functional ability was assessed using the Alzheimer’s Disease Cooperative Study Activities of Daily Living inventory (ADCS-ADL) , 15, 16,Information on healthcare resource use by patients and caregivers was collected using the Resource Utilization in Dementia (RUD) instrument  and seveMonthly costs values were estimated by applying country-specific unit costs of services and products  to the healthcare and social care resource use data collected over the 36-month follow-up period. Full details of the unit costs applied and their sources are presented in Online Resource 1.Total societal costs were evaluated using an opportunity cost approach taking into account productivity loss for working caregivers and lost leisure time for non-working caregivers. Costs were calculated for the month before each visit. Total societal costs were calculated by combining direct costs, consisting of patient healthcare costs  and patient social care costs , and indirect costs, consisting of caregiver informal care costs (including the time spent giving care and missing work). Total patient costs were analysed as patient healthcare costs and patient social care costs combined. The list of resource use items collected in the section above reflects all cost categories included in the cost analyses.The following imputation rules were applied for missing data: for institutionalised patients, mean monthly costs from the last visit were used for the period until institutionalisation and monthly costs for institutionalisation were used from institutionalisation up to 18 months for the UK and up to 36 months for France and Germany. For patients who died, last observation carried forward was used such that costs from the last known visit were extrapolated up to the date of death (no costs after death were computed). For patients with other reasons for discontinuation, the multiple imputation regression method  stratifiThe unit cost of caregiver time for working caregivers was the value of lost production time based on the national average wage per country; for non-working caregivers, it was the value of lost leisure time based on 35% of the national average wage per country population . In theTime to institutionalisation was taken from the reasons for discontinuation from the study, where the date of institutionalisation was collected if the reason for discontinuation was reported as patient institutionalisation. The current analysis did not include temporary admission to an institution or full-time care at home based on caregiver time or responses to specific basic ADL questions. Time to institutionalisation was measured in days from the date of the baseline assessment to the date of institutionalisation.Demographics and baseline characteristics were summarised using descriptive statistics and were based on non-missing observations.Competing risk analysis , where iFactors associated with time to institutionalisation were explored using Cox proportional hazards models of the 36-month data (18 months for the UK data); time to institutionalisation was censored at the time of last follow up or time to death for those subjects who did not report being institutionalised. The only competing risk in the Cox proportional hazards models was patient death, which was treated as a censored event . One hundred different models using forward and backward selection were run, selecting 67% of subjects at random for inclusion in the model, and the factors identified in each model summarised. Entry and exclusion of individual factors was based on a significance level of 0.05. Patient characteristics considered in the models were age, gender, years of education, time since diagnosis of AD, comorbidities, and baseline scores for MMSE, total ADCS-ADL, instrumental ADCS-ADL, basic ADCS-ADL and NPI-12 total. Caregiver characteristics considered in the models were age, gender, relationship with patient (spouse yes/no), and caregiver working for pay (yes/no).Additional analyses examined the effects of including AD medication as a factor in the model selection, or including the four subdomains of ADCS-ADL  or four subdomains of NPI  in the list of variables for consideration in the models, as well as interactions between the scores for cognition (MMSE) and function (ADCS-ADL). If the final selection suggested a subdomain rather than a main effect, then the models were compared based on their Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) model fit statistics. If the difference was less than 3 then the model with total score rather than subdomain was used.Any factor found to be significant in over 75% of the models was included in the parametric models used to predict time to institutionalisation. Again, the only competing risk in the parametric models was patient death, which was treated as a censored event in the analysis. To allow for different assumptions around the distribution of the data, the parametric models considered exponential, log-logistic, Weibull, log-normal and gamma distributions. Model fit was assessed using AIC and BIC model fit statistics, and the best fitting model was selected for use in the model that estimated societal and patient costs as a function of time to institutionalisation.y) as a function of time to institutionalisation (x). Separate models were developed for total societal costs, total patient costs  and patient healthcare costs, determined using the cost category definitions described earlier. These three cost categories were chosen as they were considered to be the most important for payers. For each patient, the predicted time to institutionalisation (Pred_Inst) was calculated from the parametric model. Then, for each 6-month visit, the patient’s time to institutionalisation (Pre-Inst) was calculated as: Pre-Inst = Pred_Inst-visit. Each individual subject time point was treated as independent, had an associated cost and any missing cost visits used the imputation methods described earlier. The cost analysis only used data for the first 18 months so that data from all three countries could be used.Models were fitted to estimate costs .The GERAS study included 1495 patients at baseline: 566 (37.9%) patients had mild AD dementia, 472 (31.6%) had moderate AD dementia and 457 (30.6%) had MS/S AD dementia. Table Of the 1495 patients at baseline, 307 (20.5%) were institutionalised during the 36-month follow-up period: 122/419 (29.1%) patients from France, 91/550 (16.5%) patients from Germany, and 94/526 (17.9%) patients from the UK (18 months’ follow up for the UK). According to baseline AD dementia severity, the number (%) of patients institutionalised during follow up was 66 (11.7%), 111 (23.5%) and 130 (28.4%) in the mild AD, moderate AD and MS/S AD dementia groups, respectively. An additional 152 (10.2%) patients died overall before institutionalisation during the 36-month follow-up period. Other reasons for discontinuation included lost to follow up [57 (3.8%) patients], physician decision [32 (2.1%) patients], sponsor decision [1 (0.07%) patient], subject decision [206 (13.8%) patients] and subject entered clinical trial [2 (0.1%) patients].p < 0.001).Of the 1495 patients, only 11 were excluded from the time to institutionalisation analysis due to missing data. Figure Table Additional analyses (data not shown) demonstrated that when AD medication was included as a factor, it did not enter into the final model. When the model selection included the baseline scores for the four ADCS-ADL subdomains , the communication subdomain was selected and had lower AIC values than instrumental ADCS-ADL but the difference was not significant. When the NPI subdomains were included in the model, the NPI-12 total score was more predictive than if it were replaced with a specific subdomain.Figure From the time-to-institutionalisation models, we chose the log-normal distribution to model the relationship between costs and time to institutionalisation as it had the best fit of the available data . Table Our results in community-dwelling patients with AD dementia from the GERAS study showed that, during the 5 years prior to institutionalisation, estimated monthly total societal costs increased steeply and total patient costs almost doubled, without a substantial rise in patient healthcare costs . These findings reflect escalating caregiver informal care costs and patient social care costs as a patient approaches institutionalisation, which are probably due to increasing patient dependence resulting from worsening disease severity.The difference between total societal costs and total patient costs is likely a result of caregiver informal care costs, which are the main component of societal costs  and remaA recent insurance claims data analysis of patients with dementia in Germany showed that healthcare service utilisation  and related costs increased in the 3 months leading up to institutionalisation and then stabilised or decreased . That stThe regression model found that having a non-spousal caregiver, poorer cognition (MMSE), greater functional impairment in instrumental ADL and worse behavioural disturbance (NPI-12) were all independently associated with a faster time to institutionalisation over 36 months.Yaffe et al.  showed tOur finding that several patient factors are associated with time to institutionalisation is in accordance with previous research. There is now considerable evidence that poorer cognition and lower functional abilities at baseline are associated with earlier institutionalisation of patients with dementia , 32‒35. Our result showing that worse patient behavioural problems at baseline were associated with a faster time to institutionalisation is also consistent with some previous research , 38, 39.In a 3-year prospective naturalistic study in Sweden, multivariate analyses showed that worse baseline MMSE and instrumental ADL scores were associated with a faster time to institutionalisation, as were the patient living alone, a lower versus higher dose of acetylcholinesterase inhibitor, an increase of more than 7 h per week of home help services and an increase of more than 3 days per week of adult day care . When ADImportantly, we observed differences in the time to institutionalisation when extrapolating beyond the 36-month study period using models with different distributions see Fig. . This