Dengue is a vector-borne viral disease of humans that endemically circulates in many tropical and subtropical regions worldwide. Infection with dengue can result in a range of disease outcomes. A considerable amount of research has sought to improve our understanding of this variation in disease outcomes and to identify predictors of severe disease. Contributing to this research, patterns of viral load in dengue infected patients have been quantified, with analyses indicating that peak viral load levels, rates of viral load decline, and time to peak viremia are useful predictors of severe disease. Here, we take a complementary approach to understanding patterns of clinical manifestation and inter-individual variation in viral load dynamics. Specifically, we statistically fit mathematical within-host models of dengue to individual-level viral load data to test virological and immunological hypotheses explaining inter-individual variation in dengue viral load. We choose between alternative models using model selection criteria to determine which hypotheses are best supported by the data. We first show that the cellular immune response plays an important role in regulating viral load in secondary dengue infections. We then provide statistical support for the process of antibody-dependent enhancement  in the development of severe disease in secondary dengue infections. Finally, we show statistical support for serotype-specific differences in viral infectivity rates, with infectivity rates of dengue serotypes 2 and 3 exceeding those of serotype 1. These results contribute to our understanding of dengue viral load patterns and their relationship to the development of severe dengue disease. They further have implications for understanding how dengue transmissibility may depend on the immune status of infected individuals and the identity of the infecting serotype. Dengue is an important vector-borne disease that infects four-hundred million individuals annually. Infection results in a wide range of clinical symptoms. Though many risk factors of dengue are known, the mechanisms explaining why an individual will suffer severe symptoms are poorly understood. Clinical studies have shown characteristics of viral load kinetics of dengue-infected individuals may be indicators of disease severity. However, viral load measurements vary considerably by individual. Here we use statistical methods to empirically test hypotheses that may explain variation in dengue viral load patterns by clinical manifestation and by serotype. We show that there is statistical support for antibodies being responsible for higher disease severity during secondary dengue infections and for high viral infectivity rates of dengue serotypes 2 and 3 relative to dengue 1. These results further understanding of the relationship between viral load patterns and severe dengue disease and have important implications for dengue transmissibility. Dengue is an important arthropod-borne virus whose incidence and spatial extent have increased dramatically in recent years . The virDespite limitations in our current understanding of dengue pathogenesis, key risk factors for the development of severe dengue disease have been identified in longitudinal epidemiological studies –13. ThesLongitudinal epidemiological studies have further indicated that the identity of the infecting serotype can impact the risk of developing severe dengue disease. For example, a study in Nicaragua indicated that primary DENV-1 infections were associated with more severe clinical manifestations than primary DENV-2 infections . A studyIn vitro studies have shown that the Asian I genotype of DENV-2 appears to have a higher replication rate than the American genotype of this serotype [There is further evidence indicating that dengue genotypes can differ in virulence . The Asiserotype . Other sserotype  and in pserotype , both ofTo better understand the mechanisms underlying these important risk factors for the development of severe disease, viral load patterns of dengue-infected patients have become increasingly characterized. High viral loads are generally associated with severe dengue disease –25, and Here, our aim is to improve our understanding of the within-host mechanisms leading to observed variation in viral load dynamics across dengue-infected patients. Insight into these mechanisms can shed light on why certain viral load features may be useful for predicting dengue disease outcome and on the mechanistic basis behind identified risk factors. Our approach is statistical in nature: we fit mechanistic within-host dengue models to viral load measurements from infected patients and use a Bayesian approach to choose between the models to identify significant drivers of viral load variation between patients. Bayesian methods have become an increasingly popular approach for parameter inference , in partBayesian inference via Markov chain Monte Carlo (MCMC) has been used for inference of within-host HIV models , 30, as To complement this work, we here consider the viral load dynamics across dengue serotypes DENV-1, DENV-2, and DENV-3, while still taking into account that variation in viral load might arise from differences in the immune status, clinical manifestation, and/or infecting serotype of patients. Specifically, we consider models of increasing complexity that reflect hypotheses that have been put forward in the dengue literature. We first consider a model in which all observed individual viral load measurements follow the same within-host dynamics. We build on this model by allowing within-host dynamics of secondary heterologous infections to differ from those of primary infections in their ability to rapidly invoke a cellular immune response. Following these models, we consider processes that have been invoked to explain differences in clinical manifestation outcomes: original antigenic sin (OAS) of T-cells and antibody dependent enhancement (ADE). Finally, we consider models that capture serotype-specific differences in viral infectivity rate, the strength of the elicited innate immune response, and the strength of the elicited T-cell response. Comparison of model fits using both the Bayesian Information Criterion (BIC) and the Deviance Information Criterion (DIC) first indicate that variation in viral load is in part explained by individual immune status (primary or secondary infection), with ADE contributing to whether a patient develops DHF in a secondary dengue infection. Variation in viral load is further explained by the identity of the infecting serotype, with the best supported hypothesis having serotypes differ in viral infectivity rate. Specifically, our analysis shows that viral infectivity rates of DENV-2 and DENV-3 are significantly higher than those of DENV-1. In the Discussion, we consider the implications of these results for understanding patterns of dengue disease severity and transmissibility.We statistically analyze individual-level dengue viral load data from a clinical trial of the antiviral drug chloroquine. This trial enrolled adult dengue patients at the Hospital for Tropical Diseases in Ho Chi Minh City, Vietnam , 33. PreX is the number of uninfected cells, Y is the number of infected cells, V is the concentration of free virus, N is the number of natural killer (NK) cells, and T is the number of T-cells. Free virus infects target cells at rate β, NK cells clear infected cells at rate α, T-cells clear infected cells at rate δT, infected cells produce free virus at rate ω, free virus is cleared at rate κ, infected cells stimulate the production of NK cells at rate q, NK cells decay at rate dN, the interaction of T-cells and infected cells stimulate the production of T-cells at rate qT, and T-cells decay at rate dT. These equations are based on a previously published within-host mathematical model for dengue that is capable of reproducing characteristic features of both primary and secondary dengue infections [We consider dengue within-host dynamics to be governed by the following set of equations:fections .T0) to zero in primary infection model fits. Note that β can implicitly incorporate the role of antibodies in enhancing viral infectivity in certain subjects. In both primary and secondary infections, the innate immune response (represented by NK cells) plays a pivotal role in regulating viral dynamics. This assumption is based on empirical studies that have shown support for the innate immune response (over the adaptive immune response) in clearing primary dengue infections , 36. TheX0, Y0, V0, N0, and T0) and 9 model parameters   . We firsq and α and parameters β and ω cannot be independently estimated. Due to a lack of knowledge of the value of either q and α, we assigned α an arbitrary value of 10−3 per day for all analyses. We further assign ω a value of 104 copies/cell/day based on a virological study  and NK cells (N0) both to 0. Following [X0) to 107 cells/ml  based on literature estimates , we do not attempt to estimate IP. Instead, we rely on results from a study that used Bayesian time-to-event models to estimate IP durations using observations from 35 empirical dengue studies [log(IPg) where IPg is 5.9 days [σI.Because the viral load data were reported as a function of time since the onset of symptoms and  studies . This st studies . We set 5.9 days  and esti10 viremia measurements have normally-distributed measurement errors.To fit IPj for individual j, the likelihood of the model parameters γ given this individual’s viral load data Dj is given by:Given an incubation period n of individual j. A given viral load data point k is provided at time tk, measured in days since the onset of symptoms. Dj(tk) is thus the viremia measurement from individual j at tk days since the onset of symptoms. To account for the incubation period, the model-predicted viremia measurements M are measured at times tk + IPj. Both Dj and M viremia levels are on the log10 scale. ϕ is the Gaussian probability density function (pdf), where the parameter quantifying the standard deviation of the measurement error is σϵ, which, as in [Dj(tk) only if the data point Dj(tk) lies above the limit of detection (LOD). Φ is the Gaussian cumulative distribution function (cdf). We use the Gaussian cdf to calculate the probability of observing a data point Dj(tk) at or below the LOD. Rather than sample the IPjs individually, we integrate them out numerically, assuming the probability density of IPj is given by The likelihood is evaluated over the number of viral load data points h, as in , 32, we V0 are close to 0 but cannot be 0, instead of estimating V0 directly we estimate log10(V0). We use uniform improper priors for all estimated parameters.We use the Metropolis-Hastings algorithm for MCMC parameter estimation with a multivariate normal proposal distribution truncated to the positive quadrant as the model parameters shown in The MCMC algorithm was run for a total of 300,000 iterations, with a burn-in of 150,000 iterations. Each model we considered was run for 4 different sets of initial conditions of all parameters estimated. We assessed model convergence by calculating the Rubin-Gelman convergence diagnostic for each model and ensuring that arameter , as wellj we marginalize over the individual incubation periods during the process of parameter estimation , our plotting of individual incubation periods is simply to show the most likely incubation periods for individuals of a certain group relative to individuals of other groups. Draws from the joint posterior are obtained by sampling from the conditional distributions of the incubation periods given sampled parameters γ, proportional to p(IPj|logN(log(IPg), σI))Lj(γ|Dj).In some of the figures shown in the Results section, we plot estimates of individual incubation periods. Since for each individual We estimate parameters of T0 = 0, qT is not estimated). It further assumes that viral load dynamics do not differ by the infecting serotype or by disease manifestation (DF/DHF). For this model we fit four parameters  and one initial condition V0.The first model we fit (model 0) is the most basic dengue model: it assumes that viral load dynamics are regulated by the innate immune response, and that the adaptive immune response plays a negligible role in regulating viral dynamics in both primary and secondary dengue infections  and one initial condition V0.The second model we fit (model 1) assumes that T-cells are important in clearing infected cells during secondary infections, but not in primary infections. We implement this model by fitting the primary infection data to 1 and model OAS2). OAS proposes that fast-acting memory T-cell populations cause a strong pro-inflammatory response during secondary heterologous dengue infections. Yet, because these populations have low avidity to the infecting (secondary) virus, the protective effects of the T-cells in lysing infected cells are suboptimal [The next models we consider aim to address whether there are differences in viral load dynamics between individuals presenting with different clinical manifestations (DF vs DHF), and, if so, the mechanism(s) responsible for these differences. We first consider models that incorporate the process of original antigenic sin of T-cells  in which we allow the rate of T-cell lysis of infected cells (δT) to vary by clinical manifestation (DF or DHF) during a secondary infection, with the expectation that δT would be lower in DHF-manifesting individuals relative to DF-manifesting individuals. Second, we consider a model (OAS2) in which in addition to the T-cell lysis rate δT, the T-cell activation rate qT also varies by clinical