The Escape and Radiate Hypothesis posits that herbivorous insects and their host plants diversify through antagonistic coevolutionary adaptive radiation. For more than 50 years, it has inspired predictions about herbivorous insect macro‐evolution, but only recently have the resources begun to fall into place for rigorous testing of those predictions. Here, with comparative phylogenetic analyses of nymphalid butterflies, we test two of these predictions: that major host switches tend to increase species diversification and that such increases will be proportional to the scope of ecological opportunity afforded by a particular novel host association. We find that by and large the effect of major host‐use changes on butterfly diversity is the opposite of what was predicted; although it appears that the evolution of a few novel host associations can cause short‐term bursts of speciation, in general, major changes in host use tend to be linked to significant long‐term decreases in butterfly species richness. We test several key predictions of the Escape and Radiate Hypothesis using comparative phylogenetics in nymphalid butterflies. We find that although it appears that the evolution of a few novel host associations may have caused bursts of speciation, in general, major changes in host use tend to be linked to significant decreases in butterfly species richness. Although more is known of host use in some other groups of insects, for example scale insects . First, we reconstructed the history of associations between nymphalids and their twelve most commonly used host‐plant orders with a maximum nymphalid genus host‐breadth size of five orders. This required dropping 23 of the most polyphagous nymphalid genera, along with nine additional genera for which we lacked host‐use data, leaving 357 genera  for analysis. Second, we reconstructed ancestral use of host‐plant families over a set of nymphalid subclades using an R script Appendix  to cut tDEC estimations of ancestral host use were performed with the R package BioGeoBEARS Matzke, . Specifi2.1.2To test the predictions of the Escape and Radiate Hypothesis, we used a linear modeling approach. We first sought to explain the variation across nymphalid lineages in species diversity and diversification rates with their phylogenetic history of host use. For these models, we had a single predictor variable.Host‐use change was a factor with three levels, indicating at each internal node if a host was gained, lost, or no change in use occurred.  This classification allowed us to test if host gains and losses tend to boost or throttle diversity.We used three response variables: (a) phylogenetically independent contrasts of speciation waiting times, (b) phylogenetically independent contrasts of extant species diversities, and (c) gamma statistic values . We then performed the same calculation for the focal node's sister node. The difference in average waiting times between sister nodes was used as a phylogenetically independent contrast of speciation rates. The sign and magnitude of these contrasts can show us how the gains and losses of novel host associations tend to immediately affect speciation rates. In short, this is just a comparison between two sister nodes, of the average time for subsequent lineage divergence.total extant species diversities spanning each internal node; that is, we contrasted the extant diversity of a clade descended from an ancestor that gained or lost a new host, with the extant diversity of its sister clade, while ignoring whether or not extant taxa use (or do not use) that host . Phylogenetic ages were taken from the TimeTree database  Host diversity was simply in the current species richness of the host family or order, according to the Catalogue of Life database  2.1.4To repeat, we sought to address two questions: Do host‐use gains tend to increase diversity and if so, do novel associations corresponding to greater ecological opportunities spark greater diversity gains? For the first question, we looked at how host‐use gains and losses affected extant species diversity, speciation waiting times, and the overall dynamics of diversification (as measured with the gamma statistic). For the second question, we looked at the same response variables, but only for host gains, and tried to explain the variation in the response variables with several indices of the breadth of ecological opportunity afforded by a novel host group. Two of the response variables—speciation waiting times and extant species diversities—were phylogenetically independent contrasts. Therefore, we could use standard linear modeling methods to estimate the fixed effects of the predictor variables on their variance. (We used the built‐in lm R function.) To account for uncertainty in the DEC* reconstructions, we weighted each model covariate with a vector of the proportional probabilities for each estimated ancestral host‐use state. For these models, as is standard for models of phylogenetically independent contrasts, we forced the regression to pass through the origin.Values of the third response variable, the gamma statistic, were not phylogenetically independent. To account for this, we fit linear mixed models in which phylogenetic relatedness between nodes was expressed using a pedigree structure and included this as a random effect with the Bayesian approach implemented in MCMCglmm , while nodes with