MC1R) gene locus, which controls the production of black and red pigments that determine basic colours. Other interacting genes also influence cattle phenotypes. In view of the complexity of the genetic factors influencing cattle coat colour, this study investigated the genetic basis of different coat colours of Central European cattle breeds.Animal coat colour has been the subject of numerous studies for many years. While most phenotypic features of animals result from the interaction of genetic and environmental factors, coat colour is considered to be almost exclusively genetically determined. Differences in coat colour underscore the distinct character of a given breed or group of animals, testify to its uniqueness, and sometimes serve as selection criteria. Observations of changes in cattle coat colour are an important source of information used to track domestication processes and discover how animals were selected for breeding. One of the genes responsible for variation in coat colour is the melanocortin receptor (MC1R) plays an important role. The aim of the study was to characterize genetic variation in Central European cattle breeds based on polymorphism of the MC1R gene and factors determining their coat colour. The study was conducted on 290 individuals of the following breeds: Polish White-Backed (PW), Lithuanian White-Backed (LW), Polish Red (PR), Lithuanian Red (LR), Carpathian Brown (CB), Ukrainian Grey (UG), and Slovak Pinzgau (SP). Polymorphism at the MC1R gene locus was analysed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) using two restriction enzymes: Cfr10I and SsiI. The proportions of alleles and genotypes in the MC1R locus indicates a strong relationship between polymorphism and the coat colour of cattle: The ED allele proved to be characteristic for the breeds with a white-backed coat (PW and LW), while the dominant allele in the red breeds (PR and LR) was E+. It is noteworthy that coat colour in the SP population was determined only by the recessive e allele, which resulted in the formation of a separate clade in the phylogenetic tree.There are many genes responsible for the appearance of different coat colours, among which the melanocortin 1 receptor gene ( A characteristic feature of most phenotypic traits of animals is that they are the result of the interaction of genetic and environmental factors. The coat colour of mammals depends primarily on the proportions of individual pigments: Pheomelanin (red pigment) and eumelanin (determining brown or black colour) ,2. The pMC1R), which binds to melanocyte-stimulating hormone (α-MSH), which induces eumelanin synthesis [ASIP , by acting antagonistically to the E locus causes blockage of α-MSH receptor interaction, resulting in the production of pheomelanin instead of eumelanin [The E locus encodes the melanocortin 1 receptor . Hair bulbs were used as biological material.Polish White-Backed cattle (PW) were raised in eastern Poland, in a temperate climate. The animals had black sides and a continuous stripe running from the muzzle, which was dark, along the entire body . A totalLithuanian White-Backed cattle (LW) were raised in central Lithuania, in a temperate climate. The animals had black sides and a continuous stripe running from the muzzle, which was dark, along the entire body . A totalPolish Red cattle (PR) were raised in Southern Poland, in a temperate climate. The animals had a solid red coat and a dark muzzle . A totalLithuanian Red cattle (LR) were present in central Lithuania, in a temperate climate. The animals had a solid red coat and a dark muzzle . A totalCarpathian Brown cattle (CB) were raised in Western Ukraine (Zakarpattia), in a transitional temperate climate . The animals had a solid coat colour, from dark brown to light grey, with a characteristic white ring around the nostrils . A totalUkrainian Grey cattle (UG) were raised in Eastern Ukraine (steppe region), in a continental climate (hot summer and freezing winter). The animals had a solid grey or light grey coat colour, often with a darker shade on the neck, chest or abdomen . A totalSlovak Pinzgau cattle (SP) were raised in Northern Slovakia, in a cool temperate climate. The animals had a red coat with a white stripe running from the withers and crossing the tail, udder and underbelly, and a solid red head . A totalMC1R locus in the seven Central European cattle breeds.The genomic DNA of cattle was isolated using a ready-made commercial kit for nucleic acid isolation from biological traces  according to the procedure provided by the manufacturer. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was used to analyse polymorphisms at the MC1R gene locus. The first primer pair was 5′-GTGCCTGGAGGTGTCCATC-3′ (forward primer) and 5′-GAAGTTCTTGAAGATGCAGCC-3′ (reverse primer), and the second primer pair was 5′-CAAGAACCGCAACCTGCACT-3′ (forward primer) and 5′-GCCTGGGTGGCCAGGACA-3′ (reverse primer). PCR-RFLP was used to amplify the MC1R gene fragment.Primers given by Klungland et al.  were useCfr10I and SsiI. The digestion reaction was carried out at 37 °C.The amplified fragments were digested with Thermo Scientific FastDigest™  restriction enzymes The fragments obtained from the PCR-RFLP reaction were subjected to electrophoretic separation on a 2% agarose gel stained with 0.01% ethidium bromide (EtBr). Molecular size markers  were used to track the electrophoresis process and assess the length of the fragments.O) and genetic distances according to Nei [To visualize the genetic diversity of the analysed cattle breeds, the results were subjected to statistical analysis in POPGENE v. 3.2. statistical software. The following indicators were estimated: Frequency of alleles and genotypes, observed heterozygosity (Hg to Nei . The obtMC1R locus was carried out in two stages. Restriction digestion of the PCR product (739 bp) with the Cfr10I enzyme resulted in genetic variants ED/e and E+/e, where the lengths of the fragments were 739 bp, 531 bp and 208 bp; ee with a length of 739 bp; and ED/ED, ED/E+ and E+/E+, with fragments of 531 bp and 208 bp  enabling survival in tropical conditions.Research by Putra et al.  has demoMC1R locus and the observed variations in coat colour in the Central European cattle breeds may also be due to their geographic distribution. In a study by Niemi et al. [MC1R locus in native Scandinavian breeds, the phenotypes were strongly influenced by the environment of the region where the animals were kept. High proportions of heterozygotes (from 26% to 57%) were observed among individuals of the native Scandinavian breeds, with both uniform and multi-coloured coats. In contrast, the level of heterozygosity at the MC1R gene locus in Western, Central and Southern European breeds was much lower (from 0% to 16%). In our research, the average percentage of heterozygous individuals among all analysed Central European cattle breeds was high, amounting to 53%. The high heterozygosity levels at the MC1R gene locus for the PW, LW, UG and CB breeds are likely due to the population bottleneck effect. Previous studies have shown that the current structure of these cattle populations is due to a sudden decline in the number of individuals and its subsequent restoration from the group of surviving individuals [The estimated frequency of alleles and genotypes at the i et al. , which aividuals ,38.MC1R gene locus) makes it possible to track domestication processes and identify the characteristics of a given breed. The authors’ research on individuals of Chinese cattle breeds offers a new view of evolutionary history and the features of domestication of Chinese cattle. Ludwig et al. [According to Mei et al. , analysig et al.  also stag et al. .MC1R alleles exhibits changes over time that are similar to those found in studies of mitochondrial DNA and Y chromosome haplotypes. At the locus of the MC1R gene as well as the other two markers there was a significant change in genotypes in Scandinavian cattle from the late Iron Age to the Middle Ages, followed by a period of slower change, continuing up to the present day.In addition, a comparative analysis by Niemi et al.  of ancieMC1R gene locus contribute to knowledge of the genetic variation between Central European cattle breeds, their phylogenetic relationships, and the characteristics of a given breed. The proportions of alleles and genotypes at the MC1R locus indicate a strong relationship between polymorphism and the coat colour of cattle. The ED allele was characteristic of the white-backed breeds (PW—0.46 and LW—0.50) but was not present in the other breeds analysed. In the PR and LR breeds, the wild-type allele E+ had the largest share . The alleles were evenly distributed in the CB and UG breeds, and all individuals were heterozygous (E+/e). The e allele occurred in all breeds, with the homozygous e/e genotype found only in the SP breed (100% of the studied population). Given the complexity of the genetic mechanisms responsible for the appearance of varied coat colours, such as the characteristic coat of the White-Backed breeds and the SP breed or the uniform coat colours found in the red breeds and the CB and UG breeds, research should be expanded to include analysis of polymorphism at other loci associated with coat colour in cattle.The results of analyses of polymorphism at the