manifestation. For model OAS2 we expect qT will be higher in DHF-manifesting individuals relative to DF-manifesting individuals. Note that in model 1 we fixed the value of δT at 10−6 per day. In fitting both the OAS1 model and the OAS2 model, we therefore chose to fix OAS2 we parameterize We formulate two variants of the OAS model. First, we consider a model  during a secondary infection. We assume the same β value for primary DF and secondary DF individuals. We parameterize β values for secondary DHF cases by letting βDHF = βDF + Δβ, and estimating Δβ and βDF, where Δβ may be positive or negative.We next consider a model that incorporates the process of antibody dependent enhancement (model ADE). ADE proposes that antibodies produced in a primary infection cannot completely neutralize virus present in a secondary heterologous infection. However, the resulting partially neutralized immune complexes can enter Fc-ectivity , 10. Stuin vitro , 49 and [in vivo . The ADEIn vitro studies have shown that the DENV-2 genotypes that cause more severe disease have higher replication rates than those that cause milder disease [β to differ by serotype (model SSβ). Due to problems of identifiability discussed above, we do not consider a model that varies the viral replication rate (ω).We also consider models that aim to address whether there are differences in viral load dynamics between individuals as a function of their infecting serotypes, and, if so, the mechanism(s) responsible for these differences. These models do not include variation in any parameter by clinical manifestation. Our first serotype-specific model is motivated by DENV-2 infections, which often, though not always , result  disease . We therin vitro study showing that dengue serotypes can differ in their ability to block type I interferon signaling [q) to vary by serotype (model SSq).Our second serotype-specific model is motivated by a recent ignaling . Specifiin vivo and in vitro studies that suggest that there may be intrinsic differences in the reactivity of CD8+ T-cell responses to infecting serotypes [qT).Our third and final serotype-specific model is motivated by multiple erotypes , 52, 53.SSβ, we let β1, As for the clinical manifestation models, we reparameterize serotype-specific parameters for DENV-2 and DENV-3 using DENV-1’s parameter value as a reference point. For example, for model β to vary by serotype and additionally allow β to vary by clinical manifestation, fitting a total of 11 parameters, where 6 of these parameters are viral infectivity parameters.Finally, we consider a model in which parameters vary both by clinical manifestation and between dengue serotypes  + kln(n), where ln(L) is the highest log-likelihood of the model samples, k is the number of free model parameters to be estimated, and n is the total number of viral load measurements (n = 2415 data points). A difference in BIC between two models of 2–6 is considered positive, 6–10 strong and > 10 very strong support in favor of the model with lower BIC [We select between models of increasing complexity by using two criteria for model selection: the Bayesian Information Criterion (BIC)  and the ower BIC . DIC is ower BIC . DIC is ower DIC .δT, the rate of T-cell lysis, with qT estimates compensating for changes in δT , with our expectation being that OAS2. −6 per day for OAS1 and OAS2. For both models, OAS1, the median log-likelihood is not higher than that of model 1, despite an additional free parameter  can account for differences in viral dynamics between individuals that differ in clinical manifestation, we fit model ADE (described above), with the expectation that the viral infectivity rate in DHF-manifesting individuals would exceed the viral infectivity rate in DF-manifesting individuals:  model 1 . Due to β from lower incubation periods IP for DHF compared to DF patients. To see if we could disentangle differences in β from differences in IP, we fit model 1 and model ADE to the subset of the viral load data in which viral peaks were present . Instead of fitting log(V0) and setting IPg to 5.9 days, we fit IPg and set log(V0) = −3.2 log copies/ml, the median posterior value of log(V0) for model 1 , 0.15), to ensure that the mean IPg value is between the expected 3–10 days [One reason why we might find only slight differences in viral infectivity rates between DHF-manifesting and DF-manifesting individuals is because most of the viral load data shown in  model 1 . We adop–10 days . Table 4βDF on this subset of the data. Estimates for βDF, as expected under ADE. β, which allows viral infectivity rates to differ by serotype. Since DENV-2 is thought to have a high replication rate [β2 > . β for this model. These density estimates indicate that  > β1, partially consistent with our hypothesis. Our finding that the replication rate of DENV-3 is as high as DENV-2’s may be accounted for by a study finding that some DENV-3 clades cause high incidence of severe dengue disease [To determine whether difference between serotypes may explain some of the observed variation in viral load dynamics seen in ion rate , and DENion rate , we hypo disease .q to the viral load data. Based on [q to vary by serotype, with q3 > q1 > q2. q for this model. The results of this model are contrary to expectation, with q3 estimates significantly lower than the estimates for q1 or q2.To address whether there is support for serotypes differing in their ability to block interferon signaling, we fit model SSBased on , we expeT2, qT3) > qT1. This hypothesis is based on work by Bashyam and coauthors [qT for this model. The model fits are consistent with our hypothesis that qT2 and qT3 values exceed the value of qT1.Finally, to address whether there is support for serotypes differing in their elicitation of the T-cell immune response, we fit model oauthors  that fouβ), followed by model β. In contrast, the magnitude of the T-cell response for DENV-2 and DENV-3 is much higher than for DENV-1 under the dynamics . As expeβ and model SSβ fits the data significantly better than model β, when fit to this subset of data, are shown in β do not appear to differ by serotype (results not shown), regardless of whether the model is fit to the entire viral load dataset or only the data containing viral peaks. That incubation periods do not differ by serotype is consistent with findings from [Because we did not see peak viral load in many individuals, it was difficult to distinguish between model SSngs from .β  alongside viral load data from all individuals experiencing a DENV-1, DENV-2 or DENV-3 infection, stratified by primary infection . This IP variation may arise because of individual differences in viral inoculum sizes, or because of individual differences in the reporting of symptoms. It may also arise because of variation in viral infectivity rates. Indeed, our results indicate that secondary DHF patients generally have shorter IPs than do primary and secondary DF patients , with βo, DHF2 > βo, DF2 > βo, DF1, providing evidence for ADE. In their most recent work in which they fit more complicated models to DENV-1 and DENV-2 viral load and antibody data, they found that the model that best fit the data was one in which they assumed antibodies directly neutralize free virus, as opposed to a model in which antibodies indirectly kill infected cells via ADCC. However, they found that the estimated infected cell lifespan of this model was low, suggesting that another viral clearance mechanism, such as T-cells, as we found here, may also be needed to clear the infection.In previous work by Clapham and coauthors , a diffeAs in , we madeβ, with DENV-2 and DENV-3 having higher viral infectivity rates than DENV-1. This finding stands in contrast to Clapham and coauthors’ work, which did not find differences in DENV-1 and DENV-2 parameter values that contribute to viral clearance, [In fitting our serotype-specific models to the viral load data, we showed that DENV-1, -2, and -3 differ from one another in their viral infectivity rates earance, , though earance, . Studiesearance, , 43. Unfearance, . We do nOur serotype-specific findings are consistent with a recent meta-analysis of dengue virus kinetics in non-human primates . This meAedes Aegypti has been shown to increase with viral load of the infected individual [Our results are further relevant to understanding serotype-specific differences in population-level transmissibility. For many infectious diseases, viral load levels are thought to affect transmission potential . This isdividual . This suOur ability to distinguish between models was in part limited by the absence of viral load data early in infection. For example, though shorter time to viral peak and high viremia magnitude is consistently associated with more severe dengue disease –25, 56, Dengue human infection model experiments currently underway  will becA final shortcoming of the viral load data we analyzed is that we do not know the primary infecting serotype for individuals experiencing a secondary heterologous infection. Because order of infection is known to be an important risk factor for severe disease , knowledDespite limitations inherent in the data, our analyses indicate that inter-individual variation in dengue viral load patterns can shed light on which within-host processes are important in regulating viral dynamics. Collection of individual dengue viral load data at fine temporal resolution, spanning the entire infection duration, will be important for further understanding the relationships between dengue viral load, disease severity, and dengue transmissibility.S1 Text(PDF)Click here for additional data file.S1 FigEach color shows a different MCMC run. Every 1000 iterations are shown.(TIFF)Click here for additional data file.S2 FigEach subplot shows correlations between two parameter estimates of model 1. Samples are shown for every 100 iterations after burn-in .(TIFF)Click here for additional data file.S3 FigδT. Dotted lines show limits of detection of the assays used. (b) Simulated T-cell dynamics under different assigned values of δT. In (a) and (b), simulations shown use parameter estimates of β, κ, q, qT, and V0 that yielded the median posterior. Parameter values used are as follows: δT = 10−5: β = 6.6 × 10−10, κ = 4.9, q = 5.9 × 10−4, qT = 5.9 × 10−7, log(V0) = −6.1. δT = 10−6: β = 4.6 × 10−10, κ = 4.8, q = 6.6 × 10−4, qT = 1 × 10−6, log(V0) = −3.3. δT = 10−7: β = 4.1 × 10−10, κ = 5.1, q = 6.1 × 10−4, qT = 1.3 × 10−6, log(V0) = −2.2. δT = 10−8: β = 3.9 × 10−10, κ = 5.3, q = 8.2 × 10−4, qT = 2.1 × 10−6, log(V0) = −1.9.(a) Simulated viral load dynamics under different assigned values of (TIFF)Click here for additional data file.S4 FigV during primary infections. (b) Dynamics of free virus V during secondary infections.Black solid lines and dashed lines show simulations of model 1 for placebo and chloroquine-treated groups, respectively, using median likelihood estimates. Red and yellow shaded regions show 95% posterior credible intervals for placebo and chloroquine-treated groups, respectively (orange regions show overlap of credible regions). Credible intervals were constructed from 100 simulations of each model, where parameters are sampled from the posterior for each simulation. (a) Dynamics of free virus (TIFF)Click here for additional data file.S5 FigX. (b) Dynamics of free virus V. (c) Dynamics of NK cells N. (d) Dynamics of T-cells T in secondary infections.Black solid, short-dashed lines and long-dashed lines show simulations of primary DF, secondary DF and secondary DHF infections, respectively, using median likelihood estimates. Green, orange, and blue shaded regions show 95% posterior credible intervals of primary DF, secondary DF and secondary DHF infections, respectively. Credible intervals were constructed from 100 simulations of model ADE, where parameters are sampled from the posterior for each simulation. (a) Dynamics of uninfected target cells (TIFF)Click here for additional data file.S6 Figβ. (d-f) Model (a-c) Model SS(TIFF)Click here for additional data file.S7 Figβ for model β for model β1, β2 and β3 estimates by clinical manifestation for each model are: (a) −12 . −11 . −11. (b) −11 . −11 . −11 .(a) Density estimates for clinical manifestation-specific and serotype-specific viral infectivity rates (TIFF)Click here for additional data file.S1 TableMedian marginal posterior parameter estimates are reported, with 95% posterior credible intervals for each parameter in parentheses. Units are the same as in (PDF)Click here for additional data file.S2 TableMedian marginal posterior parameter estimates are reported, with 95% posterior credible intervals for each parameter in parentheses. Units are the same as in (PDF)Click here for additional data file.S3 TableV0 = 10−3.2 copies/cell.Median marginal posterior parameter estimates are reported, with 95% posterior credible intervals for each parameter in parentheses. Units are the same as in (PDF)Click here for additional data file.S4 TableMedian log-likelihood values, BIC and DIC values for all models considered are reported.(PDF)Click here for additional data file.S5 TableMedian log-likelihood values, BIC and DIC values for all models considered are reported.(PDF)Click here for additional data file.S6 TableMedian log-likelihood values, BIC and DIC values for all models considered are reported.(PDF)Click here for additional data file.S7 TableMedian log-likelihood values, BIC and DIC values for all models considered are reported.(PDF)Click here for additional data file.S8 TableMedian log-likelihood values, BIC and DIC values for all models considered are reported.(PDF)Click here for additional data file.


