Understanding the hydrodynamic properties of polymeric coatings is crucial for the rational design of molecular transport involving polymeric surfaces and is relevant to drug delivery, sieving, molecular separations, etc. It has been found that the hydrodynamic radius of a polymer segment is an order of magnitude smaller than its physical size, but the origin of this effect does not seem to be well understood. Herein, we study the hydrodynamic properties of polymeric coatings by using molecular dynamics simulations, navigated by the continuous Navier-Stokes-Brinkman model. We confirm that the averaged hydrodynamic radius of a polymer bead is about one order of magnitude smaller than its physical radius, and, in addition, we show that it exhibits a strong dependence on the degree of polymerization. We relate this variation of the hydrodynamic radius to the structural properties and hydrodynamic shielding by surrounding polymer beads. This is done by separating the effects originating from near and far beads. For the near beads, shielding is mainly due to the two nearest beads (of the same polymer) and leads to about a 5-fold reduction in the hydrodynamic radius. Assuming the additivity of the hydrodynamic shielding by far beads, we suggest a simple model, which captures correctly the qualitative behaviour of the hydrodynamic radius with the degree of polymerization. The revealed shielding effects provide important insights relevant to the advanced modelling of hydrodynamic properties of polymeric coatings. Grafting polymers on surfaces is a versatile way to tune their physicochemical properties, which has numerous applications ,2,3. ForElectrokinetic transport of hard-core particles is relatively well understood now ,19, but To model more accurately the hydrodynamic properties of polymers, it is necessary to consider the hydrodynamic interactions at nanoscale ,28. ThisN of polymers. We rationalize the reduction of N, by relating the structural and hydrodynamic properties of polymer’s beads in coatings.The aforementioned studies of polymeric coatings on macro- and microscopic levels have unveiled some of the most important hydrodynamic properties. However, several key questions remain unanswered: Why the Stokes radius of the polymer bead in a polymeric coating,  systems . In thisElectro-osmotic flow (EOF) in a polymer-coated nanoslit was modeled by using molecular dynamics (MD) simulations and the continuous Navier-Stokes-Brinkman (NSB) model. The MD simulations were used to calculate the velocity profile of the EOF across the slit . The NSB equation was applied to obtain the flow field across the slit and to infer the Stokes radius of the polymer’s bead, x and y direction was 9.88 w is the distance between the innermost layers of the two walls, and z direction. System parameters are summarized in The MD system is shown in eriments . The walof water ,36,37. TIn order to focus on the hydrodynamic properties, unobscured by chemical details of real intermolecular interactions, we used the Weeks-Chandler-Andersen (WCA) interaction potential  between (0.56 M) . We used(0.56 M)  (Table S2−CH2)n −OH, was simulated by using the united-atom model, in which every backbone atom (carbon and oxygen) is modeled as LJ spheres (beads) with the same force-field parameters, while the hydrogen atoms are ignored; also the solvent (water) was consistently modeled as a single bead with the same LJ parameters (n of free monomers and Polyethylen glycol (PEG), H− [x direction of strength MD simulations were performed by using simulation package Gromacs 4.5.1 . The cut solvent . Electroensions)  with a cermostat ; the preF is the Faraday constant, i, The Navier-Stokes-Brinkman (NSB) equation is 1)ddzμ. No-slix-direction along the slit. From molecular dynamics (MD) simulations, we obtained the averaged concentration profiles across the slit, which are shown in We have considered a slit filled with an aqueous electrolyte; the slit walls were positively charged and grafted with polyethylene glycol (PEG) polymers  radius of the polymer bead, N . This result is a new finding and implies that The hydrodynamic radius decreases at a decreasing slope with increasing the degree of polymerization, e et al. .We rationalized these findings by studying the structure and the hydrodynamic properties of polymeric coatings, as described in detail below.The crucial assumption in the continuous NSB approach is that polymer beads are treated as isolated resistance centers screening the flow. However, the polymers are chains of connected beads and the screening properties of a bead shall be affected by the beads surrounding it; in other words, the effective hydrodynamic radius of the polymer beads is expected to depend on the structure of a polymer coating , which, N). The first peak in the RDF results from two neighbouring beads, and the second peak from the next-nearest beads, of the same polymer. The noticeable dependence of the RDF on N is apparent only for distances larger than To reveal the conformational structure of the grafted polymers, we have calculated the gyration radius of polymers  and the It is reasonable to expect that the hydrodynamic properties of polymer beads are different in these two regions, and we thus studied such properties separately for near and far beads.f, that is, the surface density of beads on a sphere of radius r as a function of orientation, To understand better the configuration of, and the hydrodynamic shielding by near beads, we first calculated the orientation distribution function, Having gained some insight into the near-bead structure, we decided to study the hydrodynamic shielding and its dependence on the bead orientation. For clarity, we focused on a simplified model, consisting of three aligned beads located in a channel under electro-osmotic flow. The beads were placed at a given angle with respect to the flow direction both sides; this means that the forces acting on the left and right sides of these beads partially compensate each other, and the magnitude of the total force decreases drastically.ail bead . As the For the same reason, the hydrodynamic drag on the central bead, v, at the same height (z coordinate) as the position of the central bead  over θ , which gcoatings e. More iN. Similarly as for the near beads, the distribution of far beads is practically independent of their orientation . entation c,d; herer from the complex and at an angle x direction. The placement of far beads was chosen such as to avoid complications with balancing collisions of the solvent from both sides of the complex. All far and near beads in this system were frozen and disallowed to fluctuate. We then calculated the hydrodynamic radius of the central bead in the complex as F is the drag acting on the central bead, v is the ambient velocity at a given z position and r and saturates at To understand the hydrodynamic screening by far beads, we considered first a simplified model system, consisting of a three-bead complex with two “far” beads placed symmetrically at a distance r to infinity for generality, we have arrived at abeaar beads , assuminar beads c,d.N obtained by using Equation  simulations and the continuum Navier-Stokes-Brinkman (NSB) model. By matching the velocity profiles from the MD and NSB approaches, we inferred the effective hydrodynamic radius of polymer beads,  studies ,34. Furtng slope e.We rationalized the reduced hydrodynamic radius by linking the structural and hydrodynamic properties of polymer coatings. We observed that, at short distances, the polymer structure is unaffected by the polymer length . In thisN (N seems to be captured properly.The hydrodynamic shielding by far beads turned out to be more intricate. Using MD simulations, we showed that the effective hydrodynamic radius is practically unaffected by far beads if they are beyond a ‘shielding radius’ (which depends on the orientation and size of a screening object; N f. Althoucharged polymers is three times larger than that of neutral polymers. Our work suggests that this might in fact be related to different structures adapted by these polymers, in particular that the polyelectrolytes prefer ‘stretched-up’ configurations [The reported shielding effects may have a broad impact on the understanding of the hydrodynamic properties of polymer coatings. Indeed, we demonstrated a subtle correlation between the hydrodynamic and structural properties, particularly that the hydrodynamic radius is larger in the mushroom region and smaller for the brush configurations of polymer coatings. It has been shown by Hill  that theurations , in cont


Although the development of effective viral vectors put gene therapy on the road to commercialization, nonviral vectors show promise for practical use because of their relative safety and lower cost. A significant barrier to the use of nonviral vectors, however, is that they have not yet proven effective. This apparent lack of interest can be attributed to the problem of the low gene transfer efficiency associated with nonviral vectors. The efficiency of gene transfer via nonviral vectors has been reported to be 1/10th to 1/1000th that of viral vectors. Despite the fact that new gene transfer methods and nonviral vectors have been developed, no significant improvements in gene transfer efficiency have been achieved. Nevertheless, some notable progress has been made. In this review, we discuss studies that report good results using nonviral vectors in vivo in animal models, with a particular focus on studies aimed at in vivo gene therapy to treat cancer, as this disease has attracted the interest of researchers developing nonviral vectors. We describe the conditions in which nonviral vectors work more efficiently for gene therapy and discuss how the goals might differ for nonviral versus viral vector development and use. Currently, cancer therapy around the world consists mainly of surgery, chemotherapy, radiotherapy, and multimodality therapy. However, as the progression of cancer still cannot be controlled, gene therapy is attracting increasing attention.Gene therapy using viral vectors has recently been proven to be an efficient approach, as exemplified by excellent results obtained using viral-mediated cytotherapy to treat diseases caused by genetic deficiencies, a clear area of interest for gene therapy-based approaches ,2. AmongGene therapy is not limited to correcting genetic defects. A variety of gene therapy approaches have been attempted for the treatment of many kinds of diseases. Regarding nonviral vectors, safety is often mentioned as an advantage and inefficiency as a disadvantage ,5. ConveOwing to the unproven safety of viral vectors, nonviral vectors are not used, and the majority of researchers do not seem to be interested in them. We searched the literature and found that in 2017, papers related to nonviral vectors comprised only 0.24% of all articles on gene therapy. Moreover, although the number of publications per year on gene therapy has increased 1.5 times over the last 5 years, no change has been observed in the number of publications per year on nonviral vectors. The unpopularity of nonviral vectors is likely attributable to reports of their low transfer efficiency compared with viral vectors. Indeed, the gene transfer efficiencies of existing nonviral vectors appear to be too low for practical use. It seems possible that nonviral approaches might never be practical as gene therapy specifically targeting genetic deficiencies, but this does not mean that they are without potential value. This raises the question, what types of improvements might make nonviral approaches useful for some types of gene therapy?In this review, we outline the current state of cancer gene therapy with nonviral vectors. We then introduce case studies where in vivo treatment using nonviral vectors resulted in a survival benefit in animal models of cancer, for which a high gene transfer efficiency is required. Analyzing successful examples of gene therapy with nonviral vectors might be helpful in identifying appropriate ways to use them therapeutically. We also refer readers to other reviews for information on new technologies and novel nonviral vectors currently in development ,9,10,11.Several nonviral transfection methods, namely microinjection, electroporation, hydrodynamic injection, and magnetic microparticles, and even chemical methods (cationic lipoplexes), have been reported . The