What is the estimated association between added-sugar labeling and obesity-related cancer rates in the US?This economic evaluation of Nutrition Facts added-sugar labeling and obesity-related cancer rates estimated that implementing the policy was associated with a reduction of 30 000 new cancer cases, 17 100 cancer deaths, and $1600 million in medical costs among US adults over a lifetime. This policy would generate net savings of $704 million from a societal perspective and $1590 million from a health care perspective.These model findings suggest added-sugar labeling would be associated with reduced costs and lower rates of obesity-related cancers. Obesity-associated cancer burdens are increasing in the US. Nutrition policies, such as the Nutrition Facts added-sugar labeling, may reduce obesity-associated cancer rates.To evaluate the cost-effectiveness of Nutrition Facts added-sugar labeling and obesity-associated cancer rates in the US.A probabilistic cohort state-transition model was used to conduct an economic evaluation of added-sugar labeling and 13 obesity-associated cancers among 235 million adults aged 20 years or older by age, sex, and race/ethnicity over a median follow-up of 34.4 years. Policy associations were considered in 2 scenarios: with consumer behaviors and with additional industry reformulation. The model integrated nationally representative population demographics, diet, and cancer statistics; associations of policy intervention with diet, diet change and body mass index, and body mass index with cancer risk; and policy and health-related costs from established sources. Data were analyzed from January 8, 2019, to May 6, 2020.Net costs and incremental cost-effectiveness ratio were estimated from societal and health care perspectives. Probabilistic sensitivity analyses incorporated uncertainty in input parameters and generated 95% uncertainty intervals (UIs).Based on consumer behaviors, the policy was associated with a reduction of 30 000  new cancer cases and 17 100  cancer deaths, a gain of 116 000  quality-adjusted life-years, and a saving of $1600 million  in medical costs associated with cancer care among US adults over a lifetime. The policy was associated with a savings of $704 million  from the societal perspective and $1590 million  from the health care perspective. Additional industry reformulation to reduce added-sugar amounts in packaged foods and beverages would double the impact. Greater health gains and cost savings were expected among young adults, women, and non-Hispanic Black individuals than other population subgroups.These findings suggest that the added-sugar labeling is associated with reduced costs and lower rates of obesity-associated cancers. Policymakers may consider and prioritize nutrition policies for cancer prevention in the US. This economic evaluation examines the association between the use of added-sugar labeling of food products in the US and the incidence of cancer from societal and health care perspectives. This study aimed to estimate the cost-effectiveness of the Nutrition Facts added-sugar labeling and obesity-associated cancer rates among US adults and further evaluate the association between implementing the policy and health disparities in population subgroups by age, sex, and race/ethnicity.In 2016, the US Food and Drug Administration (FDA) announced a mandatory labeling policy for all packaged foods and beverages to include the amount of added sugars on the Nutrition Facts label.11 a probabilistic cohort state-transition model to perform an economic evaluation of the added-sugar labeling policy and obesity-related cancer rates among 235 million US adults aged 20 years or older over a simulated lifetime  reporting guideline for economic evaluation studies.We used the Diet and Cancer Outcome Model,n the 24 . Data weThe 2013-2016 National Health and Nutrition Examination Survey data were used to simulate the US adult population aged 20 years or older in 32 subgroups by age , sex (men and women), and race/ethnicity  , facilitating more realistic and conservative estimates. The relative risks of cancer associated with each 5-point increase in BMI for 13 cancers were based on systematic reviews and meta-analyses of prospective cohort studies conducted by the World Cancer Research Fund/American Institute for Cancer Research Continuous Update Project and the International Agency for Research on Cancer  with change in BMI (the diet-BMI associations) and the estimates of BMI and cancer risk (BMI-cancer relative risks). The diet-BMI estimates were obtained from a pooled analysis from 120 997 US men and women followed up over 12 to 20 years in 3 prospective cohort studies  .12,26 Th27 We projected the cancer incidence from 2015 to 2030 based on the incidence of cancer from 2006 to 2014 using the average annual percent change method  and additional industry reformulation . The association with consumer behavior was obtained from a meta-analysis of food labeling interventions that reported a 6.6%  reduction in calories from menu calorie labeling.8 on the added-sugar labeling policy and the findings from the UK sugar reduction strategy.9 Based on these reports, we assumed no reformulation during the first year of implementation. In each of the subsequent 2 to 5 years, 7.5% to 9.0% of sugar-containing products would be reformulated to achieve a cumulative reduction of 25% in added sugar content of reformulated products,9 with no further changes thereafter. In total, an average 8.25% reduction in the added sugars would result from industry reformulation over 5 years  gained and cost-effectiveness from health care and societal perspectives. The health care perspective assessed net costs as the government costs of implementing the policy minus savings in the direct medical costs for cancer care. The societal perspective assessed net costs as the difference between the total policy costs (including government and industry costs) and the health-related cost savings (including direct and indirect costs for cancer care). All costs were inflated to 2015 US dollars using the Consumer Price Index or Personal Health Care Index, with all costs and QALYs discounted at 3% annually. Incremental cost-effectiveness ratios (ICERs) were calculated as the difference in costs divided by the difference in QALYs between policy vs no policy. ICERs falling below a willingness-to-pay threshold of $150 000 per QALY gained were considered to be cost-effective.24 Cost-effectiveness analysis was further conducted among population subgroups by age, sex, and race/ethnicity to evaluate policy associations with health disparities.Following the recommendations from the Second Panel on Cost-effectiveness in Health and Medicine,We performed 1-way sensitivity analyses by varying key input parameters, including policy association, diet-BMI association, changes in medical expenditures associated with cancer care, policy implementation costs, and discounting rate. In addition, we conducted threshold analyses to identify the minimum policy change needed for achieving cost-saving or cost-effective status, evaluated the cost-effectiveness at a 10-year horizon for stakeholders interested in near-term returns, and further identified the time frame during which the policy reached cost-effectiveness. Probabilistic sensitivity analyses were conducted to incorporate the probability distributions around the point estimates of all parameters jointly . A totalThe simulated cohort of US adults in 2015 had a mean age of 47.8 years, comprising 65.0% non-Hispanic White individuals and 71.4% overweight or obese adults  new cancer cases (0.09% reduction of incident cancer) and 17 100  cancer deaths , and an increase of 116 000  QALYs among 235 million US adults over a median follow-up of 34.4 years . With adImplementing the policy would cost the government an estimated $9.24 million ($7.18 million-$12.8 million) and the industry $1660 million ($1410 million-$1960 million) in compliance costs over a lifetime of the current US population . The polThe policy intervention was cost-saving and associated with savings of $704 million  from a societal perspective and $1590 million  a health care perspective . FurtherThe policy was associated with greater health gains per 100 000 individuals among young adults  than middle-aged adults , women than men (14 vs 12 new cancer cases averted), and non-Hispanic Black individuals (15 new cancer cases averted) than Hispanic individuals (10 new cancer cases averted) . The numThe cost-effectiveness of the policy was most sensitive to varying assumptions of policy impact, diet-BMI association, and discounting rate  and overweight/obese (0.04 points) individuals. The policy was estimated to be associated with approximately 30 000 new cancer cases and 17 100 cancer deaths among US adults over a lifetime and with substantial health care savings, resulting in net savings of $704 million from the societal perspective and $1590 million from the health care perspective. With additional industry reformulation, the policy was associated with approximately doubled health gains and economic benefits. Greater health gains and cost savings were expected among young adults, women, and non-Hispanic Black individuals compared with other population subgroups.32 For example, mammography screening starting at age 45 years was estimated to have an ICER of $40 135/QALY among a large cohort of women born in the 1960s over a lifetime.30 Colonoscopy screening starting at age 45 years achieved an ICER of $33 900/QALY among US men and women,31 and prostate-specific antigen screening had an ICER of $70 831 to $136 332/QALY among a cohort of US men beginning at age 40 years over a lifetime.32 These cancer screening strategies are cost-effective, but none of them achieved net savings. Our results suggest that a population-based nutrition intervention, such as the added-sugar labeling policy, is not only cost-effective but also cost-saving. These findings underscore the need to consider and prioritize nutrition-related policy interventions as cost-effective or cost-saving strategies for cancer prevention.Our findings suggest that the added-sugar labeling policy could be a cost-saving strategy for cancer prevention. Earlier simulation studies reported that medical screenings were cost-effective interventions in the primary or secondary prevention of specific cancers.33 A meta-analysis of interventional studies found that package, menu, and other point-of-purchase labeling policies were associated with industry reformulations, including an 8.9% reduction in sodium content.7 Thus, with the passage of the added-sugar labeling, government and advocacy strategies should be prioritized to encourage industry reformulations for achieving larger benefits.Modest industry responses to the labeling policy could be associated with substantial additional health gains and economic benefits for obesity-related cancers. Earlier studies reported that, after the US Congress passed a national law requiring chain restaurants with 20 or more outlets to list calories on menus in 2010, the mean calorie content per menu item fell from 327 kcal in 2008 to 318 kcal in 2015 in 44 of the 100 largest chain restaurants.36 but not others,37 suggest that individuals with lower educational levels or income may respond less to nutrition labeling than those with greater educational levels or income. However, such a varying response might alter the relative benefits within the subpopulation but would be unlikely to alter the robust overall cost-savings.The study had limitations. First, our investigation cannot prove a causal relationship between the implementation of this policy and cancer outcomes. Yet, cost-effectiveness analysis provides flexibility to test and evaluate different policy scenarios and reasoned assumptions. Second, we did not include potential health benefits on other sugar- or obesity-associated diseases  or the potential associations with reducing cancer burdens through reduced childhood obesity. Thus, the health benefits and cost savings of the policy could be underestimated. Third, in the absence of direct evidence, we used the estimated association of menu calorie labeling and calorie reduction as a proxy for added-sugar labeling, and limited evidence has suggested an association between menu calorie labeling or nutrition fact information and eating behaviors. However, in the sensitivity analyses with more conservative estimates, the policy remained cost-effective from both health care and societal perspectives. Fourth, insufficient data were available to incorporate potentially differing responses to labeling policy by socioeconomic status. Some studies,This economic evaluation is, to our knowledge, among the first to suggest that the added-sugar labeling policy is cost-saving and associated with reduced costs and lower cancer rates. Nutrition policies can play important roles in reducing cancer burdens and disparities in the US.