a host‐use loss had on average 36.94 fewer extant descendant species than their sister nodes . In the inclusive‐contrast family‐level model, nodes with a host‐use gain had on average 20.50 fewer extant descendant species than their sister node , while nodes with a host‐use loss had on average 35.88 fewer extant species than their sister node  descended from an ancestor that gained a host group to the total species diversity of its sister group. In the exclusive contrasts family‐level model, nodes with a host‐use gain had on average 16.37 fewer extant descendant species than their sister nodes : Apiaceae, Apocynaceae, Boraginaceae, Cyperaceae, Euphorbiaceae, Rosaceae and Verbenaceae. For full results, see Table A state‐dependent early burst model of phylogenetic branching was a better fit than a constant diversification rate model for just seven of 128 host‐plant families . Major host shifts tend to have negative long‐term effects on diversity. And as in the genus‐level analyses, we found a negative correlation between extant diversity contrasts and the early adoption index ; if a nymphalid lineage colonized a novel host group early in its independent evolutionary history, that tended to reduce the negative effects on extant species richness. Analysis of the nymphalid species‐level data also revealed a positive correlation between extant nymphalid diversity and host‐plant family age . For full results of the species‐level models, see Tables Results of the nymphalid species‐level analyses add nuance. As in the genus‐level analyses, both host gains and losses tended to be linked to decreasing in extant species richness (gains coefficient: −9.782, 4Does the Escape and Radiate Hypothesis predict phylogenetic patterns of host‐use and species diversity? For the most part, this does not seem to be the case. We find no support for the broadest prediction that the evolution of major new host associations tends to boost herbivorous insect diversity over long phylogenetic time scales. To the contrary, we find evidence that novel associations tend to decrease extant butterfly species richness. It could be that major evolutionary changes in host use are caused by major declines in fitness on ancestral hosts. Such declines could be due to a variety of factors including increased competition for diminished host‐plant resources, the evolution of novel host defenses, the invasion of host herbivore assemblages by new species, and changes in natural enemy communities (Bird, Kaczvinsky, Wilson, & Hardy, Alternatively, it is possible that major new host associations are, in fact, the realization of ecological opportunities, but such opportunities do not tend to promote speciation of nymphalids in the long term. We found evidence of early burst diversification linked to the evolution of the use of several specific plant families. This suggests that coevolutionary adaptive radiation may indeed have played a role in the diversification of herbivorous insects. But such events might have been relatively rare in the history of nymphalids, and short‐term increases in speciation might come with tight constraints on long‐term diversity.The Escape and Radiate Hypothesis can also be used to predict that a novel host association should affect species diversity in a way that is proportional to the scope of ecological opportunity the new host group offers. Two of our indices of ecological opportunity could explain some of the variation in our butterfly diversification variables. First, we found that gamma statistic values were more negative—indicating an earlier burst of diversification (Gavrilets & Losos, post hoc analyses of diversity patterns. In the second, we fit explicitly state‐dependent models of early burst diversification for nymphalid genera, but on a series of binary host‐use characters. The shortcoming of the first approach is the assumption of state‐independent diversification. The shortcoming of the second is that when a multi‐state trait such as host use is modeled as series of binary traits we tend to reconstruct ancestors without hosts (Hardy, Of course, this study has several limitations. Most importantly, we worked within the limits of the current models for reconstructing the phylogenetic history of a complex trait like host use. As mentioned above, it is currently not possible to reconstruct the ancestral states of a highly multi‐state discrete trait while accounting for state‐dependent variation in speciation and extinction rates (Maddison et al., s Hardy, . The picAt first blush, the results presented here may appear to contradict those of Hardy and Otto , who fouWe framed our analyses as tests of the Escape and Radiate Hypothesis. In our view, this is consistent with its current usage and connotations, but to be sure, those connotations have evolved since Ehrlich and Raven . In factIn sum, our tests yield evidence against a key prediction of the Escape and Radiate Hypothesis: that the evolution of major new host associations will tend to boost the species diversity of herbivorous insect lineages. We find some evidence that the scope of ecological opportunity afforded by a novel host association governs its effects on diversification dynamics. But since the overall effect of a major new association appears to be negative, it seems that large new opportunities function to primarily diminish negative effects, rather than to inflate positive effects. To be sure, this evidence is conditioned on the adequacy of the models we have used to infer ancestral host use and diversification dynamics, but it is consistent with another recent comparative phylogenetic study finding that patterns in the networks of evolutionary associations between butterflies and their host plants are inconsistent with coevolutionary adaptive radiation (Braga, Guimarães, Wheat, Nylin, & Janz, None declared.CK performed most of the scripting; CK and NBH jointly conceived of the project and helped draft the manuscript. All authors gave final approval for publication and agree to be held accountable for the work performed therein. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