mosSeveral studies have reported that nucleofection mediates gene transfer more efficiently than other nonviral methods with various resistances to cell transfection ,16,17,18In addition, several RNA delivery systems continue to be used for RNA interference, including small interfering RNAs (siRNAs) and microRNA. The RNA delivery system mechanism was derived from classic gene transfer techniques. The objectives of RNA delivery approximate for plasmid transfection. When the messenger RNAs are delivered to the cell and translated, they offer the potential of protein expression ,20. CircMeanwhile, several studies have advocated the importance of plasmid size for transfection efficiency. Presumably, the nonviral vector design is of prime importance ,23,24. IThe difference in gene transfer efficiency between viral and nonviral vectors is striking ,27. The A comparison of the efficiencies of nonviral and viral vectors provides useful guidance for the further refinement of nonviral approaches ,34. GainA related approach for improving gene transfer efficiency is to harness the same endogenous cellular functions utilized by viruses. For example, most viruses, including adenoviruses, enter cells via endocytosis. It has been discovered that a fragment of Del1, an extracellular matrix protein, enhances endocytosis and gene transfer ,39. Thishttp://www.wiley.co.uk/genmed/clinical). A search of PubMed for papers related to nonviral vectors revealed that the most common type of disease explored in the development of nonviral vectors was cancer, followed by cardiovascular disease [For which diseases have “powerless” nonviral vectors been studied? To date, 2597 gene therapy clinical trials have been conducted worldwide . Compare disease ,44,45. IAs mentioned above, viral vector–based treatment of genetic diseases, such as that based on constitutive gene expression following genome integration, has met with some success. Although nonviral vectors have also been explored as a treatment for genetic diseases, the expression of introduced genes decreases rapidly, limiting the success of this approach . TherefoLow transfer efficiency might be a problem for other types of diseases as well. The efficiency of gene transfer with transfection reagents is so low that in some cases, there is no discernable difference in the efficiency of gene transfer with transfection reagents versus the introduction of naked DNA. For example, Zabner et al.  reportedDespite concerted efforts by many researchers, no studies showing more successful cancer gene therapy for the treatment of cancer patients using nonviral vectors have been reported ,58. HoweTo get a sense of the outlook for future development, we reviewed the literature from 2008 to the present and identified 17 studies using nonviral vectors in in vivo animal models of cancer. Among these reports, we were able to identify some studies reporting a significant improvement in survival. Learning what these approaches have in common might provide us with clues for effectively using nonviral vectors. Therefore, we discuss the reports in more detail below.In our literature search, we found nine studies in which treatment of animal models with nonviral vectors led to an improved prognosis. Three of the nine studies were reported by Klutz et al. ,61,62 anp53 gene for the treatment of explanted prostatic cancer in a p53 null mouse. The tumor suppressor protein p53 is encoded by the tumor protein p53 gene. Additionally, p53 plays a primary role in a number of signal pathways, including the cell cycle, proliferation, differentiation, and apoptosis [Sharma et al.  used a npoptosis ,65. Theyp27 gene therapy using a nonviral gene delivery strategy on pulmonary metastatic tumors. Specifically, they tested the effects of systemic intravenous injection into colon cancer cells using the p27kip gene. They performed multiple rounds of directed combination therapy involving cisplatin and nonviral gene therapy. As for the Sharma et al. study [Sun et al.  examinedl. study , althougl. study .In the following studies, DNA was injected locally. Finoccharo et al.  tried toCasey et al. reported treatment of a fibrosarcoma model mouse by gene transfer using naked DNA encoding B7-1 and GM-CSF . Both trFewell et al.  created Kitano et al.  used a mIt is essential to discuss the routes of administration of DNA used in these studies. As reported by Sharma et al. , local iWe identified some reported cases for which the systemic administration of nonviral vectors was effective. It is interesting that DNA complexes accumulate in tumors without target-directed ligands, presumably because of the enhanced permeability and retention made possible by tumor vessels . For intOne of the simplest approaches to increasing the level of transgene expression is to perform repeated rounds of gene therapy. This is a realistic way to make use of the advantages of nonviral vectors, including safety in the cases of both local and systemic injection. Repeated intravenous administration of a small amount of a DNA complex could enhance accumulation in a tumor without leading to the side effects associated with gene transfer and transfection into healthy tissue.A previously reported method leading to the accumulation of expressed proteins in the extracellular matrix also appears to be effective . In suchUltimately, the success of cancer gene therapies largely depends on the choice of genes to be expressed. Nonviral vectors cannot realistically be used to introduce DNA into all cancer cells. Thus, treatments with autonomously acting “anti-tumor genes” such as p53 or TK might not be appropriate routes of development when it comes to nonviral vectors. Innovation in the field depends on new ideas regarding how to compensate for the weaknesses identified for nonviral vectors. In the studies described above, proteins originating from transfected cells appear to exert positive therapeutic effects on surrounding untransfected cells. Choosing proteins that can change the cancer microenvironment is common among successful studies using nonviral vectors.Recently, cancer stroma has been reported to play important roles in proliferation and metastasis ,81. FactCommonalities among successful studies with nonviral vectors include local injection, repeated administration, and the use of proteins that either modify the cellular microenvironment or function well at low concentrations. For cancer and other diseases, experimental systems with these characteristics have worked well. Therefore, it appears that nonviral vector–mediated gene therapy is suitable for some therapeutic approaches.Nonviral vectors should be used in protocols tailored to their specific strengths and weaknesses. These protocols are likely to be different from those considered appropriate for viral vectors. Viral vectors appear to be uniquely well suited for the treatment of genetic defects. It might be useful to think about nonviral vector-based treatments in the same manner that we think about drug treatments. With drug treatments, the types of drugs and approaches to their administration are chosen according to the nature and condition of the disease. We propose that the targets and protocols for gene therapy using nonviral vectors should be chosen accordingly. With this in mind, we suggest that in medicine, nonviral vectors should not be compared with viral vectors. Instead, nonviral vectors should be compared to drugs, such as in terms of their medical and economic efficiencies.Gene therapy performed via the nonviral vector-mediated introduction of a cDNA encoding an endogenous protein is effective, safe, and economical. Physiologically active substances are more effective and safer than novel chemicals, and manufacturing DNA is relatively inexpensive. In addition, the stability of DNA is likely to reduce the costs associated with transportation and storage; for example, Sun et al.  reported


We determined the complete genome sequence of a virus isolated from a mantled guereza that died of primary effusion lymphoma. The virus is closely related to Kaposi’s sarcoma–associated herpesvirus (KSHV) but lacks some genes implicated in KSHV pathogenesis. This finding may help determine how KSHV causes primary effusion lymphoma in humans. Macaca nemestrina, M. fascicularis, and M. mulatta macaques; and closely related viruses of other Old World primates , as well as of the RV2 lineage viruses rhesus macaque rhadinovirus, Japanese macaque rhadinovirus, and M. nemestrina RV2, have been generated from cultured viruses or directly from clinical material by conventional or high-throughput sequencing (Kaposi’s sarcoma–associated herpesvirus (KSHV) causes Kaposi sarcoma, primary effusion lymphoma, and the plasma cell variant of multicentric Castleman disease in humans  that died of primary effusion lymphoma. The virus belongs to the RV1 lineage, together with KSHV and RFHVMn. Apart from KSHV, all fully sequenced Old World primate rhadinoviruses have been found in primates of the genus https://blast.ncbi.nlm.nih.gov) analysis revealed the best match to be RFHVMn. Using a commercial microarray , we detected antibodies to lymphocryptovirus but not to simian immunodeficiency virus, simian retrovirus, herpes B virus, simian T-cell leukemia virus, measles virus, rhesus macaque rhadinovirus, human cytomegalovirus, or simian foamy virus (data not shown).In 2014, a 3-year-old male mantled guereza at a zoo in Germany died suddenly after developing severe anemia (<5 g/dL hemoglobin), subcutaneous edema, and leukocytosis. A necropsy conducted at the German Primate Centre, Göttingen, Germany, led to a diagnosis of primary effusion lymphoma. Large numbers of abnormal leukocytes were found in the vascular system of several organs. The pleura pulmonalis and the pleural space were severely infiltrated with pleomorphic round cells , panel Ahttps://www.illumina.com). The 22,978,561 trimmed reads were depleted of host sequences by screening against the human genome sequence. The remaining 3,082,106 reads were assembled de novo into contigs, the largest of which was 126,024 bp. Assemblies of the initial trimmed reads with this sequence, followed by manual extension and incorporation of smaller contigs, resulted in a final, complete viral sequence of 133,999 bp. This essentially circular sequence consists of a unique region  followed by a copy of a terminal repeat unit  and then by a partial copy of TR (727 bp). A total of 84,532 (0.4%) of the initial trimmed reads aligned with this sequence at an average depth of 170 reads per nucleotide. Inspection of the read alignment indicated that most genomes (85%) lack a 7,045-bp region toward the right end of U. In addition, a telomere-like tandem repeat was noted near the left end of U. A similar feature is present in the genome of RFHVMn but not KSHV.DNA extracted from a spleen necropsy specimen was sequenced by using an Illumina MiSeq  and 59% identical to that of RFHVMn . Phylogenetic analysis of these 3 sequences with those of viruses in the RV2 lineage, using Epstein-Barr virus (a lymphocryptovirus) as the outgroup, confirmed that the novel virus clusters in the RV1 lineage with RFHVMn and KSHV . The novWe named the 78 protein-coding genes annotated in the CbGHV1 genome according to the KSHV nomenclature  Figure 1We identified and sequenced the complete genome of a novel KSHV-like virus (CbGHV1) from a mantled guereza. The animal died of primary effusion lymphoma, which we assume was caused by CbGHV1. CbGHV1 and its close relatives KSHV and RFHVMn cluster in the RV1 lineage. The presence of a telomere-like tandem repeat near the left end of U in the CbGHV1 and RFHVMn genomes suggests that an ancestral virus may have been integrated into the host genome, and its persistence suggests that these viruses may retain the ability to integrate.Herpesviridae. Orthologs of 8 KSHV genes  are absent from CbGHV1. In KSHV, some of these genes, such as K2 and K12, encode proteins  that have been linked to viral pathogenesis. Their absence from CbGHV1 suggests that they may not be needed for the development of primary effusion lymphoma. Because ORF10 and ORF11 are related and may have arisen by duplication from an ancestral deoxyuridine triphosphatase gene (The CbGHV1 genome contains all genes that are conserved in all members of the family Additional methods and results for study of novel virus related to Kaposi’s sarcoma–associated herpesvirus from colobus monkey.