Transcriptograms, a Bioinformatics tool to analyze genome-wide gene expression data, allow us to define an appropriate context-dependent threshold for mechanistic relevance of gene differential expression. Without knowing in advance which genes are relevant, classical analyses detect every gene with statistically-significant differential expression, leaving us with too many genes and hypotheses to be useful. Using a Transcriptogram-based top-down approach, we identified three major, differentially-expressed gene sets comprising 219 mainly immune-response-related genes. We identified timescales for alterations in mitochondrial activity, signaling and transcription regulation of the innate and adaptive immune systems and their relationship to viral titer. The methods can be applied to RNA data sets for SARS-CoV-2 to investigate the origin of differential responses in different tissue types, or due to immune or preexisting conditions or to compare cell culture, organoid culture, animal models and human-derived samples.To understand the difference between benign and severe outcomes after Coronavirus infection, we urgently need ways to clarify and quantify the time course of tissue and immune responses. Here we re-analyze 72-hour time-series microarrays generated in 2013 by Sims and collaborators for SARS-CoV-1  MERS) in 2012 . Because of the complexity of the many patterns of response to SARS-CoV-2, we critically need ways to identify important biological mechanisms which act at different phases of infection and allow us to reliably identify differences in pathway and gene activity between individual patients, tissues within patients, individuals with pre-exiting conditions, sex and ethnic differences and age. The immune system is complex, sensitive and dynamic, with a delicate balance of triggers, high-gain feed-back loops, and complex interactions between its many agents, complicating interpretation of experimental measurements of immune-response components and the origins of their variation between individuals. In this case, for diagnostic, prognostic, and therapeutic purposes, detailed mathematical models of patient-specific immune responses might help us understand the range of possible immune responses, and how they depend on patient-specific variables, ranging from initial exposure level and coinfections, to age, sex, preexisting conditions and medications, etc. Furthermore, in serious cases, COVID-19 symptoms may also include blood and vascular disruption, meaning that the co-activation of other pathways with deleterious effects may play an important role in disease outcomes  or a mutant SARS-CoV-1 strain (DORF6 samples) that does not express the accessory protein ORF6 at high concentration (a multiplicity of infection MOI of 5), so that the probability of cell contamination in the culture approached 1. As controls, they also inoculated cultures with a sterile solution (Mock samples). After inoculation, they incubated the cultures at 37°C for 40 min, then changed their medium. They then harvested samples for microarray assays in triplicate at times they labeled et al. (GEO) under accession number GSE33267 (http://www.ncbi.nlm.nih.gov/geo) and we used these data for our analyses. More details on the experiments are available in Here, as a pattern for future Transcriptogram analyses of SARS-CoV-2 data and to illustrate the power of the method in quantifying the detailed and complex temporal pattern of immune response to viral infection in cell culture, we present Transcriptogram analyses for SARS-CoV-1 time-series data sets of Sims et al. . Sims et , a wild type SARS-CoV-1-containing (WT) solution, or a mutant SARS-CoV-1-containing (DORF6) solution (GEO accession number GSE33267 (http://www.ncbi.nlm.nih.gov/geo)). We first assessed patterns of expression change shared by large numbers of genes, then considered smaller gene sets which had strongly covariant temporal signatures, and finally examined single genes whose variance was statistically significant withing these sets. At each stage, we filtered the data based on the statistical significance of the subseries variability. The next section briefly discusses the Transcriptogram method and relevant parameters. We focused our analyses on the cellular response to WT virus inoculation. We present the data and Transcriptograms for the Mock and Wild Type (WT) virus-strain time courses in the main text and the Transcriptograms for DORF6 in the WT-infected and non-infected samples. We identified 219 genes whose time courses for the WT-samples show fold-changes larger than two compared to their pair-matched Mock samples at at least one time point. We clustered these genes by time-course similarity and identified 3 clusters. We determined the time evolution for each cluster's mean expression for both WT and DORF6 samples. These clusters show complex non-monotonic time courses, which we compare to the viral titer. From the changes in behaviors presented by the gene clusters’ mean expression, we infer the typical patterns and timescales for Calu3 2B4 epithelial-cell immune response to SARS-CoV-1 infection, correlate these timescales with viral titer and identify single genes that may be possible targets for therapy development.We performed a Transcriptogram-based ,15 top-dTranscriptograms are expression profiles, obtained by running a window average for expression levels of multiple genes, previously organized in an ordered gene list, representing the whole human genome. Here we consider windows of radius 30, that is, intervals around a given gene position including 30 genes to its left and 30 genes to its right in the ordered gene list. We choose radius 30 based on the analyses presented by da Silva and collaborators  terms or KEGG pathways   . The genGO:BP)  a much mTranscriptograms provide a powerful test for sample normalization by revealing undesired variable offsets in expression levels between samples. We ensure sample normalization as follows: we plotted the Transcriptograms of radius 30 for the normalized data available in the GEO database shown in  online arelative Transcriptograms by dividing the Transcriptogram profile values at each point in the ordered gene list by the Transcriptogram profile value for a control sample at the same position in the list.We obtain differential Transcriptograms between two time-series by obtaining the relative Transcriptogram for the WT samples at a given time point w.r.t. time-matched Mock samples. In a time-series for differential Transcriptograms, the control sample differs for each time point.We obtain http://amigo.geneontology.org/amigo)  .i from a gene set with differential expressionWT or Mock transcriptomes’ normalized datasets.For each gene covariance matrixWe define the WT and Mock samples at different times by generating relative Transcriptograms as described in the Statistical Methods section, taking as the control the Transcriptogram for the Mock sample at time Mock (blue lines) and WT (red lines) samples. The relative Transcriptogram for the control expression levels , indicating that cells are already responding to viral infection at the time of first RNA sampling. Figure Mock and WT samples differ significantly from the Mock-sample mean profile at WT relative Transcriptogram values at Figure entclass1pt{minimaMock samples and WT samples, probably reflecting contact inhibition of proliferation in confluent in vitro cultures. However, WT samples show some expression recovery of genes linked to the cell cycle. This recovery may reflect the onset of cell cycle after the death of some infected cells, which reduces contact inhibition of proliferation, or another tissue-recover mechanism. Changes in expression across multiple functional bands of the relative Transcriptogram appear at The important messages in Figure WT and control cultures, from the effects of infection, we considered time-matched Mock expression profiles as controls for the WT expression profiles. We define the differential Transcriptogram profile as the ratio of the WT transcriptogram value at a given time and gene position to the time-matched Mock transcriptogram value at the same gene position. Using a time-matched control helps reduce the signal from tissue-culture effects common to both WT and control samples and accentuates specific differential infection effects. Differential profiles do not show changes in expression of cell-cycle-related genes, for example, since both control and WT expression change in the same way in time. Figure Mock sample at the corresponding time. Figure WT(t)/Mock(t)) at three time points, To distinguish cell-culture effects, which affect both https://david.ncifcrf.gov/tools.jsp and label them accordingly (see below). As in Figure Differential Transcriptogram profiles show noticeable alterations after ID tools  at httpsentclass1pt{minimaWT to find the Reactome Pathways that enrich this set of 219 genes. Among others, we find that 51 of these genes participate in ‘Cytokine signaling in immune system,’ 36 participate in ‘Innate immune system,’ 18 participate in ‘Toll-like-receptor cascade,’ 15 participate in ‘Interleukin 4 and Interleukin 13 signaling’ and 8 genes are participate in ‘TNFR2 non-canonical NF-κB pathways.’ Additional file AF3 in the P < 0.05, Buonferroni corrected). Of the 219 genes, 35 have not been classified as forming a representative set for any Reactome Pathway. Thus 84% of the 219 significantly variant genes participate in pathways either directly or indirectly involved in components of the immune response.The Transcriptogram analyses identify 219 genes in differentially-expressed Transcriptogram bands which are also individually differentially expressed relative to their time-matched Proceeding with our top-down strategy, we look for temporal patterns in the differential expression of single genes . To find genes with similar patterns of temporal evolution, we first calculated the covariance matrix for the differential expression time series of the 219 genes . When two genes have the same pattern of time change of differential expression, their temporal covariance approaches one. Using the covariance matrix, we ordered genes into covariance clusters Figure  A. We idFigure e.g. Oxidation-Reduction and Retinol metabolism. As expected, this cluster shows reduced gene expression after viral infection. Cluster B (52 genes) is mainly enriched with terms related to cytokine production and the MAPK cascade. Finally, genes in Cluster C (124 genes), correlate with a broader spectrum of terms, but many genes relate to cytokine response (39 genes), signal transduction (62 genes) and regulation, including phosphorylation (60 genes). Terms or pathways can participate in more than one cluster simultaneously, but each gene participates in only one cluster. Genes in different clusters may, of course, interact.To characterize each cluster, we associate its genes with KEGG pathways and Gene Ontology: Biological Function terms using DAVID tools . AdditioWT samples w.r.t. the Mock samples. Genes in cluster B decrease  localizes to the outer mitochondrial membrane, manipulating host-cell mitochondria, and disturbing mitochondrial anti-viral signaling (In Cluster A (43 genes) average differential expression does not differ significantly from the control until ignaling . This inWT samples relative to the control, indicating that some genes change their expression very rapidly w.r.t. the control, during the 40 min incubation time before RNA harvesting GO) terms linked to ‘response to stimulus,’ with 12 specifically tagged as ‘response to cytokines.’ The great majority of the products of these genes localize either to the extracellular matrix, indicating signaling activity, or to the nucleus, indicating response to signaling. Other terms associated with genes in Cluster B link to ion transport.The genes in Cluster B (52 genes) have the richest temporal dynamics of mean differential expression. At e.g. ‘I-kappaB kinase/Nf-kappaB signaling’ (20 genes); other immune functions include ‘response to stimulus’ (98 genes), ‘response to cytokine’ (39 genes), ‘response to hormone’ (19 genes) and ‘cytokine production’ (31 genes).In Cluster C (124 genes), after w.r.t time-matched controls at at least one point of the time series. This selection of relevant genes would not be possible without the previous use of the Transcriptograms. To show the difficulty of identifying biologically-relevant genes from the mass of statistically-significant genes using classical bioinformatics approaches, we applied two classical analysis approaches (Volcano plots and correlation with virus titer) and a more sophisticated one, Gene Set Enrichment Analysis (GSEA) (GSEA) comparing WT and Mock samples using three different combinations of data sets and metrics: signal-to-noise ratio and ratio-between-classes for normalized expression values and ratio-between-classes for values using Transcriptogram-averaged data. These Volcano plots, correlations and GSEA confirm the significance of the clusters found using our Transcriptogram analyses but, on their own, cannot identify these clusters directly from the original data, showing that the Transcriptogram analyses are more powerful in this context than the classical methods and GSEA. In the present analyses, Transcriptograms define a biological-relevance criterion, rather than a statistical-significance one, by suggesting the scale of differential expression at each time that should be considered for further investigation of statistical significance. Naturally, when increasing the threshold required to select gene sets to be further analyzed, and hence decrease the number of false-positive errors, we will simultaneously increase the incidence of false-negative errors. We stress that the present analysis tries to address problems caused by the abundance of false-positive errors in finding biologically-relevant genes in experiments showing genome-wide transcription alterations. The role played by this biological-relevance criterion in the selection of genes to be further analyzed becomes evident when we compare differential Transcriptograms at Our analysis first considered Transcriptograms, which allowed us to select peaks in the differential Transcriptograms, representing 590 genes. That selection identified 219 genes with expression values (without Transcriptogram averages) that presented 2-fold or larger changes s (GSEA) , that seIn summary, we identified three gene clusters  with distinct temporal profiles. Clusters B and C have the most distinctive patterns of temporal change, probably reflecting their specific functional roles during early infection. To verify the power of our method, we selected six genes from these clusters and analyzed their differential expression evolution. We then discuss their possible roles in the cellular response to virus inoculation. As we show below, these genes have well-known roles in immune response, showing that the Transcriptogram analysis was reasonable in identifying their variation as significant.w.r.t. the control, after which it gradually decreases to an 8-fold change. The reduction in anti-inflammatory Cortisol signaling between We begin by considering the gene pair HSD11B1 (from cluster C) and HSD11B2 (from cluster B). Both genes are linked to Cortisone-Cortisol balance. Cortisol is anti-inflammatory, secreted by the adrenal gland and present in plasma. It can be converted to inactive Cortisone by the enzyme 11-beta-hidroxysteroid dehydrogenase type 2 (HSD-2), the product of the HSD11B2 gene . The tiMock and WT samples compared to Mock sample expression at WT and Mock samples.Figure entclass1pt{minimaWT sample gradually decreases as the viral titer decreases.TNFAIP3 encodes the protein A20, a negative regulator of the NF-κB protein complex. TNFAIP3 is thus a negative regulator of inflammation and is known to be rapidly induced after Toll-like receptors interact with a pathogen or respond to TNF-α or IL-1 cytokines . Figure Figure } Figure ,29. Furt} Figure , and EGR} Figure . The inf} Figure . T-cells} Figure . Both T-} Figure  and SARS} Figure  patients} Figure , its actDORF6 sample data sets produced by Sims et al. in the same experiment which generated the WT and Mock data series. DORF6 samples, showing that genes in cluster B carry the main differences between the immune response to DORF6 and WT infection.Finally, in et al. , in infected fibroblasts, EGR3 activation could link to observed fibrosis, and in infected endothelial cells, if EGR3 activation could explain the endothelialitis, thrombosis, and angiogenesis reported in COVID-19. We suggest that these hypotheses would be worth investigating in future experiments.Our analysis identified as differentially expressed two genes from a well-known feed-back loop which regulates Cortisol-Cortisone balance. Infection perturbs the resting Cortisone-Cortisol homeostasis and we would expect that each gene would follow a different time course in response to infection. We find that differential expression of the proinflammatory HSD11B2 follows Cluster B and peaks with viral titer, while the anti-inflammatory HSD11B1 follows Cluster C and peaks later. We will examine the remaining 213 genes identified as significant by our Transcriptogram analsysis in future worka priori, which genes are relevant, both classical and GSEA analyses would yield the same results as our Transcriptogram analysis. However, without knowing in advance which genes are relevant, classical analyses detect every gene with statistically-significant differential expression, leaving us with too many genes and hypotheses to be useful. The previous filtering using Transcriptograms reduced the number of genes of interest to a tractable set, allowing us to suggest shared mechanistic functions for the observed gene expression patterns. These gene sets are defined by the data directly, not by reference to previously defined pathways or biological functions.Transcriptograms allowed us to define an appropriate context-dependent threshold for mechanistic relevance of gene differential expression. If we knew, https://www.imagwiki.nibib.nih.gov/working-groups/multiscale-modeling-and-viral-pandemics).Transcriptogram analysis selects and aggregates the biologically-relevant components of the experimental time series in a way that will support detailed mathematical modeling of the inflammatory response of lung cells to SARS-CoV. This approach to aggregating time-series information to yield quantitative net functional responses is new and useful for the complex undertaking of explaining virus effects on inflammatory response, an explicit goal of, for example, the NIH new IMAG/MSM Working Group for multiscale in-host modeling of viral infection and immune response . The same methods could identify critical differences in cell responses to SARS-CoV-1 and SARS-CoV-2 infection or among responses to infection by other respiratory viruses. We could also study differences in response between cells derived from different possible loci of infection , or to compare infection responses between classical cell culture and organoids, between organoids derived from different donors, or between different initial infection intensities.GEO) under accession number GSE33267 (http://www.ncbi.nlm.nih.gov/geo).The analyses have been performed on a previously published dataset by Sims and collaborators . The dathttps://lief.if.ufrgs.br/pub/biosoftwares/transcriptogramer/ and is also available as an R-Bioconductor function. The ordered gene list we used is available as a text file and is included in the The program we used for our analyses is freely available at lqac020_Supplemental_FilesClick here for additional data file.