BPI) average pain scores by patient global impression of improvement (PGI‐I) category and the cut‐off for clinically important difference (CID) were different between Asian and Caucasian patients with chronic pain due to osteoarthritis. This analysis used data from 3 (Caucasian) and 2 (Asian) randomized, placebo‐controlled, 10‐ to 14‐week duloxetine studies for the treatment of patients ≥40 years of age with osteoarthritis pain. The receiver operating characteristic (ROC) analysis was used to characterize the association between changes in BPI average pain scores and PGI‐I levels at study endpoint. The CID was characterized by PGI‐I, and the cut‐off point for CID in BPI average pain scores was determined by the intersection of a 45‐degree tangent line with each ROC curve. Data from 668 Asian and 868 Caucasian patients were available for analysis. Baseline BPI average pain ratings including worst and least pain were comparable between Asians and Caucasians. Ratings for percentage change from baseline to endpoint for BPI average pain scores in Asian patients and Caucasian patients were similar across the 7 PGI‐I categories, regardless of age, gender, study, and treatment. The ROC analysis results of cut‐off points in BPI average pain scores demonstrated the raw change cut‐off was −3.0, and percentage change cut‐off was −40% for both Asian and Caucasian patients. Overall, the present analysis concludes changes in BPI average pain scores by PGI‐I category and the cut‐off for CID were similar for Asian and Caucasian patients with chronic pain due to osteoarthritis.The objective of the present analysis was to determine whether changes in Brief Pain Inventory ( Osteoarthritis (OA) is one of the most prevalent chronic musculoskeletal disorders worldwide, especially in the elderly.Ogawa et al.Clinical pain studies have demonstrated cultural variations in response to painful stimuli.Duloxetine is a potent and selective inhibitor of serotonin and norepinephrine in the central nervous system and is efficacious for chronic musculoskeletal pain.This analysis used data from 3 (Caucasian)In all studies, the BPI ratings for worst, least, and average pain intensity were collected at baseline, and the BPI average pain and the PGI‐I ratings were collected at several post‐baseline visits. The analysis was carried out using the values collected at the last visit of the double‐blind treatment phase. The percentage change from baseline in BPI average pain severity using the 0‐to‐10 numeric scale compared to the 7‐category PGI‐I assessment at study endpoint was computed for each patient. The relationship between percentage change in BPI scores and the endpoint PGI‐I scores was plotted by displaying the BPI average change within each PGI‐I category. Two categories on the PGI‐I scale, “very much worse” or “much worse,” were combined because of the small numbers of patients in these groups. Reduced calculations for BPI average pain scores from baseline to endpoint by PGI‐I category, according to race (Asian and Caucasian) and study participation, were evaluated. In addition, subgroup analyses by age groups , gender , and study treatment group (duloxetine vs. placebo) were performed to explore whether they influence or confound PGI‐I distribution between the races. Moreover, the percentage change from baseline in BPI average pain scores by PGI‐I category according to race (Asian and Caucasian) in duloxetine‐treated patients was assessed. All comparisons were made using descriptive statistics. The percentage change data are summarized in graphical format, while results from receiver operating characteristic (ROC) analysis are presented in tabular format.In the present analysis, 3 different levels of improvement as characterized by the PGI‐I were examined: “a little better” or higher ; “much better” or higher (PGI‐I score = 1 or 2); and “very much better” (PGI‐I score = 1). CID was characterized by the PGI‐I and was defined as “much better” or “very much better.” The ROC curves were generated by computing the sensitivity and specificity in a contingency table (2 × 2) for both the raw and percentage changes for BPI average pain scores, and for each level of the PGI‐I, using the proportion of patients who reported each possible level of change in pain rating as the exposure and the preselected level of the PGI‐I as the outcome. The appropriate cut‐off point for the BPI average pain percentage change was determined using ROC analysis. ROC analysis is a standard statistical analysis used to best characterize the association between change in the BPI average pain score (independent variable) and different levels of the PGI‐I (dependent variable) at the study