deThis methodology could be applied to country-specific cost-effectiveness models through the use of country-specific data sources (as shown in Online Resources 2–4).The study has a number of strengths. It consisted of a large, community-based sample of patients with AD, and included patients across a wide range of AD severity, assessed using standardised instruments and followed for up to 3 years. We also used a transparent methodology for our cost estimations. Only 11 patients were excluded from the time to institutionalisation analysis due to missing data. The numbers of missing patients in the cost analyses were not related to the severity of disease, as the proportion of missing patients was similar in each disease severity group.However, there are some limitations. First, in the models used, we assumed a log-normal distribution when extracting beyond the 36-month observational period. However, as can be seen in Fig. This proof-of-concept model demonstrates that total societal costs and total patient costs rise steeply as community-dwelling patients with AD dementia approach institutionalisation, whereas patient healthcare costs increase only moderately. The main cost impact is from caregivers and community services, which varies according to patient functional ability and dependence. Caregiver factors are important in understanding the risk of institutionalisation in community-living patients with AD dementia. The key caregiver factor in this community-based cohort is being a non-spouse of the patient, which was associated with a faster time to institutionalisation (over 2.5 years). The implications of this finding are that we may need to invest in better community support for caregivers who want to keep patients with AD at home, although this will be more costly from a societal perspective. There is a need for long-term data to fully understand the patterns and costs of institutionalisation.Below is the link to the electronic supplementary material.Supplementary material 1 (DOCX 29 KB)Supplementary material 2 (DOCX 29 KB)Supplementary material 3 (DOCX 49 KB)Supplementary material 4 (DOCX 29 KB)


With the advent of advanced sequencing technology, studies of RNA viruses have shown that genetic diversity can contribute to both attenuation and virulence and the paradigm is that this is controlled by the error-prone RNA-dependent RNA polymerase (RdRp). Since wild-type yellow fever virus (YFV) strain Asibi has genetic diversity typical of a wild-type RNA virus, while 17D virus vaccine has limited diversity, it provides a unique opportunity to investigate RNA population theory in the context of a well-characterized live attenuated vaccine. Utilizing infectious clone-derived viruses, we show that genetic diversity of RNA viruses is complex and that multiple genes, including structural genes and NS2B and NS4B genes also contribute to genetic diversity. We suggest that the replication complex as a whole, rather than only RdRp, drives genetic diversity, at least for YFV. One paradigm to explain the complexity of viral RNA populations is that the low fidelity of the RNA-dependent RNA polymerase (RdRp) drives high mutation rates and consequently genetic diversity. Like most RNA viruses, wild-type yellow fever virus (YFV) replication is error-prone due to the lack of proofreading by the virus-encoded RdRp. However, there is evidence that replication of the live attenuated YF vaccine virus 17D, derived from wild-type strain Asibi, is less error-prone than wild-type RNA viruses. Recent studies comparing the genetic diversity of wild-type Asibi and 17D vaccine virus found that wild-type Asibi has the typical heterogeneous population of an RNA virus, while there is limited intra- and interpopulation variability of 17D vaccine virus. Utilizing chimeric and mutant infectious clone-derived viruses, we show that high and low genetic diversity profiles of wild-type Asibi virus and vaccine virus 17D, respectively, are multigenic. Introduction of either structural (pre-membrane and envelope) genes or NS2B or NS4B substitutions into the Asibi and 17D backbone resulted in altered variant population, nucleotide diversity, and mutation frequency compared to the parental viruses. Additionally, changes in genetic diversity of the chimeric and mutant viruses correlated with the phenotype of multiplication kinetics in human alveolar A549 cells. Overall, the paradigm that only the error-prone RdRp controls genetic diversity needs to be expanded to address the role of other genes in genetic diversity, and we hypothesize that it is the replication complex as a whole and not the RdRp alone that controls genetic diversity. Flaviviridae. The disease (YF) is controlled by live attenuated 17D vaccine strain, which was derived by 176 serial passages of wild-type Asibi virus in mouse and chicken tissue (1Yellow fever virus (YFV) is a mosquito-borne virus that is the prototype member of the family  tissue 13. Despit tissue 1, 5.−7 misincorporations per copied nucleotide  that encodes three structural protein genes  and seven nonstructural (NS) protein genes in the gene order 5′-C-prM/M-E-NS1-NS2A-NS2B-NS3-NS4A-NS4B-NS5-3′ , 7. RNA cleotide . These rComparative genomic sequencing of wild-type Asibi strain and 17D vaccine showed that the vaccine differed from wild-type Asibi strain by 9 and 11 shared amino acid substitutions in the structural and NS protein genes, respectively , 14. Thein vitro and in small animal models -derived viruses were recovered in two separate experiments, and their genomes were subjected to next generation sequencing (NGS); in total, 16 viruses were evaluated. IC-derived chimeric and mutant viruses were named based on the backbone , followed by the mutation   for 17D/Asibi NS4B-M95I exceeded 1% of the population . The nuc10.1128/mBio.01871-18.5TABLE S4Table S4, DOCX file, 0.01 MB.Nucleotide diversity comparison between NS4B-95 mutant viruses and parental IC-derived Asibi and 17D-204 viruses. Download Copyright © 2018 Collins et al.2018Collins et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Overall, the altered genetic diversity profiles of the NS2B and NS4B mutant viruses suggests that mutations outside the NS5 RdRp contribute to the genetic diversity profile of wild-type Asibi and 17D vaccine virus.To investigate whether or not the genetic diversity profile of the chimeric and mutant viruses translated to phenotypic differences, multiplication kinetics of parental IC-derived Asibi and 17D viruses were compared to the chimeric and mutant viruses in human alveolar A549 cells, which have a functional interferon-α/β receptor .P value = 0.007), 36 , and 48  hours postinfection (hpi)  ; infectiP value = 0.001 and P value = 0.003, respectively) compared to those of IC-derived Asibi virus and statistically lower at 36 hpi  compared to those of IC-derived 17D-204 virus.The infectivity titers of chimeric structural prME viruses displayed an intermediate phenotype compared to the two parental IC-derived viruses, such that the infectivity titers of both Asibi/17DprME and 17D/Asibi prME viruses were statistically different from their respective parental virus at 36 and 48 hpi . ImportaP value of 0.004 and P value of 0.003, respectively) and 48 hpi  compared to IC-derived Asibi virus. Conversely, Asibi/17D NS2B-I109L and Asibi/17D NS4B-I95M replicated to significantly lower titers at 36 hpi  and 48 hpi  compared to IC-derived 17D virus.The infectivity titers of NS2B-109 and NS4B-95 mutants were the most intriguing of the findings compared to backbone viruses  and D. 1Taken together, the genetic diversity of prME chimeric viruses and NS2B-109 and NS4B-95 single-site mutant viruses translated to phenotypic differences in terms of multiplication kinetics of the viruses. However, the infectivity data for both 17D/Asibi NS2B-L109I and 17D/Asibi NS4B-M95I suggest that the contribution of both NS2B-L109I and NS4B-M95I amino acid substitutions to the genetic diversity of 17D vaccine are independent of multiplication kinetics.This study is the first to use IC-derived viruses to differentiate the genetic diversity profile of a live vaccine strain from the wild-type virus from which it was derived, as well as identifying structural and NS gene regions and mutations that contribute to genetic diversity. The results of this study suggest that the low genetic diversity profile of the 17D vaccine virus is due to multiple mutations, involving both structural (prM and E) and NS genes (at least NS2B and NS4B), and not exclusively the NS5 RdRp.RNA viruses have high mutation rates, rapid multiplication kinetics/lysis time, possessing an error-prone RdRp due to lack of error-proofing replication, replication machinery, and elements within the terminal noncoding regions, all of which contribute to high mutation rates , 35, 36.40–In this study, we show that, in general, introduction of 17D vaccine genes  into the wild-type Asibi virus backbone altered the genetic diversity of the chimeric and mutant viruses such that they differed compared to parental IC-derived Asibi virus. Not only were changes seen in the number and percentage of variants identified, but nucleotide diversity in some genes was not statistically different from IC-derived 17D-204 vaccine virus. Interestingly, introduction of Asibi genes  into the 17D backbone appeared to affect only nucleotide diversity and mutation frequency, and not the variant population. It was noted that both introduction of Asibi prME and NS4B-M95I genes into the 17D backbone accumulated two mutations in the 3′ NCR, while introduction of the Asibi NS2B-L109I gene did not. Thus, we cannot exclude the possibility that one or more of these nucleotide changes in the 3′ NCR could contribute to genetic diversity. However, taken together with the other results, they support the conclusion that the limited genetic diversity of the 17D vaccine is multigenic, where reversions in multiple genes would be necessary to return to a diversity profile consistent with wild-type Asibi virus. Support for this conclusion has been shown in vaccinees, where the attenuated phenotype was maintained along with minimal mutations of 17D vaccine in vaccinees . It has Arthropod-borne viruses (arboviruses) occupy a unique position in terms of virus genome diversity, as they have a transmission cycle that