Patients with multimorbidities have the greatest healthcare needs and generate the highest expenditure in the health system. There is an increasing focus on identifying specific disease combinations for addressing poor outcomes. Existing research has identified a small number of prevalent “clusters” in the general population, but the limited number examined might oversimplify the problem and these may not be the ones associated with important outcomes. Combinations with the highest  secondary care costs may reveal priority targets for intervention or prevention. We aimed to examine the potential of defining multimorbidity clusters for impacting secondary care costs.We used national, Hospital Episode Statistics, data from all hospital admissions in England from 2017/2018 (cohort of over 8 million patients) and defined multimorbidity based on ICD-10 codes for 28 chronic conditions . We identified the combinations of multimorbidity which contributed to the highest total current and previous 5-year costs of secondary care and costs of potentially preventable emergency hospital admissions in aggregate and per patient. We examined the distribution of costs across unique disease combinations to test the potential of the cluster approach for targeting interventions at high costs. We then estimated the overlap between the unique combinations to test potential of the cluster approach for targeting prevention of accumulated disease. We examined variability in the ranks and distributions across age (over/under 65) and deprivation  subgroups and sensitivity to considering a smaller number of diseases.There were 8,440,133 unique patients in our sample, over 4 million (53.1%) were female, and over 3 million (37.7%) were aged over 65 years. No clear “high cost” combinations of multimorbidity emerged as possible targets for intervention. Over 2 million (31.6%) patients had 63,124 unique combinations of multimorbidity, each contributing a small fraction (maximum 3.2%) to current-year or 5-year secondary care costs. Highest total cost combinations tended to have fewer conditions  affecting a relatively large population. This contrasted with the combinations that generated the highest cost for individual patients, which were complex sets of many (6+) conditions affecting fewer persons. However, all combinations containing chronic kidney disease and hypertension, or diabetes and hypertension, made up a significant proportion of total secondary care costs, and all combinations containing chronic heart failure, chronic kidney disease, and hypertension had the highest proportion of preventable emergency admission costs, which might offer priority targets for prevention of disease accumulation. The results varied little between age and deprivation subgroups and sensitivity analyses.Key limitations include availability of data only from hospitals and reliance on hospital coding of health conditions.Our findings indicate that there are no clear multimorbidity combinations for a cluster-targeted intervention approach to reduce secondary care costs. The role of risk-stratification and focus on individual high-cost patients with interventions is particularly questionable for this aim. However, if aetiology is favourable for preventing further disease, the cluster approach might be useful for targeting disease prevention efforts with potential for cost-savings in secondary care. Jonathan Stokes and co-workers explore patterns of multimorbidity and implications for the organization and costs of care. Multimorbidity, the presence of 2 or more chronic conditions in an individual, results in worse outcomes for patients and higher costs to health systems.Because existing interventions show little success, researchers have begun looking for specific combinations, “clusters,” of conditions that might be treated more effectively.Existing analyses have focused on combinations of conditions that are common in the general population, but these may not be the combinations that are associated with the most important outcomes from a health service perspective.We used national hospital data to realign research into multimorbidity by starting with outcomes relevant to the healthcare system, total costs of secondary care, and costs of  emergency admissions.We identified all unique combinations of 28 conditions present in a cohort of over 8 million people admitted to a hospital in England between April 2017 and March 2018.We examined the distribution and top contributors of total costs of these combinations over 1 and 5 years for the whole cohort and the average costs per individual.We additionally highlighted overlaps between unique combinations, which may merit follow-up in terms of development of further disease and costs.We identified over 60,000 unique disease combinations. No combination accounted for more than 3.2% of total costs for patients with multimorbidities. The combinations with the highest average cost per patient were not the same as those with highest total cost.There are no clear discrete disease combinations at which to target interventions, which implies a generalist/multidisciplinary team approach will remain important rather than pathways/guidelines based on a few specific disease clusters.Combinations containing the highest cost patients (the current focus of many interventions) were different to those accounting for the highest total costs, implying the need to develop interventions beyond only high-risk patients.There might be scope to use clusters to understand and develop preventative interventions, but focusing on addressing well-known disease risk factors  with public health/primary care interventions might provide the most efficient route to benefit systems financially and benefit many patients with multimorbidities. There are several outcomes that are important to health systems. However, especially in turbulent economic times, the costs (and cost-effectiveness) of care persistently rank high on the policy agenda. Particularly, the prevention of disease and expanded primary care are increasingly a global policy focus, aiming to reduce overall costs of comparatively expensive secondary care and potentially preventable emergency admissions ,2. ThesePatients with multimorbidities have the most contact with and generate the highest expenditure in the health system . Multimo28 minus 29) >268 million unique combinations to consider.Given this lack of success, emphasis is now moving from simply describing “the problem of multimorbidity,” by counting and documenting the number of conditions, towards potentially more useful stratifications. This has taken the form of a focus on “disease clusters,” examination of specific combinations of cooccurring conditions ,5. But, There are primarily 2 approaches in the current literature for simplifying multimorbidity to a manageable handful of subgroupings: (1) cluster analysis (grouping diseases); and (2) latent factor analysis (grouping patients) . Both meHowever, there is also a danger of oversimplification and impracticality in the output of these analyses. These complex statistical methods involve a number of modelling choices where adjustments to the technical implementation can give very different results . There iMost importantly, not all clusters will be of equal significance in terms of the outcomes that policymakers want to address . ClusterNational hospital data provide the opportunity to realign research into multimorbidity by starting with outcomes of relevance to the healthcare system rather than focusing on common sets observed in the general population. It also allows analysis of all observed disease combinations and their distribution rather than requiring selection of only a handful. We use national, administrative data from all National Health Service (NHS) hospital admissions in England in 2017/2018 to construct a cohort and examine the potential of defining multimorbidity clusters for impacting secondary care costs  via:targeting interventions, examining: (i) if there are observable combinations of conditions contributing significantly to the secondary care costs/ “preventable admission” costs that can be targeted; (ii) if there are combinations of conditions with particularly high costs per patient that can be targeted.targeting prevention of accumulated disease: (iii) combinations of conditions observed cross-sectionally might hide interrelationships that develop in the future through accumulation of further conditions. Examining whether the overlap between the unique combinations suggest conditions which merit follow-up in terms of development of multimorbidity.This study is not based on a specific prospective analysis plan. Analyses were planned in author discussions in January 2020. The study was reported in line with the REporting of studies Conducted using Observational Routinely-collected Data (RECORD) guidelines  data, an individual-level record of all contacts with NHS hospitals in England, from financial year, April to March, 2009/2010 to 2017/2018. Conditions are recorded using International Classification of Diseases  coding. However, multiple comorbidities are only recorded in admission records, not visits to outpatient departments nor emergency department attendances. We restrict our sample to any patient with at least 1 elective or emergency admission in the most recent year, 2017/2018, ensuring the possibility of multimorbidity being recorded. We exclude maternity or other admission types, such as from high-security psychiatric hospitals see . We recoWe address any potential underrecording by using all previous data available for an individual patient, from 2009/2010, to backfill any missing diagnoses. Whenever a relevant ICD-10 code (one of the 28 chronic conditions) is observed, the condition “switches on” and is then carried forward over subsequent years. The final multimorbidity count for each patient, therefore, encompasses all accumulated conditions over the 2009/2010 to 2017/2018 period.Our outcomes of interest are: (1) total costs of secondary care; and (2) costs of potentially preventable emergency admissions. We apply the national tariff costs to each of the inpatient, outpatient, and emergency department visits , and sumWe first describe the sample, comparing across number of chronic conditions to exemplify the outcomes across counts and then by each individual condition recorded. We then aggregate the patient