endpoint. The cut‐off point for CID was determined by the intersection of a 45‐degree tangent line with each ROC curve; thus, the pain intensity changes can be best associated with each of the categories of improvement on the PGI‐I.Data from 668 Asian and 868 Caucasian patients, who had recorded baseline pain intensity and at least 1 post‐baseline measure on the BPI and PGI‐I, were included in the analyses. A summary of patient demographics and baseline characteristics of the combined studies is presented in Table The ratings for percentage change from baseline for BPI average pain scores were similar across the 7 PGI‐I categories for Asian and Caucasian patients Figure . Asian aTable The present analysis of pooled data from 5 studies of duloxetine in patients with chronic pain demonstrated a close association between changes in BPI average pain scores and PGI‐I scores in Asian and Caucasian patients. In Asian and Caucasian patients, the relationship is highly consistent across all analyzed studies, the treatments administered (duloxetine or placebo), and the patient demography characteristics (age and gender). The BPI average pain (percentage) reduction from baseline to endpoint was similar across 7 PGI‐I categories for Asian and Caucasian patients, regardless of age, gender, study, and treatment. The consistency of these results suggests that Asian and Caucasian patients interpret changes in BPI average pain scores and PGI‐I scores similarly, irrespective of age, gender, study, and treatment.In the present analysis, close associations were found between BPI average pain ratings (raw and percentage change) and PGI‐I scores among Asian and Caucasian patients, as demonstrated by the area under the ROC. The raw levels of change in the BPI average pain scores associated with specific PGI‐I improvement levels could vary based on the baseline pain ratings. Therefore, the percentage change was calculated to adjust the baseline pain rating of each patient; hence, the relationship between the reported change in pain and the patients’ perception of improvement becomes more linear as the mean rating of the BPI average pain change and the PGI‐I categories becomes more consistent.In Asian patients, a percentage change of 40% best correlated with our a priori definition of a CID, namely the PGI‐I category of “much better” or “very much better.” Similarly, a 63% reduction in pain from baseline correlated with the highest degree of improvement on the PGI‐I scale. In Caucasian patients, a percentage change of 40% best correlated with our a priori definition of a CID. Likewise, a 56% reduction in pain from baseline was associated with the highest degree of improvement on the PGI‐I. Also, raw change in BPI average pain scores for “much better” or “very much better” was −3 for the Asian patients and −3 for the Caucasian patients. These results indicate that the percentage and change cut‐off values for CID were similar in Asian and Caucasian patients with OA of the knee or hip. The present analysis results suggest that these values are suitable for use in the interpretation of clinical study results for the design and analysis of future clinical trials of chronic pain treatment. In comparing our results with those of other published studiesThe strength of the present analysis is the consistency observed across all 5 large duloxetine studies conducted in multiple countries with essentially identical measurement instruments and methodology. The common design among the studies eliminates many of the sources of variability, making the association between the change in the BPI average pain scores and PGI‐I scores less likely to have alternative explanations. The present analysis includes patients of different ethnicity (Asian and Caucasian patients). This may provide robust support for the external validity of these results. There are some limitations to the present analysis. The present analysis was performed only in a setting of OA; thus, these findings may not generalize to all chronic pain conditions. Additionally, the PGI‐I score may be affected by patients’ expectations of treatment, and patients’ responses to pain may be influenced by their perception of other improvements in their condition. The present analysis did not examine the effect of these other components, but the consistency of the demonstrated associations across patient populations, age, race, and sex improves the generalizability of our findings. Apart from the concept of symptomatic improvement in a patient's condition, the concept of satisfaction with care could also be used to assess a cut‐off point. However, satisfaction with care has a distinct theoretical concept and has not been included in most pain clinical trials, including those analyzed here.The findings of the present analysis can help design future studies and estimate appropriate sample sizes of pain clinical trials. The presented data also facilitate the comparison of results across Asian and Caucasian patients, which helps to determine the value of a therapy in clinical practice. The findings of this study provide information about the range of BPI average pain scores associated with various degrees of global improvement , irrespective of ethnicity, age, and gender, which can facilitate the evaluation of treatments for chronic pain.In the present analysis of 5 clinical studies of duloxetine in chronic pain due to OA, a