The factors affecting the transmission and geographic translocation of avian influenza viruses (AIVs) within wild migratory bird populations remain inadequately understood. In a previous study, we found that environmental transmission had little impact on AIV translocation in a model of a single migratory bird population. In order to simulate virus transmission and translocation more realistically, here we expanded this model system to include two migratory bird flocks. We simulated AIV transmission and translocation while varying four core properties: 1) Contact transmission rate; 2) infection recovery rate; 3) infection-induced mortality rate; and 4) migration recovery rate; and three environmental transmission properties: 1) Virion persistence; 2) exposure rate; and 3) re-scaled environmental infectiousness; as well as the time lag in the migration schedule of the two flocks. We found that environmental exposure rate had a significant impact on virus translocation in the two-flock model. Further, certain epidemiological features  in both flocks strongly affected the likelihood of virus translocation. Our results further identified the pathobiological features supporting AIV intercontinental dissemination risk. Highly pathogenic avian influenza (HPAI) viruses have repeatedly caused emergent outbreaks in poultry and pose a serious threat to both the poultry industry and human health. Since its first emergence in China in 1996, HPAI H5N1 has caused multiple outbreaks in poultry, and more than 10 million domestic birds have been killed by infection or culled to control the virus ,3. In adIn 2014, another novel reassortant virus, HPAI H5N8, emerged from the H5N1 Gs/GD lineage and caused large outbreaks in East Asia . The virPrevious research has shown that live-poultry trade and wild bird migration are critical means for spreading HPAI viruses. Experimental studies have indicated that these viruses are not pathogenic to domestic or wild ducks, which is a pre-condition for virus transmission among ducks . HPAIVs A number of studies have analyzed the intercontinental dispersal of H5N8 and shown that it is mainly attributable to the long-distance migration of wild birds. For example, international poultry trade data from the Food and Agriculture Organization are inconsistent with the pattern of H5N8 virus spread from Asia . In contIn a previous study, we used a compartmental model with environmental transmission to simulate the transmission and geographic translocation of an AIV within one migratory bird population . We founIn this paper, we modified our previous compartmental model by adding a second susceptible flock and again included environmental transmission in the model. We used this model framework to study AIV translocation from bird wintering grounds to summer breeding grounds within two flocks as a function of epidemiological properties, environmental parameters, and the time lag in the migration schedule of the two flocks. Specifically, we varied four core epidemiological properties: 1) Contact transmission rate; 2) infection recovery rate; 3) infection-induced mortality rate; and 4) migration recovery rate; and three environmental transmission properties: 1) Virion persistence; 2) exposure rate; and 3) re-scaled environmental infectiousness. The results further describe how environmental transmission affects AIV translocation and identify the features that support HPAI intercontinental spread.We developed a compartment model based on the form presented in Galsworthy et al.  and Li eI) and recovered (R) compartments were divided into I1 and were unable to migrate. Infected birds in I1 could either recover to R1 and subsequently regain migratory ability upon transition to R2, or first regain migratory ability while still infectious and move to I2, then subsequently recover to R2 (v). Birds recovered from infection—from r). Note that the migration recovery rate and the infection recovery rate are independent of one another. We included a mortality rate induced by infection in the Virus transmission occurs through direct contact within the flock and through environmental reservoirs. For each patch and each flock, virus transmission was simulated using a susceptible-infectious-recovered (SIR)-type model (Equation (2a)–(2k)). Because an infection-induced migration delay was considered for birds, the infected  , with Fly et al. , birds rc); exposure rate (t) between migration start time of the two flocks. The parameter ranges used are shown in To simulate viruses with different epidemiological features, we varied four core parameters for each of the flocks: Contact transmission rate  based on the ranges given in To measure the likelihood of virus survival and translocation, for each patch and combination of parameters, we calculated the fraction of the 100 stochastic simulations for which the virus was present, or, conversely, extinct, in a bird flock at a given patch. We then examined all the simulations to determine the combination of pathobiological features favoring virus persistence and geographic dispersal, or, conversely, virus extinction and geographic isolation.We focus on the presence and extinction of virus during spring migration (Patches 3 and 5), as Patch 5—the summer breeding ground—is where bird populations from different regions would potentially mingle. We assumed that a virus with a higher likelihood of reaching Patch 5 was more likely to disperse intercontinentally. −1). For the parameter combinations with an infection period of 13 days in Flock A, the virus translocated to Patch 5 with a likelihood of approximately 0.6. Patterns could also be seen for the infection recovery rate in Flock B. A lower infection recovery rate in Flock B increased the probability of reaching the summer breeding patch. This effect was more evident when the infection recovery rate in Flock A was higher. Conversely, sensitivity to the infection recovery rate in Flock B was minimal when the infection recovery rate in Flock A was lower. Sensitivities were also apparent for the other three core parameters. The likelihood of reaching Patches 3 and 5 rose as transmission rates in both flocks increased, and a lower mortality rate in both flocks increased the probability of reaching the summer grounds, as did a higher migration recovery rate.Sensitivity was found for each of the four parameters in both flocks with the parameters in Flock A exhibiting a greater impact. Among the four parameters, infection recovery rate exhibited the strongest effect. The infection recovery rate in Flock A significantly determined the likelihood that the virus reached Patches 3 and 5, with a lower infection recovery rate strongly increasing the likelihood of virus translocation. The virus reached Patch 3 with a 0.6 or higher likelihood for parameter combinations with an infection period of 10–13 days in Flock A  based on the results shown in https://cran.r-project.org/web/packages/ggplot2/index.html) in R (version 2.5.2). To further investigate the features that always support translocation to a given patch versus never support translocation during spring migration, we plotted the two-dimensional kernel density for each of the four parameters . Always We found that the likelihood of always translocating to Patches 3 and 5 increased as Flock A infection recovery rate decreased. In addition, there was a decreasing likelihood of virus never translocating as Flock A infection recovery rate decreased. In contrast, Flock B infection recovery rate only had a slight impact on the likelihood of both always translocating and never translocating. The patterns for mortality rate and migration recovery rate were quite similar. The likelihood of always translocating increased with a lower mortality rate in Flock A, as well as a higher migration recovery rate. The mortality rates in Flock A and Flock B combined affected the likelihood of never translocating—there was a decreasing likelihood of the virus never translocating as the mortality rate in both flocks decreased. An increase in the migration recovery rate in both flocks also decreased the likelihood of the virus never translocating. For the transmission rate, parameter combinations with lower transmission rates in both flocks supported the virus never translocating. However, a transmission rate in Flock A around In this study, we expanded a dynamic model of AIV in migratory birds to further explore the impact of environmental transmission and pathobiological features on HPAI translocation. Our results showed that for this two-flock model system, the epidemiological features of Flock A  largely determined the likelihood of the virus translocating to the summer breeding patch; however, the epidemiological features of Flock B (the fully susceptible flock) also appeared to affect the likelihood of overall virus translocation during bird migration. Further, there existed a significant impact of environmental transmission on virus translocation. The pathobiological features identified here favoring virus translocation were similar to the findings of our previous study using a single flock model. The present results corroborated that viruses with a lower infection recovery rate and infection-induced mortality rate were more likely to spread intercontinentally. With a lower infection recovery rate, an AIV has more opportunity to spread due to its longer infectious period. Similarly, with a lower mortality rate, more infected birds survive and continue transmitting the virus. The migration recovery rate also had an impact on AIV spread; at lower migration recovery rates, infected birds remained in a migratory patch for longer, which helped to isolate healthy susceptible birds from infected birds, thereby suppressing transmission.In addition, higher transmission rates increased the overall likelihood of translocating the virus to the summer breeding patch. Here, the greater transmission rate increased the efficiency and speed of virus transmission; however, at the same time, a higher transmission rate decreased the likelihood of always translocating to the summer breeding patch. This difference may be due to depletion of the number of susceptible individuals during spring migration when transmission rates are high, such that for some stochastic runs the virus goes extinct prior to reaching the breeding patch. This effect needs to be further investigated experimentally or through field observation.Previously, we found that environmental transmission had little impact on the translocation of a virus in a single-flock model; however, with two flocks, the virus shed into the environment by Flock A showed potential to infect the fully susceptible flock (Flock B). Environmental exposure rates largely determined the likelihood that birds in Flock B were infected, which in turn further affected the overall virus translocation rates. Due to environmental transmission, the pathobiological features of AIV in Flock B could also significantly affect virus translocation, though the spread of the virus in Flock B, to some extent, appeared to depend conditionally on