level dataset to the level of each unique combination of multimorbid conditions.We rank the combinations on each outcome  and then examine the distribution and focus in more detail on the top 10 unique combinations that attract the highest hospital costs. This is done overall and separately for costs per patient by dividing by the count of patients contributing to that cost.We examine the number of overlapping combinations also containing the conditions in the top 10 ranked and present a total cost for all related combinations using a bubble plot, a scatter plot allowing a third dimension represented by the size of the circle. We weight the circle size by proportion of potentially preventable (ACSC) costs.To examine heterogeneity of the findings, we repeat the above stratifying by age (over/under 65) and area deprivation  . We focuFollowing peer-review feedback, we were asked to explore the effects of dropping hypertension as a condition, as arguably it might be considered a symptom or risk-factor, and to consider the effects of only including a smaller number of conditions on the interpretation of results. We added these as sensitivity analyses, rerunning the main analysis exploring the distribution of costs and top 10 conditions contributing to total secondary care costs for all unique: (1) combinations of 27 conditions, excluding hypertension from our original list; and (2) combinations of 15 conditions, the 15 conditions with explicit NHS guidance on comorbidity recording  . Over 4 Overall, patients with no chronic conditions (42.4% of the sample) contributed to 23.3% of the total secondary care costs, patients with 1 chronic condition (25.8% of the sample) to 21.4% of the total costs, patients with multimorbidities (31.8% of the sample) to 55.3% of the total costs.We found 63,124 unique combinations of conditions for patients with multimorbidities in 2017/2018 .Only 7 of the 63,124 unique combinations of co-conditions contribute any more than 1% of total cost of secondary care for patients with multimorbidities, with a maximum of approximately 3.2% in any 1 unique set . Only 2 The combinations with the highest total costs of secondary care in 2017/2018 are the same as those with the highest cost over 5 years. There is also a lot of overlap with the rank of combinations with the highest potentially preventable (ACSC) costs. Hypertension is common to all of the highest cost combinations. The majority of combinations are dyads (with 2 triads at ranks 4 and 9).This is in sharp contrast to the highest cost combinations for any individual patient, combinations which have many conditions  but only affect 1 or 2 patients overall .As expected, the over 65s and most deprived subgroups exhibited the most complexity, a larger number of unique combinations of conditions compared to under 65s and the wealthiest. However, the distributions and top ranked combinations of conditions contributing to each outcome were highly consistent across subgroups. The most deprived and under 65s also featured combinations including depression and alcohol misuse in the top 10 see .Both sensitivity analyses gave broadly the same results as above, no clear cost driving combinations, and combinations with highest cost those with fewer conditions but affecting a larger proportion of the population. When dropping hypertension, the highest cost combination was chronic kidney disease and diabetes, contributing to 2.4% of total secondary care costs for patients with multimorbidities, 1.1% of costs for all patients see . When anWe analysed a sample of over 8 million patients with any inpatient admission in 2017/2018 using national data in England. There are no clear multimorbidity combinations for a targeted cluster intervention approach to reduce secondary care costs significantly for the health system. Aiming interventions at the highest cost patients alone appears to be particularly questionable, since complex combinations which were highest cost for individual patients were negligible in terms of total system spend. However, all overlapping combinations containing chronic kidney disease + hypertension, or diabetes + hypertension, made up a significant proportion of total secondary care costs for patients with multimorbidities. Combinations containing chronic heart failure + chronic kidney disease + hypertension had the highest proportion of total cost that is from potentially preventable emergency admissions, which might offer relative priority targets if accumulation is preventable after further examination of aetiology.Early evidence has shown that there is a curvilinear, near-exponential, relationship between disease count and costs , and mulSimilar to previous research examining potential to reduce costs of secondary care ,32, our The most recent latent class analysis of multimorbidity clusters in England analysed a sample of 113,211 patients with multimorbidities in primary care. The study identified 20 patient clusters across 4 age strata, calculating mean number of hospitalisations for patients in each cluster. The 2 highest utilising clusters were described according to the 3 conditions estimated to be most distinctive in the cluster: (1) chronic pain (81% of patients in cluster), coronary heart disease (53%), and depression (45%), with an average of 1.6 hospital spells per year; (2) coronary heart disease (61%), atrial fibrillation (53%), and chronic heart failure (49%), with an average of 1.5 hospital spells per year. Over 70% of patients in each of these clusters also had hypertension . We builThe study incorporates data from a large national cohort of over 8 million hospitalised patients, all admitted patients in England in 2017/2018, and examines costs of hospital utilisation by all observed combinations of 28 common chronic conditions.However, we are only able to analyse a population with a hospital admission, which is likely to be different than a multimorbid population receiving other care services. Nevertheless, this is the costliest care setting and likely to contain the most complex patients. It also includes elective, including day cases, as well as emergency admissions totalling 15% of the total population of England. Accordingly, this would appear to be the most relevant population for our research question. We also observed only a fraction of the 268 million possible condition combinations, so there are likely to be many other drivers of costs in other patients in other years. However, we do not expect the distribution of costs or costliest combinations to change drastically with the inclusion of more rare combinations.Without data from other settings, we are also only able to record costs in the hospital setting. Costs in other settings are likely to be highly relevant, particularly social care, primary care, and care home (long-term care) costs, and condition combinations are likely to exhibit different cost structures across different settings. However, arguably we are focusing on the most relevant setting in terms of NHS spending, since social care and care homes are largely paid privately by individuals with a proportion paid publicly by Local Authorities, and primary care costs on a predominantly per capita basis.Unfortunately, there is no commonly agreed definition or list of ICD-10 codes for multimorbidity research , but we The definition of potentially preventable (ACSC) emergency admissions is not perfect, either. Eight of the 28 conditions we count overlap directly with the list of ACSC admissions meaning people with combinations containing these conditions have a higher probability of having an ACSC admission. However, this definition of potentially preventable admission is widely used and comparable with other studies.Our results indicate no clear target combination(s) of diseases for intervention in terms of impacts on secondary care costs. The plurality likely indicates that a generalist or multidisciplinary team approach to management will remain important rather than pathways/guidelines based on specific clusters.We identified large differences in combinations contributing to high costs for the system (whole population) compared to high costs for an individual patient. Many of the current models of care attempt to target high-cost individuals with risk stratification tools and case management . DevelopThe individual conditions contributing to the top costs overall are already known to be linked to risk factors, such as obesity, diet, exercise, and deprivation ,36. FocuExamining accumulation of conditions and ordering will be important in future studies, preferably using primary care or linked data. Examining the costs for combinations of multimorbidity across other settings would also be valuable and possible with other data sources. The effects of current payment tariffs on multimorbidity costs and differences across condition combinations also merits further work.There are no clear multimorbidity combinations for a cluster-targeted intervention approach to reduce overall secondary care costs. The role of risk stratification and focus on individual high-cost patients with interventions is particularly questionable for this aim. However, if aetiology is favourable for preventing further disease, the cluster approach might be useful for targeting disease prevention efforts with potential for cost-savings in secondary care. Given the individual conditions, a focus on reducing well-established risk factors in the general population is also likely to be beneficial for many patients with multimorbidities and might be more cost-effective.S1 AppendixChecklist of items, extended from the STROBE statement, that should be reported in observational studies using routinely collected health data.