consistent relationship between the change in BPI average pain scores and the PGI‐I category was demonstrated in Asian and Caucasian patients, regardless of age, gender, study, and treatment. Moreover, raw and percentage change cut‐off values for CID were similar in Asian and Caucasian patients with OA of the knee or hip. Overall, the present analysis concludes that the ratings for percentage change in BPI average pain by PGI‐I category and the cut‐off for CID were similar for the Asian and Caucasian populations. Applying these findings to future studies may help to standardize the definition of a CID in clinical trials of chronic pain therapies. Use of a standard outcome across chronic pain studies would greatly enhance the comparability, validity, and clinical applicability.All authors are Eli Lilly and Company employees, except for Li Yue. Hiroyuki Enomoto, Shinji Fujikoshi, Levent Alev, Yan Yolanda Cheng, and Vladimir Skljarevski are minor stakeholders. Li Yue and Jianing Wang have no conflicts of interest to declare.


This study reviewed the serial real‐time reverse‐transcription polymerase chain reaction (rRT‐PCR) results of 37 patients admitted to our hospital in Wuhan, China, who had three or more sequential negative results before discharge. Of these 37 patients, 14 (~38%) had a positive rRT‐PCR result after a negative result during convalescence, and 5 (~14%) had a positive rRT‐PCR result after two consecutive negative results during convalescence. These results suggest that it may be necessary to require that patients have three consecutive negative results before discharge, to ensure that they do not spread infection among members of their household, or in the community. We believe that our study makes a significant contribution to the literature because it is not currently the standard of care to require patients to have three consecutive negative results before discharge. Our results suggest that a relatively high proportion of patients may continue to shed severe acute respiratory syndrome coronavirus 2 after they have clinically recovered, and thus may transmit the infection to others. In this study, 14% COVID‐19 patients after clinically recovered were found to have consecutive false‐negative rRT‐PCR test result. That indicated patients who met with discharge criteria with 2 consecutive negative results may continue to shed SARS‐CoV‐2 and contribute to potential infection transmission. COVID‐19corona virus disease 2019FNfalse‐negativerRT‐PCRreal‐time reverse‐transcription polymerase chain reactionSARS‐Cov‐2severe acute respiratory syndrome coronavirus 2TNtrue‐negative1Since December 2019, novel coronavirus disease (COVID‐19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection, has widely spread to various countries and formed a global pandemic.2The study protocol was approved by the Ethics of Committees of The First Affiliated Hospital of China Medical University and the Union Hospital, Tongji Medical College, Huazhong University of Science and Technology. The requirement for informed consent was waived because the study was based on a retrospective review of medical records.We retrospectively reviewed the records of consecutive patients with laboratory‐confirmed COVID‐19 admitted to the Wuhan Union Hospital, China from 9 February to 28 March 2020.The treatment of patients in this study was in line with the Guidelines for the Diagnosis and Treatment of COVID‐19 Pneumonia published by the National Health Commission of the People's Republic of China.3Thirty‐seven patients were included in this study, median age was 62 years old, 17 (45.9%) were male. The most common symptoms were fever , cough , shortness of breath , and fatigue . Hypertension, cardiac disease, and diabetes mellitus was found as comorbidities in seven, five, five patients, respectively, only two patients had pre‐existing pulmonary disease. 27 patients had three consecutive TN rRT‐PCR results, seven had four TN results for verification and three had five TN results for verification before discharge. Fourteen of the 37 patients (38%) had one or more FN result. The median time from symptom onset to a FN result was 25 days, ranging from 14 to 37 days; the median time from symptom onset to the first TN result was 33 days, ranging from 10 to 63 days (Figure 4Previous studies have revealed the occurrence of FN rRT‐PCR test resultsLimitations of this study include the small number of cases, and the time variation of the sampling interval. Ongoing viral load and serum antibody monitoring are needed in future studies.The authors declare that there are no conflict of interests.GW participated in the design of the study and draft the manuscript. NY performed the statistical analysis. WX participated in the acquisition, analysis, or interpretation of data. CZ conceived of the study and participated in its design and coordination and helped to draft the manuscript. ZW participated Critical revision of the manuscript for important intellectual content and supervised this study. All authors read and approved the final manuscript.The study protocol was approved by the Ethics of Committ ees of The First Affiliated Hospital of China Medical University and the Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, the approval number is 2020191. The requirement for informed consent was waived because the study was based on a retrospective review of medical records.