At present, surveillance data on how Australian adolescents are performing against these guidelines are lacking. This study aims to describe the extent to which Australian secondary school students are adhering to the physical activity, sedentary recreational screen time and sleep duration recommendations outlined in the national 24-hour movement guidelines for children and young people. It also examines whether there are socio-demographic differences in levels of compliance and if there have been significant changes in these behaviours over time.n=13,790), 2012-2013  and 2018 . Students’ self-reported physical activity, screen time and sleep behaviours were assessed using validated instruments administered in schools via a web-based questionnaire.A repeated national cross-sectional survey of students in grades 8 to 11 (ages 12-17 years) was conducted in 2009-2010  did not meet any of the 24-hour movement guidelines, while only 2% of students met all three. Adherence to the sleep duration recommendation was highest (67%), with substantially smaller proportions of students meeting the physical activity (16%) and screen time (10%) recommendations. Differences in adherence by sex, grade level and socio-economic area were apparent. Students’ compliance with the screen time recommendation has declined over time, from 19% in 2009-2010 to 10% in 2018. However, there has been no significant change in the proportion meeting the physical activity (15% in 2009-2010 cf. 16% in 2018) and sleep duration (69% in 2009-2010 cf. 67% in 2018) recommendations. Compliance with all three guidelines has remained very low (<3%) across each survey round.There is considerable scope to improve Australian adolescents’ physical activity and sedentary behaviours in line with the national 24-hour movement guidelines. Policy proposals and environmental interventions, particularly those focused on replacing sedentary screen time with physical activity (e.g. promotion of active commuting to/from school), are needed to better support Australian adolescents in meeting the 24-hour movement guidelines. In recent years, the Australian Government has released age-specific 24-hour movement guidelines that focus on achieving a healthy balance of high levels of physical activity, low levels of sedentary behaviour and appropriate sleep duration each day , 2. AdapSince the adoption of 24-hour movement guidelines in Australia, there have been some efforts to document compliance among young children , 10. UsiThe most current national data on Australian adolescents’ physical activity and sedentary behaviour, collected via personal interviews in 2011-12, indicated that 8% of 13- to 17-year-olds were physically active for at least one hour each day, one in five (20%) limited their sedentary screen-based activity to two or less hours each day, and just 2% met both these recommendations . It is iThe purpose of this study was to address a knowledge gap in the literature by describing the extent to which Australian secondary school students aged 12 to 17 years are adhering to the physical activity, sedentary recreational screen time and sleep duration recommendations outlined in the national 24-hour movement guidelines, both individually and in combination in order to provide insight into particular areas of concern. It also aimed to examine whether there are socio-demographic differences in levels of compliance, which may indicate a need for targeted intervention strategies to promote healthy movement behaviours in specific adolescent sub-groups. Trends were assessed to determine whether there have been significant changes in these behaviours over the past decade among this population segment.n=13,790 from 238 schools), and subsequently repeated in 2012-2013  and 2018 , the NaSSDA Survey is a national cross-sectional study of Australian adolescents in grades 8 to 11 (ages 12 to 17 years). For each survey round, a stratified two-stage probability design was used, with schools randomly selected at the first stage of sampling and classes selected within schools at the second stage. Schools were stratified by education sector  and randomly selected to ensure the sample reflected distributions of sector within each Australian state and territory. Where a selected school declined to participate, they were replaced in the sample by a school with similar characteristics . A school response rate of 39% was achieved in 2009-2010, with this figure decreasing to 21% in 2012-2013 and then further to 8% in 2018. Within participating schools, one class group comprising a relatively random group of students (i.e. not formed on the basis of selective criteria) was selected from each grade. Additional classes were selected where class sizes were small, consent rates were expected to be low, and/or the school did not enrol students in all grades. The student response rate was comparable in 2009-2010 (54%) and 2012-2013 (53%) before increasing to 67% in 2018.Data were obtained from students participating in the National Secondary Students’ Diet and Activity (NaSSDA) Survey. First conducted in 2009-2010 (Informed consent was obtained from both parents/carers (active in 2009-2010 and 2012-2013 and either active or passive (opt-out) in 2018 as per the requirements of each individual state and territory education authority) and all participating students. The NaSSDA Survey was administered in classrooms across Australia by an independent data collection agency. Approvals were obtained from Cancer Council Victoria’s Human Research Ethics Committee, relevant state and territory education authorities and school principals. The demographic characteristics of the student sample in each survey round are summarised in Table 60-minute Moderate-to-Vigorous Physical Activity (MVPA) screening measure [r=0.40, p<0.001) with accelerometer data [Data on students’ dietary, physical activity, screen time and sleep behaviours were collected via a web-based, self-report questionnaire. Physical activity was assessed using a single-item adapted from the  measure . This meter data . Studentter data .Adolescent Sedentary Activity Questionnaire (ASAQ), which has shown good to excellent test-retest reliability among school-aged young people [Sedentary recreational screen time was assessed using a subscale from the g people . Studentg people . Those wg people .Australian Health and Fitness Survey [Sleep duration was assessed using questions adapted from the s Survey . Students Survey . In lines Survey , studentStudents reported their sex, grade level and residential postcode. A measure of socio-economic area was determined using the Socio-Economic Index for Areas (SEIFA) Index of Relative Socio-economic Disadvantage based on student’s residential postcode . Studentp<0.01 was accepted throughout.Data were analysed using Stata MP version 16.1  and weighted to bring each sample in line with the population of students enrolled in Australia by state, sex, grade level and education sector –25 and tAs shown in Table p<0.001). Similarly, a lower proportion of females met the combination of physical activity and screen time recommendations  as well as the combination of physical activity and sleep duration recommendations .Female students were less likely than male students to report meeting recommended levels of physical activity  and Year 11 . This same pattern of results was also observed for the combination of screen time and sleep duration recommendations . Students in Year 11 were less likely than the youngest students to be meeting the physical activity recommendation, both individually  and in combination with the screen time recommendation .Differences in compliance with the 24-hour movement guidelines by grade level were also apparent. Specifically, compared with Year 8 students, lower adherence to the screen time recommendation was reported by those in Year 10  and high  socio-economic areas were less likely to be meeting none of the 24-hour movement guidelines. Conversely, adherence to the sleep duration recommendation was higher among students residing in mid  and high  socio-economic areas. Students residing in high compared to low socio-economic areas were also more likely to be meeting the combination of screen time and sleep duration recommendations .Compared with students residing in low socio-economic areas, students residing in mid  and 2009-2010 . Similarly, the proportion of students meeting the combination of screen time and sleep duration recommendations was lower in 2018 than in previous survey rounds . Compliance with all three 24-hour movement guidelines has remained very low (<3%) across each survey round. However, students were more likely to be meeting none of the 24-hour movement guidelines in 2018 compared to 2009-2010 .As Table p<0.001) and 2009-2010 . Conversely, a higher proportion of students met only one of the three recommendations in 2018 compared to 2009-2010  reflecting a similar increase over time observed for meeting none of the 24-hour guidelines.Changes over time in the total number of recommendations being met by students were evident see Fig. . SpecifiThe results of the present study provide much needed data on how Australian adolescents are performing against the national 24-hour movement guidelines. Only a very small minority (2%) of students in our sample reported meeting all three recommendations for physical activity, screen time and sleep, and this has remained consistent over time. This figure is comparable to what has been found in cross-sectional studies conducted with adolescents in Asia –28, SoutThis Girl Can  limiting their daily sedentary recreational screen time to two hours or less. The observation that adolescent females are faring worse than their male counterparts with regard to physical activity is a disparity that is evident globally . Adolescn the UK  and VicHn the UK ) that han the UK , 44 may n the UK , 46.Our study also indicates that older Australian adolescents are less likely to be meeting the physical activity recommendation. This is consistent with findings from longitudinal studies which describe declines in physical activity levels during adolescence , 48. AltDespite previous reports of low adherence to physical activity and screen time recommendations among Australian adolescents , 50, ourSeveral study limitations should be acknowledged. Due to cost and feasibility issues associated with conducting large school-based surveys, information on students’ physical activity, screen time and sleep behaviours were self-reported and hence are subject to over- and under-estimations, potentially resulting in misclassification. As such, objective measurements of these behaviours are needed to validate our study findings. Further, our measure assessing sleep duration was calculated based on when students reported going to bed and turning the lights out and did not take into account varying lengths of time to get to sleep, interrupted sleep patterns, sleep quality and/or the use of electronic devices in bed after turning out the light. We also only measured sleep duration on school nights. Previous research suggests that adolescents are more likely to be meeting minimum sleep recommendations on non-school nights, possibly due to making up for inadequate sleep throughout the school week and/or being able to choose their own waking time on non-school mornings . Our metIn conclusion, our study indicates nearly universal non-compliance among Australian adolescents with the national 24-hour movement guidelines, with considerable scope for improvement with regard to the individual physical activity and screen time recommendations. These findings underscore the need for policy proposals and environmental interventions to better support all Australian adolescents in meeting the 24-hour movement guidelines. Our study also justifies the implementation of targeted strategies to redress socio-demographic disparities, given that we observed particularly low physical activity levels among females and older students, as well as inadequate sleep among students residing in low socio-economic areas.


