Pestivirus of the family Flaviviridae. Interspecies transmission of BDV between sheep, cattle, and pigs occurs regularly, sometimes making diagnosis a challenge. BDV can yield substantial economic losses, including prenatal and postnatal infections in lambs, which are the primary source of infection and maintenance of the virus in the population. Since BDV is antigenically and genetically related to bovine viral diarrhea virus (BVDV), it might pose a significant risk to cattle, influencing BVDV eradication campaigns. Similarly, the presence of BDV in swine herds due to pestivirus spillover between small ruminants and pigs might cause uncertainty in classical swine fever virus (CSFV) diagnostics. Therefore, knowledge of BDV epidemiology in different geographical regions will help prevent its spread and optimize control measures. Previous epidemiological studies have shown that various BDV genotypes are predominant in different countries. This review provides an overview of the spread of BDV world-wide in different host species.Border disease virus (BDV) belongs to the genus  Rupicapra pyrenaica pyrenaica) causing a dramatic decrease (over 80%) in the chamois populations that threatened the species with extinction [Border disease virus (BDV) is an important pathogen of sheep, and occasionally goats, reported globally, responsible for congenital disorders, abortion, stillbirths, birth of weak lambs experiencing tremors, abnormal body conformation, hairy fleece, immunosuppression, and increased risk of other infections, incurring substantial yet underestimated economic loss to the livestock industry . The firtinction ,8,9.Pestivirus, family Flaviviridae, which comprises four major species, namely bovine viral diarrhea virus type 1 (BVDV-1), type 2 (BVDV-2), classical swine fever virus (CSFV), and border disease virus (BDV), and a growing number of additional putative Pestivirus species from various domestic and wild animals. Recently, a revision of the taxonomy of the genus Pestivirus was proposed, and the original members were reclassified into 11 viral species named from A through K [Pestivirus H, atypical ruminant pestivirus, or HoBi-like viruses) and of non-bovine species such as antelope , pigs ), small ruminants , and giraffes (Pestivirus G or Giraffe 1).BDV belongs to the genus hrough K ,11. A nuPestivirus I, Turkey) pestiviruses are phylogenetically closer to CSFV than to BDV [pestivirus (OVPV), highly divergent from known pestivirus species, was found to be genetically and antigenically closely related to CSFV [Current segregation of BDV field isolates from sheep and goats, and the designation of genotypes by genetic analyses indicate that BDV can be phylogenetically identified into at least eight genotypes, from BDV-1 to BDV-8 ,14,15,16n to BDV ,19,20,21 to CSFV ,23, and Several studies have reported the adaptive plasticity of BDV to cross the species barrier and infect pigs ,25,26,27Melophagus ovinus, a blood-sucking ectoparasite of sheep, collected from the body surface of sheep in Xinjiang (China), emphasizing the potential role of Melophagus ovinus as a carrier of BDV [Lepus europaeus) to be a potential wild reservoir of pestivirus for livestock, particularly associated with high mortality outbreaks in Pyrenean chamois [Usually, transmission is due to direct contact with infected animals via the oronasal route. Vertical transmission via the placenta plays an important role in the epidemiology of the disease . Recentlr of BDV . Moreove chamois .Clinical signs also include the birth of weak lambs and persistent infections (PI) in newborns because fetuses are exposed to the virus with an immature immune system ,57. ThesBased on their effects on susceptible tissue cultures similar to BVDV, two biotypes can also be distinguished in the BDV species: cytopathogenic (cp), which causes vacuolization and lysis of infected cells, and non-cytopathogenic (ncp), which does not lead to microscopically visible signs of infection ,61. The Currently, there are no proven effective vaccines for BDV, although in the USA and some European countries, killed whole-virus vaccines have been produced but are not commercially available ,66. Occarns, E1, and E2), and seven to eight non-structural proteins , which are flanked by 5′ and 3′ large untranslated regions (UTRs) [BDV is an enveloped virus with a spherical shape (40–60 nm), and its genome consists of a single-stranded, positive polarity RNA, composed of approximately 12,300 nucleotides. BDV is easily inactivated by heat, drying, detergents, and UV light . A singls (UTRs) ,70,71. Cpro, and E2 gene sequences to classify new virus isolates. The 5′-UTR is the most conserved region of the viral genome, and the non-structural protein, Npro, codes for the N-terminal autoprotease that has no counterpart in other flaviviruses, whereas the E2 protein plays a major role in virus attachment and entry and is also important to induce neutralizing antibody production. To date, genotyping using 5′-UTR, Npro, and E2 sequences has provided consistent results to group isolates using phylogeny [Pestiviruses were designated according to the host species, localization in specific tissues and organs, transmission route and symptoms of the disease, year of collection, and region of origin . Neverthhylogeny ,74,75,76hylogeny ,78.Several epidemiological studies have shown that BDV genotypes can affect a wide range of ungulates, but generally, sheep seem naturally sensitive to pestivirus infection. BDV infection is globally distributed and was reported in different European countries such as Austria ,46,79,80BDV has also been reported outside of Europe, in Australia ,70,92, CHigh seroprevalence from 30% to 98%, depending on the geographical area, was reported. Indeed, a number of epidemiological reports concern only the seroprevalence of BDV infection in many countries, such as Austria , Chile , DenmarkSerology is an ineffective tool because of the antigenic similarity of BDV with other related pestiviruses and the cross-infection of cattle, sheep, goats, pigs, and of some non-domesticated species. Indeed, BVDV and BDV are frequently detected in cattle, sheep, goats, and pigs, with low species specificity in serological tests. The transmission of pestiviruses between different animal species is often associated with common pasture or other forms of close contact between animals ,13,14. IAlthough it was virtually impossible to monitor temporal changes in the presence of genotypes in various countries, antigenic and genetic classification of isolates from different geographical regions is essential to improve knowledge on the epidemiology of BDV. According to the International Committee on Taxonomy of Viruses (ICTV), the pestivirus species are demarcated using a range of criteria, including complete coding nucleotide sequences and deduced amino acid sequence relatedness, antigenic relatedness, and host of origin ,12,73. WThe systematic typing of the BDV isolates, based on the 5′-UTR, Npro, and E2 regions, led to the identification of eight phylogenetic groups (BDV-1 to BDV-8) .The country-wise segregation of BDV isolates into genotypes is summarized in BDV-1, with putative subgroups BDV-1a and BDV-1b, and BDV-2, which consists solely of German isolates, were originally referred to as BDV-A and BDV-B, respectively . BDV-3 ipestivirus group, an atypical pestivirus, was detected in Italy [pestivirus sequences confirmed the evidence of the novel genotype BDV-7 [p-distance slightly exceeding the values 0.24 and 0.15, proposed by Smith et al. (2017) [In 2005, the first member of a putative novel in Italy . Furtherpe BDV-7 ,50, whic. (2017)  to be th. (2017) .pro genomic region, the sequences of the strains reported from Italy matched 82–92% (nt) and 86–93% (aa) to the corresponding region in the Swiss strains.BDV-8 was subsequently reported by Peletto et al. 2016) and Caruso et al. (2017)  and Caru, respectIt is also noteworthy that Italy is the country where the highest number of BDV genotypes has been documented, indicating a greater genetic diversity of the virus compared to other countries. However, the data are insufficient to draw conclusions about the overall epidemiological situation of BDV infection, since data from countries in Central and Eastern Europe, Africa, and South America are still missing .Chlamydophila spp., Campylobacter spp., Salmonella spp., E. coli, and Corynebacterium spp. It affects reproduction, reduces growth rate, and hence delays slaughter in commercial conditions, yielding economic loss. Molecular investigations represent a useful tool to prevent and control the spread of disease, and to identify the changes in genetic characteristics of isolates by monitoring pestivirus strains in ruminants, particularly in mixed herds. Molecular epidemiology is instrumental in classifying novel viruses and studying their evolutionary dynamics. Phylogenetic analysis of BDV seeks to establish epidemiological patterns by tracing globally circulating virus isolates to individual countries. This improves our understanding of pathogenesis and infection routes, establishes genetic relationships between different isolates, and evaluates their temporospatial distribution in animal populations. Additionally, due to the spread and risks of BDV, and the challenges in controlling the disease, determining the antigenic relationship among pestivirus isolates plays a major role in diagnosis, planning immunization strategies, and controlling infection.In previous years, border disease often went unnoticed, as its symptoms, mainly related to fertility, rather than morbidity or mortality in animals, were not directly ascribed to BDV infection, and were sometimes confused with other pathogens, such as BVDV, This review aimed to provide comprehensive knowledge on the variability of BDV and the genetic relatedness among circulating strains, thus avoiding inconsistent or conflicting allocation of BDV isolates into genotypes. At least eight different BDV genotypes, spread world-wide, have been described so far. It can be speculated that this genetic variability and the similarity observed among some viruses isolated from distant countries might be associated, at least partially, with animal trade.While genetic comparisons are useful, the relationship between genetic and antigenic differences is largely unknown. This prompts the question on whether the genetic diversity of field strains can affect the diagnostics. Previous studies  have demLocally, a flock management program should aim to identify and remove all PI animals from the flock, and prevent contact with or introduction of BDV-infected animals from outside. It would be imperative to only purchase from BDV-free flocks or isolate all newly purchased animals until they can be tested free of BDV.The circulation of BDV may also substantially influence BVDV and CSFV eradication measures . There iGenetic changes in the BDV genome can alter virulence, and novel ovine pestiviruses can emerge that could impact the epidemiology of the disease. Therefore, it is essential to study the genetic diversity and unequivocally designate BDV genotypes, considering that the heterogeneity of pestiviruses will probably increase in the future. In many geographical regions, BDV circulation is not yet studied, which is necessary to obtain a comprehensive epidemiological picture of BDV infections, especially in countries where sheep and goat rearing is traditionally an important source of income.