Flock A. When the conditions in Flock A are more suitable for the development of infection, more virus particles can be released into the environment, which creates a greater likelihood of Flock B infection. Once infected, the virus will transmit within Flock B mainly through direct contact; however, virus translocation appears more dependent on the features in Flock A. If the features in both flocks favor virus translocation , the virus has a high likelihood of spreading geographically; if the features in Flock A support virus translocation, but not in Flock B, virus translocation is still likely; however, if the features in Flock A do not favor virus translocation, but the features in Flock B do, translocation to the summer breeding patch remains possible but not as probable.Our research has some limitations and highlights directions for future research. Firstly, our results focus on virus translocation during spring migration, and we assume that the virus with a larger probability of reaching the summer breeding grounds is more likely to spread intercontinentally. However, bird contact behavior at breeding grounds and subsequent migration—which are not included in this study—may also act as important determinants of intercontinental dissemination. Future studies should simulate these processes to more fully identify the ecological and pathobiological factors supporting intercontinental dispersal.Environment transmission of AIVs is affected by many factors not represented in our model, including habitat temperature, pH and lake volume. Lower temperature and higher pH favor the persistence of viruses in the environment ,32. In aA number of other factors, including age structure, loss of immunity and cross immunity, will also need to be considered in the future. For example, birds previously infected with certain low pathogenic avian influenza (LPAI) have some immunity to HPAI. Considering these effects in future studies could help improve understanding of how the age structure and immunity in wild populations can affect AIV transmission and dispersal.In summary, the results of our 2-flock model indicate that environmental transmission significantly affect virus translocation within migratory populations. We further identified that certain epidemiological features  in both flocks affect the likelihood of virus translocation and further support AIVs intercontinental dissemination risk.


For children and adolescents with mental health problems, there is a lack of data as to whether the type of residential area  influences healthcare costs for affected individuals. The aim of this study was therefore to explore potential urban vs. rural healthcare cost differences in children and adolescents with conduct disorder (CD), one of the most frequent and cost-intensive child and adolescent psychiatric disorders. Additionally, we aimed to compare healthcare costs of youths with CD, and of youths without this diagnosis.N = 6337), and an age- and sex-matched group without this diagnosis (control group). For both groups, annual costs per person for outpatient and inpatient healthcare were aggregated, stratified by area of residence .We analysed data from a German health insurance company, extracting all youths with a CD diagnosis in 2011 , inpatient treatment costs were significantly higher in rural areas  vs. 1199 EUR , p < 0.0005). For outpatient healthcare costs, the reverse effect was found, with significantly higher costs in individuals from urban areas of residence  vs. 581 EUR , p < 0.0005).While mean annual overall costs in the CD group did not differ significantly between urban and rural areas of residence  EUR vs. 795 (±4425) EUR).This study is the first to demonstrate urban vs. rural differences in healthcare costs among youths with CD. The higher costs of inpatient treatment in rural regions may indicate a need for alternative forms of service provision and delivery in rural settings. According to the International Classification of Diseases, 10th revision , depression, anxiety disorders, and substance misuse . WithoutStudies from the UK and from Germany demonstrate an above-average healthcare utilisation and subsequent costs in individuals with conduct disorders –11.In a British follow-up study, individuals diagnosed with conduct disorder in childhood had caused 8.8 times higher direct health costs in adulthood than same-aged individuals without childhood conduct disorder . The samApart from health services utilisation, conduct disorder related costs are also incurred in the education system, within the family (through lost productivity), and – due to increased delinquency – in the justice system , 12. EleWhile there is evidence that in adults with psychiatric disorders the type of residential area  significantly influences health service use and associated healthcare costs , 18), su. Harden et al. studied 10- to 17-year olds in the UK, and did not find support for the hypothesis that living in densely populated counties influenced youth delinquency [The few existing studies on urbanicity in relation to conduct disorders mainly focus on the influence of urban or rural environments on the prevalence of conduct disorders and antisocial behaviour: A study in US schoolchildren showed that while behaviour problems at home did not vary significantly across urban and rural settings, children in urban settings exhibited higher rates of school conduct problems . Harden inquency .There is evidence that children and adolescents with conduct disorder frequently show complex and multiple service use patterns, e.g. primary healthcare, specialist child mental health services, emergency department visits, behavioural therapy, social services, contacts with the criminal justice system etc. , 21. In Are there differences in overall, outpatient and inpatient healthcare costs in children and adolescents with a diagnosis of conduct disorder from urban vs. rural places of residence?Are there differences in the composition of healthcare costs in relation to type of cost and type of consulted medical specialty?Are there differences in overall, outpatient and inpatient healthcare costs in children and adolescents with a diagnosis of conduct disorder and of youths without this diagnosis?This study therefore aimed to explore the following questions:This study used secondary data from the AOK Nordost health insurance company, which is a statutory health insurance company that covers about 1.8 million inhabitants of the German federal states Berlin, Mecklenburg-Western Pomerania and Brandenburg. Nearly 90% of children and adolescent in Germany are insured in a statutory health insurance company, whereas the remainder are privately insured . AlthougAs compared with the entire German population, AOK insurees have a slightly lower socio-economic status, and more psychiatric morbidity .Out of all children and adolescents who were insured in each quarter of 2011, we extracted all who were 5 to 18 years old in 2011, and for whom one of the following ICD-10 conduct disorder diagnoses had been documented: F90.1, F91.0, F91.1, F91.2, F91.3, F 91.8, F91.9, F92.0, F92.8 or F92.9. These children constituted the conduct disorder group. For the control group, a random sample of age-, sex-, and postcode-matched children without any of the afore-mentioned conduct disorder diagnoses in the years 2006–2011 was extracted from all AOK Nordost insurees in 2011.For both groups, the prevalence of comorbid psychiatric diagnoses (remaining ICD-10 F codes) was evaluated.Annual healthcare costs per individual for both groups were calculated by adding up the following cost data from the AOK data for the calendar year 2011: inpatient hospital costs, outpatient GP costs, outpatient specialist costs, prescription of pharmaceuticals, rehabilitation costs, costs for medical aids, and medical remedies, and reimbursement of healthcare-related travel expenses.The variable “urban” vs. “rural” region was operationalised conferring to the number of inhabitants , according to the definition of the German Federal Institute for Research on Building, Urban Affairs and Spatial Development  .p-values, effect sizes were calculated, based on the test statistics of the Mann-Whitney test.Because of the non-normal distribution of the data, Mann-Whitney tests were computed for the analysis of differences between health services utilisation costs in rural and urban regions. Additional dependent-samples analyses of variance were applied, based on the matched index-control pairs. This model enables the analysis of interactions between the conduct disorder group and the control group, and between urban and rural regions. In addition to The conduct disorder group and the control group each consisted of 6337 individuals, 68.8% of which were male. The mean age was 11.2  years. In both groups, 43.2% of insurees were from the federal state of Berlin, 30.6% were from Brandenburg, and 26.1% were from Mecklenburg-Western Pomerania. 51.2% were from an urban area of residence, and 48.8% lived in a rural area of residence. The proportion of individuals of non-German nationality was 26.2% in Berlin, 3.1% in Brandenburg, and 2.3% in Mecklenburg-Western Pomerania.p = 0.348). In the control group, the corresponding prevalence rates were 24.0%, and 25.8%, respectively (p = 0.086).In the conduct disorder group, 93.3% of urban residents, and 92.7% of rural residents had at least one psychiatric comorbidity (p = 0.253).The mean overall annual healthcare costs per person were 3162 (±5934) EUR, with overall healthcare costs not differing significantly between patients from urban and from rural regions  is shown in Table Data on outpatient healthcare costs by type of physician  are presented in Table p = 0.698)  EUR. Overall costs did not show a significant difference between urban and rural areas of residence , while in urban areas costs for outpatient healthcare were the most important cost type (29.7%). Costs for inpatient treatment in rural regions of residence were significantly higher than those in urban areas. For outpatient healthcare costs, this relationship was inverse.Visits to paediatricians incurred the largest part of outpatient costs Table . In urbaIn comparison, the mean annual overall healthcare costs per person in the conduct disorder group were 3.98-fold higher than those in the control group. With regard to inpatient healthcare, costs in the conduct disorder group were 6.64-fold (urban) and 7.46-fold higher , respectively, than in the control group.The main finding of this exploratory study is the inverse relationship between inpatient and outpatient costs in the conduct disorder group: While inpatient costs were significantly higher in individuals from rural areas, outpatient costs were significantly higher in individuals from urban areas of residence.2) had the highest density of registered psychotherapists of all federal states . In contrast, the two least populated, rural federal states Brandenburg and Mecklenburg-Western Pomerania (about 70–80 inhabitants/km2) had a ratio of 12.5 and 10.9 psychotherapists per 100,000 inhabitants, respectively [A possible cause for these cost differences is an unequal availability of medical resources in urban vs. rural areas of residence. In Germany, there are considerable differences between rural and urban areas of residence in regard to the number and density of statutory health insurance-accredited physicians and mental health professionals. In 2011, for example, the most densely populated German federal state of Berlin  are especially remarkable as there is no good evidence for lasting effects of inpatient treatment for children and adolescents with conduct disorders . Vice veYet, an Australian study focusing on mental health services use for anxiety and mood disorders in the general population did not find rural residence itself to be a limiting factor for access to mental health services. Instead, other factors, including education and distress, were more important predictors of mental health services utilisation .Another possible explanation for the differences in inpatient and outpatient healthcare costs found in this study could be an urban vs. rural difference in terms of symptom severity or psychiatric comorbidity. Regarding symptom severity, this parameter was not included in the secondary data underlying this study, thus precluding such an analysis. Concerning psychiatric comorbidity, there were no significant urban vs. rural differences in the respective prevalence rates, which makes an influence of comorbidity on cost differences unlikely.Concerning the distribution of costs according to service type, our results correspond with an economic analysis of adolescents with conduct disorder by Ewest et al. , with inRegarding the composition of outpatient costs, consultations of child and adolescent psychiatrist consultations naturally yielded the highest costs in the conduct disorder group. Regarding the higher costs for GP consultations in rural areas, these results are in agreement with a recent study demonstrating that children and young people in rural German areas use outpatient general medical care to a greater extent than those in urban areas , but alsIn terms of the four times higher overall healthcare costs for children and adolescents with conduct disorders, our data is in line with the study by Ewest et al. , who fouBeyond the above-mentioned studies, there are currently no other comparable studies available which focus on urban vs. rural differences in the utilisation of healthcare services and health insurance expenses in youth with psychiatric disorders. Interestingly, our results largely comply with those of the few studies performed on rural vs. urban cost differences in adult psychiatric patients. These studies also did not find rural vs. urban differences in overall healthcare costs , and repWhile Tiainen et al.  and ZillIncredible Years programme or the 8-week Triple P programme, which do not only improve conduct disorder symptoms, but have also proven to be cost-effective [Multisystemic Therapy [Drawing on our data, an improved healthcare provision for conduct disordered children and adolescents appears to be necessary primarily in rural areas. Given the scarcity of mental health professionals in these areas, improvements in healthcare provision for conduct-disordered youths should focus on dissemination and implementation of evidence-based prevention and intervention programmes  that do ffective –42. Anot Therapy , which h Therapy , and canAn alternative would be the provision of distance- or internet-based interventions, for whose effectiveness there is a growing body of evidence , 46.Beyond the reduction of healthcare costs, an improved provision of healthcare as delineated above would also have the potential for reducing non-medical costs associated with conduct disorders  , 47, 48,In terms of limitations, it is important to note that we used secondary data, so no external validation of the conduct disorder diagnoses was possible. Also, the data did not contain information on clinical factors potentially influencing service utilisation and associated costs, e.g. symptom severity , 13). MoIn order to match control group and index group, not only age and sex but also the residential area (postcode) was taken into account, in order to serve as a proxy for socioeconomic status. Nevertheless, the lack of individual socioeconomic status data is a significant limitation, which should be mended in future studies, as some studies have demonstrated associations between socioeconomic status and health services use in children, albeit in different directions , 51. MorAnother methodological issue is the definition of areas as urban and rural, with towns with more than 100,000 inhabitants being defined as urban. It is debatable whether this classification is appropriate in the case of Berlin, which simultaneously constitutes a city and a federal state. However, the afore-mentioned classification has also been employed within the German KiGGS study, a representative epidemiological study on child health , so we aAdditionally, it has to be noted that in Germany the unit costs for inpatient and outpatient treatment do not reflect the real costs of the service providers, but are rather a price negotiated between the health insurance company and the service providers.Finally, it should be kept in mind that the effect sizes corresponding with the respective significant differences from our analysis are all small in dimension, thus qualifying the urban vs. rural differences.A strength of this study is its use of administrative data, which allows to study real-world utilization patterns in a large and unselected population without nonresponse or recall bias problems. Moreover, this study is of special interest, as it constitutes the first approach to investigate rural vs. urban differences in the provision of healthcare for children and adolescents with conduct disorders.This study is the first to demonstrate urban vs. rural differences in healthcare utilisation and costs among youths with conduct disorder. The higher costs of inpatient treatment in rural regions may indicate a need for alternative forms of service provision and delivery in rural settings.


West African countries Liberia, Sierra Leone, and Guinea experienced the largest and longest epidemic of Ebola virus disease from 2014 to 2016; after the epidemic was declared to be over, Liberia, Guinea, and Sierra Leone still experienced Ebola cases/clusters. The United States Centers for Disease Control and Prevention (US CDC) participated in the response efforts to the latter Ebola clusters, by assisting with case investigation, contact identification, and monitoring. This study aims to estimate the cost to the US CDC of responding to three different Ebola clusters after the end of the Ebola epidemic in 2015: i) Sierra Leone, Tonkolili ; ii) Guinea, Nzerekore ; iii) Liberia, Somali Drive .After interviewing team members that had participated in the response, we estimated total costs (expressed in 2016 US Dollars [USD]), where total costs correspond to travel costs, deployed personnel costs, costs to prepare for deployment, procurement and interagency collaboration costs, among others. We also estimated cost per cluster case ; and cost per case-affected-region . We found that the response cost varied sixteenfold between USD 113 166 in Liberia and USD 1 764 271 in Guinea, where the main cost drivers were travel and personnel costs. The cost per cluster case varied tenfold between 37 722 in Liberia (three cases) and USD 347 226 in Sierra Leone, and the cost per case-affected-region varied threefold between USD 37 722 in Liberia and USD 88 214 in Guinea.Costs vary with the characteristics of each cluster, with those spanning more regions and cases requiring more resources for case investigation and contact identification and monitoring. These data will assist policy makers plan for similar post-epidemic responses.The online version of this article (10.1186/s40249-018-0484-6) contains supplementary material, which is available to authorized users. Please see Additional file From 2014 to 2016, West Africa experienced the largest and longest epidemic of Ebola virus disease (EVD). Over 28 500 cases and 11 300 deaths were reported to the World Health Organization (WHO) from Guinea, Sierra Leone and Liberia [To aid planning for future such operations involving the CDC or other international organizations, we analysed costs, from the perspective of the CDC, of emergency responses to three clusters: i) Sierra Leone, Tonkolili ;  ii) GuinWe assessed resource usage during the responses by consulting the teams involved in the response, since specific costs are managed by different centres inside CDC. Such costs are limited to the time scope of the cluster and/or personnel deployment. We consulted in-country CDC team members that were or had been stationed in West Africa and had been in charge of the cluster response. We also consulted personnel located in the Emergency Operations Center in the CDC headquarters in Atlanta responsible for setting up travel, and providing logistical materials for the deployed personnel. We thus interviewed 21 Emergency Operations Center and in-country CDC team members to obtain data on the three emergency responses under study. Since the study was performed from the perspective of the CDC, we did not include resources expended by the Ministries of Health in the affected countries or by other supporting partners.We included travel costs; deployed personnel time (in hours and wages); funds necessary for collaboration with other agencies; costs needed to for procured materials (such as Rapid Diagnostic Tests), if required; cost of local hires in the region where the response took place; cost of vaccination and medical clearance costs required to personnel preparing to be deployed; transport and other logistical costs such as fuel and security, if required; communication equipment/service for deployed personnel; planning and scheduling travel costs (“backstage costs”); and costs required to reimburse partners for support given for the cluster in terms of personnel and resources for emergency evacuation procedures, if required. When addressing personnel compensation, we considered personnel deployed from the CDC’s Atlanta headquarters, personnel from other agencies assisting in the response via an interagency agreement, and CDC teams aggregated from already stationed and CDC-contracted personnel in the country of the response . We did not include the costs of personnel stationed in Atlanta that did not travel or was not involved in travel preparation.We calculated total costs. Given wide variability in total costs, we also estimated cost per cluster case, equal to the total costs divided by the total number of cluster cases; and cost per case-affected-region, which is equal to the total costs divided by the product of the number of cases times the number of regions affected. That is, if there are 10 cases spread over two regions, one would divide total costs by 20. To allow for comparison among countries, we expressed costs in US Dollars (USD) in 2016 prices (USD 2016).The cost