JCI, Kanchan et al. leveraged the LEAP trial data to examine molecular genetic mechanisms of early sensitization. A previously identified HLA risk allele for peanut allergy (DQA1*01:02) was associated with peanut-specific IgG4 levels in consumers. Notably, IgG4 antibodies likely provide protection by reducing the binding of allergen to IgE. The association of the same allele with peanut allergy in avoiders while potentially conferring protection in consumers reinforces the need to integrate genetic information toward a personalized therapeutic strategy for the best outcome in addressing food allergies.The rising incidence of food allergy in children underscores the importance of environmental exposures; however, genetic factors play a major role. How the environment and genetics interact to cause food allergy remains unclear. The landmark Learning Early About Peanut Allergy (LEAP) clinical trial established that early peanut introduction protects high-risk infants, consistent with the tolerizing effects of gut exposure. In this issue of the  JCI, Kanchan et al.  and candidate gene association studies (CGAS). Both approaches reveal a robust association between food allergy and HLA class II region , althougn et al.  address n et al. .Kanchan et al.  discoverFuture work may focus on more comprehensive genotyping of the HLA locus. It is important to consider, though, that all the HLA alleles in the host present various epitopes in concert, and studying the full complement of HLA allelic variants would best determine the cumulative impact of such presentation. Alternative pipelines affording parallel, comprehensive analysis of HLA-DR, -DP, and -DQ alleles are needed to effectively attribute immune response to the HLA molecule.Kanchan and authors did not observe an association between HLA alleles and circulating (plasma) IL-4 and IL-10 levels in the consumption-arm participants . HoweverKanchan et al. invoked a dual–allergen exposure hypothesis, which posed that primary allergen exposure through skin is sensitizing and through the gut is tolerizing , 6. ThusMALT1 to strongly associate with peanut-specific IgE as well as Ara h 1–, 2–, and 3-specific IgE, primarily in the avoidance group  in the context of LEAP-ON trial (The outcome for participants who carry risk alleles, including ON trial  is of inMALT1 associates with the immune response to many peanut components, HLA-DQA1*01:02 is specific for Ara h 2, consistent with a narrower role (The results of Kanchan et al.  demonstrwer role . An impoKanchan et al.  also higAs the authors point out, consumption is so effective in preventing peanut allergy that it is not possible from their data to assess whether HLA-DQA1*01:02 associates with tolerance in consumers. Thus, the clinical relevance of this gene-environment interaction awaits future work. Despite the limitations, Kanchan et al. (


Background: Inflammatory markers, such as C-reactive Protein (CRP), Interleukin-6 (IL-6), tumor necrosis factor (TNF)-alpha and fibrinogen, are upregulated following acute stroke. Studies have shown associations of these biomarkers with increased mortality, recurrent vascular risk, and poor functional outcome. It is suggested that physical fitness training may play a role in decreasing long-term inflammatory activity and supports tissue recovery.Aim: We investigated the dynamics of selected inflammatory markers in the subacute phase following stroke and determined if fluctuations are associated with functional recovery up to 6 months. Further, we examined whether exposure to aerobic physical fitness training in the subacute phase influenced serum inflammatory markers over time.Methods: This is an exploratory analysis of patients enrolled in the multicenter randomized-controlled PHYS-STROKE trial. Patients within 45 days of stroke onset were randomized to receive either four weeks of aerobic physical fitness training or relaxation sessions. Generalized estimating equation models were used to investigate the dynamics of inflammatory markers and the associations of exposure to fitness training with serum inflammatory markers over time. Multiple logistic regression models were used to explore associations between inflammatory marker levels at baseline and three months after stroke and outcome at 3- or 6-months.Results: Irrespective of the intervention group, high sensitive CRP (hs-CRP), IL-6, and fibrinogen  were significantly lower at follow-up visits when compared to baseline . In our cohort, exposure to aerobic physical fitness training did not influence levels of inflammatory markers over time. In multivariate logistic regression analyses, increased baseline IL-6 and fibrinogen levels were inversely associated with worse outcome at 3 and 6 months. Increased levels of hs-CRP at 3 months after stroke were associated with impaired outcome at 6 months. We found no independent associations of TNF-alpha levels with investigated outcome parameters.Conclusion: Serum markers of inflammation were elevated after stroke and decreased within 6 months. In our cohort, exposure to aerobic physical fitness training did not modify the dynamics of inflammatory markers over time. Elevated IL-6 and fibrinogen levels in early subacute stroke were associated with worse outcome up to 6-months after stroke.Clinical Trial Registration:ClinicalTrials.gov, NCT01953549. Over the past decades, progress in stroke treatment has led to lower mortality rates but at the same time, increased numbers of stroke survivors implicate an increasing need for post-stroke rehabilitation. Stroke is the third leading cause of Disability Adjusted Life Years; 40% of stroke survivors are disabled between 1 month and 5 years after the event (Biomarkers have become a relevant topic in stroke research and multiple interesting markers have been identified . AmongstWe investigated the dynamics of high-sensitive C-reactive protein (hs-CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha  and fibrinogen in the subacute phase following stroke. With our intervention group receiving a 4-week fitness training program, we explored whether aerobic physical fitness training in the subacute phase is associated with the course of inflammatory biomarkers. Furthermore, we determined if fluctuations in inflammatory markers are associated with functional outcome after stroke.PHYS-STROKE trial. The details of the PHYS-STROKE study design and the main results have been published previously (PHYS-STROKE study can be found in PHYS-STROKE study (BAPTISe (“Biomarkers And Perfusion – Training-Induced Changes After Stroke”) and received additional blood biomarker measurements and MRI scans before and after the intervention .This exploratory analysis is part of an observational biomarker sub-study nested within the multicenter, randomized controlled, endpoint blinded eviously , 22. In eviously . PatientKE study . A subsarvention . PHYS-STClinical follow-up visits took place at study enrollment (baseline), after the 4-week trial intervention period (v1), as well as 3- (v2) and 6-months (v3) after stroke. Screening of patients was performed by the trial physician of the respective site. Study outcomes were assessed and documented by trained study assessors at each visit. Study assessors and the trial statistician were blinded to the intervention. Detailed descriptions of outcome parameters were reported previously , 22. ForFasting blood samples were retrieved at the recruiting rehabilitation center at each follow-up visit. Laboratory analyses of serum inflammatory markers were performed within 6 h at the “Labor Berlin” in Berlin, Germany. Blood levels of hs-CRP, IL-6 and TNF-alpha were determined using solid-phase, chemiluminescent immunometric assays . Measurement of fibrinogen levels was performed based on the Clauss method in citrated blood plasma by using the HemosIL® Q.F.A. Thrombin (Bovine) kit, Instrumentation Laboratories. Standard laboratory reference values were used to define elevated serum levels for each inflammatory marker: <3.0 mg/l for hs-CRP, <3.8 ng/l for IL-6, <8.1 pg/ml TNF-alpha, and 2–4 g/l for fibrinogen.Primary research questions of this project were the dynamics of inflammatory markers over a time course of 6 months and differences in inflammatory biomarkers between the two treatment groups. Secondary aims were the associations of inflammatory markers at baseline with functional outcome parameters at 3- and 6-months after stroke.Using Generalized estimating equation (GEE)-models, we investigated the dynamics of inflammatory markers, as well as associations of levels of inflammatory markers over time with respective patient characteristics and cerebrovascular risk factors. GEE-models allow estimating associations for repeated measurements . We usedp ≤ 0.1 in univariate analyses) to our models. Multivariate models were calculated using backward selection.We used multivariate logistic regression models to analyze associations between levels of inflammatory biomarkers at baseline and 3 months after stroke and functional outcome parameters up to 6 months. Functional outcome parameters  were dichotomized by the median and used as dependent variables in the multivariate models. As independent variables, we used absolute levels of single inflammatory markers (as continuous variables). Furthermore, we added sex, National Institutes of Health Stroke Scale (NIHSS) scores at baseline and age, as well as arterial hypertension  were randomized in the fitness and 95 (48%) in the relaxation group. The mean age of the study cohort was 69 years (SD ± 12) and 119 (60%) of our participants were male. One hundred eighty-one (90.5%) participants suffered from ischemic stroke, 19 (9.5%) from hemorrhagic stroke. At study enrollment, our cohort presented with a median NIHSS of 8 (IQR: 5–12), a median mRS of 4 (IQR: 4.00–4.00), a median BI of 50 (IQR: 35–60) and a median MWS of 0.30 m/s (IQR: 0.13–0.66 m/s). No significant differences between the two groups regarding the baseline characteristics were determined. Between September 2013 and April 2017, 200 participants were enrolled in the p < 0.01 for all three inflammatory markers and all time points from v1 to v3 as compared to baseline). TNF-alpha was elevated post-stroke and declined over the study period; however, no statistically significant decline was observed .The median time from stroke onset to the start of intervention was 28 days (IQR: 17–40). At baseline, the mean levels of all investigated inflammatory markers were above the