requires two natural hosts, an arthropod and a vertebrate, that have very different selection pressures on restricting diversity/mutation rates. The necessity to replicate in two hosts allows for viral diversity and evolution; however, it also creates bottlenecks and subjects the virus to various selection pressures that influence viral diversity, including increased diversity in the arthropod host and decreased diversity in the vertebrate host, resulting in a lower rate of evolution of arboviruses than for other RNA viruses –53. Dudl–51–Culex quinquefasciatus  and NS genes in the genetic diversity profile of the 17D vaccine virus. As anticipated, introduction of either prME genes of 17D into the Asibi backbone or prME genes of Asibi into the 17D backbone altered the multiplication kinetics of the viruses compared to their respective parental viruses. The NS2B-109 and NS4B-95 amino acid substitutions had differing effects depending on the backbone virus; the NS2B-109 and NS4B-95 substitutions did not alter the multiplication kinetics of IC-derived 17D virus, while they did alter the multiplication kinetics of IC-derived Asibi virus. In comparison, previous studies showed that differences in the multiplication kinetics of poliovirus and Chikungunya virus (CHIKV) fidelity mutants were not observed , 39, 41;asciatus . Thus, d7D virus . FurtherOverall, the phenotypes obtained from studies of multiplication kinetics support the genetic analysis of the viruses, namely, substitutions in the structural (pre-membrane and envelope), NS2B, and NS4B protein genes of Asibi and 17D viruses that alter genetic diversity also correlate with phenotypic changes. Additionally, it is speculated that the contribution of the NS2B-109 and NS4B-95 mutations to the genetic diversity profile is likely associated with their roles in the RC –25. This–Escherichia coli (MC1061), and plasmid was harvested by large-scale extraction. Briefly, 200-ml cultures were incubated at 37°C and 200 rpm for 12 to 14 h. The culture was then pelleted at 4°C by centrifugation and resuspended in cold glucose-Tris-EDTA. A 0.2 N NaOH–1% SDS solution was used for cell lysis, followed by 3 M KOAc (pH 4.8) to neutralize the reaction. The plasmid was pelleted by isopropanol precipitation and then resuspended in TE buffer, followed by treatment with RNase A for 30 min at 37°C, and purification by phenol-chloroform extraction. The purified plasmid was further concentrated using Clean and Concentrate kit according to the manufacturer’s instructions (Zymo).YF infectious clones (IC) were generated by site-directed mutagenesis (SDM) of either pANCR 17D (IC) or pANCR Asibi IC as a template. A QuikChange XLII SDM kit (Stratagene) and mutagenic primer sets were used to generate infectious clones per the manufacturer’s protocol. Stock cDNA YF IC were amplified in in vitro transcription of purified cDNA using SP6-Scribe standard RNA in vitro transcription kit (Cellscript) according to the manufacturer’s protocol; the reaction mixture was incubated at 37°C for 2.5 h. The entire reaction mixture was electroporated into 6.7 × 106 monkey kidney Vero cells in a prechilled 2-mm cuvette using a Bio-Rad Gene Pulser set at 1.5 kV, infinite ohms, and 25 µF. Electroporated cells were allowed to recover for 10 min at room temperature and then transferred into 15 ml of prewarmed MEM supplemented with 8% fetal bovine serum (FBS), L-glutamine, sodium bicarbonate, and penicillin-streptomycin in a T-75 culture flask. Cell supernatant was collected when infected cells showed 80% cytopathic effect (CPE). RNA from the YF ICs were extracted using QIAamp Viral RNA minikit (Qiagen), following the manufacturer’s guidelines.cDNA YF ICs (4 μl/ml) were linearized by digestion with XhoI for 17D backbone viruses and NruI for Asibi backbone viruses at 37°C for 2 h. Linearized cDNA was further digested with 50 μl/ml proteinase K, purified twice by phenol-chloroform and once by chloroform extraction, followed by ethanol precipitation. Full-length YF RNA was generated by https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6614).RNA was extracted from IC-derived viruses, and NGS libraries were constructed using TruSeq RNA v2 kit (Illumina) as recommended by the manufacturer and then sequenced on an Illumina HiSeq1500 instrument. Paired-end reads were quality trimmed to minimum 35 bases and a minimum quality score of 35 using trimmomatic (version 0.22) and then realigned to a reference genome for wild-type Asibi virus (GenBank accession no. AY640589) using Bowtie2 local alignment mode and very sensitive setting (version 2.2.4). PCR duplicates were removed using Picard-tools (version 1.120) using default settings and an optical duplicate distance of 0. Qualimap (version 2.2) was used to assess the quality and determine the mean read coverage of NGS data sets. The NGS data sets were matched by random down-sampling using Picard-tools (version 1.120) to the virus with the lowest mean coverage. Vphaser2 (version 2.0) was used to determine variant population; all single nucleotide variants controlled for false discovery and strand bias were considered in the analysis . SequencP value = 0.12 (ns [not significant]), P = 0.033 (*), 0.002 (**), and P < 0.001 (***) .Diversity indices utilizing the NGS data sets were determined for the ORF by using deepSNV (version 1.16.0) and local R scripts (version 3.2.4). Briefly, nucleotide counts for genomic positions were determined, and then mutation frequencies were calculated for each genomic position; the total number of mutant bases called for a genomic position was divided by the total number of bases called at the genomic position as previously described . Nucleot2 for at least 18 h. Adherent cells were infected at an MOI of 0.1 in triplicate and incubated at room temperature for 30 min. After the inoculum was removed, cells were washed 3 times with PBS and then incubated with 2 ml of MEM supplemented with 2% FBS, L-glutamine, sodium bicarbonate, and penicillin-streptomycin at 37°C under 5% CO2; cell supernatant collected at 0, 12, 24, 36, and 48 hpi. Cell supernatant was stored at −80°C until assayed. Infectious virus was titrated in duplicate by immunohistochemical focus-forming assay (FFA) in Vero cells. FFAs were fixed with 1 part acetone to 1 part methanol at 4 dpi and stained using YFV Asibi mouse immune ascitic fluid polyclonal antibody as the primary antibody. Multiplication kinetics of IC-derived Asibi and 17D-204 viruses were first compared using Student’s t test, before comparing the IC-derived chimeric and mutant viruses using one-way ANOVA and Tukey’s multiple-comparison test; P value = 0.12 (ns), P = 0.033 (*), P = 0.002 (**), and P < 0.001 (***) .A549 cells were purchased from ATCC and maintained according to ATCC guidelines in minimal essential medium (MEM) supplemented with 8% fetal bovine serum, L-glutamine, sodium bicarbonate, and penicillin-streptomycin. Prior to infection, A549 cells were allowed to adhere to plates at 37°C under 5% CO


Publicly available transcriptomic datasets have become a valuable tool for the discovery of new pathogens, particularly viruses. In this study, several coding-complete viral genomes previously not found or experimentally confirmed in alfalfa were identified in the plant datasets retrieved from the NCBI Sequence Read Archive.Medicago spp. transcriptomic datasets were retrieved from the NCBI SRA database. The raw reads were first mapped to the reference genomes of Medicago sativa and Medigago truncatula followed by the alignment of the unmapped reads to the NCBI viral genome database and de novo assembly using the SPAdes tool. When possible, assemblies were experimentally confirmed using 5′/3′ RACE and RT-PCRs.Publicly available Cactus virus X, for which coding-complete genomic sequences were obtained by a de novo assembly.Twenty three different viruses were identified in the analyzed datasets, of which several represented emerging viruses not reported in alfalfa prior to this study. Among them were two strains of cnidium vein yellowing virus, lychnis mottle virus and The results improve our knowledge of the diversity and host range of viruses infecting alfalfa, provide essential tools for their diagnostics and characterization and demonstrate the utility of transcriptomic datasets for the discovery of new pathogens. Medicago sativa L.) is one of the most extensively cultivated forage legumes in the world [Alfalfa enamovirus-1 [Alfalfa virus S, a new species of the family Alphaflexiviridae discovered in alfalfa samples exhibiting chlorosis and stunting [Marafivirus [Alfalfa leaf curl virus found in plants displaying leaf curling symptoms [Alfalfa  ovirus-1 ; Alfalfastunting ; alfalfaafivirus ; and Alfsymptoms . Alfalfasymptoms , 12.Medicago sativa samples employed in the generation of the deposited datasets contained viruses. Several emerging viruses were identified that had not been reported to infect alfalfa prior to this study or had not been experimentally confirmed in the plant.Publicly available transcriptomic datasets have became a valuable tool for the discovery of new pathogens, particularly viral sequences , 13–16. https://www.ncbi.nlm.nih.gov/sra). The raw sequencing reads were first mapped to the reference genomes of Medicago sativa (http://www.medicagohapmap.org/downloads/cadl) and Medicago truncatula (http://www.medicagogenome.org/) with the Bowtie2 software described below. Those reads that did not map to the reference genomes were assembled into contigs using SPAdes (v.3.12.0) and searched  against other plant genomes  to check for possible cross-run contamination. Reads that did not map to any plant species were aligned to the NCBI viral genome database (https://www.ncbi.nlm.nih.gov/genome/viruses/). The alignments were performed using BBMap (v. 37.66), SeqMan Ngen  and Bowtie 2 (v.2.3.4) with the following very sensitive settings: -D 20 -R 3 -N 0 -L 20 -i S, 1, 0.50 and -p 20 (threads). In general, the very sensitive settings increase the search effort of Bowtie2  by increasing the cutoffs for which Bowtie would stop searching. The length of the seed substring (L) was shortened to 20 from 22 and the interval (i) between substrings was decreased to 0.50. The reads that were mapped to the reference viral sequences related to the viruses of interest and to the assembled viral