To investigate the prevalence and correlating factors of binge drinking among middle and high school students in Zhejiang Province, China.We performed a cross-sectional study using data from a school-based survey. A total of 23 543 (response rate=97.5%) eligible adolescents from 442 different schools  were asked to fill in an anonymous self-administered behaviour questionnaire between April and May 2017. Multivariable logistic regression models were used to examine the associations of sociodemographic and behavioural factors with binge drinking.The mean (SD) age of participants was 15.6 (1.7) years and 51.3% were boys. The proportions of students from middle schools, academic high schools and vocational high schools were 51.9%, 27.5% and 20.6%, respectively. In total, 22.8% (95% CI 21.6 to 23.9) of students reported drinking alcohol in the past 30 days and 9.2% (95% CI 8.5 to 10.0) of students reported binge drinking  during the past month. The prevalence of binge drinking was highest among vocational high school students . Older age, studying at high school, poor academic performance, higher levels of physical activity, excessive screen-time, loneliness, insomnia, previous suicide attempt, cigarette smoking, fighting, being bullied and sexual experience were found to be positively associated with adolescent binge drinking.Binge drinking is common among middle and high school students in Zhejiang, China. Efforts to prevent binge drinking may need to address a cluster of sociodemographic and behavioural factors. Our findings provide information to enable healthcare providers to identify students at high-risk of binge drinking and to inform planning of intervention measures for at-risk students. This is a school-based study with a relatively large sample size, a high response rate and a study population which is representative of Provincial China.The study questionnaire includes a wide range of factors, which might help us better understand how to prevent adolescent binge drinking.The cross-sectional study design prevents establishment of causal relationships between sociodemographic or behavioural factors and binge drinking.Only students attending schools participated in the survey. Students who have been expelled or suspended from school, or who stopped attending, may be more likely to binge drink, and the overall prevalence of binge drinking in our current study might therefore represent an underestimate of the true prevalence.Alcohol consumption has been identified as an important risk factor for chronic disease and injury.9–11Binge drinking is defined as episodic excessive drinking. Prevalence of binge drinking among adolescents varies between countries, and is estimated to be 8% in Iceland and 56% in Denmark.Parental alcohol-related practices have been shown to be important risk factors for adolescent drinking.21 22et alet al25Previous studies have found some inconsistencies in sociodemographic patterns of adolescent binge drinking. For instance, Miller Zhejiang Province, in the east of China, has a population of 56 million. It has experienced rapid economic development in the past 30 years, which has been associated with increased exposure of adolescents in Zhejiang to western culture. As a result of this, patterns of alcohol use in this area might differ from those in other regions of China. The current study was designed with the aim of examining the prevalence of binge drinking and its correlating factors among students in Zhejiang Province.The survey used a three-stage sampling design. In stage 1, 30 counties, including 12 urban areas and 18 rural areas, were sampled randomly from all 90 counties  of Zhejiang Province according to socioeconomic status. In stage 2, 10 classes of middle school, 5 classes of academic high school and 5 classes of vocational high school were selected randomly within each chosen counties, respectively. In stage 3, all students in the chosen classes were invited to participate in the study. In China, after 6 years of primary school education, children usually attend middle school  for 3 years (grades 7–9). After graduation from middle schools, they enter high school  for a further 3 years (grades 10–12). The survey questionnaire was modelled from existing surveys including the Youth Risk Behaviour Survey, conducted by the Centers for Disease Control and Prevention (CDC),2×P×(1−P)/d2. Means and 95% CI (2-sided for µ=1.96) were determined; the prevalence of binge drinking (10%) obtained in the China was used as a measure of probability (P);The sample size was calculated by using the formula: n=deff×µStudy participants were healthy students and no patients were involved in the study. Students and their parents were not involved in the design and conduct of study. Due to an anonymous survey, our findings will be disseminated to Department of Health and Department of Education, not directly to participating students.For the purposes of this study, alcoholic drinks include beer, wine, wine coolers, rice wine and liquor such as Chinese liquor, rum, gin, vodka or whiskey. Current drinking was assessed through the question: "During the past 30 days, on how many days did you have at least one drink of alcohol?” Answer options include: ‘0 days’, ‘1–2 days’, ‘3–5 days’, ‘6–9 days’, ‘10–19 days’, ‘20–29 days’ and ‘30 days’. Participants were considered as current drinkers if they reported drinking alcohol on at least 1–2 days during the past 30 days. Binge drinking was assessed by the question: "During the past 30 days, on how many days did you have 4 or more drinks of alcohol (if you are girl) or 5 or more drinks of alcohol (if you are boy) during a period of 1 to 2 hours?"Physical activity was assessed by the question: "During the past 7 days, on how many days were you physically active for a total of at least 60 min per day?" Answer options included: ‘none’, ‘1 day’, ‘2 days’, ‘3 days’, ‘4 days’, ‘5 days’, ‘6 days’ and ‘7 days’, and these were further categorised into four groups: ‘none’, ‘1–2 days/week’, ‘3–5 days/week’ and ‘6–7 days/week’. Current smoking was assessed by the question: "During the past 30 days, on how many days did you smoke cigarettes?" Answer options included: ‘none’, ‘1–2 days’, ‘3–5 days’, ‘6–9 days’, ‘10–19 days’, ‘20–29 days’ and ‘all 30 days’. Current smoking was defined as smoking cigarettes on at least 1–2 days in the past 30 days. Screen-time was estimated through the question: "On an average school day, how many hours do you play video or computer games or use a computer for something that is not school work?" Answer options included: ‘none’, ‘<1 hour/day’, ‘1 hour/day’, ‘2 hours/day’, ‘3 hours/day’, ‘4 hours/day’, ‘≥5 hours/day’, and these answers were further categorised into four groups: ‘none’, ‘<1 hour/day’, ‘1–4 hours/day’, ‘≥4 hours/day’. Suicidal attempt was assessed using the question: "During the past 12 months, how many times did you actually attempt suicide?" Response options included: ‘none’, ‘1 time’, ‘2–3 times’, ‘4–5 times’, ‘6 or more times’, and suicide attempt was defined as at least once in the past 12 months. Fighting was assessed by the question: "During the past 12 months, how many times were you in a physical fight?" Answer options included: ‘none’, ‘1 time’, ‘2–3 times’, ‘4–5 times’, ‘6–7 times’, ‘8–9 times’, ‘10–11 times’, ‘12 or more times’, and fighting was defined as at least once in the past 12 months. Being bullied was assessed by the question: "During the past 12 months, how many times has someone threatened or injured you with a weapon such as a gun, knife or club on school property?" Answer options included: ‘none’, ‘1 time’, ‘2–3 times’, ‘4–5 times’, ‘6–7 times’, ‘8–9 times’, ‘10–11 times’, ‘12 or more times’. Being bullied was defined as being threatened or injured by someone at least once in the past 12 months. More detailed information on covariates is provided in The survey was conducted by trained surveyors from the local CDC. In order to improve response rate, every recruited student was given a pencil box as a gift, and the survey was anonymous.Written informed consent was obtained from all participants and their guardians before the survey.2 test. To assess the associations between each correlating factor and binge drinking, univariate and multivariable logistic regression analyses were performed using the PROC SURVEYLOGISTC procedure, to take into account the complex survey sampling methods. We first determined which factors were associated with binge drinking in univariate analyses (P<0.05), and variables significant in the univariate analyses were entered in a multivariable logistic regression model. All statistical tests were two tailed, and P values <0.05 were considered to be statistically significant.All analyses were performed using SAS software V.9.3. A weighting factor was applied to each student record to adjust for non-response and for the varying probabilities of selection. The weight used for estimation in this survey is given by: W=W1×W2×f1×f2. W1=the inverse of the probability of selecting the county; W2=the inverse of the probability of selecting the classroom within the county; f1=a student-level non-response adjustment factor calculated by class; f2=a poststratification adjustment factor calculated by grade.A total of 24 157 students were invited to participate. Due to missing or incomplete questionnaires and refusal to participate, 23 543 eligible participants (response rate 97.5%) were included in the current analyses. Twelve thousand sixty-eight (51.3%) participants were boys and, overall, mean age was 15.6 years. Twelve thousand two hundred seven (51.9%) participants were middle school students, 6477 (27.5%) were academic high school students and 4859 (20.6%) came from vocational high schools.The percentage of students that came from non-intact families was 9.9 ; 11.4% oAs shown in The prevalence of binge drinking was 9.2% (95% CI 8.5 to 10.0) overall, and was 6.3%, 7.7% and 17.9%, respectively in middle school, academic high school and vocational high school. The associations of binge drinking with sociodemographic and behavioural factors were similar to those of current drinking. The prevalence of binge drinking was higher among boys than among girls (11.8% vs 6.5%), among students with previous suicide attempts (31.5% vs 8.4%), among students who reported smoking cigarettes (51.2% vs 6.8%), involvement in a physical fight (21.5% vs 7.0%), being bullied (14.1% vs 8.5%), previous sexual experience (39.2% vs 8.1%), poor academic performance, longer duration of screen-time and higher severity of loneliness or insomnia . There was no urban-rural difference in the prevalence of binge drinking (9.8% vs 9.0%).After adjusting for other variables included in the model, multivariable analysis showed that, compared with boys aged ≤13 years, older boys were more likely to binge drink . CompareIn this study of middle and high school students in Zhejiang, China, we examined the prevalence of current drinking and binge drinking, and identified and quantified the associations of sociodemographic and behavioural correlates of binge drinking, providing information to enable development of interventions to prevent binge drinking among this population group.Due to different definitions of binge drinking, direct comparisons between studies are difficult. Since using a 5-drink measure for high school students in 1975, most national surveys have defined binge drinking as 5 or more