of responding to an Ebola cluster varied widely between responses, ranging from USD 113 thousand in Liberia, to USD 1 800 thousand in Guinea  and personnel costs . These costs are related to the need to deploy personnel expert in epidemiology to perform case investigation, contact identification and monitoring, which were CDC’s main contributions to response , which are specific to the particularities of each cluster. The need for these resources increases very rapidly with the number of cases and geographic spread of the cluster. For the responses studied, the complexity of the cluster in Guinea (which included ten cases over two different regions) demanded an additional number of resources to do contact tracing and epidemiologic investigations.Additionally, the resources required for some responses included procurement costs for rapid diagnostics tests (RDTs) . The need for RDTs is judged on a case-by-case basis; whether more RDTs are required is a function of the numbers of possibly exposed persons and suspected cases as well as the diagnostic testing algorithm adopted.In what concerns the resources necessary to prepare for deployment , they varied from USD 1384 in Liberia  to USD 35 988 in Guinea ; with costs in Sierra Leone  also high given the number of regions affected by the cluster and the consequent need to deploy more personnel. Finally, it was also necessary to take into account logistical costs (fuel and security) which varied from almost USD 24 000 in Liberia and Sierra Leone to USD 12 500 in Guinea . Such costs depend on the geographic characteristics of the region where the cluster takes place (whether cases are spread over a wide area or not), and on the need or not to hire an extra security detail. Thus, they depend more on the context where the cluster unfolds then on the characteristics of the cluster itself.The cost per cluster case varied almost tenfold between 37 722 in Liberia (three cases) and USD 347 226 in Sierra Leone (two cases); in Guinea, cost per case was USD 176 427. The cost per case and affected region varied almost threefold between USD 37 722 in Liberia  and USD 88 214 in Guinea ; it was USD 69 445 in Sierra Leone .For the clusters studied, the cost per case-affected-region varied almost threefold between USD 37 722 in Liberia and USD 88 214 in Guinea. Total costs varied sixteenfold between USD 113 166 in Liberia and USD 1 764 271 in Guinea. This study illustrates the broad range of resources needed for responding to an international emergency response. The large differences in estimates of cluster costs seem to be due to varied response activity, which depends on the particular characteristics of the cluster (number of cases and number of affected regions). The need for resources seems to increase with both the number of cases and with the geographical dispersion of the cases, with a higher than linear increase on costs per case-affected region.Note, however, that we did not include costs of personnel that did not travel or were not involved in travel preparation. We also did not include costs from other partners, such as other international organizations and Ministries of Health. That is, the costs presented in this manuscript represent the costs to respond to Ebola clusters, from the perspective of the CDC, and are thus limited to a portion of total response costs. To calculate total costs, the resources used by different partners would also have to be taken into account. Countries that increase their capacity to detect-and-respond to clusters may also experience similar types of response costs, scaled to the size of their Ministries of Health/response teams and the specific requirements for the teams . In addition, costs were estimated via interviews; given that we were unable to consult logistical records such as receipts, there may be cost under-reporting. We have tried, however, to collect data for different clusters in order to be able to appreciate the variation of response costs with cluster characteristics (number of cases and regions affected).This study is a first step towards quantifying resource usage during the response to an emergent disease outbreak. We found that total response costs varied sixteenfold for the studied outbreaks; they varied threefold by case and region affected. This suggests that the resources required to respond to clusters vary crucially with the characteristics of each outbreak, namely the regions affected and the total number of cases. As a result, a comprehensive assessment of the characteristics of the cluster and regions affected is necessary to gauge the total means necessary to the response. This information is useful for policy makers considering the resources required to mount emergency responses and thereby enhance global health security.Additional file 1:Multilingual abstracts in the five official working languages of the United Nations. (PDF 192 kb)


Herpes simplex virus (HSV) has various presentations, depending on the patient’s immune status, age, and the route of transmission. In adults, HSV type 1 is found predominantly in the oral area, and HSV type 2 (HSV-2) is commonly found in the genital area. HSV-2 infection without genital lesions is uncommon. Herein we report a unique case of pharyngotonsillitis as an initial manifestation of disseminated HSV-2 infection without genital involvement.A 46-year-old male was admitted to our hospital with a 1-week history of fever and sore throat. His past medical history included hypereosinophilic syndrome diagnosed at age 45 years. Physical examination revealed throat congestion, bilaterally enlarged tonsils with exudates, tender cervical lymphadenopathy in the left posterior triangle, and mild epigastric tenderness. The laboratory data demonstrated bicytopenia, elevated liver enzyme levels, and hyperferritinemia. A bone marrow smear showed hypocellular marrow with histiocytes and hemophagocytosis. The diagnosis of HLH was confirmed, and the patient was treated with methylprednisolone pulse therapy on days 1–3. On day 5, despite initial improvement of the fever and sore throat, multiple, new, small bullae developed on the patient’s face, trunk, and extremities. Additional testing showed that he was positive for HSV-specific immunoglobulin M and immunoglobulin G. Disseminated HSV infection was suspected, and intravenous acyclovir (10 mg/kg every 8 h) was begun. A subsequent direct antigen test of a bulla sample was positive for HSV-2. Moreover, tonsillar and esophageal biopsies revealed viral inclusion bodies. Immunohistochemical staining and a quantitative real-time polymerase chain reaction (PCR) assay confirmed the presence of HSV-2. Disseminated HSV-2 infection with multiple bullae, tonsillitis, esophagitis, and suspected hepatic involvement was diagnosed. After a 2-week course of intravenous acyclovir, his hematological status and liver function normalized, and his cutaneous skin lesions resolved. He was discharged on day 22 in good general health and continued taking oral valacyclovir for viral suppression due to his immunosuppressed status.Disseminated HSV-2 infection should be considered as one of the differential diagnoses in patients with pharyngotonsillitis and impaired liver function of unknown etiology even if there are no genital lesions. Herpesviridae family of viruses, which includes the herpes simplex virus-1 (HSV-1), herpes simplex virus-2 (HSV-2), varicella zoster virus, cytomegalovirus, Epstein-Barr virus, human herpes viruses 6 and 7, and Kaposi’s sarcoma-associated herpesvirus (type 8) [Herpes simplex virus (HSV) is a large, double-stranded DNA virus belonging to the ubiquitous (type 8) .HSV-1 infection usually involves the face and skin “above the waist.” On the other hand, HSV-2 infection usually involves the genitalia and skin “below the waist” in sexually active adolescents and adults [A 46-year-old male was admitted to our hospital with a 1-week history of fever and sore throat. His past medical history included hypereosinophilic syndrome diagnosed at age 45 years. An extensive workup failed to disclose any underlying diseases. The patient had been receiving oral prednisolone (8 mg/day) and azathioprine (100 mg/day) regularly for approximately nine months, and his hematological status was stable. He denied any recent travel, illicit drug use or exposure to arthropods. He is married and reported monogamous sexual activity with his wife.On initial presentation, his temperature was 39 °C, his blood pressure was 145/103 mmHg, his pulse rate was 111 beats per minute and regular, his respiratory rate was 20 breaths per minute, and his percutaneous oxygen saturation was 99% on ambient air. Physical examination revealed throat congestion, bilaterally enlarged tonsils with exudates, tender cervical lymphadenopathy in the left posterior triangle, and mild epigastric tenderness. An examination of the genital area revealed no abnormal findings such as an ulcer or blisters, and the remainder of the examination was unremarkable.4 /μL), elevated liver enzyme levels (aspartate aminotransferase (AST): 1558 U/L; alanine aminotransferase (ALT): 1007 U/L; lactate dehydrogenase (LDH): 2688 U/L; alkaline phosphatase: 265 U/L; total bilirubin: 0.9 mg/dL), and hyperferritinemia . Serologic tests for hepatitis A, B, and C viruses and human immunodeficiency virus (HIV) were negative. Serum antibodies confirmed past infection by the Epstein-Barr virus, cytomegalovirus, and varicella zoster virus. Computed tomography demonstrated prominent hepatosplenomegaly and multiple low-density areas in the liver. An upper gastrointestinal endoscopy revealed multiple vitiligo lesions in the esophagus. A bone marrow smear showed hypocellular marrow with histiocytes (3.0%) and hemophagocytosis  using the search terms, “disseminated,” “herpes simplex virus,” and “type 2,” we identified 15 case reports of adult disseminated HSV-2 infection in the English-language literature since 2000 [In our search of the PubMed database  for HIV infection reactivated HSV-2, possibly via immune reconstitution inflammatory syndrome (IRIS) . IRIS deHerpesviridae account for 62% of cases of virus-associated HLH including Epstein-Barr virus (43%), cytomegalovirus (9%), and other herpes viruses (10%) [In the present case, the disseminated HSV-2 infection triggered the development of HLH. Ramos-Casals M et al. reported that es (10%) . There aes (10%) , 13, 20.es (10%) . On the The present study reported the first case of disseminated HSV-2 infection associated with pharyngotonsilitis, esophagitis, and HLH without genital involvement in an immunocompromised host. The patient recovered after receiving intravenous acyclovir and methylprednisolone pulse therapy. HSV-2 pharyngotonsillitis is rare and difficult to diagnose, especially if genital lesions are not present. This report emphasizes and promotes awareness of the importance of the early recognition and appropriate clinical management of HSV-2 infection. Additional data from similar cases and further studies to characterize HSV-2 infection and HLH in adults, particularly those who are immunocompromised, are needed.