contigs from the datasets, were sequestered and assembled de novo using SPAdes (v.3.12.0) [Medicago sativa amalgavirus 1 (MsAV1). Plant materials infected with other viruses were not available for experimental confirmation of the transcriptomic data. For the MsAV1 experiments, the same RNA samples employed in Nemchinov et al. [http://web.cbio.uct.ac.za/~brejnev/). The SIAS tool that calculates pairwise sequence identity and similarity from multiple sequence alignments (http://imed.med.ucm.es/Tools/sias.html) was used to predict amino acid identities between the Pro-Pol regions of cnidium vein yellowing virus (CnVYV) and lychnis mottle virus (LycMoV).Viral sequences were identified in alfalfa datasets retrieved from the NCBI Sequence Read Archive  and for one virus that had not been experimentally confirmed in the plant (MsAV1).We performed a systematic survey of 655 alfalfa transcriptomic datasets publicly available from the NCBI. The survey indicated that approximately 90% of Medicago sativa amalgavirus 1 sequence was originally reported by Wang and Zhang in 2013 under the GenBank accession GAFF01077243.1 and later dubbed MsAV1 by Nibert et al. [The . The 5′/3′-terminal sequences of the viral genome were determined experimentally by RACE Amalgaviraviridae , 21 at bSecoviridae, order Picornavirales [Cnidium officinale) in Korea [Cnidium vein yellowing virus (CnVYV) is a bipartite, linear positive-sense ssRNA virus, a tentative member of the family avirales . Two isoin Korea . To the in Korea , 26.Medicago spp. were detected: 96% of the contigs had BLAST hits that corresponded to either M. sativa or M. truncatula.For this study, the datasets were retrieved from NCBI Sequence Read Archive (SRA) and the contigs were individually assembled as described in the Materials and Methods. A total of 603,368 out of 108,082,074 (0.55%) raw Illumina pair-ended reads were found in accessions SRR2089795 and SRR2089796 of the BioProject PRJNA289195  that mapThe virus reads from both accessions SRR2089795  and SRR2089796  were assembled into coding-complete bipartite viral genomes, consisting of RNA1 and RNA 2 segments from two different strains of CnVYV-related virus. The virus strains were provisionally designated CnVYV-A1 and CnVYV-A2.The genome of CnVYV-A1 assembled from the accession SRR2089795, had two RNA molecules of 6983 and 3588 nucleotides in length, respectively, excluding the poly (A) tails . The P1 had 79.6% amino acid identity to the polyprotein 1 of CnVYV-1 (AKN59243.1). Other top BLAST hits included polyproteins from the CnVYV-2 isolate (AKN59245.1) , lychnis mottle virus (LycMoV),  and strawberry latent ringspot virus (SLRSV), . Thus, BLAST searches based on the amino acid alignments of P1 indicated the close relationship of the CnCVYV-A1 not only to the reference CnCVYV strains (1 and 2), but also to LycMoV and SLRSV. LycMoV is a tentative member of the family e family .The RNA 2 segment of the CnVYV-A1 sequence retrieved from the accession SRR2089795 was 77.03% identical to the RNA 2 from LycMoV isolate Andong   tail. It was 77.29% identical to the RNA 2 of the CnVYV-2 Yeongyang isolate . Other BLAST hits (LycMoV and CnVYV 1) had a low coverage (1%). The RNA 2 translated into a polyprotein of 994 aa-long, which, similar to the P2 in CnVYV-A1, showed a top BLAST hit for LycMoV . Other closely related species were SLRSV  and CnVYV 2 . The RNA 2 segments of the CnVYV-A1 and A2 strains were 90.7 and 97.9% identical at the nucleotide and amino acid levels, respectively.Secoviridae  of the CdaeFig. .Fig. 7PWe thus concluded that CnVYV-A1 and CnVYV-A2 represent isolates of the same virus strain adapted to alfalfa, for which we propose the name CnVYV-A .Secoviridae that was first reported in Lychnis cognata, a flowering plant in the family Caryophyllaceae [Vincetoxicum acuminatum in Japan and the complete nucleotide sequence of LycMoV-J was obtained [Lychnis mottle virus (LycMoV) is a tentative member of the family yllaceae . In 2017obtained .A total of 400,676 out of 56,610,334 (0.7%) raw Illumina paired-end reads were found in the accession SRR2089796  that mapBLAST searches showed that the RNA1 segment of LycMoV-A had 74% identity to that of the LycMoV Andong isolate . SLRSV was the second hit with a low query coverage (22%). However, when the RNA 1 was translated into a putative polyprotein 2201 aa in length and analyzed by BLAST, the top hit was CnVYV-1 . LycMoV was the second highest hit . A translation of the nucleotide sequence presumably corresponding to LycMoV, into an amino acid sequence resembling that of the CnVYV suggested that CnVYV and LycMoV represent the same viral species.The RNA 2 segment of LycMoV-A was 77.3% identical to that of the LycMoV Andong isolate at the nucleotide level . At the amino acid level, P2 had 90.9% percent identity with that of the LycMoV-J isolate . The Andong isolate of LycMoV  was the second highest hit and SLRSV  was the third highest.Cnidium officinale and the Andong isolate of LycMoV from Lychnis cognata, we compared the amino acid identities between their Pro-Pol and CP regions, which are currently used as species demarcation criteria by the ICTV [8). Although the identities were slightly different from the ones generated by BLASTP due to the different formula used for the calculations, all the conserved Pro-Pol values were higher than 80% (the ICTV criteria for species demarcation are less than 80% identity) and all the CP values, except that for SLRSV, were higher than 75% (ICTV criteria for species demarcation are below 75% identity). We speculate that CnVYV-A, together with all the other viruses in this study group  and with the exception of SLRSV due to the low identity of its CP region, constitute individual strains of the same viral species isolated from different hosts, for which a collective name reflecting its biological characteristics and taxonomic position is required. We suggest the following temporary name for the species: cnidium vein yellowing-like virus (CnVYLV).To determine whether LycMoV and CnVYV are strains of the same virus species or correspond to the distinct species as was suggested by Yoo et al. , 28 for the ICTV . The SIAthe ICTV  predictePhylogenetic analyses based on the alignments of the Pro-Pol region between the protease CG motif and the RdRp GDD motif (CG/GDD) in RNA1 and the predicted CP region in the RNA 2 segment supported this conclusion: CnVYV-A1 and CnVYV-A2 were grouped together with the reported strains of the CnVYV and LycMoV Fig.  and the Cactus virus X (CVX) is a member of the family Alphaflexiviridae, genus Potexvirus. It infects many species in the plant family Cactaceae worldwide [orldwide . Prior torldwide .In this work, we have detected 2028 paired-end CVX-related reads in the publicly available alfalfa transcriptomic dataset SRR7751381 from the NCBI BioProject PRJNA487676  . The raThe 5’UTR of the CVX-A consisted of 74 nucleotides preceding the initiation codon of ORF1 and began with the sequence CCAACACCAA, which is most likely missing a few nucleotides (~ 5 nt) at the very 5′ terminus. The 3’UTR consisted of 99 nucleotides downstream of the termination codon of the ORF5 (pos. 6502–6504) and appeared nearly complete, missing at most one or two nucleotides. Viral RNA translated into five putative ORFs encoding for RdRp ; triple gene block protein 1 ; partially overlapping with the ORF2 triple gene block protein 2 ; triple gene block protein 3 overlapping with the ORF 3 ; and a putative coat protein separated from the ORF 4 by a 15 nt-long intergenic region .We predicted that the genome organization of the CVX-A largely resembles that of the CVX . BLASTP M. sativa prior to this study. Among them were two strains of cnidium vein yellowing virus, lychnis mottle virus and Cactus virus X, for which coding-complete genomic sequences were obtained by a de novo assembly. Based on the amino acid identities of the conserved Pro-Pol region, the CP sequences and the phylogenetic analyses, we suggested that CnVYV-A1, CnVYV-A2, CnVYV-1, CnVYV-2, LycMoV-A, LycMoV, and LycMoV-J represent different strains of the same viral species, tentatively called cnidium vein yellowing-like virus (CnVYLV). We have also presented the first in silico and experimental identification of Medicago sativa amalgavirus 1 in US alfalfa germplasm, which is significant because amalgaviruses are known to be vertically transmitted through seeds. Although amalgaviruses are not considered pathogenic, they could be of economic importance due to their potential role in mixed infections [Publicly available transcriptomic datasets are becoming an increasingly valuable tool for the discovery of new pathogens, particularly viral sequences. In this study, we performed a systematic survey of 655 alfalfa transcriptomic datasets publicly available at the NCBI. Twenty three different viruses were found in the analyzed transcriptomes, including several emerging viral pathogens not previously reported in fections  and ubiqfections .The reported results improve our knowledge of the diversity and host range of viruses infecting alfalfa and provide essential tools for their diagnostics and characterization.Medicago sativa samples employed in the generation of the deposited datasets contained viruses. Several emerging viruses were identified that had not been reported to infect alfalfa prior to this study or had not been experimentally confirmed in the plant. Coding-complete genomic sequences were obtained for cnidium vein yellowing virus (CnVYV), lychnis mottle virus (LycMoV) and Cactus virus X (CVX), all of which have not been diagnosed in alfalfa until now. Further research is required to confirm the in silico identification of these viruses and to determine their symptomatology, geographic distribution and economic importance to the alfalfa industry.The systematic survey of alfalfa transcriptomic datasets publicly available at the NCBI indicated that approximately 90% of Additional file 1: Primers used in the 5′/3′ RACE and RT-PCR reactions for amplification of the MsAV1-derived products.Additional file 2: Overview of viral contigs identified in 655 alfalfa transcriptomic datasets.Additional file 3: Nucleotide sequences of the alfalfa strains of cnidium vein yellowing virus (CnVYV-A), lychnis mottle virus (LycMoV-A) and Cactus virus X (CVX-A).