clinical cut-offs. The mean serum concentrations of inflammatory markers showed a falling tendency over the 6-month observation period. The mean values of hs-CRP, IL-6, TNF-alpha, and fibrinogen over a time course of 6 months are depicted in p = 0.03), IL-6  and TNF-alpha  levels over time correlated with increasing age. Additionally, levels of inflammatory markers over time were associated with prevalent cardiovascular risk factors at baseline: participants with pre-existing arterial hypertension had significantly increased levels of hs-CRP  and smokers had significantly elevated hs-CRP  and IL-6  levels over the course of 6 months after stroke. Participants presenting with a history of cardiovascular, but not cerebrovascular, disease had significantly increased TNF-alpha levels over time , compared to participants without cardiovascular disease. Fibrinogen levels over time did not show any associations with investigated risk factors in GEE-models. GEE models demonstrated that elevating hs-CRP .The dynamics of inflammatory biomarkers over the 6-month observation period separated by treatment groups are presented in In univariate models, increased baseline levels of all investigated markers were associated with worse functional outcome measurements at 3 or 6 months after stroke. Median splits of functional outcome parameters and the results of univariate analyses can be found in the Supplementary Material , 4. TablIn the presented study we analyzed consecutive serum levels of inflammatory markers of patients with subacute stroke. We observed upregulated inflammatory activity after stroke and a decline over a period of 6-months. Levels of investigated inflammatory markers were not modified in magnitude or dynamic by early aerobic physical fitness training. Elevated levels of IL-6 and fibrinogen in the early subacute phase showed associations with functional impairment up to 6-months.Hs-CRP is the marker that has received the most attention over the past decades when it comes to inflammatory biomarkers in stroke research. Many studies have underlined an elevation of hs-CRP after stroke and an association of higher levels with an unfavorable outcome . We founThere is growing evidence that elevated blood levels of IL-6 in acute stroke are associated with stroke lesion volume, stroke severity, post-stroke infection as well as worse short- and long-term outcome and death , 30–32. There is good evidence that fibrinogen is involved in cardiovascular disease and negatively associated with clinical outcome –36. EvidStudies on TNF-alpha and stroke have yielded inconsistent results. Zaremba et al. indicated that elevated levels of TNF-alpha within 24 h of stroke-onset  are correlated with lower BI scores up to 2 weeks . In contPHYS-STROKE study analyses . Blood levels of inflammatory markers might therefor vary between participants, with participants included at a later time-point post-stroke showing decreased inflammatory activity. Secondly, the effect that aerobic fitness training has on inflammatory markers might differ with different time points of training-initiation, resulting in us finding no significant effects in our cohort. Thirdly, we did not adapt for possible differences in activities outside the study intervention, as well as activities post-intervention. That could have influenced our study results. Moreover, we did not exclude patients with a diagnosis of infection or other conditions that lead to an upregulation of blood inflammatory markers.Our study contributes to the literature on understanding the dynamics of inflammatory processes in stroke. In association with clinical outcome, the observed fluctuations can help to comprehend the role of inflammatory processes post-stroke. Including the measurement of inflammatory markers in routine stroke management can help identify patients that are at risk for long-term functional impairment. Models of outcome prediction including blood levels of inflammatory markers (single markers as well as a panel of markers) need to be validated in large, independent cohort studies. To understand the impact of fitness training on inflammation in stroke patients, researchers must first identify the ideal training program for counteracting inflammation. This research will support the important goal of identifying treatment options to blunt the neurotoxic effects of post-stroke inflammation in clinical practice. It remains unclear whether targeted anti-inflammatory treatment for stroke patients could prevent long-term stroke deterioration and this work is certainly deserving further research.Serum levels of hs-CRP, IL-6 and fibrinogen decrease up to 6 months following stroke. Aerobic fitness training did not modify levels of inflammatory markers compared to relaxation over time. Increased IL-6 and fibrinogen levels in early and late subacute stroke are associated with worse outcome up to 6-months after stroke.The raw data will be made available upon reasonable request to the authors.The studies involving human participants were reviewed and approved by Institutional review board of Charité–Universitätsmedizin Berlin. The patients/participants provided their written informed consent to participate in this study.AF, MEb, AN, and TR conceived or designed the study and supervised the study. BK drafted the manuscript and contributed in statistical analysis. TR, AF, MEb, and MEn critically revised the manuscript for important intellectual content and gave final approval of the version to be published.MEn reports grants from Bayer and fees paid to the Charité from AstraZeneca, Bayer, Boehringer Ingelheim, BMS, Daiichi Sankyo, Amgen, GSK, Sanofi, Covidien, Novartis, Pfizer, all outside the submitted work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


P < .0001).ß-d-N4-hydroxycytidine (NHC), the parent nucleoside of molnupiravir, a COVID-19 antiviral, was quantified at SARS-CoV-2 transmission sites in 12 patients enrolled in AGILE Candidate-Specific Trial-2. Saliva, nasal, and tear NHC concentrations were 3%, 21%, and 22% that of plasma. Saliva and nasal NHC were significantly correlated with plasma (Clinical Trials Registration. NCT04746183. An extended therapeutic goal of antiviral therapy is the prevention of infection in individuals who have been exposed to an infected person. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection occurs through inhalation or inoculation of virus onto upper respiratory airways and mucosal surfaces. In order to be an effective prophylactic agent, a drug must penetrate into these sites in sufficient quantities.Molnupiravir EIDD-2801; MK-4482), a prodrug of the ribonucleoside analogue β-d-N4-hydroxycytidine (NHC), has recently been licensed in the United Kingdom and received US Food and Drug Administration FDA (FDA) emergency use authorization (EUA) in the United States for the treatment of symptomatic coronavirus disease 2019 (COVID-19) in adults with at least 1 risk factor for developing severe disease. Following oral administration, molnupiravir is rapidly hydrolyzed by esterases to NHC, which is phosphorylated by host kinases to active intracellular metabolite EIDD-1931-5’-triphosphate (EIDD-2061) ; n = 16); the majority of individual ratios were between 0.01 and 0.04 (n = 12). Individual NHC RNP:P for nasal secretions and tears were more variable  and, overall, approximately 6-fold higher than saliva RNP:P . Geometric mean (CV%) NHC RNP:P stratified by molnupiravir dose and study day are described in NHC saliva concentrations were approximately 3% that of plasma , whereas statistically significant relationships were not observed for paired tear and plasma NHC concentrations . Time post-dose was included as a repeated effect, but addition of random effects for intercept and slope did not improve the models.NHC concentrations in saliva and nasal secretions were significantly associated with paired plasma on day 1 and day 5 , are orally administered antivirals licensed in the United Kingdom and with FDA EUA in the United States for early treatment of mild to moderate COVID-19 in adults with at least 1 risk factor for developing severe disease. Molnupiravir is currently under phase 2 evaluation within AGILE including mild to moderate COVID-19 without risk factors and in both unvaccinated and vaccinated patients. Molnupiravir is also being investigated for prophylactic use in household contacts of symptomatic COVID-19 patients (MOVe-AHEAD). Knowledge of drug accumulation within the upper airways and mucosal secretions will inform and support future research in this area.0-4 ratios). Of the measured saliva, nasal, and tear samples, 6%, 50%, and 61%, respectively, were within or above the NHC 90% effective concentration (EC90) against SARS-CoV-2 in primary human airway epithelia cultures [We observed saliva NHC concentrations that were 3% that of plasma, whereas exposure in nasal secretions and tears was higher at approximately 20% that of plasma . A strong correlation between saliva and plasma NHC concentrations implies (assuming a 1-compartment model) that salivary accumulation is dependent on the plasma concentration. Mucosal permeability and protein binding are major factors in determining salivary drug accumulation, since only unbound drug is available for diffusion into saliva . NHC exiNP:P; contributed to data variability; and limited data interpretation. Finally, the active triphosphate metabolite, EIDD-2061, was not quantified. Despite these limitations, these data add to our understanding of NHC pharmacokinetics, principally at sites of COVID-19 infection.There are a number of limitations of this study. The small sample size, which is typical of early-phase studies, only allowed for a descriptive interpretation of NHC pharmacokinetics and was underpowered for statistical comparisons between matrices. We used a truncated sampling schedule between 0 and 4 hours to limit infection risk; therefore, NHC elimination over the 12-hour dosing interval could not be fully assessed in nonplasma compartments. Missing samples led to exclusions from the analysis, particularly for evaluation of RTo our knowledge, we are the first to describe penetration of NHC into nasal secretions and tears and, to a lesser extent, into saliva. These data support the evaluation of molnupiravir as prophylaxis for SARS-CoV-2 infection.Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.Supplementary materials are available at ciac199_suppl_Supplementary_MaterialClick here for additional data file.