Health system constraints are increasingly recognized as an important addition tomodel-based analyses of disease control interventions, as they affect achievable impactand scale. Enabling activities implemented alongside interventions to relax constraintsand reach the intended coverage may incur additional costs, which should be considered inpriority setting decisions. We explore the use of group model building, a participatorysystem dynamics modelling technique, for eliciting information from key stakeholders onthe constraints that apply to tuberculosis infection prevention and control processeswithin primary healthcare clinics in South Africa. This information was used to designfeasible interventions, including the necessary enablers to relax existing constraints.Intervention and enabler costs were then calculated at two clinics in KwaZulu-Natal usinginput prices and quantities from the published literature and local suppliers. Among theproposed interventions, the most inexpensive was retrofitting buildings to improveventilation (US$1644 per year), followed by maximizing the use of community sites formedication collection among stable patients on antiretroviral therapy  andintroducing appointments systems to reduce crowding (US$9302). Enablers identifiedincluded enhanced staff training, supervision and patient engagement activities to supportbehaviour change and local ownership. Several of the enablers identified by thestakeholders, such as obtaining building permissions or improving information flow betweenlevels of the health systems, were not amenable to costing. Despite this limitation, anapproach to costing rooted in system dynamics modelling can be successfully applied ineconomic evaluations to more accurately estimate the ‘real world’ opportunity cost ofintervention options. Further empirical research applying this approach to differentintervention types (e.g. new preventive technologies or diagnostics) may identifyinterventions that are not cost-effective in specific contexts based on the size of therequired investment in enablers. Estimating the full costs of health system investments to successfully implementtuberculosis infection prevention and control interventions in South African clinics isessential for priority settingGroup model building, a participatory system dynamics modelling (SDM) technique, mayassist with designing feasible interventions and identifying activities that can becosted to overcome health system constraints and achieve implementation targetsInterventions requiring large upfront capital investments, such as retrofittingbuildings to improve ventilations or installing UV germicidal irradiation systems, arecheaper over time than interventions requiring behaviour change (e.g. ensuring windowsand doors are kept open) due to the higher relative share of enabling costs (e.g. ofintensified training and supervision) required to make them sustainableSDM-informed costing allows for a comprehensive view of the health system influences onintervention impact and feasibility, in a way that may be superior to other, lessparticipatory stakeholder consultation methodsMycobacterium tuberculosis(Mtb) in primary care clinics and other health care settings is apriority on tuberculosis (TB) infection prevention and control (IPC) agenda in South Africa.Transmission of drug-resistant (DR) Mtb is documented within healthfacilities (Mtb transmission in clinics in high human immunodeficiency virus(HIV) burden settings may be higher than contact data would suggest for both health careworkers and patients , and a set of enabler activities identified during the group modelbuilding as necessary to ensure interventions are feasible in South African clinics (e.g.increased training and district-level supervision). Pathways of action of the identifiedinterventions and enablers were described through a process of iterative review andrevision of the causal loop diagrams and free lists generated by stakeholders during theworkshops, integrated with the qualitative evidence gathered by the wider UmoyaOmuhle project.This information then fed into the design of interventions that would be effective atreducing nosocomial Umoyaomuhle study facilities, who were asked about input requirements, includingstaff time, for carrying out hypothetical tasks. Capital investments and other start-upcosts were annualized using a 3% discount rate for future costs. All costs are presentedin 2019 US$.Unit costs for core intervention activities and enablers were estimated using price andquantity data from the published literature and quotes from local suppliers. Unit costscaptured the incremental economic costs of all core activities, including the opportunitycost of staff time, recognizing that even activities that are not time-consuming and thatare already implemented to some degree, such as opening windows and doors to improveventilation or directing queuing patients, will need dedicated staff to increase theirfeasibility and impact compared to current levels , above service-level  or outside the remit ofthe Department of Health (e.g. improving transport links to clinics to ensure theviability of appointment systems). Other activities were acknowledged as central to theintervention but excluded from the costing exercise, as they did not represent an actualcost, i.e. they referred to barriers that could not be overcome through financialinvestments (e.g. having to obtain permission from the district to carry out clinicbuilding modifications).The final list of interventions included in the cost model is presented in Incremental annual costs of each intervention option, including upfront capital investmentand recurrent costs of all intervention activities and enablers, are reported in The most expensive input to intervention implementation was the time of clinic staff. Forthis reason, relatively simple but human resource-intensive interventions, such as ensuringthe regular opening of windows and doors or implementing a queuing system that allows forcoughing patients to be rapidly triaged and for other patients to wait in a sheltered areaoutdoors, were found to be more expensive than those interventions relying on capitalinvestments and technology, such as installing ultraviolet germicidal irradiation (UVGI)systems.Enablers identified ranged from relatively inexpensive capital investments, such aselectric heaters to ensure thermal comfort in winter when windows are kept open, to morecostly enhancements to training programmes for clinic staff and consultations with communityrepresentatives, to ensure lasting changes in work culture and local ownership of theinterventions. Overall, the proportion of total intervention costs represented by theenablers was inversely proportional to the size of capital investment required by theintervention; and directly proportional to the intervention’s reliance on changes in thebehaviour of patients and/or staff .CorrespThis analysis applied group model building, an established SDM approach, to elicitinformation on the health system constraints that operate in TB IPC in South Africa. Thisinformation was then used in a novel way to build a model of the local incremental costs ofa set of TB IPC interventions implemented at two clinics in KwaZulu-Natal. The interventionswere designed bearing in mind potential barriers to implementation and necessary investmentsto overcome these; then costed iteratively, based on feedback given by SDM participants. Theresulting unit costs thus reflect information linking the implementation process tooutcomes, and are a closer representation of the full opportunity costs of theseinterventions compared to those generated with standard costing methods, as they include thecosts of relaxing health system constraints.Despite the addition of enabling costs, the TB IPC interventions considered aresubstantially less expensive than other interventions for preventing TB transmissioncurrently included in the South African National Strategic Plan for HIV and TB as well as inthe Investment Case for TB, such as improving the timeliness and yield of facility-based TBscreening by using more sensitive algorithms and contact tracing . IntensiFurther cost savings could be realized by considering the proposed TB IPC interventions asa package, thus allowing the costs of those enablers that are shared by more than oneintervention to be spread across them. An example would be the costs of enhancing routinestaff training and supervision, which are shared by all the interventions analysed with theexception of building retrofits. Similarly, gains in efficiency could be realized fromscaling up the interventions to the regional and/or national level  or that are otherwise ‘uncostable’. This may bebecause the interventions and enablers consist of novel activities for which sources ofprice and quantity data cannot be readily identified, such as setting up new CCMDD pick uppoints; or they may consist of high-level activities, such as redesigning training formatsand materials based on data collected from routine monitoring and evaluation, the costs ofwhich are above-site and difficult to allocate to specific interventions; or they mayconsist of activities that fall outside the remit of the health sector, such as improvingpublic transport links to health facilities to support the implementation of a clinicappointment system that spaces patient visits throughout the day. While activities that donot incur a cost and those that fall outside the health sector might be excluded from aneconomic evaluation (depending on the perspective taken), additional data collection isneeded for costing novel activities and for allocating and scaling the costs of sharedabove-site enablers, e.g. from pilot/demonstration projects or feasibility studies.SDM-informed costing allows for a comprehensive view of the health system influences onintervention impact and feasibility, in a way that may be superior to other, lessparticipatory stakeholder consultation methods. By providing several occasions forinteraction between different TB IPC stakeholders at the national and decentralized level,as well as between stakeholders and researchers, the group model building exercise presentedin this analysis enabled a thorough process for validating and refining costing assumptions,including on the details of intervention and enabler design. For successful application ofthis approach in economic evaluation, further research is needed into ways of integratinginsights from SDM, that are well suited to identifying above-service level costs, with moretraditional costing methods, which usually focus more on service-level inputs. Such acombination can, e.g. smooth the process of linking costs into transmission model outputs,which are usually service-level units, as well as potentially inform the choice of afunctional form for modelling costs at scale. SDM can also be useful for identifyingintervention types that might not be cost-effective based on the share of total costsrepresented by the required enablers. Further analyses of interventions that are more orless reliant on capital investments or behaviour change, such as new preventive ordiagnostic technologies, are needed to fully assess its potential applications in economicevaluation and priority setting.czab155_SuppClick here for additional data file.