(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.S3 Appendix(DOCX)Click here for additional data file.S4 Appendix(DOCX)Click here for additional data file.S5 Appendix(DOCX)Click here for additional data file.S6 Appendix(DOCX)Click here for additional data file.S7 Appendix(DOCX)Click here for additional data file.S8 Appendix(DOCX)Click here for additional data file. 3 Sep 2020Dear Dr Stokes, Thank you for submitting your manuscript entitled "Multimorbidity combinations, costs of hospital care and potentially preventable emergency admissions: Cross-sectional analysis of a national dataset of over 8 million people" for consideration by PLOS Medicine.Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.Please re-submit your manuscript within two working days, i.e. by .https://www.editorialmanager.com/pmedicineLogin to Editorial Manager here: Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review. plosmedicine@plos.org if you have any queries relating to your submission.Feel free to email us at Kind regards,Helen Howard, for Clare Stone PhD Acting Editor-in-ChiefPLOS Medicine plosmedicine.org 30 Sep 2020Dear Dr. Stokes,Thank you very much for submitting your manuscript "Multimorbidity combinations, costs of hospital care and potentially preventable emergency admissions: Cross-sectional analysis of a national dataset of over 8 million people" (PMEDICINE-D-20-04285R1) for consideration at PLOS Medicine. Your paper was evaluated by a senior editor and discussed among all the editors here. It was also evaluated by three independent reviewers, including a statistical reviewer (r#1). 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For instructions see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it. Please ensure that the paper adheres to the PLOS Data Availability Policy  of the study's methodology.*Please clarify if the analytical approach reported here corresponds to one laid out in a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.a) If a prospective analysis plan  was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript. b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place. c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.https://www.equator-network.org/reporting-guidelines/record/) may be most appropriate here, designed to support reporting of studies done using routinely-collected health data. If using this guideline please note that in the methods section and include a completed checklist as a supporting information file . *It would be helpful to use an appropriate reporting guideline to support reporting of the study, and perhaps also to trigger filling in some of the key details noted by reviewers (below). The RECORD guideline . But this is a very interesting problem, statistically speaking. As the authors note, with so many conditions, there are millions and millions of possible combinations, far more than even the large number of subjects. The authors look at every combination that appeared in the data, no matter how complex. This runs the risk of overfitting the data. Although the authors did not do regression or something similar, they did look at the cost per patient of some very complex combinations. But it seems likely that many of the combinations that were not observed would have very high costs, too. I think a couple of additional analyses would be very useful.First, what if all the costs per patient were randomly assigned in some fashion? Second, the authors could look at all unique dyads and triads of conditions. With 28 conditions there are 28*27/2 = 378 dyads and 9828 triads. This suits the sample size. Third, perhaps some of the conditions could be combined? This is more a medical question than a statistical one, so I can't really suggest combinations. But, as an educated layperson, it seems to me that there should be some combination possible. With 10 "meta-conditions" there would be 2^10 -11 - 1013 combinations and this is much more manageable. Even 15 conditions wouldn't be too bad. Peter Flom-----------------------------------------------------------Reviewer #2: REVIEW of "Multimorbidity combinations, costs of hospital care and potentially preventable emergency admissions: Cross-sectional analysis of a national dataset of over 8 million people".The article provides interesting insights into the usefulness, limits, and potential of the cluster analysis approach to healthcare usage, has a very large sample. However, the paper need further improvement efforts to be published:-TitlePlease state that your study's population is not general national population, but hospitalized patients population-MethodsPlease give some context information about UK NHS If you collected outcome into five years, this is not a cross sectional design but a retrospective cohort designPlease explain how do you count for the five years costs and which time period do you analyze.Please provide a unique subheading "statistical analysis" grouping the actual subheadings "analysis", "targeting intervention", "targeting prevention"….First phrase of methods could be moved into the discussion.The part of analyses, which you define as sensitivity analyses, could be better defined as analyses of subgroups, please revised in the text-ResultsThere's no mentioning of the prevalence of the most common conditions among your population sample. I suggest you to provide a table one with the description of sample characteristics and the prevalence of the most common conditions among your sample (regardless of combinations) to understand how it is representative on entire population. You can create a figure with two columns representing in one the distribution of number of conditions in the sample  and in other the distribution of total costs by condition group (23.3% of cost for no condition)In table 1 is lacking sometime of labels of the outputs (for example age will be reported mean and standard deviation). Moreover, please provide percentages out of bracket and absolute numbers inside. Please provide both currencies reported in the labels. Why there are so many with missing age it not compulsory in health care records to add date of birth? In table 2 do you report only in pounds? You refer the labels of columns as "total costs", but do they refer to "average total costs"?In table 1 and 2 please provide how frequent  are the combinations in the MM patientsPlease give at least one decimal in the figure of percentage reported in the textFig.1: The chart's meaning is a bit difficult to understand at first sight. I suggest you introduce it with a more descriptive title, and elaborate its description a bit further in the main text. Also, I'd move the NB note above, as part of the figure's description. Finally, the chart's key placement is unfortunate: I suggest you move it on the top right corner and frame it, in order for the reader not to confuse it with the actual data displayed by the figure.-DiscussionPlease move the subheading "findings in the context" before "limits"The prescribing expenditure/drugs is not on per capita basisYou state "….so overall system saving are by no means guarantee….." this doubt is verified in the cost effectiveness analyses, which consider costs for each health care level. There are some evidences that found that prevention interventions even if they increase primary care costs but reducing other costs, produce net savings.-Conclusion "There are no clear multimorbidity combinations for a targeted intervention approach to reduce secondary care costs. The role of risk-stratification and focus on individual high-cost patients is particularly questionable." The link between these two phrases isn't very apparent. I suggest you add proper logical connectors, or re-arrange the paragraph to make it sound more consequential.-----------------------------------------------------------Reviewer #3: SummaryThe goal of this paper is to estimate clusters of comorbidities and examine these clusters with high health care costs and avoidable hospitalizations using health administrative databases. The question and approach are interesting; however, there needs to be additional detail added to clarify the methods and outcomes. Comments1. I found the points in the introduction very interesting, but they could be organized so that it is easier for the reader. I would suggest separating the analytic problems form the clinical/intervention gaps. So first clearly explain why the clusters are hard to action from an intervention perspective, and second why the methods used to identify these clusters may be limited. This study then aims to address both those gaps.2. The time element of the questions was not defined. The authors pose very interesting questions regarding the relationship between different conditions with health system outcomes , but when? Within a year? Within 30 days? It wasn't until the midpoint of the methods that it seems like it cost over five years - but unclear form when (from the first diagnosis?). The period for when the hospitalization and costs accrual relative an index date could be more precise and justified.3. Justification for the 28 conditions should be included, more than just the reference to the previous study (i.e. why were those the conditions chosen in the last study).4. The validity of the definitions used for the 28 conditions is unclear. Many of the administrative data definitions should be validated, and those should be mentioned. Although the authors cite diagnostic accuracy, it's unclear if they are referring to only one coding or where in the diagnostic field the code should occur (i.e. most responsible diagnosis or can be a secondary field). Generally, multiple codes/visits are needed, but it was unclear if this was done. 5. Interesting that 15 conditions are given explicit guidance on coding given the NHS guidance. This, however, may introduce bias in that these conditions are more likely to be coded and therefore conditions not on this list may be missed. Can the authors comment on this?6. Hypertension is very dominant, and many argue not a condition but rather a risk factor for a chronic condition (such as diabetes or CHF). If hypertension was not included, the results would indeed be dramatically different. Can the authors comment on if this?7. I agree overall with the interpretation that focusing on specific combinations is not likely to yield a specific path; however, I think that this does not necessarily lead to a generalist approach mentioned in the Discussion. Instead, multidisciplinary health care teams may be an equally beneficial approach - given a different mix of specialists depending on the patient's needs.