Proliferative verrucous leukoplakia is considered by the WHO as an oral potentially malignant disorder that presents the highest tendency to recurrence and malignant transformation rate. However, to date limited evidence-based prognostic data for oral carcinomas developed in patients with proliferative verrucous leukoplakia (PVL-OC) have been published, and these carcinomas probably perform better than conventional oral carcinomas. In this study we present a systematic review and meta-analysis to evaluate the current evidence in relation to the prognosis of oral carcinomas developed in patients PVL-OC.p = 0.03). Univariable meta-regression verified that case series that presented higher proportions of verrucous carcinomas showed a better survival of PVL-OC (p = 0.05), but not with higher proportion of oral squamous cell carcinomas (p = 0.74). Significant differences were not found for other relevant variables such as follow up period (p = 0.44) or multiple tumor development (p = 0.74). In conclusion, PVL-OC show favorable prognostic parameters, especially with regard to the mortality rate.Proliferative verrucous leukoplakia (PVL) is contemplated by the World Health Organization (WHO) as an oral potentially malignant disorder (OPMD) with a high the highest malignant transformation ratio among all OPMD (approximately 50%). Our aim was to evaluate the current evidence in relation to the prognosis of oral carcinoma developed in patients with proliferative verrucous leukoplakia (PVL-OC). We searched PubMed, Embase, Web of Science and Scopus for published studies (upper date limit = June 2021). We evaluated the quality of studies (QUIPS tool). We carried out meta-analyses, examined inter-study heterogeneity through subgroup and meta-regression analyses, and performed sensitivity and small-study effects analyses to test the stability and reliability of results. 23 studies met inclusion criteria . The meta-analyzed overall mortality rate was 21.29%  for PVL-OC, clearly lower than the 34.7–50% mortality rate for conventional oral cancer reported in previous studies. In comparison with a single study reporting on conventional oral cancers, mortality was significantly lower for PVL-OC (hazard ratio = 0.29 [95%CI = 0.10–0.89],  An international seminar convened by the World Health Organization (WHO) Collaborating Center for Oral Cancer in Glasgow (Scotland) in 2020 has reported an update on nomenclature and classification of oral potentially malignant disorders (OPMD) . ProlifeOne of the groups that has studied this aspect in greater depth has reported that in its series of 55 patients with PVL, 27 patients developed oral cancer of which 11 patients developed multiple tumors (40.74%) . One migBased on this background, it seems pertinent to evaluate current evidence in relation to the prognosis of PVL-OC, to determine the mortality rate of PVL-OC and to explore the impact of potential covariables on survival .Our study was designed complying with high standards criteria developed by Cochrane Handbook for Systematic Reviews of Interventions , Cochranwww.crd.york.ac.uk/PROSPERO; CRD42021267896 code was assigned) ). There was a significant high degree of inter-study heterogeneity . A considerable high degree of heterogeneity was found among studies . A high degree of heterogeneity was observed , M- status (M+: PP = 0.07% [95%CI = 0.00–18.63]), and present an early (I/II) clinical stage (PP = 89.88% [95%CI = 59.21–100.0]). Nevertheless, these meta-analyses were conducted over low sample sizes (n = 2 to 3 studies), showing imprecision  and therefore, a low quality of evidence  than the probability of death from a conventional carcinoma. The reason for justifying this low mortality is currently unknown. There are not enough studies that offer data on the TNM parameters and PVL-OC stage, so we cannot provide any information in this regard. However, in relation to the influence of the PVL-OC lineage, some interesting considerations should be made. Our meta-analysis shows that 33.66% of the patients developed verrucous carcinomas while 72.21% developed OSCCs. Likewise, we verified that those series that presented higher proportions of verrucous carcinomas showed a better patient survival (p = 0.05), which is absolutely logical if we take into account that verrucous carcinoma exclusively develop local malignancy with no tendency to metastasis; on the contrary, in those series that presented higher proportions of OSCCs, mortality did not worsen (p = 0.74), which indicates in our view that PVLs generate OSCC in themselves with a good prognosis. Our group has recently reported that OSCCs developed on oral lichen planus (OLP) also present a good prognosis with low mortality rates (15.48%) [Our current systematic review carried out on a total of 23 studies and 543 patients with PVL indicates that 288 patients developed 504 oral carcinomas. Today it is accepted that PVL is the OPMD that presents the highest malignant transformation rate, close to 50% of cases ,56,57. D(15.48%) , which wp = 0.74). This observation has also been made and reported by our research group in patients who develop multiple carcinomas in OLP [p = 0.44), which is probably also indicating the good behavior of PVL-OC related on characteristics inherent to their own biopathology.Our results regarding multiple tumor development in PVL are also of interest. It could be expected that the development of multiple tumors in PVL could lead to higher patient’s mortality. However, this is not the case in our meta-analysis, mortality was not affected by PVL giving rise to multiple tumors  future studies investigating the behavior of oral carcinomas in PVL patients report in greater detail the demographic characteristics of patients with cancer development; (2) the follow-up periods must be long and well communicated; this is essential in prognostic studies, where it must be guaranteed a minimum period of time that allows the appearance of the investigated event ; (3) studies should diagnose patients with PVL based on exhaustive diagnostic criteria, preferably evidence-based ; (4) th2 statistic, which estimates the proportion of the inter-study variance explained by covariates; important explanatory sources of heterogeneity were found after the application of this method . Second, the sample sizes of the primary-level studies were very small . However, due to the low prevalence of PVL, this limitation represents a difficult challenge to overcome. Future multicenter cohort studies should be developed to obtain more precise results, with narrower confidence intervals, providing a higher quality of evidence. Finally, an inherent limitation of the included primary-level studies -in our methodological reflections on RoB- was the failure to report relevant information and parameters, limiting the number of observations in secondary analyses . Given the methodological and clinical relevance of these variables, future studies should publish their datasets in a more rigorous way, preferably reporting individual patient data. Despite the above limitations, our systematic review and meta-analysis is innovative—showing for the first time more precise and evidence-based results, derived from-208 PVL-OC which developed 504 oral carcinomas—robust, and reliable; it is also supported by our sensitivity analyses and funnel plots and provides recommendations for the design of future studies on this topic.Our study also presents some potential limitations that should also be discussed. First, a considerable degree of inter-study heterogeneity was observed, a frequent finding in meta-analyses of proportions . ConsequIn conclusion, our systematic review and meta-analysis presents consistent results and evidence that PVL-OC show a favorable prognostic behavior hypothetically related to characteristics inherent to the tumor’s own biopathology. Future studies on this topic should report prognostic clinical–pathological data for these carcinomas in order to elucidate the reasons for this statement.