drinks among both women and men.et alNo association was found between parental educational levels and binge drinking in our study. In a systematic review, including 20 studies from 10 countries or areas, parental socioeconomic status, defined as the educational level, income or occupation, was weakly positively associated with binge drinking in low-income and middle-income countries. However, no such association was not found in high-income countries.A cohort study, including 89 university students who were followed up for 2 years, found persistent binge drinking was associated with verbal memory and monitoring difficulties.et alBuscemi In our study, nearly one in five students reported >4 hours screen-time per day. Previous studies have mainly focused on television watching as a potential risk factor for various diseases.et alet alAlcohol drinking can damage neurons, decrease neurogenesis, and cause cognitive and affective dysfunction, especially among adolescents.et alAs demonstrated in our study, with higher levels of insomnia, the odds of adolescent binge drinking was higher. A previous study by Popovici and French,et al8Alcohol drinking is a well-established risk factor for suicide attempts,Our study had several limitations. First, the a cross-sectional study design prevents establishment of the causal relationships between sociodemographic and behavioural factors and binge drinking. Second, all data were self-reported by students, and self-reported alcohol consumption may be susceptible to recall and social desirability biases. Third, only students attending schools participated in the survey. Students who had been expelled or suspended from school, or who stopped attending, may be more likely to binge drink, and the overall prevalence of binge drinking in our current study might therefore represent an underestimate of the true prevalence.Despite these limitations, our study identified correlates of binge drinking among middle and high school students in Zhejiang and quantified the strength of these associations, providing insight to inform binge drinking prevention strategies. Efforts to prevent binge drinking may need to address a cluster of correlating factors, including cigarette smoking, excessive screen-time, suicide attempt, fighting, being bullied, loneliness, insomnia and sexual behaviour. The presented findings provide evidence to assist healthcare providers in identifying students at high risk of binge drinking, which will aid in the planning of prevention and intervention measures for at-risk students.


T is a quasi-nonexpansive mapping and In this paper, we introduce a new problem, the modified split generalized equilibrium problem, which extends the generalized equilibrium problem, the split equilibrium problem and the split variational inequality problem. We introduce a new method of an iterative scheme  C be a nonempty closed convex subset of a real Hilbert space H. The set of fixed points of T is denoted by quasi-nonexpansive ifLet T is firmly nonexpansive,Let variational inequality is to find a point α-inverse strongly monotone if there exists a positive real number F be a bifunction of equilibrium problem for Let entclass1pt{minimaexample, . Let F bduced by  in 1994 duced by –8.F be a function of generalized equilibrium problem is to find Let C, Q be nonempty closed convex subsets of real Hilbert spaces Throughout this section, let split feasibility problem  which is to find a point In 1994, Censor and Elfving [CQ algorithm whose sequence L is the spectral radius of the operator A. Then the CQ algorithm converges to a solution of the SFP, whenever solutions exist. If there are no solutions of the SFP, the CQ algorithm converges to a minimizer of the functionTo solve the SFP, Byrne  introducsplit common fixed points problem (SCFPP) [Let  (SCFPP) , 12 is tU and T are firmly quasi-nonexpansive mappings. Then the sequence z, where In 2017, Wang  introducsplit inverse problem (SIP) which is a generalization of the split common fixed points problem. In this, there are given two vector spaces X and Y and a linear operator 1, is formulated in the space X and the second one, denoted IP2, is formulated in the space Y. Given these data, the split inverse problem is formulated as follows:Censor et al. , 14 intrsplit equilibrium problem (SEP) [The em (SEP)  is to fisplit variational inequality problems  were introduced and studied by Cencor et al. [The r et al. : find \dexample,  and the example, , 17.modified split generalized equilibrium problem (MSGEP) which is to find By investigating SEP and SVIP, we introduce the If we put If we put In the case of bifunctions 4). From , 1.12)  \documenMSGEP is a generalization of the generalized equilibrium problem, the split equilibrium problem and the split variational inequality problem. So, this problem can be used in sensor networks, data compression, practice as a model in intensity-modulated radiation therapy (IMRT) treatment planning, robustness to marginal changes and equilibrium stability etc.Let Then A is a bounded linear operator on H, T is a quasi-nonexpansive mapping on H, f is a contraction with coefficient a under suitable conditions of the parameters γ and ω. By assuming T is demiclosed on H.In 2012, Tain and Jin  introducT by assuming T is demiclosed on H; a difficult proof. Motivated by [Motivated by SFP and SVIP, we introduced a new problem, the modified split generalized equilibrium problem, which extends the generalized equilibrium problem, the split equilibrium problem and the split variational inequality problem. Many authors proved strong convergence theorem involving a quasi-nonexpansive mapping vated by , we intrvated by , 12 and vated by , we intrH be a real Hilbert space with inner product Opial’s condition [Let ondition , i.e., fF is monotone, i.e., for each for each For solving the equilibrium problem, we assume that the bifunction LetCbe a nonempty closed convex subset ofHand letFbe a bifunction ofintosatisfying (A1)–(A4). LetandThen there existssuch thatAssume thatsatisfies (A1)–(A4). Fordefine a mappingas follows:for allThen the following hold: is single-valued,is firmly nonexpansive, i.e., for anyis closed and convex.LetHbe a real Hilbert space, letCbe a nonempty closed convex subset ofHand letAbe a mapping ofCintoH. LetThen, forwhereis the metric projection ofHontoC.LetCbe a nonempty closed convex subset of a real Hilbert spaceH. Letbe a finite family of quasi-nonexpansive mappings ofCintoHwithand letwithThenIn this lemma, we show that To start with, it is easy to see that From Lemma Letbe a sequence of nonnegative real numbers satisfyingwhereis a sequence inandis a sequence such thatThenLetCandQbe nonempty closed convex subsets of a real Hilbert spacesandrespectively. Letbe a bounded linear operator. Letandbe the bifunctions satisfying (A1)–(A4). Letbe aρ-inverse strongly monotone mapping andbe a firmly nonexpansive mapping. Thenandare nonexpansive mapping,for allwhereLis the spectral radius of the operatorandis the adjoint ofA, defined byfor allanddefined byfor allρ-inverse strongly monotone mapping and Let Next, we show 2 is true. From Lemma LetCbe a nonempty closed convex subset of a real Hilbert spaceHand letbe a quasi-nonexpansive mapping withThenT is a quasi-nonexpansive mapping, we getLet LetCbe a nonempty closed convex subset of a real Hilbert spaceH. Letbe a finite family of quasi-nonexpansive mappings ofCinto itself withThenfor allwherewithandLet Next, we prove a strong convergence theorem for solving the modified split generalized equilibrium problem (MSGEP).LetCandQbe nonempty closed convex subsets of a real Hilbert spacesandrespectively. Letbe a bounded linear operator. Letbeα, β-inverse strongly monotone mappings, respectively. Letandbe the bifunctions satisfying (A1)–(A4). Letbe a finite family of quasi-nonexpansive mappings ofCinto itself withLetbe aρ-inverse strongly monotone mapping andbe a firmly nonexpansive mapping. AssumeFor givenand letandbe sequences generated bywherewithLis the spectral radius of the operatorandis the adjoint ofA. Alsoare sequences inwithfor allSuppose the following conditions hold: (i)and(ii)for somefor all(iii)and(iv)Thenandconverge strongly toα-inverse strongly monotone mapping, we obtainρ-inverse strongly monotone mapping and Let Afterward, we show that To show this, choose a subsequence →∞. From , we obtandition, , we obta1). From , we have}j→∞. By  and usinndition,  and  a a\docume=PFu. By , we get=PFu. By  and –(A4). LetTbe a quasi-nonexpansive mapping ofCinto itself. Letbe aρ-inverse strongly monotone mapping andbe a firmly nonexpansive mapping. AssumeFor givenand letandbe sequences generated bywhereLis the spectral radius of the operatorandis the adjoint ofA. Alsoare sequences inwithfor allSuppose the conditions (i)–(iv) of Theorem hold. Thenandconverge strongly toLetCbe nonempty closed convex subset of a real Hilbert spaceLetbeα, β-inverse strongly monotone mappings, respectively. Letbe the bifunction satisfying (A1)–(A4). Letbe a finite family of quasi-nonexpansive mappings ofCinto itself withLetbe aρ-inverse strongly monotone mapping. AssumeFor givenand letandbe sequences generated bywherewithAlsoare sequences inwithfor allSuppose the conditions (i)–(iv) of Theorem hold. Thenandconverge strongly toLetCandQbe nonempty closed convex subsets of a real Hilbert spaceandrespectively. Letbe a bounded linear operator. Letbeα, β-inverse strongly monotone mappings, respectively. Letandbe the bifunctions satisfying (A1)–(A4). Letbe a finite family of quasi-nonexpansive mappings ofCinto itself withAssumeFor givenand letandbe sequences generated bywherewithLis the spectral radius of the operatorandis the adjoint ofA. Alsoare sequences inwithfor allSuppose the conditions (i)–(iv) of Theorem hold. Thenandconverge strongly toT is demiclosed; a difficult proof in a framework of Hilbert space.If we take The following knowledge is used to prove Theorem akahashi .In 2009, Iemoto and Takahashi  proved tT with T.A nonspreading mapping LetHbe a Hilbert space, letCbe a nonempty closed convex subset ofH, and letSbe a nonspreading mapping of C into itself. Thenis closed and convex.S-mapping generated by In 2009, Kangtunyakarn and Suantai introducC be a nonempty convex subset of a real Banach space. Let C into itself. For each Let S-mapping generated by This mapping is called an LetCbe a nonempty closed convex subset of a real Hilbert space. Letbe a finite family of nonspreading mappings ofCintoCwithand letwherefor allandfor allLetSbe the mapping generated byandThenandSis a quasi-nonexpansive mapping.By using these results, we obtain the following theorems.LetCandQbe nonempty closed convex subsets of a real Hilbert spaceandrespectively. Letbe a bounded linear operator. Letbeα, β-inverse strongly monotone mappings, respectively. Letandbe the bifunctions satisfying (A1)–(A4). Letbe a finite family of nonspreading mappings ofCintoCwithand letwherefor allandfor allLetSbe the mapping generated byandLetbe aρ-inverse strongly monotone mapping andbe a firmly nonexpansive mapping. AssumeFor givenand letandbe sequences generated bywhereLis the spectral radius of the operatorandis the adjoint ofA. Alsoare sequences inwithfor allSuppose the conditions (i)–(iv) of Theorem hold. Thenandconverge strongly toBy using Corollary In this section, an example is given for supporting Theorem Let rated by  as folloIn Example Let Let rated by , where \ rewrite  as folloTable Example 3.4Table Theorem U and T are firmly quasi-nonexpansive mappings. Then the sequence z, where r, s, a, γ, L, By using the concept of Picard iteration, Wang  defined rding to  and U anfined by  converge