Early identification through newborn screening is the first step in active management of sickle cell disease (SCD). Uganda currently screens newborns and infants under 2 years for SCD in high HIV-burden districts using isoelectric focusing with dried blood spot samples. Our analysis sought to estimate the costs per child screened for SCD using this method in Uganda and then to use those data to estimate the price threshold for screening with a point-of-care (POC) test.We estimated the financial and economic costs per child screened for SCD using data from health facilities and the Central Public Health Laboratory. These costs included sample collection, transportation, and laboratory processing. Price thresholds for a POC test were estimated using two scenarios.The price threshold of an SCD POC test used for diagnosis would be $3.77 when taking into account only financial costs and $5.14 when taking into account economic costs. Thresholds for a POC test used for screening would be $3.07–$3.51 and $4.38–$5.09, respectively, depending on test specificity.The price threshold of a POC test for SCD will depend on the assumptions on how it will be used – either as a screening or diagnostic test. If used for screening, test specificity will have significant impact. Results from this type of costing study can allow developers to incorporate quantitatively estimated price thresholds for innovative products into target product profiles early in the product development cycle. Sickle cell disease (SCD) is an aggregate of several genetic blood disorders that involve the presence of a sickle cell gene. In SCD, at least one of the beta-globin subunits in hemoglobin is replaced with hemoglobin S. In sickle cell anemia, which is a common form of SCD, hemoglobin S replaces both beta-globin subunits in the hemoglobin.6A few LMIC have initiated SCD screening programs, including Uganda where the prevalence of children with SCD was estimated to be 0.73% and the prevalence of children with sickle cell trait was estimated to be 13.3% in 2015.® , a multiplexed qualitative POC immunoassay used for the rapid diagnosis of SCD has recently been commercialized at a price point of around $4.50 per test.12Newborn screening with a point-of-care (POC) test that is performed at primary health facilities with results given to caregivers during the same visit would allow earlier detection, education, and management to more effectively prevent early mortality in infants and children with SCD.These POC tests are, however, relatively unavailable in LMIC currently and the question remains regarding what the target price of an SCD POC test should be in order for the test to be affordable to public sector programs in LMIC. The target price is one of the characteristics in a target product profile (TPP), which technology developers should develop in the early stages of their product development process in order to ensure alignment between the performance and operational characteristics of the products that they develop and the needs of end users. Unfortunately, this important aspect of early product development does not often take place with the appropriate rigor. Qualitative analyses, such as interviews and discussions with experts, have often been employed to determine the target price for a product used in low-resource settings. Also, potential buyers’ past spending on similar products and competitive products that are on the market are also sometimes taken into account.Costing analyses of SCD newborn screening in LMIC are sparse. One study of a newborn screening program in Angola that used DBS for screening estimated that the cost per infant screened was $15.36 in 2011 international dollars.We selected Uganda for this costing study because it has been implementing a newborn screening program for SCD, and therefore it has a program in place where we could obtain data to estimate economic cost per child screened. Six health centers  were selected for inclusion in the costing study, as these were the ones performing SCD screening. These health facilities were providing newborn screening for SCD in urban and peri-urban areas in and around Kampala.As mentioned before, Uganda is using IEF with DBS for SCD screening and diagnosis. IEF requires laboratory equipment, infrastructure, and technical ability to implement successfully. Electrophoresis equipment or automated systems require electricity, and consumables in the form of reagents and gels require refrigeration. Structured questionnaires were used to collect data from staff at six health facilities and from the CPHL, concerning the quantity and unit cost of resources used in screening and diagnosing SCD. Either one nurse or one laboratory staff responsible for specimen collection was interviewed at each of the six health centers to provide information on the resources used to collect DBS from a typical child. One laboratory staff at the CPHL was also interviewed to provide information on supplies, reagents, instruments, and equipment used to process the DBS and human resource time for each laboratory process. The CPHL staff also provided information on batch processing and capacity in the laboratory to enable estimation of costs per DBS sample processed. All interviews were conducted in October 2016. Study staff obtained verbal consent from study participants prior to initiating the interviews. Study participants did not receive any compensation for their involvement in the costing interview, which took about 45 minutes to complete.The costs for the resources included in this study were 1) costs of supplies used for sample collection (the DBS kit) at the health facility and gloves; 2) costs of health facility staff time spent on counseling the caregiver about SCD and on sample collection and packaging; and 3) sample transportation costs. The DBS kit contained a filter paper card, alcohol swab, lancet, and capillary tube in a plastic bag with a desiccant. Costs for the motorcycle couriers used for sample transportation included the annualized purchase costs of the motorcycles, fuel, service and maintenance, and the courier/motorcycle driver allowance. The couriers transported DBS samples for both the EID and the SCD screening programs and also other specimens. The cost per sample or specimen was estimated by dividing the courier costs by the total number of samples and specimens transported per year.At the CPHL, the cost included the costs of supplies, reagents, instruments, and equipment used to process the DBS cards and staff time spent on sample processing. The CPHL staff reported that they processed 80 specimens per batch and ~200–300 specimens per day. We used these numbers to estimate cost of sample processing.Both financial and opportunity costs were collected and included in this study. As mentioned above, financial costs represent actual expenditures for goods and services and these costs are usually included in the budgeting process. Opportunity costs capture the cost of foregone use of existing resources, eg, human resource time for staff who are already employed by the government. Economic costs are the sum of the financial and opportunity costs. We included both financial and opportunity costs in our estimates because the financial costs are the ones that have budgetary implications, but it is also important to consider the opportunity costs (and hence the full economic costs) as this will show the economic burden to the program.We made certain assumptions when estimating the costs for the current program or when estimating the costs for a program using an SCD POC test. These assumptions include: 1) each staff works 20 days a month and 8 hours a day; 2) the courier serves 25 health facilities each month and collects samples for various tests, including the DBS card samples for the SCD and EID programs and other specimens; 3) health workers spend the same amount of time providing SCD counseling to caregivers regardless of whether the current program or a program using an SCD POC test is utilized; 4) collecting a sample and running an SCD POC test takes 25 minutes; and 5) an SCD POC comes as a full kit containing a lancet, capillary tube, and alcohol swab, and therefore health facilities additionally require only gloves to conduct an SCD POC test, just as they now do for the current program.Unit costs of resources that were locally procured were obtained from the Joint Medical Store price list. Unit costs for resources that were imported  were obtained from the Internet and verified through a price quotation from the main supplier. The analysis was done using Microsoft Excel . All costs were reported in 2016 US dollars and an exchange rate of 3,458 Ugandan shillings per US$1 was used for currency conversion. We first estimated the financial and economic costs per child diagnosed under the current SCD screening program by summing the costs of all the resources used. We then used these estimates to calculate the price thresholds (the possible price) of an SCD POC test in order for the financial and economic costs per child screened and diagnosed to be comparable between the current program and a program using an SCD POC. These calculations were done using the Excel Goal Seek function.In order to estimate the price thresholds, we considered two scenarios of an SCD POC test: 1) use as a screening test and 2) use as a diagnostic test. “Screening” refers to a single use (one DBS sample or POC test), and “diagnosis” refers to conducting the test a second time (a second DBS sample or POC test) to confirm the screening test result. In the screening use case, positive samples would be collected on DBS cards and tested with IEF at the CPHL. We assumed that the sensitivity of the SCD POC test would be 99%, similar to Sickle SCAN, and varied the specificity at 99%, 95%, 90%, and 85% to explore its impact on the estimated price thresholds. Based on Ndeezi et al (2016)This study protocol and verbal consent process was approved by the Mulago Research Ethics Committee at Mulago National Referral Hospital in Kampala and the Uganda National Council for Science and Technology. Verbal consent was received from study participants prior to initiating the interviews.The staff at health facilities reported that they spent an average of 7.5 minutes (range 1.5–15 minutes) providing SCD counseling to caregivers before sample collection, an average of 4 minutes (range 2–5 minutes) collecting and packaging a DBS sample, and an average of 6.75 minutes (range 3.5–7.5 minutes) communicating SCD screening results to caregivers. The total cost of staff time at health facilities per child screened for SCD was estimated at $0.24 (range $0.14–$0.38), as shown in At the CPHL, cost per SCD sample processed was estimated at $4.14 including the time of the laboratory staff and the cost of lab suppliers including reagents. As a result, the average economic cost per child screened for SCD with the current method of DBS was estimated to be US$5.88 on average (range $5.73–$6.11) of which $3.67 were financial costs .If an SCD POC test was to be used