The translation of genome sequencing into routine health care has been slow, partly because of concerns about affordability. The aspirational cost of sequencing a genome is $1000, but there is little evidence to support this estimate. We estimate the cost of using genome sequencing in routine clinical care in patients with cancer or rare diseases.We performed a microcosting study of Illumina-based genome sequencing in a UK National Health Service laboratory processing 399 samples/year. Cost data were collected for all steps in the sequencing pathway, including bioinformatics analysis and reporting of results. Sensitivity analysis identified key cost drivers.Genome sequencing costs £6841 per cancer case (comprising matched tumor and germline samples) and £7050 per rare disease case (three samples). The consumables used during sequencing are the most expensive component of testing . Equipment costs are higher for rare disease cases, whereas consumable and staff costs are slightly higher for cancer cases.The cost of genome sequencing is underestimated if only sequencing costs are considered, and likely surpasses $1000/genome in a single laboratory. This aspirational sequencing cost will likely only be achieved if consumable costs are considerably reduced and sequencing is performed at scale. However, this expectation likely only reflects the consumables component and does not consider the overall costs of the sequencing process, which include sample processing (including library preparation and sequencing), bioinformatic data processing and analysis, interpretation and reporting of sequencing results, and data storage. Clinical interpretation in particular can be lengthy and costly. This dichotomy has led to descriptions of “the $1000 genome and the $100,000 analysis.”2Next-generation sequencing (NGS) technologies provide high-throughput simultaneous testing of multiple genes and allow either the whole genome or parts of it (via exome sequencing or targeted panels) to be sequenced in hours, at great depth and increasing sensitivity. These technologies have been in use, largely on a research basis, since 2008. Prior to 2008, the use of Sanger-based technologies meant that resequencing was substantially more expensive—for example, a human genome cost an estimated $20–25 million in 2006.3 Few cost analyses presented data transparently, and many publications did not state which components were included in cost estimates. In addition, resource use and unit costs were rarely reported in a disaggregated manner, and many studies did not calculate the actual cost of exome or genome sequencing, instead using prices charged by commercial operators. Furthermore, few studies have accounted for the number of samples that realistically must be sequenced to achieve a diagnostic result; at least two samples are required for cancer cases and three are often required for rare disease cases, with the proband and both parents often sequenced as a trio.To ensure that NGS technologies are not merely an expensive addition to patient care, demand is increasing for accurate figures on the “complete” costs of the entire sequencing process. There is considerable variation in the costs reported in academic papers and the media. A review of economic evaluations of exome and genome sequencing in 2018 reported that cost estimates for a single test ranged from £382 ($555) to £3592 ($5169) for exome sequencing and from £1312 ($1906) to £17,243  for genome sequencing.This paper reports a microcosting study that we undertook to provide comprehensive and detailed estimates of the complete costs of using genome sequencing to identify pathogenic variants. This study was undertaken in the context of routine care for patients with cancer or rare diseases in one National Health Service (NHS) laboratory in the United Kingdom (UK).4 and was carried out from June 2016 to December 2017.We undertook a detailed microcosting study of clinical-grade genome sequencing using the Illumina HiSeq 4000 in the Oxford Molecular Diagnostics Center (OMDC), an accredited NHS laboratory in the UK. Although clinical grade, genome sequencing is not yet routine in NHS clinical practice and the sequencing described herein was funded as part of a translational research grant. Microcosting is a highly detailed health economic costing approach in which all of the underlying resources required for an intervention or activity, such as equipment, consumables, and staff time, are identified, and then unit costs are attached to this resource use to generate an overall cost. This microcosting study was undertaken in line with the methods outlined by Drummond et al.,5Patients with rare diseases or cancers suitable for genome sequencing were referred for sequencing via participating clinicians. Rare disease referrals were triaged at a Genomic Medicine Multi-Disciplinary Team (MDT) meeting, based on whether prior genetic testing for known genes (panel tests and arrays) had been carried out . Pediatric and adult patients with a broad spectrum of rare disease  were recruited. Family trios comprising the proband and both parents were recruited where possible, since knowledge of the genetic variants and affection status of parents allows many variants to be eliminated in the filtering process. Based on our experience, this greatly improves the success rate for identifying pathogenic variants while reducing analysis time.Cancer cases were also reviewed at an MDT meeting for suitability for genome sequencing. Patients with a broad spectrum of cancer types were recruited, including breast, colorectal, prostate, and endometrial cancers. Tumor and germline DNA samples were obtained for each patient; however, tumor sequencing was only undertaken if the pathologist’s report indicated that >40% of the tissue was tumor. Following sequencing, somatic variants were identified by subtraction of the germline variants from those in the tumor.We first determined the precise testing pathway for genome sequencing in the OMDC laboratory. The standard operating procedures for the HiSeq 4000  were used to develop costing questionnaires to collect resource use information. These questionnaires were completed by staff at the Oxford Biomedical Research Center . All steps in the genome sequencing pathway, from sample reception to data interpretation, reporting, and archiving were considered  guidelines.7 Variants classified as pathogenic, likely pathogenic, and of uncertain significance within the in silico panels were detailed in a clinical report. Secondary findings were also investigated in any patients or participants who had consented to this, by applying a 50-gene in silico panel as recommended by the ACMG.10The bioinformatics phase included both clinical and research bioinformatics. Clinical bioinformatics analysis, which all samples passed through, consisted of a standardized pipeline to identify variants in genes known to be pathogenic for the presenting condition . This provided industry standard encryption, offsite storage at multiple locations, and near disk retrieval speeds.Data were collected on resource use and unit costs for each step in the pathway. This included the average staff time for each activity and salary data, use of equipment  and consumables  and error rates. Resource use data were adjusted to reflect the different requirements of genome sequencing in cancer and rare diseases. In cancer cases, two DNA samples were sequenced—one extracted from the tumor tissue (TT), which required a sequencing depth of at least 75×, and one germline sample at a minimum of 30× depth. This meant that in the sequencing and bioinformatics stages, TT samples required different quantities of consumables compared with germline samples. For rare disease trios, three samples were sequenced (the proband and both parents), each at a minimum of 30× depth.https://www.gov.uk/government/col-lections/exchange-rates-for-customs-and-vat).The resources that were used were then linked to their associated unit costs. Unit cost data was extracted from OMDC purchasing records where possible. If this was not possible, unit costs were obtained from commercial laboratory equipment suppliers. When cost data were only available for a kit as a whole, kit costs were apportioned equally across all items in the kit. Costs that were specified in US dollars were converted to British pounds based on the average exchange rate of $1.23:£1 in November 2016. All comparator costs reported for other studies are also presented in 2016 values (The most expensive item was the HiSeq 4000 sequencing machine (Illumina), which cost £474,373, with an annual maintenance cost of £55,641. This sequencing system requires two consumable kits (a HiSeq 3000/4000 Sequencing by Synthesis [SBS] Kit costing £4207 and a HiSeq 3000/4000 Paired End [PE] Cluster Kit costing £2597), with half of each kit required per case.Sequencing data and results were assumed to be archived in variant call format (VCF) files for five years at the end of the genome sequencing pathway. Rare disease samples were assumed to require 0.4 GB of storage space each year . For cancer, germline samples were assumed to require 0.4 GB per year and subtracted tumor samples were assumed to require 1 GB per year.Information on staff salaries was extracted from national salary scales from March 2016 for NHS staff and from University of Oxford salary scales for the year 2016 for university staff  was excluded.11 as well as the inclusion of VAT calculated at 20%. Other parameters that were varied included the overhead rate, all inputs to staff cost calculations, the length of data archiving, kit costs, and the cost of the HiSeq 4000, whether or not rare disease cases required research bioinformatics, the size of rare disease cases, and error rates. We also evaluated the impact of archiving binary alignment map (BAM) files alongside VCF files. For both cancer and rare disease cases we assumed that this would require 150 GB of storage per year. Finally, annual throughput was varied between 100 and 2000 samples, and we tested the joint impact of both a reduction in consumable costs and an increase in sample throughput. Annual throughput of 1000 samples was tested in this analysis as this was the projected annual capacity of the HiSeq 4000 sequencing machine in this OMDC setting. The effect of increasing throughput to 2000 samples was also investigated to assess whether costs plateau when volume increases.Sensitivity analysis was performed to explore the structural and methodological uncertainty in this microcosting study and to assess how changes in key variables would affect test costs. This ensures that our results are more generalizable to other settings. Discount rates of 1.5% and 5% were evaluated, as per UK recommendations,Table Table The use of genome sequencing as a diagnostic tool is £209 more expensive in rare diseases cases compared with cancer cases. Equipment costs are higher for rare disease cases than for cancer cases, reflecting differences in the number of samples sequenced per case and sequencing depth. Consumable and staff costs are, however, slightly higher for cancer cases. Staff costs are higher during the clinical interpretation stage for testing in cancer  due to differences in clinical scientist time during this stage , which reflects the fact that investigation of variants in previously undescribed genes is not included in the base case cost for rare disease cases . The difference in consumable costs is driven by lower software costs for rare disease cases during the bioinformatics stage , again because consumables for investigation of variants in previously undescribed genes are not included in the base case cost for rare disease cases. Sequencing consumable costs were