Understanding the extent to which population-level suicide risk screening facilities follow-up and engagement in mental health treatment is important as engaging at-risk individuals in treatment is critical to reducing suicidal behaviors.To evaluate mental health follow-up and treatment engagement in the Veterans Health Administration (VHA) following administration of the Columbia-Suicide Severity Rating Scale (C-SSRS) screen, a component of the VHA’s universal suicide risk screening program.This cross-sectional study used data from VA’s Corporate Data Warehouse.140 VHA Medical Centers.Patients who completed the C-SSRS screen in ambulatory care between October 1, 2018—September 30, 2020.Standardized suicide risk screening.Mental health follow-up (one or more visits within 30 days of C-SSRS screening) and treatment engagement (two or more visits within 90 days of C-SSRS screening) were examined.P<0.001). For FY19, a positive C-SSRS screen in non-mental health connected patients was associated with an increased probability of follow-up from 49.8% to 79.5% (relative risk = 1.60) and engagement from 39.5% to 63.6% (relative risk = 1.61). For mental health-connected patients, a positive C-SSRS screen was associated with a smaller increase in probability of follow-up from 75.8% to 87.6% (relative risk = 1.16) and engagement from 63.3% to 76.4% (relative risk = 1.21). Results for FY20 were similar.97,224 Veterans in Fiscal Year 2019 (FY19)  and 58,693 Veterans in FY20  received the C-SSRS screen. Across FYs, a positive C-SSRS screen was associated with increased probability of mental health follow-up and treatment engagement. Patients who were not seen in mental health in the year prior to screening had the greatest increase in probability of mental health follow-up and engagement following a positive screen (Identification of suicide risk through population-level screening was associated with increased mental health follow-up and engagement, particularly for non-mental health connected patients. Findings support the use of a standardized, comprehensive suicide risk screening program for managing elevated suicide risk in a large healthcare system. In October of 2018, the Veterans Health Administration (VHA) implemented a national enterprise-wide suicide risk screening and evaluation initiative known as the VA Suicide Risk Identification Strategy (Risk ID) . As suchRisk ID facilitates identification of suicide risk through two key processes: suicide risk screening and comprehensive suicide risk evaluation. Early implementation of Risk ID (October 2018- December 2020) consisted of two stages of screening to identify individuals with actionable risk who may require further evaluation: the primary suicide risk screen, item 9 (i9) of the Patient Health Questionnaire-9 (PHQ-9), and the secondary suicide risk screen, the Columbia-Suicide Severity Rating Scale (C-SSRS) screen. , 5.Bahraini et al.  demonstrWhile these findings are promising, further research is needed to evaluate the clinical utility of Risk ID. Ideally, a positive screen for suicide risk will facilitate timely connection to behavioral or MH services for further evaluation and/or management of risk as clinically indicated. Understanding the extent to which population-level suicide risk screening facilities follow-up and engagement in MH treatment is important, as research has shown that engaging individuals at risk for suicide in treatment is critical to reducing suicidal behaviors –10. MoreThis was a nationwide, retrospective study of pre-existing medical records for patients receiving care in the VHA. Additional details regarding Risk ID can be found in Bahraini et al. . This stData were extracted for patients who: a) were eligible for suicide risk screening during fiscal years (FY) 2019–2020 ; and, b) completed the C-SSRS screen after a positive primary screen. Patients were eligible for Risk ID screening if they had at least one outpatient visit in an ambulatory care setting  and were due for depression and/or PTSD screening at the time of the visit. As part of Risk ID, the primary screen (i9 of the PHQ-9)  was addeThe C-SSRS screen is a 6-item measure derived from the original version of the C-SSRS . The firData were extracted from the VA Corporate Data Warehouse. Dates of MH visits—defined as VHA outpatient encounters that used a 500-series Decision Support System Identifier (“stop code”) in the primary or secondary position and counted for workload credit—were used to generate the two primary outcomes for this study: MH follow-up and MH engagement. These stop codes included all MH specialty  and integrated care clinics . MH follow-up was defined as having at least one MH visit within 30 days of C-SSRS screening, and MH engagement was defined as having at least two MH visits, on separate days, within 90 days of C-SSRS screening. Additionally, these dates were used to establish whether a patient had a history of recent VHA MH treatment, which was defined as having at least one MH visit in the year prior to C-SSRS screening.th Revision (ICD-10) codes were obtained from any VHA outpatient encounter in the year prior to screening. The codes were then grouped into the presence or absence of the following five diagnostic categories: substance use disorders, defined as any F10-F19 ICD-10 code (or more specific subcode); mood disorders, including bipolar disorders, defined as any F30-F34 or F39 ICD-10 code; anxiety disorders, defined as any F40-F42 ICD-10 code; posttraumatic stress disorder (PTSD), defined as an F43.1 ICD-10 code; and other mental health-related disorders not otherwise captured in the preceding categories. A sixth domain, marked as “MH diagnoses unknown,” was used with patients for whom no diagnostic information was available because they had zero outpatient encounters that required a diagnosis across the VHA system in the prior year. The decision to create the “MH diagnoses unknown” category was made to permit inclusion of this subgroup of cases in analyses rather than remove them for having missing data and to avoid potentially mischaracterizing this subgroup as having no MH diagnoses . It should be noted that this category likely captures information regarding overall healthcare engagement in addition to MH diagnoses.Demographic information and MH diagnosis history were also extracted for eligible patients. For a given patient, MH-related International Classification of Diseases and Related Health Problems, 10Analyses were conducted using R 4.0.5 software . ModifiePatient demographic information is presented in greater increase in probability of follow-up for patients without recent MH contact who screened positive versus patients who screened positive but had a MH visit in the past year. This effect was quite substantial  had an increased probability of follow-up compared to Veterans without documented MH diagnoses in the year prior to screening.Results for MH engagement are presented in Again, the relative risks for statistically significant covariates tended to be fairly small, and Veterans without available MH diagnostic information were more likely to engage with MH compared to Veterans without documented MH diagnoses in the year prior to screening. Female gender was associated with increased probability of MH engagement in both years, as was Hispanic or Latino/a ethnicity. Compared to White Veterans, African American Veterans were more likely to have MH engagement in FY19, whereas American Indian or Alaska Native Veterans, Asian Veterans and Veterans for whom race was unknown were less likely. In FY20, compared to White Veterans, African American Veterans and Multi-racial Veterans were more likely to have MH engagement, whereas Asian Veterans were less likely.To our knowledge, this study is the first to show that population-based suicide risk screening in non-acute care settings facilities MH follow-up and treatment engagement among those identified to be at elevated risk. Overall, findings demonstrated that among patients screened in VHA ambulatory care settings, the probability of MH follow-up and longer-term engagement was higher among those who had a positive secondary screen for suicide risk, compared to those with a negative secondary screen.any outpatient VHA encounter in the year prior to screening. Yet, all of these patients reported some level of suicidal ideation on the primary suicide screen  which tThese data suggest that population-level suicide risk screening may be an effective method of not only identifying patients with elevated yet likely unrecognized risk, but also engaging these patients in MH care. The findings for non-MH connected patients are particularly compelling given that most of the studies in this area have primarily focused on outpatient treatment engagement following acute care  –10.Even among MH-connected patients, those with a positive C-SSRS screen were still 16% more likely to have timely follow-up compared to those with a negative screen. Given that most of these patients had timely follow-up regardless of screening results, even a 16% increase associated with a positive C-SSRS screen is clinically meaningful. For a subset of these patients, suicide risk screening may help facilitate timely re-engagement in MH care or it may prompt more targeted interventions aimed at reducing suicide risk.Lower probability of engagement, compared to follow-up, after a positive suicide screen were shown in both FY19 and FY20. There are several likely explanations for this finding. It may be that providers are prioritizing quick connection following a positive secondary screen for further suicide risk evaluation. Once evaluated, patients who are deemed to be at higher levels of risk may continue engaging in MH treatment over a longer period of time, while those at lower levels of acute risk may be managed in primary care or through other brief interventions . However, as demonstrated in other studies, both patient level barriers , and provider/systems level barriers  could have also contributed to lower probability of ongoing MH engagement , 17. FutAlthough results across fiscal years were not statistically compared, increased probabilities of follow-up and engagement were observed in FY20. There were also fewer C-SSRS screens completed in FY20 due to the overall decrease in healthcare visits and encounters following the onset of the coronavirus disease of 2019 (COVID-19) pandemic. Despite the decrease in encounters, it is notable that probabilities of MH follow-up and engagement following a positive screen increased in FY20. The shift to system-wide virtual care brought on by the pandemic may have facilitated increased MH engagement by reducing certain barriers to care. Veterans at risk for suicide may also have been more motivated to engage in MH treatment due to the additional stressors brought on by the pandemic . Though we did not specifically examine pre- and post-COVID-19 changes in screening and follow-up during FY20, further research examining the impact of COVID-19 on the implementation of suicide risk screening, the prevalence of positive suicide screens and follow-up care may shed further light on these findings.There are several potential limitations with this study. MH encounters were determined using the 500 series stop codes which may have led to instances in which MH encounters were missed because they were classified incorrectly or MH care was given in other integrative care settings not captured in the 500 series . Another limitation is that we examined any MH treatment encounter in 30- and 90-days post-screening and did not differentiate between the type of encounter, location of care, or procedures received. Future research examining the characteristics and delivery of MH care following a positive suicide risk screen may help inform strategies to increase treatment engagement among at-risk patients. In particular, examining differences in follow-up and engagement across MH specialty and integrated care clinics  is one area that warrants further investigation and may provide a better understanding of whether these models of care are differentially associated with ongoing treatment engagement for MH-connected and non-MH connected patients. Furthermore, it will also be important to understand which practices may be more effective at engaging patients at different levels of acute and chronic risk  in MH care. Lastly, future research should also focus on examining the impact of suicide risk screening and subsequent care processes on suicide outcomes.Connecting and engaging at-risk patients in MH care is a cornerstone of suicide prevention. This study demonstrated that a nationwide population-level suicide risk screening program facilitated follow-up MH care and longer-term MH treatment engagement among those identified to be at elevated risk. The probability of timely MH follow-up and treatment engagement was even higher among patients who were not connected to MH in the year prior to screening. Future research examining factors that impact MH treatment engagement following a positive suicide screen in medical settings is needed to ensure that patients with elevated risk are connected to needed treatment. Moreover, in order to fully understand the impact of population-level screening on suicide, studies that examine the degree to which screening facilitates access to timely evidence-based interventions shown to reduce suicidal behavior are also warranted.S1 FigThe gray bars represent having received mental health treatment in past year. The white bars represent not having received mental health treatment in the past year. Error bars represent standard error. C-SSRS = Columbia-Suicide Severity Rating Scale.(PDF)Click here for additional data file.S2 FigThe gray bars represent having received mental health treatment in past year. The white bars represent not having received mental health treatment in the past year. Error bars represent standard error. C-SSRS = Columbia-Suicide Severity Rating Scale.(PDF)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.