C. elegans, which we posit embodies the benefits of both high-throughput screening and high-resolution microscopy. We show the use of these techniques in (1) characterizing distinct responses of a transcriptional reporter to various stresses in defined anatomical regions, (2) identifying regions of high mitochondrial membrane potential in live animals, (3) monitoring regional mitochondrial activity in aging models and during development, and (4) screening for regulators of muscle mitochondrial dynamics in a high-throughput format. This platform offers a significant improvement in the quality of high-throughput biosorter data analysis and visualization, opening new options for region-specific phenotypic screening of complex physiological phenomena and mitochondrial biology.The tissue-specific etiology of aging and stress has been elusive due to limitations in data processing of current techniques. Despite that many techniques are high-throughput, they usually use singular features of the data (e.g. whole fluorescence). One technology at the nexus of fluorescence-based screens is large particle flow cytometry (“biosorter”), capable of recording positional fluorescence and object granularity information from many individual live animals. Current processing of biosorter data, however, do not integrate positional information into their analysis and data visualization. Here, we present a bioanalytical platform for the quantification of positional information  of  Methods to reliably identify causative factors of cellular phenomena at the organismal level have been laborious and time consuming due to limitations in current imaging techniques. Automated robotic systems which use multi-well plates (e.g. cell culture) have increased throughput and enabled the characterization of many cellular responses (e.g. reporter genes): however, these readouts are not always applicable to the biology of an intact animal.C. elegans, using a large particle flow cytometer(“biosorter”)6, which works very similarly to a conventional flow cytometer. However, the currently available analytical platforms do not utilize the positional data being recorded from each particle, leaving regional information out of the analysis. High resolution microscopy, by contrast, has enabled the tissue-specific measurement of various cellular processes in both live and fixed cells, but it is labor intensive, time consuming, and susceptible to high variability due to small sample size. Moreover, these modern imaging techniques have been optimized for single-celled organisms and tissue culture but do not account for the complexity found in metazoans.To date, a number of genetic screens have been carried out on the small nematode worm, Longitudinal Alignment Metabolic Profiler” or “LAMPro”) to exploit previously under-used positional information and define key regions of interest in various celluar phenomena . We posit that this platform embodies advantages of both high-throughput screening and high-resolution microscopy, while also substantially removing artefactual data. LAMPro systematically: (1) aligns and normalizes profiles  and (2) performs powerful data visualization to enable identification of subtle differences between animals/strains. While LAMPro can be used for a variety of model organisms, here we demonstrate, with applications to C. elegans biology, i.e. stress response, bioenergetics, and mitochondrial morphology/dynamics, how LAMPro’s streamlined data analysis and unprecedented data visualization capability can improve the quality and sensitivity of conventional biosorter data.Thus, with the intent to improve the quality and visualization of high-throughput, whole-organism, biosorter data we developed a methodology and freely available software  non-ideal longitudinal profiles are excluded, which reduces data variability, (2) individual profiles are aligned, allowing for positional statistics to be done on large populations, and (3) data can be effectively visualized and quantified. Thus, through the study of mitochondrial stress, bioenergetics in development and longevity, and morphology/dynamics, we outline the main features of LAMPro and its ability to improve the quality of longitudinal data analysis and visualization.Individual organism measurements done by large particle flow cytometry represent the total response per organismC. elegans expressing fluorescent, tissue-specific reporters, longitudinal measurements can be represented in an optical profile, which demonstrates the location and intensity of all parameters at each position. For example, green fluorescently labeled sensory neurons in the far anterior and posterior , which can distinguish individual neural clusters and pharyngeal bulbs, gut cells, and other fine features within a single animal.In large particle flow cytometry, optical and fluorescent intensity values are recorded as objects move past a laser detector. The length of an object is recorded as the “time of flight” . Once aligned and normalized, the points along the length of each individual animal’s optical trace (in each channel recorded) allows for representative sample-population statistics ) to be rendered digitally as heat maps for any recorded channel  and granularity/width ). An example of a heat map output is shown for the medians of sensory neurons (green) and intestine (red) at the bottom of Fig. Through our alignment and length-normalization algorithms, LAMPro software is able to align asymmetrically-distributed tissues from a broad range of longitudinal profile data  and then test for their statistical significance . Comparison of differences in various tissue regions 10. By coupling the transcriptional activity of mitochondrial chaperone genes, hsp-6 and hsp-60, to GFP production, e.g. hsp-6p::gfp and hsp-60p, several groups have identified different mechanisms by which the UPRmt is activated10. The current method for comparing UPRmt reporter activity, however, uses the “normalized” total green fluorescence of each profile  and then plots the descriptive statistics for each data set  of the following previously identified challenges: [1] disrupting the formation of electron transport chain (ETC) super-complexes , [2] removal of a protease that monitors translation and protein folding in the mitochondrial inner membrane , [3] inhibition of mitochondrial protein translation , and [4] inhibition of mitochondrial protein import into the matrix .t Figure . This ise Figure . Using Lcco-1 and tim-17(2) RNAi were very similar to each other, but the UPRmt activation profiles under spg-7 and mrps-5 RNAi were quite different from each other  RNAi was statistically identical along the profiles, while comparison of cco-1 RNAi to spg-7, mrps-5, and tim-17(1) displayed regions that differed significantly  RNAi  depend heavily on the functionality of mitochondria and indeed, mitochondrial membrane potential (Δψ) perturbation in nematodes, has been associated with lifespan extension, fertility, apoptosis, and increased stress resistance19. Here, we investigated differences in longitudinal profiling of Δψ as a function of larval development and between long-lived mutant animals.We used JC-9, a chemical dye, to characterize the spatial distribution of mitochondrial membrane potential (Δψ) in live nematodes Fig. 11–13. Ma11, which localized to mitochondria  of whole worms decreases from L1 to L4  also varied along the worm profile. Indeed, profiles of Δψ at each larval stage are clearly distinct from each other  but also have higher Δψ (~1.3x)14. Using daf-2 mutants as a control for elevated Δψ, we sought to determine if LAMPro could distinguish a panel of longevity mutants based on their longitudinal pattern of Δψ. A comparison of overall Δψ in several longevity mutants  and controls , and daf-16; daf-2: mutated downstream effector in daf-2 background ) showed that median Δψ is significantly higher than N2 “control” for all strains except age-1   gene, an inner-membrane mitochondrial protease, would not have been identified based on the TOF and green fluorescence, which was not significantly different from the control  gene, encoding a MitoFerrin implicated in iron transport to mitochondria, resulted in a lower green fluorescence  finding that immp-1 RNAi treatment overall decreased Δψ  and TOF  in the biosorter , object length  and granularity/width ) per animal6. Individual measurements taken along the length of each animal have not been utilized in such studies, ultimately overlooking positional information inherent to the data. To address this issue, several biosorter data analysis programs have been reported6, but they have limitations in regard to (1) exclusion of artefactual data, (2) alignment of profiles, and (3) data visualization. The software (LAMPro) described here addresses these issues and enables region-specific phenotypic characterization and longitudinal comparisons in large cohorts of C. elegans data. In addition, this software has broader applications because it can effectively align and visualize biosorter data from a wide range of organisms including Drosophila embryos, larval mosquito, and larval zebrafish (data not shown). LAMPro excludes aberrant profiles, thereby reducing biologically irrelevant variability. LAMPro also systematically aligns and compares longitudinal profiles of different organism models displaying a wide range of sizes, widths, and granularities  is recommended for determining the proper orientation algorithm to use. Third, the native fluorescence of animals (e.g. older worms) or fluorescence bleed-through of fluorescent proteins with similar emission spectra (e.g. GFP and YFP) can confound the outputs of the algorithms. Proper configuration of the biosorter optics (e.g. selection of filter cubes and PMT settings) to match the spectral characteristics of the fluorescent proteins will reduce complications with bleed-through. The correct setting of threshold conditions  on the sorter (e.g. “Store Gated” condition) or as “basal fluorescence” values in the Perl code (lines 897–899) can also reduce these potential biases.In our applications of LAMPro three factors were carefully considered to collect data of high quality. First, we applied an exclusion criteria to eliminate viable worms that had clumps of bacteria (from the food plate) sticking to their head or tail, which caused high Extinction peaks on the profile edges and a deformation/distortion of the fluorescent trace Figure . Notablymt exists in various C. elegans tissues. Transcriptional reporters, such as hsp-6p::gfp, are commonly used to investigate stress in C. elegans3. Longitudinal profiling of hsp-6p::gfp expression illustrated that mitochondrial stress can have disparate and unique patterns of UPRmt activation dependent on the genetic model  RNAi had mostly identical UPRmt activation profiles, while the mrps-5, spg-7, and tim-17(1) RNAi were dramatically different  cell non-autonomous activation of UPRmt in neurons  and (2) the activation of UPRmt cell autonomously in the intestine by the RNAi alone. A potential experiment which might enable one to parse out the region-specific contributions of these two forms of cell-cell communication is to compare the hsp-6p::gfp activation pattern between worms