-----------------------------------------------------------Any attachments provided with reviews can be seen via the following link:[LINK] 21 Oct 2020AttachmentReply to reviewers_vFINAL.docxSubmitted filename: Click here for additional data file. 4 Dec 2020Dear Dr. Stokes,Thank you very much for re-submitting your manuscript "Multimorbidity combinations, costs of hospital care and potentially preventable emergency admissions: Retrospective cohort analysis of a national dataset of over 8 million hospitalised people" (PMEDICINE-D-20-04285R2) for review by PLOS Medicine.I have discussed the paper with my colleagues and the academic editor and it was also seen again by xxx reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:[LINK]plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.Our publications team  and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.Please also check the guidelines for revised papers at plosmedicine@plos.org) if you have any questions or concerns.We expect to receive your revised manuscript within 1 week. Please email us , which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.Please ensure that the paper adheres to the PLOS Data Availability Policy (see plosmedicine@plos.org.Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org. If you have any questions in the meantime, please contact me or the journal staff on We look forward to receiving the revised manuscript by Dec 11 2020 11:59PM. Sincerely,Adya Misra, PhDSenior Editor PLOS Medicineplosmedicine.org------------------------------------------------------------Requests from Editors:Title: please consider revising to “Multimorbidity combinations, costs of hospital care and potentially preventable emergency admissions in England: A cohort study”Abstract- “Multimorbid patients generate the highest expenditure in the health system and..” can we rephrase this to be less stigmatising?Abstract- in the background section, could you please highlight the aim of your studyAbstract-please provide brief participant demographics. In addition "aged 65 years" in the abstract and resultsAbstract “Given the individual conditions, a focus on reducing well establishedrisk factors in the general population is also likely to be beneficial for manymultimorbid patients and might be more cost-effective” was this directly tested? I suggest removing "In this study, we noted that ..." or similar at the start of the final subsection of the abstractPage 5 – “multimorbidity is highly prevalent” could you add brief numbers to put this into context. In addition, I suggest you specify what you mean by “older” and “deprived” here.Could you please rewrite the aims so they are not posed as questionsMethods- perhaps the mention of Beveridge is not needed? Methods- Please specify that specifics ethics approval was not needed for this studyRECORD checklist- please add paragraph numbers and refer to the completed checklist at the start of the methods sectionPlease can you revise all iterations of “multimorbid patients” to “patients with multimorbidities”I suggest revising this sentence on page 15 “Aiming interventions totarget combinations with the highest cost patients alone appears to be particularly questionable” to highlight what your findings suggest.I suggest removing lifestyle on page 16 “lifestyle risk factors, such as obesity, diet, exercise and deprivation”.Please ensure all acronyms are introduced on first viewI suggest adding “strengths” to the limitations section on page 15-16Please remove funding information from the main textPlease remove spaces from the square bracketsComments from Reviewers:Reviewer #1: The authors have addressed my concerns and I now recommend publicationReviewer #2: The paper has been greatly improved and the authors modified the text accordantly to previous remarksReviewer #3: Thank you for taking the time to carefully respond to the comments. The revisions very much improve the clarity of the manuscript and the additional sensitivity analyses - especially those regarding hypertension - do provide a strong assurance of the robustness and significance of the findings.Any attachments provided with reviews can be seen via the following link:[LINK] 5 Jan 2021Dear Dr. Stokes,I am writing concerning your manuscript submitted to PLOS Medicine, entitled “Multimorbidity combinations, costs of hospital care and potentially preventable emergency admissions in England: A cohort study.”We have now completed our final technical checks and have approved your submission for publication. You will shortly receive a letter of formal acceptance from the editor.Kind regards,PLOS Medicine


Culex pipiens mosquitoes. Consequently, an increase in WNV-positive mosquitoes presents a rise of the number of patients, as it has been seen in NYC. We present a 65-year-old patient with WNV infection who presented with Guillain–Barré syndrome (GBS). She had a rapidly progressing ascending paralysis, a common feature in GBS patients but an uncommon presentation in WNV. Realizing WNV as an emerging pathogen along with its uncommon presentation of GBS can be potentially lifesaving if caught at an early stage.West Nile virus (WNV) is an arthropod-borne flavivirus belonging taxonomically to the Japanese encephalitis subgroup. Usually, it is transmitted by  Campylobacter jejuni or Epstein–Barr virus [Guillain–Barré syndrome (GBS) was first described by Guillain, Barré, and Strohribed in 1916 . It manirr virus . Two-thirr virus . There arr virus . The actrr virus , 9. Therrr virus . In addirr virus . The immrr virus , 11. Botrr virus . We reporr virus . As a reA 65-year-old Hispanic woman presented to the emergency department with a 1-week history of intermittent fever. She was seen by an emergency room doctor at that time and was sent home after being diagnosed with pneumonia after a chest radiograph was performed showing a questionable left lung base infiltrate and discharged with levofloxacin and ibuprofen. The patient returned to the emergency department 3 days later because of worsening symptoms described as weakness and inability to walk coupled with fever of 101 degrees Fahrenheit. As per the family, the patient was unusually confused, had difficulty with comprehension, and articulating her needs. In addition to these symptoms, the patient denied any other constitutional symptoms, sick contact, recent travel, or interaction with pets or animals. A thorough physical examination revealed crackles in the right middle and lower lungs; therefore, another chest radiograph was performed showing increased patchy density at the left lung base as compared with prior exam. She was started on 1 gram of ceftriaxone daily and 500 mg of azithromycin daily for treatment of the pneumonia at this time. A series of neurological examinations initially revealed a mildly confused elderly woman who was, nonetheless, oriented to person, time, and place. The patient stated she felt weak but was able to move her legs. She could move her arms but could not raise them more than approximately 10 inches. Consequently, the neurologist ordered the first CT scan which had unremarkable findings. On day 2 of admission, the patient was awake, alert, but oriented only to person. She denied headache but had a rigid neck with her speech becoming increasingly incomprehensible although she was able to follow few simple commands. Neurological exam was significant for asymmetric facial droop and left arm dropping fast than right and drop arm test. Stroke notification was called, and another noncontrast CT of the head was performed which was unremarkable.Legionella sp. which resulted negative. Findings were reported to the Department of Health (DOH), and the case was discussed with hospital medical team and family members.Later on the second day of admission, she became unresponsive and required intubation and was transferred to the Medical Intensive Care Unit (MICU). She only responded to painful stimuli by moving all extremities. For the next two days, the neurological examination showed a rapidly progressing ascending paralysis with decreased deep tendon reflexes of the upper and lower extremities. During her stay in the MICU, subsequent chest radiographs and respiratory, blood, and urine cultures were acquired, and a lumbar puncture was performed. She was started on treatment for suspected bacterial and viral meningitis with azithromycin, ampicillin, ceftriaxone, and vancomycin while awaiting test results. Acyclovir was also administered due to suspicion of a herpes simplex encephalitis. Dexamethasone and IViG were administered for suspicion of acute disseminated encephalomyelitis. Respiratory cultures were recovered from an endotracheal aspirate revealing light yeast formation. CSF obtained from lumbar puncture was positive for elevated proteins and normal white blood cell count. Considering the unique CSF findings, Guillain–Barré Syndrome (GBS) was considered, and its etiology was invested. Nerve conduction studies and an electromyography (EMG) were performed revealing acute sensorimotor axonal and demyelinating peripheral neuropathy consistent with GBS. Serology tests were subsequently ordered and West Nile virus IgM was reported positive. CSF serology was tested for Lyme and herpes simplex