In Experiment 2, ROM significantly increased (p < 0.01), and the shear elastic modulus of the rectus femoris significantly decreased (p < 0.05). It was suggested that the stretching intensity (110%) is more important than stretching duration to decrease the muscle stiffness of the rectus femoris.A previous study reported that 3-min of high-intensity static stretching at an intensity of 120% of range of motion (ROM) did not change the muscle stiffness of the rectus femoris, because of the overly high stress of the stretching. The purpose of this study was to examine the effects of high-intensity static stretching of a shorter duration or lower intensity on the flexibility of the rectus femoris than that of the previous study. Two experiments were conducted (Experiment 1 and 2). In Experiment 1, eleven healthy men underwent static stretching at the intensity of 120% of ROM for two different durations (1 and 3 min). In Experiment 2, fifteen healthy men underwent 3-min of static stretching at the intensity of 110% of ROM. The shear elastic modulus of the quadriceps were measured. In Experiment 1, ROM increased in both interventions ( Static stretching is used to improve flexibility of muscles and to prevent injuries . FlexibiMuscle strain is a frequent sports injury and occurs most often in hamstrings, quadriceps [especially the rectus femoris (RF)], and gastrocnemius . To prevRecently, the acute effects of high-intensity static stretching on the muscle-tendon unit stiffness have been investigated. The intensity of static stretching is determined according to the ROM and point of discomfort (POD) of each participant. Previous studies performed high-intensity static stretching at the intensity of 120% ROM and 120% POD and showed that high-intensity static stretching effectively decreased the muscle-tendon unit stiffness of the hamstrings ,c and thThe stress of static stretching increases with stretching duration and intensity. Two experiments were conducted (Experiment 1 and 2) to examine the effects of high-intensity static stretching of shorter durations or lower intensities than that of the previous study (intensity of 120% ROM and duration of 3 min) . For Exp∗Power 3.1 software , and the results showed that the requisite number of participants for this study was 8 and 15 for Experiment 1 and 2, respectively. There was no significant difference in baseline ROM and the muscle stiffness of the RF between the participants in Experiment 1 and 2 . All participants were informed of the requirements and risks associated with their involvement in this study and signed a written informed consent document. The study was performed in accordance with the Declaration of Helsinki (1964)1. The study was approved by the Ethics Committee of the Niigata University of Health and Welfare, Niigata, Japan, (Procedure #17677).Eleven  and fifteen healthy men  were recruited for Experiment 1 and 2, respectively. All participants did not regularly perform any flexibility or strength training. Participants who had a neuromuscular disease or lower extremity musculoskeletal injury history were excluded. During the experimental period, all participants were instructed not to perform resistance or flexibility training of the lower limbs. Previous studies that examined the effects of high-intensity static stretching reported large effect sizes for the muscle stiffness . TherefoKnee flexion ROM was measured in the same fashion as a previous study . In deta2 and set near each muscle center, with an analysis area of a 5-mm-diameter circle at the center of the stiffer region  with a SL10-2 linear probe. The participants were lying on a treatment bed in the neutral hip joint position with a 90° flexed hip and knee joint. The shear elastic moduli of the RF, VL, and VM were measured at the midpoint, 60 and 80% distal between the anterior superior iliac spine and the proximal end of the patella, respectively . The sizr region . Long-axr region , 2021, tr region .The pain of the quadriceps was assessed during each stretching intervention using a VAS with a 100-mm continuous line with “not sore at all” on one side (0 mm) and “very, very sore” on the other (100 mm). Stretching pain assessments were performed during each stretching intervention, three times.The static stretching intervention was performed in a similar fashion to the knee flexion ROM assessment. The intensities of static stretching were calculated based on the knee flexion ROM in the PRE value in each intervention . At 110 post hoc analyses using Bonferroni’s test were performed. For Experiment 2, the paired t-test and Wilcoxon signed-rank test were used to compare the PRE and POST values of knee flexion ROM and muscle stiffness , respectively. For VAS in Experiment 2, a one-way repeated ANOVA was used, and if a significance was detected, post hoc analyses using Bonferroni’s test were performed. Moreover, a mixed model ANOVA was conducted to examine the effects of time (PRE vs. POST) and intervention (static stretching with 120% ROM intensity for 3 min vs. static stretching with 110% ROM intensity for 3 min) on ROM and the shear elastic modulus. For VAS, a mixed model ANOVA was used to examine the effects of time (Set 1 vs. Set 2 vs. Set 3) and intervention (static stretching with 120% ROM intensity for 3 min vs. static stretching with 110% ROM intensity for 3 min). Partial eta squared  and r-values  were reported to reflect the magnitude of the differences among each intervention  was used to examine the effects of time (PRE vs. POST) and intervention (1 min vs. 3 min) on ROM and the shear elastic modulus. For VAS, a two-way repeated ANOVA was used to examine the effects of time (Set 1 vs. Set 2 vs. Set 3) and intervention (1 min vs. 3 min). If a significance was detected, rvention . The anap = 0.18, partial eta squared = 0.09), and no main effect for intervention , however, there was a significant main effect for time . Knee flexion ROM significantly increased in both interventions  (both p < 0.01).There was no significant two-way interaction , and no main effect for the intervention  and time   .p = 0.42, partial eta squared = 0.04), and no main effect for intervention , however, there was a significant main effect for time   and Set 2 (p < 0.05).There was no significant two-way interaction ( = 0.34) . In botht-test showed that knee flexion ROM significantly increased after the stretching . A Wilcoxon signed-rank test revealed that the muscle stiffness of the shear elastic modulus of the RF significantly decreased , but that of the VL  and VM  were not changed . Post hoc analysis revealed that the VAS value of the Set 2 (p = 0.04) and Set 3 (p < 0.01) were significantly smaller than that of the Set 1 , nor main effect for intervention , however, there was a significant main effect for time . Knee flexion ROM significantly increased in both interventions (both p < 0.01).For knee extension ROM, there was no significant two-way interaction . The shear elastic modulus of the RF significantly decreased only in 110% intensity (p = 0.02), but not 120% intensity (p = 0.36).For the shear elastic modulus of the RF, there was a significant two-way interaction , nor main effect for intervention  or time .For the shear elastic modulus of the VL and VM, there were no significant two-way interactions , but there was a significant main effect for time  and intervention . The VAS value in 120% ROM intensity was significantly greater than that of 110% ROM intensity (p < 0.01). The VAS value of Set 3 was significantly smaller than that of Set 1 (p < 0.01) and Set 2 (p < 0.01).For the VAS value, there was no significant two-way interaction . In Experiment 1, the shear elastic modulus of the RF, VL, and VM was not changed after either the 1- or 3-min of static stretching at the intensity of 120% ROM. In the present study, VAS values of