expressing a cco-1 hairpin in exclusively neurons or exclusively intestine . Finally, we cannot discount the possibility that some of the observed differences could be due to variability in the efficiency of RNAi knockdown, thus more precise experiments using serial dilutions of each RNAi expressing bacteria, in conjunction with the sorter, should enable the identification of tissues “most sensitive” to reporter activation. This future work, however, does not undermine the impact that this methodology would have in future studies requiring comparisons of region-specific transcriptional reporters and its potential use in discovery of additional regulators that maintain or vary the positional expression of biologically relevant reporters.It is currently unknown why differential sensitivity to UPRdel Fig. . Relativent Fig. . Furtherent Fig. . It is i21) and we have observed a comparable transient spike in Δψ in mitochondria isolated from egg to larval L2 stages11. Dramatically, the most significant differences in Δψ were between Day 1 and Day 2 adults, for each regional comparison, and the “Anterior” and “Posterior” regions of the transition from larval (L4) to adulthood (Day 1 adult)  display elevated Δψ and whether the elevated Δψ is positional along the length of the nematode. Comparison of longitudinal Δψ profiles of various longevity mutants  provided great insight   are mutated  when mutated33, but does not induce the nuclear localization of DAF-16 31, suggesting the Δψ is not uniquely associated with the nuclear localization of DAF-16. Undoubtedly, longitudinal profiling of Δψ could be extended to other mutants to explore the relevance of Δψ in other models of longevity and disease (e.g. neurodegeneration)35.Long-lived nematodes with mutations in the insulin receptor, ght Fig. . The dafsee Fig. . The eleted Fig. . When th29, information regarding the anatomical regions where such control takes place often requires extensive follow-up experiments. We developed LAMPro to discover new mutants that would not have been previously identified based on the GFP fluorescence of each animal measured by the biosorter, but could be defined by longitudinal profiling of a muscle-specific mitochondria-targeted GFP under various RNAi treatments, which reportedly alter muscle mitochondria morphology28. The treatments with RNAi against C24H11.6 (immp-1), W02B12.9 (mfn-1) and W10C8.5 genes were chosen to place emphasis on gene knockdowns that would not have been identified based exclusively on total GFP fluorescence prior to exclusion  led to phasic regions of high signal in lamellar mitochondria in the perinuclear regions of muscle cells in the mid region , by contrast, caused a dramatic thinning of mitochondria and a massive increase in tubulation close to the anterior region of the animal  to link UPRmt activation with mitochondrial morphology and Δψ, and then characterize hits for lifespan extension. Finally, although this wouldn’t require high-throughput techniques, one notable example where these tools could be immediately applied is in distinguishing the striking phenotypic differences  and differential lifespan extensions of nuo-6; isp-1 double mutants  and the single mutants paired with its RNAi counterpart 43.While conventional genetic screens reveal the identity of proteins that are important in global maintenance of mitochondrial morphologyion Figs . Marked ion Figs  and S8M.mal Figs  and S8F,NAi Figs  and S8G.NAi Figs  and S8M.C. elegans studies of the tissue specific etiology of complex physiological phenomena  and disease. The ability to extend these studies to phenotypic RNAi and/or drug screens to reverse the effects of aging and neurodegenerative disease models on mitochondrial health are now indeed exciting possibilities.Our development and application of the freely available bioanalytical software LAMPro to the study of mitochondrial biology has enabled the identification and characterization of region-specific mitochondrial function and morphologies. In particular, our discovery of concomitant regional changes in mitochondrial morphology and activity (Δψ) should pave the way for future daf-2(e1370), CF1038 daf-16 (mu86), CF1043 daf-2(e1370); daf-16(mu86), DA1116 eat-2 (ad1116), TJ1052 age-1(hx546), SJ4100 zcIs13 (hsp-6p::gfp), RT130 pwIs23 , AM101 rmIs110 , VS30 hjSi158 ::mCherry::HDEL::let-858 3′UTR), and IG274 frIs7 . Additional transgenic worms with stable extrachromosomal arrays were created using the following vectors pGH8 , pCFJ90 , pCFJ104 . Co-injection of any one of these plasmids with the Mos1 transposase pCFJ601 (eft-3p::Mos1 transposase) dramatically improved the likelihood of creating stable extrachromosomal arrays. These mCherry plasmids all possessed an unc-119 rescue protein to identify successful DNA injections. The germline of late larval L4 worms was injected as described previously into strain eg6703 (unc-119(ed3) III; cxTi10816 IV;) which was mutant for unc-11944. After injection worms that moved normally across the plate (or unc-119+) were picked and placed on new plates to enrich for worms with extrachromosomal arrays. One extrachromosomal line was isolated for each tissue described. The muscle-specific mitochondrially-targeted GFP strain, AGD1664, was created by fusing (1) a myo-3p muscle promoter, (2) a putative MLS from the mitochondrial gene H28016.1a, (3) a GFP cDNA, and (4) an unc-54 UTR together (or “myo-3p::MLS2::gfp::unc-54 3′UTR”) and inserting this gene into the chromosome 1 locus ttTi4348 by MosSCI integration. This strain was then crossed to strain AGD1582, containing fer-15 (b26), fem-1(hc17), and uthIs272[myo-2p::tdTomato]. The resulting strain, AGD1756, contained myo-3p::MLS::gfp, myo-2p::tdTomato, and showed partial sterility at 25 °C.The following worm strains were obtained from the Caenorhabditis Genetics Center : N2, CB1370 The ratiometric mitochondrial membrane potential sensor dye JC-9  (D-22421) was purchased from Life Technologies (now Thermo Fisher) while valinomycin (V0627) and Levamisole Hydrochloride (31742) were from Sigma Aldrich.45. Briefly, staged worms were rinsed off plates with M9  and added to Eppendorf tubes. Animals were spun down and supernatant was removed and replaced with 1 mL of M9 and 5 µl of DiO or DiI [2 mg/ml in DMF]. Tubes were rocked at 20 °C for 1 to 1½ hours and then spun down, washed with fresh M9 twice before animals were placed on fresh OP50 plates for at least 2 hours at 20 °C to allow the dye to pass through the digestive tract (eliminates non-specific labeling). Worms were then washed off the plates with fresh M9 and run through the biosorter.A subset of nematode sensory neurons was labeled in with DiO  or DiI  to facilitate orientation in Figs To label live worms for Figs 47. Worms of the specified stages were washed off plates and resuspended in autoclaved M9 into 15 ml conical tubes. We used a Union Biometrica complex object parameter analysis sorter (COPAS) Biosorter (product no. 350–5000–000) using both 561 nm and a 488 nm light sources. After the Biosorter was calibrated and flushed with sequential 10% bleach, M9, COPAS cleaning solution (#300–5072–000), and finally, M9, worms were added to the “cup” or “hopper” and data was acquired using previously optimized settings for laser PMT power and size gating. More specifically, only data from worms of the proper size  and Time of Flight  was stored (using the “Store Gated” option). This prevented the acquisition of data that resulted from debris, eggs, and unstaged (e.g. younger L1 or L2) worms from being saved when this was not the intention. *N.B. Although there is an empirically established a relationship between the EXT and object “width”, much like “Side Scatter” (or “90° scatter”) in conventional flow cytometry, these measurements also take into account additional properties including density, texture, and granularity.Worm staging was performed at 20 °C in line with previously published developmental timing and worm growth mediaE. coli containing an empty vector control or expressing double-stranded RNA. RNAi strains were taken from the Vidal library, if present, or from the Ahringer library if absent from the Vidal library. RNAi clones used were for the genes cco-1 , spg-7 , mrps-5 , and two different RNAi constructs against tim-17 ”) and Ahringer #97 G12 (“tim-17(1)”) and an empty vector “control” which consisted only of the L4440 vector. All RNAi clones were sequence verified prior to use and knockdown verified previously49. For mitochondrial dynamics experiments, L1 arrested worms were grown to L4 on RNAi strains which came from a documented and published RNAi library of genes implicated in the maintenance of mitochondrial morphology30.Worms were fed from hatch HT115 Worm profile data was collected using the Biosort 5401.1 software provided for use with the biosorter machine. Prior to running our orientation algorithm (described below), all profiles were converted from one of multiple.dat file(s) into a single.txt file using the “Export as Text” function in the Profile Reader 16.1 software  and then saved in a folder alongside the.lmd and.dat files that were saved by the Biosorter at the time of data acquisition. All orientation algorithms were written in Perl5 and interpreted in Cygwin (a Linux API that can run on Windows). Data analysis, significance testing, and data plotting was run using MATLAB scripts (version R2015a) which have been integrated into a single graphical user interface (“LAMPro Suite”). A flow chart for this “Generalized Workflow for Data Processing” is available in the Supplementary Information.n ≥ 50 animals per run . Notably, these sample sizes were reflective of the data after our software’s exclusion criteria had been applied.Regarding data processing and analysis, we discovered several conditions inherent to data acquisition in a biosorter that led to non-ideal data. We developed experiments and exclusion criteria to eliminate the effects of (1) small, non-staged animals and debris e.g. clumps of bacteria, eggs, L1 and L2 worms, (2) animals with aberrant extinction profiles , regional significance) can be found in the Supplementary Information. Of note, our software can detect and visualize when a fluorescent reporter becomes saturated  which is likely to eliminate fine differences in activation patterns (see Figure For a detailed, step-by-step, walkthrough of the workflow necessary for formatting the files and then running the software refer to the section titled “LAMPro Directions” in the Supplementary Information. Links to download all these programs freely is available through the journal’s Supplemental Material.Supplementary InformationSoftware