WHO hepatitis C elimination targets two years after the introduction of highly effective direct‐acting antiviral (DAA) treatments.To assess progress in Australia toward the 2030 RNA testing and hepatitis C treatment in Australia, January 2013 – June 2018. Changes in testing and treatment levels associated with DAA availability were assessed in an autoregressive integrated moving average (ARIMA) statistical model, and the impact by 2030 of different levels of testing and treatment were estimated using a mathematical model.Analysis of quarterly data on government‐subsidised hepatitis C Hepatitis C prevalence among people who inject drugs; annual hepatitis C incidence relative to 2015 levels; projections for the hepatitis C care cascade in 2030.DAAs) to 28 022 during 2016–18; the mean annual number of diagnostic RNA tests increased from 17 385 to 23 819. If current trends in testing and treatment continue , it is projected that by 2030 only 72% of infected people would be treated . The incidence of hepatitis C in 2030 would be 59% lower than in 2015, well short of the WHO target of an 80% reduction. The identification and testing of people exposed to hepatitis C must be increased by at least 50% for Australia to reach the WHO elimination targets.The mean annual number of treatments initiated for people with hepatitis C increased from 6747 during 2013–2015 (before the introduction of Hepatitis C elimination programs in Australia should focus on increasing testing rates and linkage with care to maintain adequate levels of treatment. The known: Despite high initial uptake of hepatitis C treatment in Australia, it is uncertain whether the hepatitis C testing rate is sufficient to sustain the treatment uptake necessary for achieving the WHO hepatitis C elimination targets by 2030.The new: Progress towards elimination in Australia was assessed in mathematical models incorporating data on hepatitis C testing and treatment from multiple national datasets. Australia is unlikely to meet the WHO targets unless the identification and testing of people exposed to hepatitis C is increased by 50%.The implications: To maintain treatment levels adequate for achieving WHO elimination targets, hepatitis C programs in Australia should focus on increasing testing rates.In 2015, the first World Health Organization viral hepatitis strategyAustralia is one of only a few countries that have had unrestricted access to DAAs for several years. In 2016, the Australian government provided about $1.2 billion to fund DAA treatments over five years, so that every Australian with hepatitis C could receive treatment with low out‐of‐pocket cost to the patient .Modelling has indicated that Australia can meet the WHO 2030 elimination targets, provided treatment uptake can be sustained among people with advanced liver disease and people who inject drugs .To achieve the WHO hepatitis C elimination goals, the level of testing must be adequate. Modelling indicates that, if testing and engagement with care does not exceed pre‐2016 levels, the number of people treated will decline as the number of diagnosed and treatment‐ready patients declines.In our study, we collated data on hepatitis C testing and treatment in Australia from several national datasets. Our key aims were to assess trends in hepatitis C testing and treatment after the introduction of DAAs, and to use mathematical modelling to assess how progress toward the 2030 WHO targets would be influenced by different testing and treatment levels.http://medicarestatistics.humanservices.gov.au/statistics/mbs_item.jsp) (The presence of serum antibodies to the hepatitis C virus (HCV) indicates exposure to the virus; testing for HCV RNA (by polymerase chain reaction assay) is required to distinguish between an active infection and an earlier, resolved exposure.tem.jsp) , part A.Quarterly time series hepatitis C treatment prescription data for the period 1 January 2013 to 30 June 2018 were obtained from the Australian Pharmaceutical Benefits Scheme (PBS). Data were disaggregated by prescriber type  and treatment regimen  , part A.Autoregressive integrated moving average (ARIMA) modelsWe applied the Burnet–Optima mathematical model of HCV transmission and liver disease progression and treatment used in our earlier studies of hepatitis C testing and treatment in Australia.For the period from 2016 , four scenarios were simulated and compared:treatment numbers remain at pre‐2016 level (before 2016: pegylated interferon‐based