as a diagnostic test, the result of an SCD POC at a health center will become the final result. Only additional item required to conduct an SCD POC test is a pair of gloves ($0.13) used for sample collection because these are not included in the POC kit. As If an SCD POC test is used as a screening test, costs associated with conducting an additional test will need to be taken into account. In this case, the specificity of an SCD POC affects the number of children who will be sent to receive an additional test and thus the costs. We therefore examined the effects of the test specificity by varying it while holding sensitivity constant. When only accounting for the financial costs of the current program, we estimated that the price thresholds for an SCD POC ranges from $3.07 to $3.51 depending on the specificity of the SCD POC test . We estiThe price threshold, or the maximum price, for an SCD POC test used for screening will always be lower than the price of an SCD POC used for diagnosis. This is because a certain number of children are always referred for a diagnostic test, incurring the cost of performing the tests, when an SCD POC is used as a screening test. When the specificity of the test is low, there will be many false positive test results from the screening test and this increases the number of children unnecessarily referred for an additional diagnostic test.Affordable POC tests to identify SCD have been hailed as an appropriate solution for LMIC.Based on the Internet prices, we noted that some of the SCD POC tests that are available or will be on the market are below the financial price threshold while some are above. POC tests currently on the market range in price from $2.30 to $9.40 per test. However, even for the SCD POC tests with Internet prices that are below the financial price thresholds we estimated, it should be noted that the landed prices for these POC tests will be higher than the listed Internet prices because shipping, customs clearance, and other in-country costs such as regulatory approval costs are not included in these online prices. Also, these online prices do not account for other supplies that are required to run these tests that are not bundled with these tests. Therefore, the online prices cannot be directly comparable with the financial price threshold estimates we generated. Also, the sensitivity of these tests to detect SCD are generally close to 100%, while specificity ranges from 90.9% with SICKLEDEX to 90%–100% for Sickle SCAN and HemeChip.We included both financial and opportunity costs in our price threshold calculations. While financial costs are the ones that are likely to have budgetary implications, it is important to consider the opportunity costs and hence the economic costs. Accounting for the opportunity costs helps to shed light on the economic burden of a test. For example, a test may be offered at a lower price but it can place a strain on existing resources by, eg, being time-consuming for health care workers to run. If only financial costs are accounted for, this type of burden on existing resources will be overlooked. Also, cost-effectiveness analyses include economic costs and hence the estimates generated will be informative for future studies that will compare the cost-effectiveness of different tests for SCD.There are some limitations to this cost study. First, estimation of the economic costs was based on data from a small sample of facilities that were located near Kampala, so these few sites may not be representative of the system in the whole country. Our costs may underestimate the economic costs, especially for transport, which may be higher for facilities that are farther away from the CPHL, which is located in Kampala. Also, our estimated sample transport costs are low because the SCD DBS samples are transported leveraging the transport system of the HIV EID and thus creating efficiencies. In other countries, where there are no existing sample transport systems to leverage, having a test that requires transport to a centralized location for processing may result in much higher sample transportation costs. Second, the cost estimates do not include several costs, such as shipping and handling charges, customs clearance for imported supplies and equipment, equipment maintenance and repair costs, supervision and management costs, and treatment costs, and hence may again underestimate the costs for the current program. In addition, the estimated price thresholds of the POC tests may be overestimated because we did not factor in the training of users to conduct the POC tests and also quality assurance and supervision. Finally, the study does not include costs borne by caregivers or their households such as transport costs to bring children to health facilities and the opportunity costs of caregiver time. If all these costs are included, the economic costs of the current program and a program utilizing an SCD POC would likely increase, and that would affect the price threshold estimates of an SCD POC test. Importantly, our analysis assumes that the two test approaches demonstrate similar levels of sensitivity and specificity and are able to detect the same hemoglobins.Technology developers frequently develop TPPs, including the target product price, in order to ensure the product that they are developing is well aligned with the needs of the users at an acceptable price. However, for developing countries, some of these TPPs are not informed by evidence from the countries where these products are intended to be used. As a result, sometimes products’ features do not fit the needs of LMIC and/or prices are beyond what the public health systems in developing countries can accept. Although our cost study has some limitations, it quantitatively shows the price thresholds for an SCD POC test. We hope this result can assist test developers and manufacturers to determine the appropriate target price for an SCD POC test from both a financial and an economic perspective. Our cost study has generated estimates that may be useful in the early stages of product development to assess one aspect of product marketability and to determine if a POC test warrants further investment for development, validation, and commercialization.The price threshold of a POC test for SCD will depend on the assumptions on how it will be used – either as a screening or diagnostic test. If used as a screening test, test specificity will have significant impact. Results from this type of costing study can allow developers to incorporate quantitatively estimated price thresholds for innovative products into TPPs early in the product development cycle.The dataset generated and analyzed during the current study is available from the corresponding author on reasonable request.


Therefore, there is high importance to analyze and forecast on reducing China’s CO2 emissions from lime industrial process. The aims of this paper are to make up the research gaps in China and provide a quantitative reference for related authorities to formulate relevant policies. The prediction method in this paper is consistent with the published national inventory, which is an activity data based method to predict carbon dioxide emissions from the industrial process of four categories of lime products. Three future scenarios are assumed. The business as usual scenario (BAU) is a frozen scenario. There are two emission reduction scenarios (ERS and SRS) assumed under different emission reduction strength considering combined industrial process CO2 emission reduction approaches from both the production side and the consumption side. The results show that between 2020 and 2050, China’s lime industrial process has an increasingly significant CO2 emission reduction potential, enabling both emission intensity reductions and total emission reductions to be achieved simultaneously. Based on the simulation results from emission reduction scenarios, compared with 2012 level, in 2050, the emission intensity can be reduced by 13–27%, the total lime production can be reduced by 49–78%, and the CO2 emissions in the lime industrial process can be reduced by 57–85%.China greenhouse gas inventories show that CO Specifi3) is thermally decomposed into calcium oxide and carbon dioxide. Calcium oxide is the effective chemical component of lime, commonly referred to as quicklime. From the perspective of the lime industry, industrial process emissions are the primary source of carbon dioxide emissions, accounting for nearly 70% of the whole industry’s emissions [In China, the raw material for lime production is mainly limestone, which is produced in lime calcination kilns. In the calcination production process, the main chemical composition of limestone, the calcium carbonate CaCO is thermmissions ,5,6,7.The Chinese government released China’s greenhouse gas inventories in 1994 , 2005 88,9, 20082 emission reduction technology that has been commercialized and specifically designed for the lime industrial process. Therefore, the three published Chinese greenhouse gas inventories use the same emission factors which are classified by different uses of lime products. The source of these emission factors is a survey of typical lime enterprises jointly conducted by Tsinghua University and the China Lime Association. 2/t lime) in China inventory.The emission factors of the lime industrial process are determined by the chemical composition of raw materials. At present, a normalized statistical mechanism for the emission factors of the lime industrial process has not been established yet in China. Also, there is no CO2/t lime [2/t lime [Emission factors: International emission factors are used because of data availability and other reasons. Due to the geographical differences in limestone raw materials, the international emission factors for lime industrial processes are mostly higher than China’s national and specific emission factors shown in 2/t lime ,13. Base2/t lime ,16, and Activity data: Some studies have not classified lime products ,17,18,19Emission reduction approaches: Most of the existing research focuses on energy-saving technologies on the lime production side ,23,24,25Due to the existence of a large number of small and medium-sized enterprises and township enterprises, existing literature looking into the lime industry emission mitigation is relatively sparse not only in China but also on a global scale. On international level, the three versions of IPCC Guidelines ,12,13 ar2 emissions from the lime industrial process are large scales and closely related to the development of its downstream energy-intensive industries, such as steel and calcium carbide. Therefore, there is high importance to analyze and forecast on reducing China’s CO2 emissions from lime industrial process. The aims of this paper are to make up the above research gaps in China and provide a quantitative reference for related authorities to formulate relevant policies as follows:Emission factors: In terms of caliber, the forecast method of this paper is consistent with the