static stretching at the intensity of 120 and 110% ROM were approximately 60 and 43.2, respectively. The previous study reported that the VAS values of static stretching at the intensity of 120% and 100% ROM were 59.5 and 13.5, respectively . These dThere were some limitations in the present study. The participants were not athletes. Therefore, it is necessary to examine the effects of high-intensity static stretching in the quadriceps of athletes who regularly engage in flexibility training, including static stretching. In addition, the intensity of static stretching was defined with reference to ROM . HoweverThe effects of different duration (1 and 3 min) of static stretching at the intensity of 120% ROM was investigated (Experiment 1). The results showed that ROM increased in both stretching interventions, but the shear elastic modulus of the RF, VL, and VM showed no change. In Experiment 2, the effects of 3-min of static stretching at the intensity of 110% ROM were examined, and an increment in ROM and decrement in the shear elastic modulus of the RF were found. It is suggested that the intensity of static stretching (110% ROM) is more important than its duration to decrease muscle stiffness of the RF.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Niigata University of Health and Welfare. The patients/participants provided their written informed consent to participate in this study.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Players reported limited ROM in ADF-KF, HE, HAB-HF, HF-KE, and KF; and asymmetric ROM mainly in HE, ADF-KE, KF, ADF-KF, and HF-KE. In conclusion, this study provides gender-specific lower extremity flexibility profile scores in BPs that can help athletic trainers and athletic and conditioning trainers to identify those players who are at higher risk of injury due to abnormal ROM scores.Analysis of the flexibility profile of basketball players (BPs) can reveal differences in range of motion (ROM) by gender and also identify those players who are at higher risk for sports injuries. A descriptive observational study was conducted to determine the lower extremity flexibility profile of sixty-four basketball players and gender differences to identify players at higher risk of injury due to limited and asymmetric ROM in one or more movements. Participants: Sixty-four  competitive athletes from the national leagues of the Spanish basketball league system participated in the present study (power of sample size ≥0.99). The eight passive ROM tests of the hip, knee and ankle were assessed using the ROM-SPORT battery. Each player completed a questionnaire on age, basic anthropometric data, dominant extremities, and training and sport-related variables. The lower extremity flexibility profile was established at 15° and 10° hip extension (HE), 39° and 38° ankle dorsiflexion with knee extended (ADF-KE), 40° and 39° ankle dorsiflexion with knee flexed (ADF-KF), 43° and 43° hip abduction (HAB), 75° and 61° hip abduction with the hip flexed (HAB-HF), 78° and 83° hip flexion with the knee extended (HF-KE), 134° and 120° knee flexion (KF), and 145° and 144° hip flexion (HF) by male and female basketball players, respectively. Sex differences in HE, HAB-HF, and KF were observed in BPs ( Muscle extensibility is defined as the ability of some muscle tissue components to deform by increasing their length under the influence of an external and internal torque ,2. ChangThe method of measuring ROM has been established internationally by institutions such as the American Academy of Orthopaedic Surgeons  and the Knowing the reference values that define the lower extremity flexibility profile for their sport is of great interest to athletic trainers and athletic and conditioning trainers ,24. The At the same time, limited ROM due to muscle tightness ,28,34,35To our knowledge, previous studies have not identified lower extremity-based specificity analysis ROM for basketball players (BPs). Accordingly, the aim of the current study was twofold: (1) to define the lower extremity flexibility profile of sixty-four BPs along with gender differences, and (2) to identify players at higher risk of injury due to limited ROM or inter-limb ROM asymmetry in one or more movements.This investigation (ID: 1702/2017) was approved by the Institutional Ethics Committee of the College of Murcia (Spain) and complied with the World Medical Association Code of Ethics, Declaration of Helsinki.Seventy-three BPs from four basketball teams were initially recruited for this descriptive observational study. The players played in the second  and fourth  levels of the Spanish basketball league. In order to recruit the BPs to participate in our study, the researchers contacted the coaches one month before the assessment session. One week prior to the evaluation, an investigator met with the technical staff and BPs to explain the objectives of the study. This was also used to familiarize the BPs with the ROM tests. Familiarization with the testing procedures took place during the BPs’ first visit to the sports center during peak competition time. During the second visit, each player signed an informed consent form and completed the questionnaire that included demographic, anthropometric, and basketball training data. In addition, during the competition period, an assessment of essential lower limb movements was performed using the eight ROM tests from the ROM-SPORT battery .http://www.randomizer.org to avoid bias that might occur if the measurements were taken in a particular order. The average of the two measurements was used for further statistical analysis. A third measurement was taken if the investigators found a variation of more than 5% between these two measurements of any test to identify the error measurement. There was a pause of more than one minute between repetitions and two minutes between each test. The data were then analyzed to confirm or reject the null hypothesis.Players were instructed not to engage in vigorous physical exertion 48 h prior to the study and were informed that they could withdraw from the study at any time. BPs performed a dynamic warm-up of approximately 20 min, following recommendations from a previous study . All ROMLater, the lower extremity flexibility profile was determined and gender differences between BPs were analyzed. To complete the comprehensive analysis, each player’s ROM values were compared to reference values  to identify players who were ROM limited by muscle tightness and inter-limb ROM asymmetry.Sixty-four BPs aged between 17 and 31 years (average of 22.6 ± 2.9 years) participated as volunteers in this study and had an average of 12 years (12.4 ± 3.9 years) of basketball experience . ThirteeThe power of the sample size for this study was analyzed as described in the Statistical Analysis section.Two exercise and sports science graduates with at least 15 years of experience in musculoskeletal assessment conducted the data collection for this study. They were the same examiners and had the same competencies in assessing ROM. The principal examiner performed the ROM tests while the assistant examiner reviewed the compensatory movements and recorded the data . In a doThe survey included questions about self-reported age, basic anthropometric data , dominant laterality (defined as the leg you would use for one-legged jumps to achieve maximum jump height), training, and sport-related variables . The data in the surveys were cross-checked with the basketball coach to increase objectivity. If the coach and player data did not match, club or federation records were used. The examiners evaluated the anthropometric measurements and verified the information provided in the questionnaires.The maximum passive hip, one knee and ankle ROMs were assessed using the ROM-SPORT battery . This baFirst, the assumptions about the normality of the data were tested using the Shapiro–Wilk normality test. Descriptive statistics with mean and standard deviation were calculated for all variables tested.t-test for independent samples was conducted to determine possible gender differences in the demographic and athletic variables. A post hoc sample size calculation was performed using the software package G*Power 3.1.9.7.  