The plasma levels of asymmetric dimethylarginine (ADMA) had been proved to be an independent cardiovascular risk factor. Few studies involved the entire arginine methylation dysfunction. This study was designed to investigate whether arginine methylation dysfunction is associated with acute coronary syndrome risk in coronary artery disease population.In total 298 patients undergoing coronary angiography because of chest pain with the diagnosis of stable angina pectoris or acute coronary syndrome from February 2013 to June 2014 were included. Plasma levels of free arginine, citrulline, ornithine, and the methylated form of arginine, ADMA, and symmetric dimethylarginine (SDMA) were measured with high-performance liquid chromatography coupled with tandem mass spectrometry. We examined the relationship between arginine metabolism-related amino acids or arginine methylation index  and acute coronary events.P = 0.88); the AMI differed significantly between 2 groups (P < 0.001). Multivariate logistic regression demonstrated that AMI was an independent risk factor of acute coronary events in patients with coronary artery disease .We found that plasma ADMA levels were similar in the stable angina pectoris group and the acute coronary syndrome group (Our study suggested that ADMA levels were very similar in the stable angina and acute coronary syndrome patients; AMI might be an independent risk factor of acute coronary events in coronary artery disease population. It has been shown that ADMA plasma levels are elevated in diseases which are linked to an impairment of the NO-pathway and endothelial dysfunction such as hypertension, hypercholesterolemia, type 2 diabetes mellitus, hyperhomocysteinemia, and chronic renal failure.The main Arg metabolism pathway includes several important enzymes Fig. . Since pNowadays few studies involved the entire arginine methylation dysfunction in atherosclerotic diseases. So we designed this case-control study to investigate whether arginine methylation dysfunction is associated with acute cardiac event in coronary artery disease population.22.1) and ACS group . Exclusion criteria included the following: renal dysfunction (serum creatinine >1.5 mg/dL), hepatic or thyroid diseases, acute/chronic infection, malignant tumors, cerebrovascular disease, history of ACS, symptoms, or signs suggestive of congestive heart failure, and other myocardiopathy. The traditional cardiovascular risk factors included age, sex, systemic hypertension, hypercholesterolemia, smoking, and diabetes mellitus. Systemic hypertension was diagnosed if blood pressure was ≥140/90 mm Hg on 2 occasions or if the patients were taking antihypertensive drugs. Hypercholesterolemia was defined as fasting total cholesterol level ≥200 mg/dL or if the patients were already taking lipid-lowering agents. Diabetes mellitus was diagnosed if there was a fasting glucose level ≥126 mg/dL and/or plasma glucose level of ≥200 mg/dL 2 hours after glucose administration, or if the patient was taking oral hypoglycemic agents or receiving insulin injection therapy for blood glucose control at the present time. Acute/chronic infection status was determined through temperature taking, physical examination, and basic laboratory tests .Two separate experienced cardiologists evaluated 412 consecutive coronary angiograms. Finally, 298 patients undergoing coronary angiography because of chest pain with the diagnosis of stable angina pectoris (SAP) or acute coronary syndrome (ACS) from February 2013 to June 2014 were included in this study. According to history and result of coronary angiograms, patients were divided into 2 groups: SAP group , HbA1c and high sensitive C-reaction protein (hsCRP). We estimated the creatinine clearance rate (CCr) with Cockcroft–Gault formula. For results of ultrasonic cardiograph and myocardial perfusion imaging, we referred to the medical records. by 2 separate experienced cardiologists. In brief, the coronary artery tree was divided into 9 segments: the left main coronary artery; the proximal, middle, and distal left anterior descending artery; the proximal and distal circumflex artery; the proximal and middle right coronary artery; and the posterior descending artery. Each of these segments was scored from 0 to 3 depending on the most severe diameter stenosis according to the following system: 0 = normal, 1 = stenosis between 1% and 49%, 2 = stenosis between 50% and 99%, 3 = total stenosis, with each of the segments distal to the occlusion arbitrarily given a score of 1. The CAS was generated as the sum of the scores in all segments.The severity of coronary atherosclerosis was summarized by “coronary atherosclerotic score” (CAS) developed by Azar et al2.2,12 Briefly, 100 μL plasma was mixed with 25 μL of internal standard 15N-L-lysine (50 μmol/L) and 500 μL of acetonitrile, and then vortexed for 30S. After centrifugation at 10,000 g for 10 minutes, 500 μL of the supernatant was collected and evaporated to dryness under a gentle stream of nitrogen at room temperature. The dryness was reconstituted with 120 μL of pure water and filtered through a 0.2 μm nylon micro-spin filter tube  at a centrifugation of 10,000g for 1 minutes. The filtration was collected and chromatographed on an Atlantis dC18 column  with water (0.1% formic acid)-acetonitrile (0.1% formic acid)  as the mobile phase. The flow rate was 0.25 mL/min and the injection volume was 10 μL. The detection was performed on the API 3000 triple-quadrupole mass spectrometer  using electrospray positive ionization . Multiple reactions monitoring (MRM) scan mode was applied to monitor the target analytes based on the following mass transitions: ADMA, 203.1 → 46.2; SDMA, 203.1 → 172.4; Arg, 175.1 → 70.0; Cit, 176.1 → 70.0; Orn, 133.1 → 70.0; 15N-Lyscine , 148.1 → 85.0, respectively. The HPLC-MS/MS method was validated via standard solution over the concentration range of 0.25 to 4 μmol/L for ADMA and SDMA, and 25 to 400 μmol/L for Arg, Cit, and Orn, respectively. Inter- and intra-day precision for the target analytes were all within 15% and the accuracy was within 85% to 115%. The limit of quantitation was 0.25 μmol/L for ADMA and SDMA, and 25 μmol/L for Arg, Cit, and Orn, respectively.Fasting blood was drawn under standardized conditions. Samples were immediately processed and stored at –80°C until analysis. Plasma levels of ADMA, SDMA, Arg, Cit, and Orn were measured by high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS).2.3t tests for continuous variables and the χ2 tests or Fisher exact tests for categorical data. The multivariate logistic regression analysis was used to evaluate the independent risk factors of ACS. The odds ratios (OR) and 95% confidence intervals (CI) were calculated. A 2-tailed P-value of <0.05 was considered significant.Data were analyzed with the SPSS 17.0 for Windows . Continuous variables were presented as means ± standard deviation (SD) and categorical variables as frequency and percentage. Comparisons between the 2 groups were performed using Student This study was carried out with the approval of ethics committee of Peking Union Medical College Hospital, and they monitored for rationality and safety.33.1P < 0.001).In total 298 patients were enrolled in this study, including 98 patients in the SAP group and 200 patients in the ACS group. There were no significant differences between 2 groups concerning age, sex, body mass index (BMI), mean artery pressure (MAP), CCr, TG, TC, LDL-C, HDL-C, left ventricle ejection fraction (LVEF), and other cardiovascular risk factors including hypertension, hyperlipidemia, diabetes, smoking habit, and family history of CAD, even secondary prevention medicine-use Table . Compare3.2P = 0.88). The plasma concentrations of Arg in the ACS group were significantly lower than the SAP group . Similarly, concentrations of Cit and Orn in the ACS group were also significantly lower than the SAP group (both P < 0.001). The AMI in the SAP group was significantly higher than that of the ACS group .As shown in Table 3.3P = 0.008; OR = 1.258, 95% CI 1.100–1.439, P = 0.001, respectively).To access whether the AMI-associated independently with ACS, the multivariate stepwise logistic regression model was calculated, and all established risk factors or risk markers for CAD and secondary prevention medicine-use were included. AMI and hsCRP were identified as the independent protector or risk factor for ACS . Also, evidence suggests a putative role of oxidative stress in CAD, especially in ACS, which was believed to be a predictive factor for CAD and even could predict major adverse cardiovascular events.,21 Aukrust et al also suggested elevated oxidative stress concomitant with inflammatory markers in ACS. This implied arginase activity might also increase in CAD and ACS conditions, which was proven in vitro, and would further induce low bioavailability of arginine. Low substrate bioavailability for NOS and arginase would explain low Cit and Orn concentrations. But whether arginase activity is even significantly higher in ACS than SAP, which will answer the question why levels of Arg, Cit, and Orn were even lower in the ACS patients, needs further investigation.Further, we first reported that plasma levels of Arg, Cit, and Orn in the ACS group were significantly lower than the SAP group. These small amino acids were important in arginine metabolism, which were related to nitric oxide generation and arginine cycle. Under normal diet, Arg exogenous intake was relatively fixed; only 5% to 15% plasma Arg was synthesized in the body (mainly in kidney), So the AMI was partially determined by the Arg residues methylation status of protein. Oxidative stress, native or oxidized LDL, angiotensin II, eNOS inhibition/uncoupling and inflammation played a pivotal role in Arg residues methylation by managing PRMT/DDAH expression–27, and meanwhile increased arginase activity reduced Arg bioavailability, both of which might contribute to lower AMI in ACS. Studies had demonstrated Arg metabolites were associated with atherosclerosis progression,,28,29 and even associated with atherosclerotic plaque burden and fragility. Notsu et al suggested that the Arg/ADMA ratio may be a sensitive marker for atherosclerosis; Tang et al demonstrated that Arg/(Cit + Orn)  might serve as a more comprehensive concept of reduced NO synthetic capacity and diminished GABR were associated with both development of significantly obstructive atherosclerotic CAD and heightened long-term risk for major adverse cardiovascular events. In this study, we used AMI as an index for metabolism dysfunction and found it was also an independent predictor of acute coronary events in patients of CAD.We also first demonstrated that AMI in the SAP group was significantly higher than that of the ACS group. Despite of diet intake influence, plasma Arg hemostasis was achieved largely via protein degradation and Arg catabolism.,32 In the future, arginine metabolic amino acids may become additional biochemical parameters for clinical routine to identify those patients at higher risk for future cardiovascular events.The unavailability of easily accessible and rapid analytical methods to quantify these small amino acid molecules has been one obstacle for using Arg metabolites as risk markers. The HPLC-MS/MS has been shown to be a useful, relatively fast, and reliable method for determination of plasma levels of these small molecules with its advantage over ELISA assay.There were some limitations in this study. First, our study was a case-control study and sample size was relatively small. Confirmation in larger perspective cohort may be mandatory. In addition, subjects were enrolled from population referred for coronary angiography, so selection bias was inevitable for cases from a single medical center, for different admission rates might exist for SAP and ACS patients. Lastly, we considered the influence of complicate coronary lesions on ADMA levels, but neglected that of systemic atherosclerosis.To conclude, this case-control study suggested that ADMA levels were very similar in SAP and ACS patients; lower AMI served as an independent risk factor of acute coronary syndrome for patients of coronary artery disease.