virus which were negative. Urine was also tested for Given the patient's presentations and the serological tests, it was determined that GBS secondary to WNV infection was most likely the diagnosis. Treatment for meningitis was discontinued, and intravenous immunoglobulin was continued for 10 days and plasmapheresis would be considered thereafter. After 4 days of 30 mg IViG, she showed substantial improvement moving her toes and closing her fist. At 10 days, she was able to relay requests with improved concentration and without facial droop, and also muscle strength improved on a daily basis. After completion of 10 days of IViG, no further plasmapheresis was initiated considering her continued improvement and supportive care was recommended. The hospital course was complicated by hyponatremia and serum hypo-osmolality considered secondary to Syndrome of Inappropriate Antidiuretic Hormone Secretion (SIADH). She was treated with fluid restriction and followed closely by the nephrology specialist adjusting her fluid intake daily. She remained in the hospital until hyponatremia was corrected and maintained. The total hospital course was 20 days, and she was thereafter transferred to a rehab facility where she continued to improve. As of the time this paper is being written, the patient has regained full functionality of her upper extremities and is no longer requiring ventilator support. Nonetheless, lower extremity weakness has remained unchanged, unfortunately.West Nile virus is a member of the flaviviridae family that is primarily transmitted to humans by mosquitoes. Although at first not endemic to North America, outbreaks in New York and other areas of the United States from 1999–2012 have led to its evolution in North America and is now considered to be endemic to North America . From 20Mycoplasma pneumoniae [M. pneumoniae, are also known to cause pneumonia [Staphylococcus coagulase negative indicating Staphylococcus (S.) aureus as new source of her pneumonia. Although S. aureus was present in cultures, it is unlikely to be the original agent in the patient's pulmonary compromise and is considered a hospital-acquired pneumonia. While it is possible that the antecedent to this patient's GBS was S. aureus, it seems more likely that her WNV infection is what led to the development of GBS, as there is a lack of research linking S. aureus to the development of GBS. Furthermore, weakness from the developing neurological deficits is more likely to have led to the development of poor respiratory effort resulting in pneumonia, as opposed to the pneumonia being the etiology of the neurological disease. Further research connecting WNV to GBS is necessary to make a definitive conclusion on which of this patient's infections led to her development of GBS. However, we believe that in this case, the patient's diagnosis of West Nile virus was the agent that led to her later complication of Guillain–Barré Syndrome.Although further studies of early diagnosis for West Nile virus infection are warranted, we propose here that putting WNV in your differential diagnosis could be lifesaving. GBS is known to be commonly preceded by a number of agents, such as varicella zoster virus, herpes simplex virus, hepatitis A, B, C, and E viruses, human immunodeficiency virus (HIV), and eumoniae . Some ofneumonia . In our


Researchers must be able to measure concentrations, sizes, and infectivity of virus-containing particles in animal agriculture facilities to know how far infectious virus-containing particles may travel through air, where they may deposit in the human or animal respiratory tract, and the most effective ways to limit exposures to them. The objective of this study was to evaluate a variety of impinger and cyclone aerosol or bioaerosol samplers to determine approaches most suitable for detecting and measuring concentrations of virus-containing particles in air. Six impinger/cyclone air samplers, a filter-based sampler, and a cascade impactor were used in separate tests to collect artificially generated aerosols of MS2 bacteriophage and swine and avian influenza viruses. Quantification of infectious MS2 coliphage was carried out using a double agar layer procedure. The influenza viruses were titrated in cell cultures to determine quantities of infectious virus. Viral RNA was extracted and used for quantitative real time RT-PCR, to provide total virus concentrations for all three viruses. The amounts of virus recovered and the measured airborne virus concentrations were calculated and compared among the samplers. Not surprisingly, high flow rate samplers generally collected greater quantities of virus than low flow samplers. However, low flow rate samplers generally measured higher, and likely more accurate, airborne concentrations of Infectious virus and viral RNA than high flow samplers. To assess airborne viruses in the field, a two-sampler approach may work well. A suitable high flow sampler may provide low limits of detection to determine if any virus is present in the air. If virus is detected, a suitable lower flow sampler may measure airborne virus concentrations accurately. Substantial evidence exists for key instances of airborne virus transmission. Sattar et al.  reportedEmerging zoonotic viruses such as H5N1 avian influenza virus, novel 2009 H1N1 influenza virus, 2012 H3N2 variant influenza virus, H7N9 avian influenza virus, and H5N2 and H5N8 avian influenza viruses have posed real or potential risks to veterinary, swine, and poultry workers –18. SwinAnimals in agricultural operations generate particles that are small enough to be transported substantial distances, and many of these particles contain viral RNA –24. HoweViral and other biological aerosols may be sampled from air using a variety of different approaches –30. ImpiA few studies have evaluated the ability of samplers to collect airborne influenza viruses, either experimentally or in the field. Fabian et al . compareSeveral studies have measured the sizes of airborne particles with which viral RNA is associated. Using a two-stage cyclone sampler, Blachere et al.  measuredWhile data about the size distribution of particles with which viral RNA is associated are valuable, it does not provide information about whether the virus is infectious or not. Processing samples to isolate and titrate infectious virus can provide data on virus infectivity as a function of particle size. This critical information is needed to evaluate the risks that virus aerosols pose. Alonso et al.  infectedTo effectively measure the sizes of particles with which infectious viruses are associated, we must identify or develop an optimal method to sample size-separated viral aerosols at a high volumetric flow rate with high efficiency to provide as low a limit of detection as possible, while keeping the viruses viable for culture-based analyses. This is the first in a series of papers in which we report the results of tests to compare a wide range of existing samplers that can collect viral aerosols using a variety of principles. The objective of our tests is to identify combinations of sampling parameters and technologies that (1) provide the greatest chance of detecting infectious virus and viral RNA and (2) most accurately measure airborne concentrations of infectious virus and viral RNA. This first paper focuses primarily on samplers that use the principles of impingement and cyclonic collection.Protocols and procedures followed throughout the study were approved by the University of Minnesota Institutional Biosafety Committee (IBC #1808-36316H).Three different viruses were aerosolized experimentally: an avian influenza virus (AIV) subtype H9N9 as a surrogate for avian influenza viruses of pandemic potential, an H3N2 swine influenza virus (SIV) as a surrogate for human origin viruses capable of transmission between pigs and humans, and MS2 coliphage used commonly as a general virus surrogate in aerosol studies. Low-pathogenic strains of AIV and SIV were chosen as surrogates for highly pathogenic zoonotic influenza strains to which animal agriculture workers might be exposed.2 atmosphere until cytopathic effects (CPE) appeared, usually within 2–4 days. The cultures were frozen and thawed one time followed by centrifugation at 3,000 x g for 15 minutes. The supernatants, 1 liter from each virus, were aliquoted in 45 mL amounts and stored at -80°C until used in experiments.The influenza viruses were grown and titrated in Madin-Darby canine kidney (MDCK) cells. The cells were grown in minimum essential medium (MEM) with Earle’s Salts and L-Glutamine  supplemented with 8% fetal bovine serum (FBS), penicillin (100 U/mL), streptomycin (100 μg/mL), neomycin (90 U/ mL), and fungizone (1 μg/mL). AIV and SIV were propagated separately in MDCK monolayers at 90% confluency. The monolayers were infected after thrice washing with Hanks’ balanced salt solution and maintained in MEM supplemented with 4% of 7.5% bovine serum albumin solution- fraction V , 0.65 U/mL TPCK-trypsin , and antibiotics as mentioned above. The infected cells were incubated at 37°C under 5% COEscherichia coli famp (ATCC 700891) as host cells following the method described previously  was autoclaved and kept molten in a 48°C water bath. Serial 10-fold dilutions (1050) method. Serial 10-fold dilutions (100 to 10−7) of the nebulizer suspensions and the samples were prepared in cell maintenance medium . Triplicate wells of 1-day-old MDCK monolayers grown in 96-well cell culture plates were infected with 100μL aliquots of each sample dilution. Cytopathic effects (CPE) of influenza viruses was examined under inverted microscope after 5 days of incubation at 37°C, 5% CO2, and humidity control. Viral titers were calculated by the Karber method Reviewers' comments:Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The paper compares the performance of