using a sample size of 33 female and 31 male basketball players, an alpha level of p < 0.05, effect size (Hedges’ g), and Student’s t-test for independent samples  [Second, a Student’s igure 2) .t-test was calculated to estimate inter-limb ROM asymmetry. To assess the effect size of the difference, Hedges’ g was calculated from the values of the paired samples t-tests and the effect was classified as no effect (g < 0.2), small: (g = 0.2 to 0.59), moderate (g = 0.6 to 1.19), large (g = 1.20 to 2.00), very large (g = 2.00 to 3.99), extremely large (g > 4.00) [Paired samples  > 4.00) . The low > 4.00) .t-test was performed to assess differences between the ROM of younger and older players.A k-means cluster analysis was performed to classify participants between younger and older players. An independent-samples t-test was performed to assess differences between normal and limited ROM groups; the Hedges g effect size was also considered. Statistical analyses were performed using SPSS software , with a significance level of at least five percent.Cut-off values for risk factors according to previous prospective cohort studies were used to identify athletes at higher risk of injury with normal vs. inter-limb ROM asymmetry  and 10° for the high ROM values as HF-KE, KF, and HF-KF) ,43,44,45The initial sample consisted of 73 players. Nine players were withdrawn from the study by the researchers. The reasons were that the players had not completed the questionnaire correctly, had not signed the informed consent form, or had received long-term rehabilitation treatment (ankle sprain and low back pain) in the past six months, including flexibility training.The variables  identified in this study resulted in a sample size significance of 0.99 for HE ROM, 1.00 for HAB-HF ROM and 0.99 for KF ROM.p = 0.029); however, the effect size of the effect found between both sides of the body was small (Hedges’ g = −0.34). Inter-limb ROM asymmetry was found only for the quadriceps in KF ROM . p = 0.01; Hedges’ g = 1.04)), HAB-HF ROM ) and KF ROM ) with moderate, very large and large effect sizes, respectively. Male players showed higher values than female players in all three ROMs.Differences were found between the group of young players (<23 years) and the group of older players (≤23 years) in the KF ROM  of the players in HE (iliopsoas), 24 (37.5%) of the players in ADF-KF (soleus), 44 (68.8%) of the players in HAB-HF (monoarticular adductors), 39 (60.9%) of the players in HF-KE (hamstrings), 37 (57.8%) of the players for KF (quadriceps), and 18 (28.1%) of the players for HF ROM (gluteus maximus) showed limited ROM .Individual analysis of inter-limb ROM asymmetry revealed a slightly greater number of lower values in the dominant limb compared to the same movement in the contralateral limb . Lower vThe present study is the first to describe the lower extremity flexibility profile in 64 BPs of both sexes using a standardised procedure. In general, the results suggest that the ROM-SPORT battery determines the first reference values of BPs competing in the national-tier leagues of the Spanish basketball league system. However, differences were observed between younger and older players in KF ROM, which is consistent with the results of previous studies ,16. The Most authors who have studied the flexibility of BPs have not addressed the analysis of sex differences ,55,56,57Second, our study demonstrates a comprehensive analysis of flexibility that provides athletic trainers and athletic and conditioning trainers a simple, individualised strategy to improve athletic performance and minimise athletic injuries in players. This comprehensive analysis observed limited ROM with a frequency of 28.1% to 68.8% of BPs for ADF-KF ROM (soleus), KF ROM (quadriceps), HF-KE ROM (hamstrings), HAB-HF ROM (monoarticular adductors), and HE ROM (iliopsoas). The cut-off values for this study are based on reference values from previous studies or clinical experience. In this sense, Backman and Danielson  presente95) of the ROM-SPORT tests is between 4° to 7° [Regarding the inter-limb ROM asymmetry, the results on the magnitude of the standardized mean differences show no inter-limb ROM differences, except for the KF ROM (quadriceps). The mean distance inter-limb ROM in the studied movements was 3° , 4° , and 5° (HF). These results are common in most studies calculating sports inter-limb ROM asymmetry. This result should not be considered valid because the sensitivity of the inclinometer is 2°, and the maximum detectable change , ADF-KE ROM (gastrocnemius), KF ROM (quadriceps), ADF-KF ROM (soleus), and HF-KE ROM (hamstrings). Compared to other team sports, the results for the frequency of inter-limb ROM asymmetry are lower than in soccer ,31, futsFuture research should focus on determining reference values in basketball with a representative sample of players from teams playing in a league. Prospective cohort studies are needed to determine the cut-off value for limited ROM and inter-limb ROM asymmetry for each test to identify BPs most likely to develop pain or injury. In addition, the reference values should be validated with a multifactorial assessment that includes physical and sports performance tests. In this regard, we recommend that researchers focus on the development and evaluation of pre-participation screening and injury prevention programs through high-quality randomised controlled trials targeting athletes at higher risk for basketball injury.Athletic trainers and athletic and conditioning trainers have a valuable tool in the ROM-SPORT battery to monitoring the ROM and conditioning in BPs. The normative data presented in this study serve as a baseline for evaluating ROM performance in BPs. A stretching routine could be developed for those players whose values fall below the basketball reference values. The design of this routine should also consider gender differences for athletes in HE, HAB-HF and KF ROMs. A comprehensive analysis may also help athletic trainers and athletic and conditioning trainers to identify musculoskeletal adaptations for sport, e.g., limited ROM by muscle tightness and inter-limb ROM asymmetry in BPs. This individualised ROM analysis is a useful strategy to improve athletic performance and reduce injury risk in competitive basketball players. However, this strategy will always be most effective when a multicomponent training program combining flexibility, strength, and neuromotor is used in basketball players.The lower extremity flexibility profiles of the sixty-four basketball players were 15° and 10° HE, 39° and 38° ADF-KE, 40° and 39° ADF-KF, 43° and 43° HAB, 75° and 61° HAB-HF, 78° and 83° HF-KE, 134° and 120° KF, and 145° and 144° HF ROMs of the male and female basketball players, respectively. Comparison of the two lower extremity flexibility profiles shows gender differences for the HE, HAB-HF, and KF ROMs. Basketball players mainly reported muscle tightness in ADF-KF, KF, HAB -HF, HF-KE, HF, and HE ROMs, and inter-limb ROM asymmetry mainly in HE, ADF-KE, KF, ADF-KF, and HF-KE ROMs. This study provides gender-specific lower extremity flexibility profile values in BPs that can help athletic trainers and athletic and conditioning trainers to identify those players who are at higher risk of injury due to abnormal ROM values. Reduction in injury risk can be achieved when professionals use a multicomponent training program that includes flexibility, strength, and neuromotor.