identical for rare disease and cancer cases because the same number of clustering and sequencing kits were used to sequence a rare disease trio (three samples at minimum 30× coverage) as a cancer case (with tumor and germline at minimum coverage of 75× and 30× respectively). Although this introduced some wastage for rare disease cases, this approach was necessary to achieve the rapid turnaround required for clinical cases. Such costs could be reduced in a high-volume facility where samples could be more effectively batched. Although equipment costs are £348 higher for rare disease cases (as 1.5× as many samples are required), this is balanced out by higher consumable and staff costs for cancer testing.Figure The results of the sensitivity analysis are presented in Table Most changes in staff-related variables had a negligible effect on the cost of testing, as did changes in the discount rate. Including the costs of research bioinformatics for rare disease cases has a significant impact on the cost of testing. Both standard cases and cases of intermediate difficulty increase test costs by 8%, to £7594 and £7583 per case, respectively (intermediate cases require more staff time than standard cases—300 minutes per case versus 120 minutes—but require software with lower licensing costs). Varying the PE and SBS consumables kit costs by 50% above and below the base case values impacted on both test costs, but did not change the cost difference between the two testing applications. Varying the cost of the sequencer by 50% above and below the base case value changed both test costs by a negligible amount, primarily because half of the sequencer cost per cancer or rare disease case (£279 for cancer cases and £418 for rare disease cases) is the annual maintenance cost for the sequencer, not the cost of the sequencer itself. Archiving BAM files as well as VCF files increased the cost per case by just 2% for both cancer and rare disease cases.The full results of the joint sensitivity analysis on throughput and consumable costs are presented in Figures This paper reports the first detailed cost analysis for the complete genome sequencing process for both cancer and rare disease cases in a single center within the UK NHS. Our results show that the costs of using genome sequencing as a diagnostic tool are similar for both cancer (£6841 per case or £3420 on average per genome) and rare disease cases (£7050 per trio or £2350 per genome). Using a medium throughput sequencer (the HiSeq 4000), the key cost drivers for cancer cases are sequencing  and, to a lesser extent, bioinformatics and reporting (£933). The key cost drivers for rare disease cases are the same: sequencing (£4659 per case) and bioinformatics and reporting (£677).5 Hence our policy has been to recruit rare disease trios. In this regard, it should be noted that the 100,000 Genomes Project in the UK has also elected to preferentially recruit trios for its rare disease cases. While proband-only testing is increasingly being used in some countries as an in silico panel that can be flexibly applied to multiple disorders, this restricts the potential for molecular diagnoses to those in previously identified disease genes, and makes identification of novel genes or genotype/phenotype associations unlikely.Although our cost estimates are similar for cancer and rare disease cases, there are some small differences in the types of costs incurred in each context based on the fact that a cancer case is considered here as two samples (tumor and germline) and a rare disease case as a trio (parents and proband). While two samples are a necessity for cancer cases to identify pathogenic variants in the tumor, a rare disease case could yield diagnostic results from the proband’s genome alone. However, based on our experience this considerably reduces the success rate for identifying pathogenic variants while also contributing substantially to analysis time and costs.3 A Canadian microcosting study of genome sequencing in autism spectrum disorder cases estimated the cost of testing to be £3016 (US $4346) for a single patient sample using the Illumina HiSeq 2500 .12 In this study, follow-up with Sanger sequencing for the proband and two parents was assumed to take place in 50% of cases, costing £98 (US $141) per sample . A similar estimate was reported by Christensen et al. in 2018. This US study reported the results of randomized controlled trials of genome sequencing in two settings, estimating the costs of testing per patient to be £3673 (US $5338) in a cardiology setting and £3641 (US $5291) in a primary care setting.13 These figures included the cost of confirmatory Sanger sequencing in the proband, which ranged from £420 to £435 (US $611 to $632) per patient. Another Canadian study by Weymann et al. reported a much higher cost for the Illumina HiSeq 2500. This study estimated the costs of using genome sequencing and subsequent analysis to guide treatment decisions in advanced cancers, reporting a cost per patient of £10,820  but it additionally included biopsy and tissue sample processing, RNASeq, panel testing, and experimental verification.14 A final Canadian microcosting study of genome sequencing in autism spectrum disorder estimated the cost of testing to be £1168 (US $1696) per genome when a trio was tested using the Illumina HiSeq X platform .15 The annual cost of follow-up using Sanger sequencing, fluorescence in situ hybridization (FISH), and quantitative polymerase chain reaction (qPCR) tests in probands and their parents ranged from £46 to £51 ($66 to $75).Our cost estimates are generally lower than those reported in previous studies when considered on a per patient genome basis .16 However, there are no national tariffs for genome sequencing.17 These cost estimates will help to fill that gap in the literature.Our study contributes to this evidence base by presenting highly detailed cost estimates in two clinical contexts, providing full information on which components were included in these estimates. We also calculate the actual cost of genome sequencing, in contrast to previous studies that have presented a commercial price. Going forward, these cost estimates will be a useful input into economic modeling studies that underpin health technology assessments of genome sequencing, and will help to inform budgetary calculations at both the local and national level. These estimates will also be informative when determining the optimal positioning of genome sequencing in diagnostic pathways. This is a particular concern in the context of rare diseases, as these disorders are commonly characterized by diagnostic odysseys. Finally, in the UK standardized unit costs exist for many elements of health-care resource use in the form of NHS reference costs, for example.Several limitations of our study should, however, be noted. First, costs were calculated for the Illumina HiSeq 4000. It is therefore unclear how our estimates relate to the costs associated with alternative sequencing platforms, particularly those with higher throughput. However, as our costs are provided at a highly detailed level this should facilitate comparisons with other sequencing platforms. Second, these costs were estimated in a UK setting, so may not be directly generalizable to other country settings. However, these estimates may be broadly indicative of the magnitude of these test costs in other settings. Furthermore, the sequencing consumables unit costs are likely to be similar between countries; the discounts that are provided for bulk orders are likely to be a more significant factor in influencing cost. Studies that estimate genome sequencing costs at a similarly detailed level in other countries are urgently required, as well as studies that consider whether these cost estimates might vary in cancer or rare disease subgroups. Third, our cost estimates are based on unit costs from 2016. These unit costs will likely be different in 2019, however it was not possible to source new quotes from suppliers to update these costs. Fourth, we included the cost of overheads in our estimates by assuming that these costs were equal to 20% of the total cost of testing. This approach implies that the overheads that are attributable to sequencing are proportional to the overall cost of sequencing. Given that consumables accounted for a large proportion of sequencing costs in both cancer and rare disease cases, this assumption may not hold. Fifth, our analysis assumed a minimum sequencing depth of 75× for tumor samples, 30× for germline samples, and 30× for probands and parents sequenced as part of a rare disease trio. It was not possible to vary these assumptions in sensitivity analysis to estimate to what extent the cost of sequencing increases if sequencing depth also increases. Finally, the moratorium on the use of non-UK cloud-based storage for NHS patient data during the study period forced us to use a UK-based archiving service (Arkivum). As there are now other cloud storage options in the UK, it is possible that storage costs will be lower. However, data archiving was not a key cost driver for either application of genome sequencing.18 The combination of bioinformatics and reporting was the second most expensive step in the testing pathway, although these costs were much lower than those associated with the sequencing step. Further automation of the bioinformatics pipeline and streamlining of clinical reporting may reduce these costs somewhat going forward, at both the single center and national scale. However, bioinformatics and reporting costs as a proportion of overall sequencing costs may increase in the short to medium term, if automation and a reduction in sequencing consumable costs due to sequencing at scale happen at the same time. Finally, our analysis did not consider the cost of any actions taken on the basis of genome sequencing test results, such as changes in patient clinical management. Future work should explore and quantify these potentially significant downstream costs.Our results have implications for policymakers, both in the UK and in other countries. Our microcosting study was undertaken in a single laboratory processing ~400 genome sequencing samples per year, so explored the impact on cost of varying sample throughput in a single center. Lower costs are, however, likely to be achievable if genome sequencing is rolled out as a routine test in clinical practice at the national level, realizing economics of scale in terms of equipment costs and providing access to bulk discounts on consumable costs. Indeed, the “$1000 genome” may already be achievable in such a system if only sequencing consumable costs are considered. Future work evaluating the costs of sequencing at scale  would help to inform decision-making at the national level regarding the appropriate configuration of sequencing services. This work is particularly relevant given the recent completion of the 100,000 Genomes Project in England and the launch of a new Genomic Medicine Service in the UK NHS.Our findings suggest that the costs of genome sequencing and clinical analysis of a cancer case or rare disease trio are £6841 (£3420 per genome) and £7050 (£2350 per genome), respectively. The costs of sequencing are yet to meet the desired $1000 per genome figure when testing is performed on relatively small numbers of patients with cancer or a rare disease in a single center with modest throughput. Sensitivity analyses indicate that high throughput—commensurate with a national-scale facility—combined with bulk discounts on consumable costs will likely have the greatest impact on the overall cost of sequencing going forward. This will be an important consideration for policymakers in this arena.Supplementary Materials