eight aerosol samplers when collecting a bacteriophage and two influenza viruses. I have minor comments and questions.When comparing samplers in a test chamber, a constant concern is always that the samplers are sampling aerosols with the same quantity and particle size distribution, since this directly affects the results. Steps taken often include placing the samplers on a rotating table, moving the samplers among the different test locations for different experiments, and/or adding fans to a test chamber to mix the air. In this study, the investigators did use a particle counter to check for significant variations in the particle concentration at different locations, but in the future they should consider taking additional steps and performing additional experiments to help ensure that the results they are seeing are due to actual differences between the samplers and not due to differences in aerosol concentrations.The investigators are using fluorescein in their experiments as a tracer, which is common in aerosols experiments. The fluorescence of fluorescein is pH sensitive. Since they did all their experiments using the same buffer, their pH was probably consistent, but it is a factor to consider in the future.https://apps.dtic.mil/dtic/tr/fulltext/u2/a384058.pdf. Accessed April 3, 2019.Kesavan, J and RW Doherty (2000). Use of Fluorescein in Aerosol Studies. Report No. ECBC-TR-103. Aberdeen Proving Ground, MD: US Army Edgewood Chemical Biological Center, 2000, 23 p. Available at In Table 1, it would be helpful to include the cut-off diameters for the different samplers. The AGI-30 in particular is not very effective for particles below about 1 micrometer. I don’t know what the cut-off diameter is for the cyclonic collector, but if it is higher, that may explain the lower performance reported in the study.The results indicate that the gamma values for MS2 and SIV were somewhat greater than 1. If my understanding is correct, theoretically 1 should be the maximum value. Do the investigators have any thoughts on why this occurred?In the discussion, the investigators say, “The results in Fig 3 suggest that, while high flow rate samplers may be better for detecting infectious virus and viral RNA in the air, airborne virus concentrations are measured more accurately by lower flow rate samplers.” They discuss a few possible explanations for this. Are there other possibilities that should be considered? Are the aspiration efficiencies lower for the higher flow rate samplers? Are they recirculating filtered air from the outlet back into the inlet?Later in the discussion, the investigators say “However, the observation that the Andersen impactor measures distributions shifted to smaller sizes may be a reflection of particle bounce that has been documented previously for cascade impactors.[48] A way to avoid particle bounce is to coat impactor stages with a material that prevents bounce, such as grease.[49-51] However, that procedure would likely interfere with virus recovery and analysis and hence is not a practical option.” The pharmaceutical industry routinely uses water-soluble coatings for impactor stages which may be more practical than grease:https://www.researchgate.net/publication/288993318Mitchell, JP (2003). Practices of Coating Collection Surfaces of Cascade Impactors: A Survey of Members of the European Pharmaceutical Aerosol Group (EPAG). Drug Delivery to the Lungs – XIV, December 11-12, 2003, London, UK, The Aerosol Society. https://www.ncbi.nlm.nih.gov/pubmed/19280348Mitchell, JP, MW Nagel, V Avvakoumova, H MacKay and R Ali (2009). The abbreviated impactor measurement (AIM) concept: part 1--Influence of particle bounce and re-entrainment-evaluation with a "dry" pressurized metered dose inhaler (pMDI)-based formulation. AAPS PharmSciTech 10(1): 243-51. https://www.ncbi.nlm.nih.gov/pubmed/29853937Khalili, SF, S Ghanbarzadeh, A Nokhodchi and H Hamishehkar (2018). The effect of different coating materials on the prevention of powder bounce in the next generation impactor. Res Pharm Sci 13(3): 283-287. In the acknowledgements, NIOSH is the National Institute for Occupational Safety and Health .Reviewer #2: The authors of this manuscript compared the performance of various low and high flow air samplers for the collection of MS2, an avian and a swine influenza virus. For each air sampler the amount of collected virus RNA and infectious virus was determined and expressed as 1) total collection of virus after 30 minutes of sampling and 2) per cubic meter. In addition, the loss of virus infectivity due to the nebulization process was determined, as well as the relative recovery rate of the viruses after air sampling. The size distribution for the different viruses collected by the Andersen Cascade Impactor and the NIOSH sampler was determined as well. The authors concluded from these experiments that high flow samplers in general collected more virus, while low flow rate samplers collected virus amounts more accurately and therefore suggest to use a combination of both kind of air samplers in field settings.General remark about the experimental set-up:All air samplers run simultaneously for 30 minutes. This would suggest that a high rate air sampler such as the SpinConII with a flow rate of 450 LPM would sample 13500 L in 30 minutes, compared to a low flow sampler like the NIOSH that samples only 105 L in 30 minutes with a flow rate of 3.5 LPM. Therefore, it is not surprising that within the same amount of time a high flow sampler might collect more virus. In addition, this also suggests that a low flow sampler is in competition with the strong air flow of high flow samplers. To get a more accurate impression of the performance of each sampler, the authors could have tested the air samplers separately and let the samplers draw the same amount of air (i.e. shorter or longer running time for either high or low flow samplers). This is a limitation of the current set-up that should be noted in the discussion.Collection medium Andersen ImpactorThe authors should provide more information on the collection medium used in the stages of the impactor. The performance of the impactor can be influenced by the collection medium used, also with regard to the shift in size distribution as it was observed in the present study.Overall this is a nice study that compared a wide range of air samplers for the collection of aerosolized viruses.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  16 Dec 2020Comments from Academic EditorComment 1: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found athttps://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdfResponse 1: Thank you for pointing out our inattention to the style requirements. We have renamed our figure and supporting information files according to the requirements. In addition, we have edited our headings and double-spaced our references to meet the style requirements. http://journals.plos.org/plosone/s/supporting-information.Comment 2: Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: Response 2: Thank you for pointing out that we did not provide a caption for our supporting information. We added a caption on the final page of the revised manuscript. We also added an in-text citation to the supporting information dataset file at the very beginning of the Results section on p. 17.Comments from Reviewer #1 Comment 3: When comparing samplers in a test chamber, a constant concern is always that the samplers are sampling aerosols with the same quantity and particle size distribution, since this directly affects the results. Steps taken often include placing the samplers on a rotating table, moving the samplers among the different test locations for different experiments, and/or adding fans to a test chamber to mix the air. In this study, the investigators did use a particle counter to check for significant variations in the particle concentration at different locations, but in the future they should consider taking additional steps and performing additional experiments to help ensure that the results they are seeing are due to actual differences between the samplers and not due to differences in aerosol concentrations.Response 3: We agree with the reviewer's comment that uniformity of the aerosol is a significant concern in tests like these. A rotating table was not a practical option due to the size, sensitive nature, and AC power requirements of some of the instruments. Our team is reluctant to use fans for mixing because our tests have shown that fans can remove some of the aerosol from the test space. Randomly assigning locations for the instruments was somewhat feasible, although the test space is small enough that this would have presented significant challenges. The isolation room building was torn down a few months after this study in favor of a replacement with somewhat larger test spaces. This may make repositioning samplers randomly for each experiment more feasible in future studies. Despite not taking the approaches suggested by the reviewer, we believe that the data show that the aerosol was uniform. Figure 3 indicates that five of the samplers yielded quite similar viral RNA concentrations for all three viruses even though they were not all placed adjacent to one another. Comment 4: The investigators are using fluorescein in their experiments as a tracer, which is common in aerosols experiments. The fluorescence of fluorescein is pH sensitive. Since they did all their experiments using the same buffer, their pH was probably consistent, but it is a factor to consider in the future.https://apps.dtic.mil/dtic/tr/fulltext/u2/a384058.pdf. Accessed April 3, 2019.Kesavan, J and RW Doherty (2000). Use of Fluorescein in Aerosol Studies. Report No. ECBC-TR-103. Aberdeen Proving Ground, MD: US Army Edgewood Chemical Biological Center, 2000, 23 p. Available at Response 4: The