Hosts influence and are influenced by viral replication. Cell size, for example, is a fundamental trait for microbial hosts that can not only alter the probability of viral adsorption, but also constrain the host physiological processes that the virus relies on to replicate. This intrinsic connection can affect the fitness of both host and virus, and therefore their mutual evolution. Here, we study the coevolution of bacterial hosts and their viruses by considering the dependence of viral performance on the host physiological state . To this end, we modified a standard host-lytic phage model to include viral plasticity, and compared the coevolutionary strategies emerging under different scenarios, including cases in which only the virus or the host evolve. For all cases, we also obtained the evolutionary prediction of the traditional version of the model, which assumes a non-plastic virus. Our results reveal that the presence of the virus leads to an increase in host size and growth rate in the long term, which benefits both interacting populations. Our results also show that viral plasticity and evolution influence the classic host quality-quantity trade-off. Poor nutrient environments lead to abundant low-quality hosts, which tends to increase viral infection time. Conversely, richer nutrient environments lead to fewer but high-quality hosts, which decrease viral infection time. Our results can contribute to advancing our understanding of the microbial response to changing environments. For instance, both cell size and viral-induced mortality are essential factors that determine the structure and dynamics of the marine microbial community, and therefore our study can improve predictions of how marine ecosystems respond to environmental change. Our study can also help devise more reliable strategies to use phage to, for example, fight bacterial infections. Cell size is a key trait for microbial systems. Replication rates and physiological traits are generally correlated with cell size, both within and across species . For virThe adsorption of viruses to host cells depends on a combination of multiple factors. Host size influences the probability of host-virus encounter , and theViral infection starts when the virus encounters a host and becomes irreversibly attached onto a specific receptor at the host surface, with the frequency of successful encounters defining the adsorption rate . Once adExperimental work has shown that these key viral traits are not fixed, but change with the host physiological state : eclipseViral plasticity has been shown to have important ecological and evolutionary implications for host-phage systems . It is yBacteria-phage coevolution has previously been studied in the context of an “arms race” , e.g., uTo fill this knowledge gap, here we use a standard host-lytic phage model that we modified to include viral plasticity. To implement coevolution, we used a numerical ecoevolutionary framework that focuses on host size and viral latent period as representative evolving traits. With this framework, we compare the traits that emerge under coevolution to those emerging when host evolves without virus, and to those obtained in previous work when host does not evolve but virus does . TheoretN), uninfected hosts cells (C), infected hosts (I) and extracellular viruses (V). The environmental conditions are set up to those of a chemostat, i.e., a well-stirred controlled environment in which the host and phage encounter each other randomly. Because we assume that, once a host is infected, its machinery is devoted to viral production, infected hosts do not replicate and therefore the host’s generation time does not constrain the viral latent period. We further consider that bacteria grow in the limited volume of the chemostat, and thus system-size effects can trigger competition for space, light, or other resources not explicitly modeled here that can limit/hinder population growth. This crowding effect can be implemented through a density-dependent term, in our case a quadratic loss term that only affects free hosts as uptake and growth stop at infection.To represent the ecological dynamics of the host- phage system, we used a well-known model that considers explicitly the delay between infection and lysis . This moi is characterized solely by its size , and a viral phenoptype j is characterized by its latent period, the equations for the dynamics of the different phenotype populations and the nutrient are given by:Thus, assuming that a host phenotype Y  to be the same for all host phenotypes. Equation (2) describes the growth of each host phenotype population, Ci, as a result of reproduction (first term), infection by the different viral phenotypes present in the chemostat at time t (second term), bacterial loss due to dilution (third term), and population growth slowdown due to crowding (fourth term). Equation (3) keeps track of the cells infected by viral phenotype j . The first term represents the new infected individuals resulting from the adsorption of that specific j viral phenotype. Infected cells disappear during dilution (last term) or due to the lysis of the cells that became infected exactly one latent period (L_j) in the past . The lysis of these infected cells releases new free phage for viral phenotype j (first term in Eq. (4), with B representing the burst size, see below). This pool of free phage then decreases due to phage adsorption (second term), and natural mortality and dilution (last term).(see definitions of symbols and units in We assume that free hosts grow according to the classic Monod equation :max(ri) is the maximum growth rate, and Kn(μmax)  is inversely correlated with the affinity of the uptake/growth machinery for this nutrient. As explained below, both traits depend on the host’s cell size, ri . This leads to the addition of a new population every time a host mutant and/or a virus mutant join the system  generates a new mutant phenotype. A new host phenotype is identical to the parental cell except for its radius, chosen randomly from a normal distribution centered on the parental value and with a standard deviation of 0.1 microns. Similarly, a new viral phenotype is identical to the parent except for the value of the latent period, chosen randomly within a normal distribution centered on the parental value with a standard deviation of 10e system . Note the system .max), which suggests different limiting factors for small and large organisms. For small cells such as bacteria, molecular transit time inside the cell can be the limiting factor whereas uptake rate limits larger cells  and its maximum potential for growth, μmax  (i) through the following data-informed expressions  . We consressions :E decreases and M increases with the host growth rate. Both functional forms show a plateau at high growth rates, which reflects the physiological limits of the host machinery to synthesizing proteins  function, and μmax, experiment is the maximal growth rate that the host reached in the experimental data used to deduce Eqs. (8)–(9); M∞ represents the upper plateau of the increasing sigmoid, αM how steeply M reaches it, and M_0 the midpoint of the function. Finally, Eq. (8) shows a finite value for μ→0, which represents the possibility of viral reproduction at very low host growth rates, observed experimentally . These values are thus host-specific: because different host phenotypes/strains show different sizes and therefore different μmax, Enon and Mnon must follow accordingly. As we could not find values for E and M for all the different host sizes used in our simulations, we estimated those values as Enon(ri)=E[μmax(ri)] and Mnon(ri)=M[μmax(ri)]. In consequence, the burst size for the non-plastic virus follows here the expression:For each host phenotype the host . In cons® to integrate numerically these equations under different environmental conditions  and, due to the limited amount of information available about the (broadly defined) crowding strength, we also vary the parameter α in a range commensurate with the rest of terms in Eq.(2). Specifically, we try α = 0 , α=10−6lcell−1d−1, and 10−7cell−1d−1.We use Matlab4 simulated days (enough for the system to reach stationarity), multiple hosts compete for the common nutrient (bottom-up regulation) while experiencing the mortality exerted by the viral populations (top-down pressure), which in turn compete for the available hosts. In our system, host size can evolve between 0.3 to 1.1 μm, range that provides trait values usually observed for E. coli  (E) among all the host mutants, which ensures a minimal latent period bigger than any eclipse period in the system. The latter is necessary because we assume that any viral phenotype can infect any available host. We also consider that both host and virus mutate at similar times. Increasing the viral mutation rate to up to 5 times that of the host did not change the probability of coexistence nor the number of functional combination (results not shown), and therefore we set the less-computationally-expensive limit of equal timing for both host and viral mutations.Thus, during 10vations) . On the L,r) randomly chosen within the ranges above, mutation and selection enable the stochastic exploration of the phenotypic space until, eventually, a combination of phenotype maximizing fitness for the host and virus emerges: the Evolutionary Stable Strategy (ESS) . Due to the stochastic character of the simulation, we run up to 500 replicates for each combination of w,N0, and α, in order to find the ESS as the average of the trait values emerging over replicates. We further compare the emergent ESS for both plastic and non-plastic viruses, running for the latter the non-plastic version of the model (Eq. 11).With this model and constraints, and starting from a pair of host-virus phenotypes with  combinations vary as w, N0, and α change. For each case, we compare the  for the plastic case to those obtained for the non-plastic description of the system. Finally, we compare (i) the LESS emerging from coevolution to the analytical expression provided by ESS emerging from coevolution to the ESS obtained in a system where bacteria evolve in the absence of viruses.We then analyze how these  that is lower than our original number of replicates. For example, at low dilution rates, virus and host cannot coexist as the associated low growth rate eventually leads to the extinction of the host (followed by the extinction of virus); at high dilution rates, viral mortality is very high and the