Pontibacter akesuensis strain AKS 1T was found in Akesu, Xinjiang Province, China, and exhibits the extraordinary ability to metabolize various substrates and is resistant to solar radiation. To gain insight into the bacterial genetic determinants for this adaptability, we report the complete genome sequence of strain AKS 1T. Pontibacter akesuensis strain AKS 1T  was selectively isolated from the desert soil of Akesu by using a traditional dilution plating method. After Biolog GN2 tests, AKS 1T indicated outstanding carbon utilization ability on 70 different compounds  sequence libraries were prepared for two types with 100 bp and 500 bp insert sizes. WGS sequence data of 780 Mb, giving approximately 167-fold genome coverage, was generated and assembled into 32 contigs, which were connected into two scaffolds based on the paired-end relationships of libraries by using SOAPdenovo  and one plasmid . The chromosome of AKS 1Tcontained 4,240 predicted genes, 12 rRNA operons, and 43 tRNAs. A total of 146 genes, zero rRNA operons, and zero tRNA were identified in the plasmid. Besides 14 microsatellites, 126 tandem repeats with a total length of 6,647 bp were predicted in the chromosome of AKS 1T.The complete genome of P. akesuensis strain AKS 1T, 2,375 genes were identified by the KEGG database. Meanwhile, 394 genes, 376 genes, and 233 genes, which were clustered using the functions “carbohydrate metabolism,” “amino acid metabolism,” and “replication and repair” separately, were the three main classification items. Furthermore, the strain contained 206 genes and 194 genes, which could be classified as “xenobiotics biodegradation and metabolism” and “energy metabolism.” Exploring the genes related to compound utilization in cruel environments, we found many genes belonging to the carbon-nitrogen hydrolase family or carbonic anhydrase family. Additionally, gene aspB, which functions as a carbon fixer in photosynthetic organisms, was also found in AKS 1T. These data provided a comprehensive description of the outstanding ability of nutrition metabolism and explained the adaption ability of AKS 1T in harsh environments.From the gene annotation results of P. akesuensis strain AKS 1T was deposited in GenBank under the accession numbers CP014766 and CP014767, respectively.Genome information for the chromosome and plasmid of


The present study aimed to elucidate the associations among self-rated oral health, clinical oral health status, oral health behaviors, subjective oral symptoms, and oral health-related quality of life (OHRQoL) in a group of Japanese university students.Of 2051 participants, 2027 (98.83%) students received an optional oral examination and answered a questionnaire including items regarding age, sex, self-rated oral health, oral health behaviors, subjective oral symptoms, and OHRQoL [The Oral Health Impact Profile (OHIP)-14]. On oral examination, the decayed, missing, and filled teeth (DMFT) score, Community Periodontal Index (CPI), the percentage of teeth showing bleeding on probing (%BOP), and malocclusion were recorded. Structural equation modelling (SEM) analysis was used to test associations.p < 0.05). CPI, %BOP, and oral health behaviors were excluded from the final model.The mean score (± SD) of OHIP-14 was 1.92 ± 5.47. In the SEM analysis, the final model showed that self-rated oral health, oral pain, malocclusion, and the DMFT score were directly associated with the OHRQoL, and subjective symptoms of temporomandibular disorders (TMD) and recurrent aphthous stomatitis were both directly and indirectly associated (OHRQoL was associated with self-related oral health, subjective symptoms of TMD, oral pain and stomatitis, DMFT, and malocclusion in this group of Japanese university students. Oral health-related quality of life (OHRQoL) represents the subjective experience of symptoms related to oral conditions that have an impact on well-being , 2. OHRQYoung university students are in a dynamic transition period of growth and development that bridges adolescence (high school students) and adulthood (people in the community) , 16. SinSelf-rated oral health is often assessed in epidemiologic studies, since it permits easy evaluation of the participants’ general oral health condition. Based on the results of previous studies, information on self-rated oral health should be included during surveillance of oral health in young people , 19. In We hypothesized that university students with better self-rated oral health, clinical status, and oral health behaviors, and less subjective oral symptoms would report better OHRQoL. Therefore, the aim of the present study was to elucidate the associations among OHRQoL, self-rated oral health, clinical status, oral health behaviors, and subjective oral symptoms in a group of Japanese university students.The first-year students of all departments of Okayama University underwent a mandatory general health examinations at the Health Service Center in April 2014. Of 2051 participants, 2027 (98.83%) students underwent an optional oral examination and completed a questionnaire. The exclusion criteria included incomplete data and age ≥20 years. To avoid the influence of age-related factors, most of the participants were 18 or 19 years old , 23. TheAll participants were mailed a questionnaire prior to the health examination. The questionnaire included the following items: age, sex, general condition, self-rated oral health, oral health behaviors, subjective oral symptoms, and OHRQoL.The question ‘In general, how do you consider your oral health?’ was used to assess self-rated oral health; the responses included ‘very good’, ‘good’, ‘fair’, ‘poor’, or ‘very poor’ .Oral health behavior was assessed by asking about the daily frequency of tooth-brushing, with the possible responses being 1, 2, or ≥3 times. Participants were also asked to indicate whether they used dental floss and had a regular dental check-up during the past year .With respect to subjective oral symptoms, the presence of oral pain and recurrent aphthous stomatitis during the past 3 months was answered as either yes or no . QuestioThe Japanese version of Oral Health Impact Profile (OHIP)-14 was selected to evaluate OHRQoL. The OHIP-14 is a short version of the OHIP-49 . The OHIFive dentists  recorded the oral health status of the participants. The DMFT score was used to evaluate dental caries status based on the World Health Organization caries diagnostic criteria . PeriodoA modified version of the Index of Orthodontic Treatment Need (IOTN) was used to assess malocclusion . Based op < 0.05 was used for the regression coefficients. “TMD”, “Oral health behaviors”, and “Clinical periodontal conditions” were set as latent variables. The latent construct “TMD” comprised “Pain in TMJ”, “Clicking,” and “Difficulty opening mouth”; “Oral health behaviors” included “Frequency of tooth brushing”, “Regular check-up”, and “Use of floss”; and “Clinical periodontal conditions” included the “CPI” and “%BOP”. Low scores indicated a good condition, that is, for self-rated oral health “1 = very good”, “2 = good”, “3 = fair”, “4 = poor”, and “5 = very poor”; for TMD, “1 = never”, “2 = rarely”, “3 = sometimes”, and “4 = frequently”; and for tooth brushing frequency, “1 = three times or more”, “2 = two times”, and “3 = 1 time or less”. For other parameters, “1 = yes” and “2 = no”.Structural equation modelling (SEM) analysis was used to test the associations among self-rated oral health, clinical oral health status, oral health behaviors, subjective oral symptoms, and OHRQoL , 30, 31.Cohen’s effect size was assessed using correlation coefficients or the standardized coefficient that corresponded to r; small, medium, and large effect sizes were 0.10, 0.30, and 0.50, respectively .A total of 243 participants who had provided incomplete data in their questionnaires and 126 participants who were 20 years or older were excluded. As a result, data from 1901 students  were analyzed. Table χ2 = 25.582, df = 17, P = 0.1423). The CFI, TLI, and RMSEA values indicated good model-data fit . The model showed that (1) self-rated oral health directly affected OHRQoL and the effect size was small, (2) the DMFT score directly affected OHRQoL and the effect size was medium, (3) subjective symptoms of TMD, oral pain and stomatitis, and malocclusion directly affected OHRQoL and the effect size was small. All pathways were significant (p < 0.05). Clinical periodontal conditions and oral health behaviors were excluded from the final model.Figure In the SEM analysis, self-related oral health, subjective symptoms , and clinical status  were positively associated with OHRQoL in university students. OHRQoL represents the subjective experience of symptoms related to oral conditions that have an impact on well-being , 2. ManyAs for the characteristics of participants, the mean score (± SD) of OHIP-14 was 1.92 ± 5.47 in this study. It was relatively low compared to those in elderly people –11, 35. Self-rated oral health was directly associated with OHRQoL, that is, poor self-related oral health resulted in poor OHRQoL, although the effect size of the path was small. The association was also observed in previous studies , 20–22, With respect to clinical status, the DMFT score and malocclusion were directly associated with OHRQoL, that is, participants with a high DMFT score and malocclusion thought that they actually had poor OHRQoL. These results were similar to those of other studies –43. FurtSubjective symptoms  were associated with OHRQoL in university students. These findings were supported by previous studies in other populations –50. SympThe path form oral health behaviors to OHRQoL did not fit the final model. However, the previous studies suggest a possible association between oral health behaviors and OHRQoL –53. The Clinical periodontal conditions such as the CPI score and %BOP were excluded from the final model and did not show a significant effect on OHRQoL in this study. The reason may be that young students with periodontal diseases generally have minimal symptoms. However, in periodontitis patients who have evident periodontal symptoms, OHRQoL was associated with clinical status –6. Thus,In the present study, the OHIP-14 scores were relatively low and all effect sizes of the paths, except for DMFT to OHRQoL, may be low. However, exploring the association between OHRQoL and other factors should not be neglected, even though the score was not relatively high. Early detection and early treatment of oral diseases are important for improving OHRQoL in young people, as well as in elderly people. Since oral examinations are not mandatory for university students in Japan, routine health examinations in universities should include an investigation of predictors of OHRQoL.The OHIP-14 was selected as an indicator of OHRQoL because the OHIP-14 is better at detecting psychosocial impacts among individuals and groups and more closely matches the main criteria for measurement of OHRQoL , 56. FurIn the SEM analysis, individual characteristics (sex) and environmental characteristics (socioeconomic factors) were not included. These factors were associated with QOL in Wilson and Cleary’s conceptual model . When thThis study has other limitations. First, it was a cross-sectional study, and to develop a better model, a prospective cohort study and an interventional study would be required. Second, the participants were all college freshmen at Okayama University, which may limit the generalizability of the findings to young Japanese students.The OHIP-14 score was positively associated with self-related oral health, subjective symptoms of TMD, oral pain and stomatitis, and clinical status  in this group of Japanese university students.