published national inventories. Emission factors are classified by 4 different uses of lime products to improve accuracy and consistency of data series.Activity data: In consistence with emission factors’ caliber, activity data are also classified into the same 4 categories by use. Different lime categories are analyzed respectively in depth to improve completeness and accuracy of data series.2 emissions forecasting and emission reduction path analysis for its lime industrial process by 2050.Emission reduction approaches: Starting from the lime production and demand sides simultaneously, this paper combines with the scenario analysis method in order to measure the comprehensive emission reduction effects of various emission reduction pathways. It provides China with COChina greenhouse gas inventories show that CO2 emissions from the lime industrial process as well as implied emission factors. In The remainder of this paper is organized as follows. In 2);i expresses the category of lime products, including four categories: construction lime, metallurgical lime, chemical lime and other lime;i is the carbon dioxide emission factor of the industrial process of lime category i in the target year (tCO2/t); EFi is activity data of the target year, that is, the output of lime category i (mt). ADThe prediction method is consistent with the published national inventory, which is an activity data based method to predict carbon dioxide emissions from the industrial process of four categories of lime products, as shown in formula (1). Based on the latest available data in 2012, the activity data (lime outputs) and emission factors for the target year are respectively predicted. Multiplying the activity data by its corresponding emission factor is the carbon dioxide emission from the industrial process of a certain lime category in the target year. The carbon dioxide emissions from the industrial process of four categories of lime are added up to obtain the total carbon dioxide emissions from the industrial process of the lime industry in the target year.i expresses the type of lime, including four categories of lime: construction lime, metallurgical lime, chemical lime and other lime;i is the carbon dioxide emission factor of the industrial process of category i lime in the target year (tCO2/t); EF2/t);For the prediction of emission factors, the application of lime production-based emission reduction technology and its influence are mainly considered. Lime production is a traditional industry. As far as the lime industrial process itself is concerned, what happens is a simple chemical reaction. The emission factor is only related to the chemical composition of raw materials, and has nothing to do with the improvement of production efficiency and energy efficiency. As a result, China has no commercially available carbon-dioxide emission reduction technology specifically for the lime industrial process. However, according to the literature study review, a technology named CALGERGY, currently at the research stage, can absorb 90% carbon dioxide emissions in the lime industrial process by planting microalgae within the boundaries of lime enterprises . The algMetallurgical lime: the flow direction of products is basically as the raw material of long-process steelmaking industry, which has a mass balance relationship  with cruChemical lime: the flow direction of products is mainly as the raw material of calcium carbide, and there is a mass balance relationship with the calcium carbide output , which cConstruction lime and other lime: the products of lime are directly used as construction materials , disinfeThe prediction of activity data (outputs) needs to start from the demand side of lime for different uses. The specific prediction methods of different categories of lime by usages are as follows:2/t);IEF is implied emission factors of the lime industrial process in the target year (tCO2); AD is total lime outputs in the target year (mt).Based on the prediction of total carbon dioxide emissions from the lime industrial process, the Implied Emission Factors (IEF) of the lime industrial process can be further calculated according to formula (6). The change of IEF is related to the progress of lime production technology and the optimization of the product structure.2 emission reduction approaches from both the production side and the consumption side. Production-based approaches include a series of CCU technologies with different technology penetration rate assumption in two emission reduction scenarios. Consumption-based approaches include control on crude steel outputs, reduction of metallurgical lime consumption coefficient, control on calcium carbide outputs, reduction of chemical lime consumption coefficient and reduction of the output of construction lime and other lime by substituting materials. There is no increasing emission trend scenario assumed in this paper since the variation of CO2 emissions from lime industry are related to technology progress and demand of its downstream industries. Based on the scenario parameters extracted from existing literatures, China will basically achieve industrialization by 2020, there is potential of technology progress in lime industry and its downstream demand will peak around 2020. Therefore, there would be little possibility of an increasing CO2 emission trend in lime industry.Three scenarios are assumed for predictive analysis. The business as usual scenario (BAU) is a frozen scenario in which the emission factors are consistent with the base year and the total outputs of lime are consistent with the projected peaking value during 2020–2050. In other words, it assumed in the BAU scenario that there would be a plateau after lime output peaking. There are two emission reduction scenarios assumed under different emission reduction strength. Both of the emission reduction scenarios, the ERS and SRS, consider combined industrial process CODetailed scenario assumptions are as follows:The business as usual scenario (BAU) is a frozen scenario. Regardless of technological progress, the emission factors of each target year are consistent with the base year, as shown in Emissions reduction scenario (ERS) considering carbon dioxide emission reduction approaches of industrial processes in both the production-based and the consumption-based of lime, as follows:Production-based approaches: To promote the CCU technologies, the technology penetration rate will reach 15% by 2050.Reinventing Fire: China [Control crude steel outputs. Crude steel production prediction data under the BAU scenario of e: China   was quoted.Control calcium carbide outputs. The prediction data of calcium carbide output in the BAU scenario of Reduce chemical lime consumption coefficient. Assuming that all advanced technologies of closed electric arc furnace are adopted in 2050, the consumption coefficient of lime in the chemical industry will decrease linearly from 1.284t/t calcium carbide in 2012 to 0.909t/t calcium carbide in 2050 .Reducing the output of construction lime and other lime by substituting materials. It is assumed that construction lime and other lime output will drop to 50% of 2012 production by 2050.Consumption-based approaches:The corresponding target parameters of emission reduction approaches in this scenario are shown in The strengthened emission reduction scenario (SRS) and the emission reduction scenario (ERS) considering the same emission reduction approaches, but with greater intensity, as follows:Production-based approaches: To promote the CCU technologies, the technology penetration rate will reach 30% by 2050.Control crude steel outputs. The prediction data of crude steel output in the remolding energy scenario of Reinventing Fire: China   was quoted.Reduce chemical lime consumption coefficient. Assuming that all advanced technologies of closed electric arc furnace are adopted in 2050 and some lime inputs are replaced by alternative raw materials, the consumption coefficient of lime in the chemical industry will decrease linearly from 1.284t/t calcium carbide in 2012 to 0.871t /t calcium carbide in 2050 .Reducing the outputs of construction lime and other lime by substituting materials. It is assumed that construction lime and other lime output will drop to 10% of 2012 production by 2050.Consumption-based approaches:The target parameters corresponding to emission reduction approaches in this scenario are shown in Since the penetration rate of emission reduction technology from limestone manufacture side in each scenario is not differentiated by lime product categories, the emission factor’s decline trend of each product is parallel to each other in each emission reduction scenario, as shown in According to the China Industrialization Process Report released by the Chinese Academy of Social Sciences, China has entered the late stage of industrialization and will basically achieve industrialization by 2020 . The for2 emissions from lime production.The production forecast shows that the product structure of the ERS scenario will not change much. The proportion of metallurgical lime, which occupies the highest proportion, varies from 47% to 51% in each target year, followed by the proportion of construction lime fluctuating between the range of 32–35%. The product structure of the SRS scenario will change significantly from 2030, and the proportion of metallurgical lime rise to 69%, while the proportion of construction lime falls to 16% by 2050. The 2 in 2020 and retain a plateau during the whole forecasting period. For both emission reduction scenarios, process emissions will peak before 2020 and decrease continuously afterwards. The main difference of the emission trends between the two emission reduction scenarios are the decrease ranges due to different emission reduction strength assumptions. By 2030, the lime industrial process emissions of the two emission reduction scenarios will drop to 109 and 72 mt CO2, respectively. The decline is 20.4% and 47.4%, respectively, relative to the 2012 level. By 2050, lime production emissions in the two reduction scenarios will further decline to 60 and 21 mt CO2. The decline is 56.2% and 84.7%, respectively, relative to the 2012 level. Details are shown in 2 emissions from the lime industrial process is faster than the decline in lime outputs.In correspondence with the three scenario assumptions, there would be three trends of future lime industrial process emissions. The lime industrial process emissions from the BAU scenario will reach a peak of 172 mt CO2/t lime. By 2030, the implied emission factors for the two emission reduction scenarios will fall to 0.656 and 0.625 tCO2/t lime, which are 4.4% and 9.0% lower than the 2012 levels, respectively. By 2050, the implied emission factors for the two emission reduction scenarios will further decrease to 0.594 and 0.501 tCO2/t lime, which are 13.4% and 26.9% lower than the 2012 levels, respectively. Details are shown in The implied emission factor for the BAU scenario is constant at 0.686 tCOThe emission reductions and emission