viral population goes to extinction, which allows the host to thrive alone.Note that extreme conditions deterministically result in a number of useable combinations (V=α=0), both the ecological and evolutionary dynamics of the host can be assessed analytically , the evolutionary stationary state of the host can be calculated numerically using the genetic algorithm described above. As the dilution rate increases, the emerging host size increases with the dilution rate and eventually saturates  cannot overcome the increasing mortality due to dilution. Increasing crowding strength increases the emerging host size, which still ultimately saturates at the same value  emerging from different replicates for a given dilution rate are clustered around a clear mean value, indicating that only one true combination of host size and latent period results from each environment. For a better visualization, we show in rESS for the plastic case than the non-plastic case, while LESS is smaller at lower dilution rates but becomes larger at higher dilution rates. At w=10d−1, rESS saturates to the host size that provides the highest growth for the fixed N_0 ], and (ii) the number of infections by influencing the adsorption rate. In order to separate the influence of those effects on the emerging traits, we also check the emerging ESSs when the adsorption rate is fixed . Our results do not change qualitatively when using a fixed adsorption rate (not shown).Host size plays two distinct roles in our model: it affects (i) host quality by influencing μrESS and LESS emerging from coevolution with the emerging (i) rESS obtained when the host evolves alone and (ii) LESS obtained when the virus evolves with only one single host phenotype. For the latter, we use the expression In order to understand the role of coevolution in the selection of the evolving traits , we compare, respectively, the rESS resulting from the plastic case departs from the virus-free case, resulting in a larger host. The rESS in the non-plastic case shows larger host sizes than the plastic cases for all dilution rates, although both plastic and non-plastic cases converge to a similar value at high dilution rates. For low dilution rates, the emergent host size in the case of the plastic virus is similar to that without the virus. LESS than in the case of viral evolution with a fixed  host phenotype. This difference is more noticeable for the non-plastic case. Increasing the dilution rate  has on these strategies. Typically, the virus drives the host to evolving defense mechanisms against phage infection, as repeatedly reported in the literature . In our rESS) shows smaller sizes in environments with lower nutrient concentration, which result from lower dilution rates. This evolutionary strategy resonates with the fact that smaller microbes are better competitors when nutrient is scarce, like is the case for smaller phytoplankton  in oligotrophic waters , the phage pressure maintains the bacterial population to a lower level than in the absence of virus. Consequently, fewer bacteria compete for the nutrient and thus the host population grows faster, which ultimately results in larger emergent sizes. A logical expectation would be for the host to show, in the presence of the virus, a smaller size than in its absence, to avoid viral infection. Instead, the observed increase in host size indicates that the increase in metabolic rate associated to the larger size compensates for being a larger target for viral adsorption. This strategy also benefits the virus as it increases adsorption, and the improved host physiological state reduces latent period and increases burst size. Because a fixed adsorption rate did not alter our results, we conclude that the evolution of host size influences viral evolution mostly by altering host quality and, indirectly, host availability.For a same environment  but lower host densities , which reveals a host quality-quantity trade-off. In nutrient-poor environments, the associated increased availability of the host with small size, which leads to small growth rates, i.e., low quality, tends to increase the latent period; this contrasts with past theory predicting that high host cell density favors phage with short latent period . Thus, aw. This result is in contrast with the definition of non-plastic virus we used here, as the latter shows the latent period expected at maximum growth rate . L value for the latter might result from the effect of host availability on the emerging latent period, compensating the effect of the smaller host growth rate. With an abundant host, the strategy of the plastic virus is to lyse hosts earlier even if the resulting burst size is smaller. On the other hand, the lower amount of available hosts and the roughly one-order-of-magnitude difference in burst size requires from the non-plastic virus a longer latent period, to avoid host “overexploitation” .Unexpectedly, the emerging latent period in the plastic case is shorter than in the non-plastic case for most dilution rates, and longer only for very high values of L and B between plastic and non-plastic virus are, however, small because the host growth rate for the former is close to its maximum. The crowding effect is an additional factor that regulates the bacterial population. The decrease in population resulting from crowding actually allows the host to reach the highest size that maximizes its growth rate for lower dilution rates, which can again be due to the lower competition for nutrient (see above). For sufficiently high α, the bacterial population is entirely regulated by the crowding effect, which explains the similar emergent size in the presence or absence of the virus.When the dilution rate is high, the burst size and host availability for both cases equalize, the only determining factor is the difference between host maximum growth rate (for the non-plastic virus traits) and realized growth rate (for the plastic virus traits) and, as a consequence, the non-plastic virus shows a shorter latent period than the plastic one. The differences in LESS larger than that obtained when the host does not evolve, for both plastic and non-plastic viruses. Therefore, we conclude that coevolution slows down viral infection, the more so for a non-plastic virus. This delay and differences between plastic and non-plastic cases are amplified in nutrient-poor environments , where the host grows far from its maximal rate and the importance of plasticity is more observable. Without further empirical information, however, we cannot hypothesize the mechanisms underlying these strategies.Finally, the virus adapts to the evolutionary response of the host showing a Our framework has a few important limitations. Although coevolutionary branching is a possibility that has been observed in laboratory and natural populations , we focuE. coli) but larger species showing a negative correlation with cell size (Although pathogens typically have detrimental effects on their hosts, in some cases the evolution of parasitic organisms may benefit the host by increasing host fitness . In our ell size . Our conThe code used to obtain the data supporting the conclusions of this article will be made available by the authors under request.JAB and MH designed the research. MC conducted the research. MC and JAB wrote the manuscript with input from MH. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


TM 4B, and an affinity chromatography was established for PCV2 purification. A total of 6.5 mg of purified PCV2a/LG with 97% purity was obtained from 120 mL of the viral culture medium, and only PCV2 was detected by electron microscopy. No significant changes in the antigenic characteristics of the purified virus were detected by a capture enzyme-linked immunosorbent assay (ELISA). Furthermore, the titer of the purified PCV2 was 100 times higher than that of the unpurified virus. This affinity chromatography method was also used to purify PCV2b/LN590516 and PCV2d/SD446F16, and the purified viruses were detected by electron microscopy, capture ELISA, and virus titration, respectively. The results showed that these two strains can be successfully purified, but the yield is lower than that of the PCV2a strain. In addition, the purified virus could be used to study the viral adsorption and invasion of PK15 cells using indirect immunofluorescence assays. A large number of PCV2 signals were detected to transfer from the cellular surface to the periphery of the nucleus of the PK15 cells after 30 min of adsorption of the PCV2 to the PK15 cells. The affinity chromatography is a simple and convenient tool to obtain PCV2 with high purity. It could be applied for virus structure analysis, antibody preparation, and viral adsorption and invasion research.Porcine circovirus type 2 (PCV2) is a DNA virus without an envelope. The viral particle is icosahedral and has a diameter of approximately 17 nm. In order to obtain the purified virus, a broad-spectrum monoclonal antibody 3A5 against PCV2 was coupled to CNBr-activated Sepharose Circovirus in the family Circoviridae. In 1991, the virus was isolated from pigs suffering from post-weaning multisystemic wasting syndrome in Canada [Porcine circovirus type 2 (PCV2) is a member of the genus The genome of PCV2 is circular, single-stranded, covalently closed, and composed of typical amphipathic DNA . The genEscherichia coli [PCV2 is a small unenveloped virus with 20-hedral symmetry (T = 1). Each virus particle consists of 60 CAP proteins, and the virus has a diameter of approximately 17 nm ,18. Cesihia coli . Furtherhia coli . HoweverIn this study, we report an affinity chromatography based on a PCV2 broad-spectrum monoclonal antibody (mAb) to obtain PCV2 with high purity.TM 4B, and 20 μL coupled CNBr-activated SepharoseTM 4B was used for the SDS–PAGE analysis. The results showed that the coupled SepharoseTM 4B contained the light and heavy chains of the mAb 3A5 . The concentrations of tube number one and tubes number 10 to number 15 were too low to be detected. The concentrations of tubes number two to nine were 345, 827, 998, 1221, 1354, 1233, 402, and 98 ng/µL respectively, as shown in 7.5 median tissue culture infectious dose (TCID50)/mL, which was 100 times higher than that of the unpurified virus. No virus was detected in the PK15 cell culture samples. To test whether the reaction characteristics of the purified virus and mAb 3A5 had changed, capture ELISA was used to assess the ELISA titers of the purified and unpurified PCV2. The peak values  of the unpurified