reviewer is correct. We should have adjusted the pH upward prior to the fluorescence measurements. We have done this in previous work; it was an oversight not to have made the adjustment in this study. However, the buffer is stable. With our calibration, therefore, the readings should still be accurate. Comment 5: In Table 1, it would be helpful to include the cut-off diameters for the different samplers. The AGI-30 in particular is not very effective for particles below about 1 micrometer. I don’t know what the cut-off diameter is for the cyclonic collector, but if it is higher, that may explain the lower performance reported in the study.Response 5: We would agree with the reviewer's comment if data were available on cut-off diameters for all of the instruments. We have already indicated in the text on p. 9 that the AGI-30 and the BioSampler have significant losses in sampling efficiency for particles smaller than 1 μm. The size intervals for the Andersen impactor and the NIOSH sampler were provided on pp. 8 and 9, and we indicated on p. 9 that the VIVAS works by growing submicrometer particles into ones that can more easily be collected. The sampling efficiency by particle size for the SpinCon II, the Bobcat, and the Cyclonic Collector have not been thoroughly characterized. We believe that we have reported the size dependent properties as thoroughly as possible in the text, and that adding data for only some of the samplers in Table 1 would not be helpful. Therefore, we have not revised the table.We have taken some very rough measurements regarding the sampling efficiency of the Cyclonic Collector, which indicate that it may not sample at 100% efficiency for particles smaller than 2 μm and larger than 8 μm. We have referred to this in the Discussion on p. 22.Comment 6: The results indicate that the gamma values for MS2 and SIV were somewhat greater than 1. If my understanding is correct, theoretically 1 should be the maximum value. Do the investigators have any thoughts on why this occurred?Response 6: The main reason that we calculate gamma is to determine if a substantial proportion of a virus is being inactivated by the nebulization process. If we were to do this for vegetative bacteria, for example, gamma values would be close to 0. In theory, the measured gamma could be greater than or less than 1. It would be less than 1 if the virus were inactivated by repeatedly being passed through the nozzle in the nebulizer. On the other hand, gamma could be greater than 1 if the virus is (a) not inactivated and (b) is also not suspended evenly over time in the nebulizer fluid so that proportionally more fluorescein would be nebulized than virus. Whether the geometric mean values are greater than or less than 1 is not very important here. As we indicate, the population of gamma values in our tests cannot be distinguished statistically from 1 for any of the viruses. This means that the viruses are not being systematically inactivated over the course of each test period, nor is the virus suspension systematically becoming non-homogeneous. The geometric means differ from 1 and the 95% confidence limits around gamma for each of the viruses are fairly large due to the inherent variability of working with viruses. In addition, a sample size of three for each virus is not ideal. With more samples, we would have geometric means closer to 1 and tighter error bars, as in some of our previous work. We have chosen not to make revisions to the manuscript around this point because we believe these explanations are tangential to the main point, which is that we do not see systematic inactivation of the virus due to nebulization.Comment 7: In the discussion, the investigators say, “The results in Fig 3 suggest that, while high flow rate samplers may be better for detecting infectious virus and viral RNA in the air, airborne virus concentrations are measured more accurately by lower flow rate samplers.” They discuss a few possible explanations for this. Are there other possibilities that should be considered? Are the aspiration efficiencies lower for the higher flow rate samplers? Are they recirculating filtered air from the outlet back into the inlet?Response 7: The reviewer makes an excellent point. We were a little limited in indicating possible reasons for the high flow rate samplers yielding lower concentrations. In response, we added two paragraphs in the Discussion section on pp. 22-23, one addressing removal and aspiration efficiency and the other addressing the possible effects of sampler exhaust. Comment 8: Later in the discussion, the investigators say “However, the observation that the Andersen impactor measures distributions shifted to smaller sizes may be a reflection of particle bounce that has been documented previously for cascade impactors.[48] A way to avoid particle bounce is to coat impactor stages with a material that prevents bounce, such as grease.[49-51] However, that procedure would likely interfere with virus recovery and analysis and hence is not a practical option.” The pharmaceutical industry routinely uses water-soluble coatings for impactor stages which may be more practical than grease:https://www.researchgate.net/publication/288993318Mitchell, JP (2003). Practices of Coating Collection Surfaces of Cascade Impactors: A Survey of Members of the European Pharmaceutical Aerosol Group (EPAG). Drug Delivery to the Lungs – XIV, December 11-12, 2003, London, UK, The Aerosol Society. https://www.ncbi.nlm.nih.gov/pubmed/19280348Mitchell, JP, MW Nagel, V Avvakoumova, H MacKay and R Ali (2009). The abbreviated impactor measurement (AIM) concept: part 1--Influence of particle bounce and re-entrainment-evaluation with a "dry" pressurized metered dose inhaler (pMDI)-based formulation. AAPS PharmSciTech 10(1): 243-51. https://www.ncbi.nlm.nih.gov/pubmed/29853937Khalili, SF, S Ghanbarzadeh, A Nokhodchi and H Hamishehkar (2018). The effect of different coating materials on the prevention of powder bounce in the next generation impactor. Res Pharm Sci 13(3): 283-287. Response 8: The literature on coatings used for pharmaceutical applications was unknown to us. We greatly appreciate the reviewer's recommendation to consider these very interesting papers! We have added two sentences on p. 23 and included the three papers suggested by the reviewer as references 52-54.Comment 9: In the acknowledgements, NIOSH is the National Institute for Occupational Safety and Health .Response 9: We thank the reviewer for catching this mistake. We have corrected the acknowledgment on p. 25.Comments from Reviewer #2 Comment 10: General remark about the experimental set-up:All air samplers run simultaneously for 30 minutes. This would suggest that a high rate air sampler such as the SpinConII with a flow rate of 450 LPM would sample 13500 L in 30 minutes, compared to a low flow sampler like the NIOSH that samples only 105 L in 30 minutes with a flow rate of 3.5 LPM. Therefore, it is not surprising that within the same amount of time a high flow sampler might collect more virus. In addition, this also suggests that a low flow sampler is in competition with the strong air flow of high flow samplers. To get a more accurate impression of the performance of each sampler, the authors could have tested the air samplers separately and let the samplers draw the same amount of air (i.e. shorter or longer running time for either high or low flow samplers). This is a limitation of the current set-up that should be noted in the discussion.Response 10: We thank the reviewer for this interesting comment, and a different way to conceive the test. Testing each sampler individually would take out interferences from other samplers, and we could control total sampling volume. However, this approach would present additional difficulties such as an impractical number of tests and would lead to some very long tests. For instance, if we decided that 15 minutes was the minimum run time for the highest flow rate sampler , then the lowest flow rate sampler would require more than 32 hours to process the same volume of air. It would be difficult to maintain a constant aerosol for that long, and we would have to deal with variations in conditions among the tests. To address the limitation perceived by the reviewer, we have added the following sentences to the Discussion on p. 21: "The finding that lower flow rate samplers generally collected less RNA and lower titers of infectious virus is not surprising given that they are processing less air. An interesting set of experiments would be to run the samplers so that they process equal volumes of air and then determine the titers from each device."Comment 11: Collection medium Andersen ImpactorThe authors should provide more information on the collection medium used in the stages of the impactor. The performance of the impactor can be influenced by the collection medium used, also with regard to the shift in size distribution as it was observed in the present study.Response 11: We are not sure what the reviewer means by the collection medium. The reviewer may be asking if anything is being used on top of the impactor stage. We have added a sentence on p. 8 to clarify that uncoated aluminum plates were used as the collection surface in the Andersen impactor, except for the final stage for which a filter was used. It is also possible that the reviewer is thinking about a Six-Stage Viable Andersen Cascade Impactor, in which a collection medium like agar is required. It should be clear to readers from Table 1 – as well as from other context in the manuscript – that we are using the Non-Viable Andersen impactor.AttachmentResponse to Reviewers 20201216.docxSubmitted filename: Click here for additional data file. 21 Dec 2020Comparison of samplers collecting airborne influenza viruses: 1. 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