reduction rates achieved in each target year of different scenarios are used to measure their emission reduction effects. The emission reduction effect is the concept of relative quantity and is related to the setting of the baseline. The following two types of benchmarks are used for measurement.2, with emission reduction rates of 20.5% and 47.3%, respectively. By 2050, the emission reductions of the two emission reduction scenarios will reach 78 and 116 mt CO2, with emission reduction rates of 56.5% and 84.5%, respectively. Results are shown in Based on the actual emissions in 2012, by 2030, the emission reductions of the two emission reduction scenarios are 28 and 65 mt CO2, with reduction rates of 36.4% and 57.9%, respectively. By 2050, the emission reductions of the two emission reduction scenarios will reach 112 and 151 mt CO2, with emission reduction rates of 65.2% and 87.6%, respectively. Results are shown in Based on the emission forecast data for the same target year in the BAU scenario, by 2030, the emission reductions for the two emission reduction scenarios are 63 and 99 mt CO2 emissions in each scenario, they are also the two largest contributors to emission reductions. Among the emission reduction sources of the two emission reduction scenarios calculated using different baselines, the total proportion of metallurgical lime and construction lime in both 2030 and 2050 is over 80%. Since metallurgical lime and construction lime account for the highest proportions of both productions and CO2 emission reduction approaches from both the production side and the consumption side. The results show that between 2020 and 2050, China’s lime industrial process has an increasingly significant CO2 emission reduction potential, enabling both emission intensity reductions and total emission reductions to be achieved simultaneously.The traditional view is that the carbon dioxide emission reduction potential of the lime industrial process is small since there are limitations of existing literature on the industry’s current emissions analysis and even sparse literature looking into the lime industry emission mitigation not only in China but also on a global scale. However, this paper makes up the research gaps in China and provides a quantitative reference for related authorities to formulate relevant policies which considers the development of policies and technologies on lime production and demand sides in a comprehensive manner and sorts out various approaches of emission reductions. By combining with scenario analysis methods, this paper quantitatively estimates the pathways of comprehensive emission reduction approaches. Three future scenarios are assumed. The business as usual scenario (BAU) is a frozen scenario in which the emission factors are consistent with the base year and there would be a plateau after lime output peaking. There are two emission reduction scenarios (ERS and SRS) assumed under different emission reduction strength considering combined industrial process CO2 emission reduction effects, the high cost of abatement is the main constraint of technology promotion. The cost of abatement of CCU technology is expected to be 58–68 US $/tCO2 [Emission intensities are the emission factors of various lime products and the implied emission factor of the whole industry. Under the preconditions of scenario analysis, the decrease of emission intensity would be achieved at different rate based on the technological progress and environmental protection policies on the lime production side. The lime production side emission reduction technology with future application prospects consists of a series of Carbon Capture and Utilization (CCU) technologies. The CCU technology penetration rate predicted in the two emission reduction scenarios reaches 5–10% in 2030 and 15–30% in 2050. On the hypothetical basis of this, the forecasted emission intensity is reduced by 4–9% in 2030 and 13–27% in 2050. Although CCU technology has obvious COS $/tCO2 ,44. Now,S $/tCO2 , and resS $/tCO2 . From thS $/tCO2 . The int2 emissions in the lime industrial process. The main demand side of lime products, including steel, chemical and construction industries, all belong to the industries which are strictly controlled by the Chinese government in terms of production technology, production capacity and environmental protection [In addition to relying on technological progress on the production side to reduce emission intensity, it is more important to rely on technological progress and total volume control on the demand side in order to reduce the total amount of COotection . With th2 emission reduction in China’s lime industrial process.The forecast results of this paper show that with the implementation of the bilateral guidance of lime production and demand sides, by 2030, China can achieve 20.5–47.3% reductions based on the actual emissions in 2012. By 2050, China can reduce emissions by 56.5~84.5%. Based on the emission forecast data for the same target year in the BAU scenario, by 2030, emissions reductions of 36.4–57.9% can be achieved. By 2050, China can reduce emissions by 65.2–87.6%. Among the emission reduction sources of the two emission reduction scenarios calculated using different baselines, the total proportion of metallurgical lime and construction lime in both 2030 and 2050 is over 80%. Therefore, metallurgical lime and construction lime are key sources of COTo achieve the above predicted emission reduction potential at lowest uncertainty, it is necessary to rely on both international and domestic policies. Main supportive international policies include the links of international carbon markets and stricter environmental indicators on import which would lead to decrease of lime industry’s downstream demand, the steel and chemical products. Chinese government would play an important role on its current industrial and environmental protection policies’ continuity and enhancement. It is crucial to implement national carbon market, carbon tax and shut down backward production facilities to promote technology progress and total output control and thus guarantee both emission intensity reductions and total emission reductions in predicted pathways.


Two captive cheetahs from a zoo in Japan died of a severe fever with thrombocytopenia syndrome–like illness. Severe fever with thrombocytopenia syndrome virus, an endemic tickborne phlebovirus, was detected systemically with secretion of infectious viruses into the saliva. These cases highlight the risk for exposure of captive animals to endemic arthropodborne pathogens. Phlebovirus, family Phenuiviridae [previously known as family Bunyaviridae])  virus  in a group of 4 cheetahs sharing the same outside enclosure; she was anesthetized for medical examination on day 3. Laboratory studies showed extremely low leukocyte and low platelet counts and slightly elevated aspartate aminotransferase, alanine aminotransferase (ALT), and total bilirubin levels . The aniHelicobacter pylori antigen, enteric bacteria, and ova-parasite. On day 3, slightly decreased leukocyte count along with continued low platelet count and high liver aspartate aminotransferase levels were detected. On day 4, the gastroscopy test showed erosion and petechiae in the stomach, and a blood-feeding tick was found and removed from the ear. In addition to the low leukocyte and platelet counts, elevated ALT, creatine phosphokinase, and lactate dehydrogenase levels were revealed by laboratory tests on day 6. Cheetah 2 vomited with hemorrhage on days 6 and 7 and died on day 7. Similar to cheetah 1, cheetah 2 had a swollen spleen with white nodules and ulcers in the stomach. No clinical signs were observed in the 2 other cheetahs.Slightly abnormal behavior of a 6-year-old male cheetah (cheetah 2) was first observed at 10 and 15 days after the death of cheetah 1. Obvious anorexia in cheetah 2 was recorded at 20 days after cheetah 1 died (hereafter referred to as day 1). Dragging of hind limbs was observed on day 2, and hematologic tests revealed a moderately low platelet count and elevated ALT . A fecalWe performed laboratory tests for virus detection using plasma from cheetah 1 and serum, spleen, and mesenteric lymph node samples from cheetah 2. SFTSV RNA genomes were detected using a quantitative reverse transcription PCR (RT-PCR) targeting the S  segment RNA in the spleen and lymph node but not in plasma and serum , panel AWe isolated infectious viruses using Huh-7 cells and Vero E6 cells from the plasma and popliteal lymph node of cheetah 1 and the spleen, lymph nodes, and brain of cheetah 2 but not from the serum of cheetah 2. Infected cells were clearly stained in an immunofluorescence assay with a monoclonal antibody YG1-7-3-3-4 raised against the recombinant SFTSV nucleoprotein of the Japanese prototype strain YG1. Conditions of the plasma and serum samples may result in negative RT-PCR results in the plasma and negative RT-PCR results and virus isolation in the serum because both samples were collected from animals supposed to cause viremia.5 50% tissue culture infectious dose (TCID50)/g or TCID50/mL in the salivary gland and oral swab samples, respectively; however, virus was not detected in the nasal and rectal swab samples  using MiSeq  with NEBNext-Ultra RNA Library Prep kit (NEB). De novo assembly on CLC Genomics Workbench  determined virtually full-length sequences of all 3 RNA segments of 2 SFTSV isolates. We manually edited the termini and remapped virus reads to contigs to define the complete full-length genome sequences (sequences deposited into GenBank under accession nos. LC325234–9). We found only 1 synonymous nucleotide difference on the coding region of the L (large) segment between the 2 isolates. Phylogenetic analyses of 3 RNA segments revealed that both cheetah isolates were clustered together with an SFTSV isolate, SPL071A, which had been reported in a human in the same prefecture as the zoo  (13).We found a fatal SFTS-like illness of 2 cheetahs naturally infected with an endemic tickborne virus, SFTSV. Disease progression of cheetah 2 was carefully tracked by daily monitoring, providing important clinical information on fatal SFTSV infection in animals. Because the genome sequences of 2 SFTSV isolates were almost identical to each other, and closely related to those of a local isolate from a human case, SFTSV circulating among ticks and wild animals in the area may intrude into the zoo. That 2 cheetahs sharing the same outside enclosure were successively infected with SFTSV within a month of each other and that they had the potential to shed infectious SFTSV into their saliva indicates the virus might have been independently transmitted to 2 cheetahs by ticks; however, the possibility of horizontal transmission through a bite of the animal is undeniable. Further investigation on ticks and animals around the zoo is ongoing. Our study highlights the zoonotic risk for SFTSV infection and the importance of monitoring this endemic arthropodborne disease in zoo animals, as well as livestock, pets, and wildlife.