virus and purified virus were similar  and 120 mL of PCV2d/SD446F16 (106.0TCID50/mL), respectively. The purified viruses were collected in ten tubes, respectively. The concentrations of the PCV2b/LN590516 products were 0, 212, 634, 763, 1034, 1067, 683, 302, 78, and 0 ng/µL, with a total amount of 4.8 mg. The concentrations of PCV2d/SD446F16 were 0, 137, 389, 624, 839, 1045, 936, 386, 57, and 0 ng/µL, with a total amount of 4.4 mg. The same virus purification products were mixed and detected for electron microscopy. The results showed that both the purified PCV2b/LN590516 and PCV2d/SD446F16 obtained pure virus particles, and there was no obvious contaminant protein . A large number of PCV2 signals (green) were detected on the cell surface . To tracEscherichia coli was purified by a single-column, high-throughput fractionation procedure based on size exclusion chromatography and the purity was 95% with a yield of 10 mg from 200 mL of bacterial culture [TM 4B was established to purify PCV2. The yield was about 6.5 mg from 120 mL of PK15 cells culture with a purity of 97%. Compared with other non-specific methods of purifying PCV2, the affinity chromatography method established in this study was simpler in operation and the purified virus had high purity [The virus purification technique is used in many applications, such as virus structure analysis, virus invasion analysis of host cells, virus-specific receptor identification, and specific antibody production ,20. As v culture . A three culture . In thish purity ,22,23. ITM 4B, but the H chain of IgG was extremely reduced compared to the L chain  free of PCV1 and PCV2 were grown in MEM  containing 10% heat-inactivated FBS  for virus propagation and titration. PCV2a/LG strain, PCV2b/LN590516, and PCV2d/SD446F16 (MK347371) were used for purification, and their origins, genotypes, and accession numbers are shown in PK15 cells (ATCC6.0 TCID50/mL) were used as virus seeds to inoculate, respectively, freshly digested PK15 cell suspensions (2 × 105 cells/mL) at a multiplicity of infection of 0.5. Viruses were maintained in MEM containing 2% FBS and 3 mM d-glucosamine . After incubation at 37 °C for 120 h, the virus was collected from cultured infected cells by three freeze-thaw cycles and then centrifuged at 2500× g for 10 min at 4 °C. The supernatants containing the virus were aliquoted and stored at -80 °C. Virus titers were measured as described previously [6.0 TCID50/mL were used for virus purification.The PCV2a/LG, PCV2b/LN590516, and PCV2d/SD446F16  according to the manufacturer’s instructions. The concentration of purified mAb was measured using a NanoPhotometer® NP80 , and its purity was analyzed by SDS–PAGE, followed by thin-layer scanning [3 solution containing 0.5 M NaCl, pH 8.3) at 4 °C for 18 h and then coupled to CNBr-activated SepharoseTM 4B  according to the manufacturer’s instructions.To obtain purified mAb 3A5, the ascites from BALB/c mice containing mAb 3A5 were produced as described previously  and werescanning . A total6.0 TCID50/mL) was centrifuged at 6800× g for 30 min, and the supernatant was filtered using a 0.45 μm filter. One milliliter of the coupled matrix was added to the clarified virus solution, and the mixture was rotated end-over-end overnight at 4 °C. The mixture was loaded onto the column  under gravity several times. To remove any proteins that were not bound to mAb 3A5, the column was fixed vertically and washed with 20 column volumes of washing buffer . PCV2a/LG was eluted from the column using elution buffer  into Eppendorf tubes containing 100 µL of 1 M Tris-HCl (pH 8.0) per 1 mL eluent. The concentrations of purified PCV2 were measured using a NanoPhotometer® NP80 .A total of 120 mL PCV2a/LG , boiled for 5 min, and separated by standard SDS–PAGE, followed by thin-layer scanning . The pur405 values ≥ 0.18, the sample was considered to be positive. At 0.15 < OD405 < 0.18, tests were repeated. At OD405 < 0.15, the sample was considered negative.Capture ELISA based on mAb 3A5 was used to test the reactivity of the purified PCV2 with its antibody as described previously . PurifieTo test whether the purification process affected the infectivity of purified PCV2, purified PCV2 (998 ng/µL) and unpurified virus were detected using virus titration as described previously .6.0TCID50/mL) and 120 mL PCV2d/SD446F16 strain (106.0TCID50/mL) were purified using the same chromatography column and method, and then UV method, capture ELISA, virus titration, and electron microscope were used to detect the concentration, reactivity, virus titer, and purity of the purified virus.Since the monoclonal antibody 3A5 can not only react with the PCV2a strain but also bind to the PCV2b and PCV2d strains, can the affinity chromatography method based on this mAb also be used to purify the PCV2b and PCV2d strains? Based on the above problems, 120 mL PCV2b/LN590516 strain  and incubated at 4 °C for 1 h. After three washes with cold MEM, the cells were cultured in fresh MEM supplemented with 2% FBS at 37 °C and were fixed at 0 min, 30 min, and 60 min with 100% cold methanol for 10 min at −20 °C. An IFA was performed to stain PCV2 as described previously . The mAb


Human parainfluenza viruses (HPIVs) are a leading cause of acute respiratory infection hospitalization in children, yet little is known about how dose, strain, tissue tropism, and individual heterogeneity affects the processes driving growth and clearance kinetics. Longitudinal measurements are possible by using reporter Sendai viruses, the murine counterpart of HPIV 1, that express luciferase, where the insertion location yields a wild-type (rSeV-luc(M-F*)) or attenuated (rSeV-luc(P-M)) phenotype. Bioluminescence from individual animals suggests that there is a rapid increase in expression followed by a peak, biphasic clearance, and resolution. However, these kinetics vary between individuals and with dose, strain, and whether the infection was initiated in the upper and/or lower respiratory tract. To quantify the differences, we translated the bioluminescence measurements from the nasopharynx, trachea, and lung into viral loads and used a mathematical model together a nonlinear mixed effects approach to define the mechanisms distinguishing each scenario. The results confirmed a higher rate of virus production with the rSeV-luc(M-F*) virus compared to its attenuated counterpart, and suggested that low doses result in disproportionately fewer infected cells. The analyses indicated faster infectivity and infected cell clearance rates in the lung and that higher viral doses, and concomitantly higher infected cell numbers, resulted in more rapid clearance. This parameter was also highly variable amongst individuals, which was particularly evident during infection in the lung. These critical differences provide important insight into distinct HPIV dynamics, and show how bioluminescence data can be combined with quantitative analyses to dissect host-, virus-, and dose-dependent effects. Human parainfluenza viruses (HPIVs) cause acute respiratory infections and can lead to the hospitalization of children. HPIV infection severity may vary due to dose, strain, patient, and whether the infection initiates within the upper or lower respiratory tract. There is a need to determine how the rates of virus spread and clearance change in different infection scenarios in order to better understand varying clinical manifestations. The significance of our research is in identifying the dominant mechanisms driving strain-, dose-, and tissue-specific HPIV infection kinetics, and in pairing bioluminescence data with quantitative analyses to determine how the same virus can yield patient-specific outcomes. This work enhances our understanding of HPIV infection and broadens our knowledge viral dynamics in the upper and lower respiratory tracts. Human parainfluenza viruses (HPIVs) are a leading cause of acute respiratory infection, with 80% of children seropositive by the age 5 . As a coThe murine parainfluenza counterpart, Sendai virus (SeV) , has beein vivo kinetics of a myriad of viral infections and have been used to analyze other respiratory viruses like influenza A virus (IAV) ,iewed in –22 and siewed in –26. A stiewed in , 28) in Here, we pair a mathematical model  togetherThe data used are from Burke et al. . BrieflyV is virus (PFU/ml), B is bioluminescence (photons/sec.), α is the maximum interaction rate, KB is the half-saturation constant, and n is the Hill coefficient. The subscript ‘L’ denotes the measurements in the lung. We also examined whether a linear function fit the data better and found that c (77 versus 86 for the rSeV-luc(M-F*) virus and 95 versus 106 for the rSeV-luc(P-M) virus).Plotting the bioluminescence data against the corresponding viral loads for individual mice and separately for each virus revealed a nonlinear correlation in the lung data and a linear correlation in the nasopharynx and trachea data . To tranγ is the intercept (PFU/ml). Subscripts ‘N’ and ‘T’ denote the measurements in the nasopharynx and trachea, respectively. This linear relation is similar to our previous work . He. He16]. fections . Compara trachea –50.Although the low dose resulted in slightly fewer infected cells in the nasopharynx, there was little change in the other processes compared to the high dose  and littI2 approximates the lung damage). Thus, our finding that the number of infected cells was significantly lower in the low d/v groups is in accordance with the reduced weight loss in these animals. In general, having fewer infected cells and/or reduced activation of host responses should lessen virus-induced lung damage and immunopathology.In the data used here, there was minimal weight loss in the animals infected with low d/v compared to high d/v , which iDistinguishing the drivers of HPIV infection heterogeneity and the impact of different strains, doses, patients, and sites of infection as we did here is central to understanding the disease course and developing effective treatments. It may also help identify the mechanisms that influence disproportionate disease in children and the immunocompromised, who may develop more severe presentations from otherwise low doses. In addition, although transmission dynamics are complex and typically associated with viral presence in the URT, further insight into tissue-specific viral dynamics and the possibility of prolonged virus shedding is vital to abrogate the disease , 67, 68.S1 TextSupplemental text, figures, and tables.(PDF)Click here for additional data file.