therapy);initial increase in treatment numbers with introduction of DAAs, followed by decline from 2019 ;treatment numbers in 2018 are maintained until 2030; andtreatment numbers in 2018 are maintained until 2030, and testing (and diagnosis) of previously undiagnosed HCV‐infected people increases by 50% .Box 1For each scenario, we assumed that treatment was not targeted to any subgroup of people with hepatitis C .The outcomes in each scenario were hepatitis C prevalence among people who inject drugs, annual hepatitis C incidence relative to 2015 levels, and the projected hepatitis C care cascade in 2030.A Monte Carlo uncertainty analysis was conducted to obtain 95% confidence intervals (CIs) for model estimates: 100 random parameter sets were drawn, with parameters for the force of infection constant for people who inject drugs, disease progression and mortality rates, and the proportion of people spontaneously clearing infection selected randomly from their uncertainty bounds. A sensitivity analysis assessed the impact of data limitations, including testing in prisons not included in our model, lower RNA test positivity rate, and different levels of uptake of testing by people who inject drugs and other patients , part B.Statistical analyses were undertaken in R 3.5.0 and modelling in Matlab 2018b.Ethics approval was not required for our analysis of publicly available and non‐identifiable aggregate data.During 1 January 2013 – 31 December 2015, 20 240 hepatitis C treatments were initiated . During 1 January 2016 – 30 June 2018, 70 056 hepatitis C treatments were initiated , including 67 180 instances of DAA therapy (96%); however, annual treatment numbers declined across this period  .During 1 January 2013 – 31 December 2015, 52 156 diagnostic HCV RNA tests were conducted (17 358 per year). During 1 January 2016 – 30 June 2018, 59 548 diagnostic HCV RNA tests were conducted (23 819 per year), including 32 519 tests (55%) for people in the 45–54 and 55–64‐year age groups . IncreasBox 2Changes in testing and treatment numbers in individual states and territories were similar to the national changes , part C.During 1 January 2016 – 30 June 2018, 35 434 of 67 393 treatments for which the prescriber type was identified (53%) were prescribed by specialists. The proportion of treatments prescribed by non‐specialists has increased since 2016: from 13 117 of 34 130 treatments (38%) in 2016 to 12 763 of 23 635 (54%) in 2017, and 6079 of 9628 treatments (63%) in the first half of 2018 . This chBox 3The introduction of DAAs was associated with statistically significant increases of 1125  diagnostic RNA tests and 5647 (3661–7634) treatment initiations per quarter from the second quarter of 2016 , part D;DAA access was associated with statistically significant increases in testing for all age/sex groups, except for those aged 15–34 years and for women aged 45–54 years , part D.Data for the two non‐diagnostic RNA testing codes are provided in Should present trends in testing and treatment continue , the model projected that by 2030 72% of people with hepatitis C would be treated , the preIf current trends in testing level continue, the number of diagnosed but untreated people would approach zero by 2025 (data not shown). In the reduced and maintained treatment scenarios, an estimated 65 000 or 49 000 people would be undiagnosed or lost to follow‐up after a positive antibody test result in 2030 . The epiIn the increased testing scenario, the WHO treatment (80% of people with hepatitis C) and incidence targets (80% reduction) are achievable , Box 5.Box 4Box 5Our major findings were not altered by our sensitivity analyses , part B.After combining complete national datasets on hepatitis C RNA testing and treatment, we found that testing levels need to be increased to eliminate the disease in Australia. To reach the WHO elimination targets of 80% of people with hepatitis C being treated and incidence being reduced to 80% of the 2015 level by 2030, the number of hepatitis C‐exposed people identified and tested needs to be increased by at least 50%. Our finding contrasts with previous modelling which suggested that Australia was on track to achieving the 2030 elimination targets;A key feature of our model is that people need to be diagnosed to receive treatment. That is, the annual number of people who commence treatment in each scenario is determined by the number of treatments available and the number of diagnosed people available for treatment initiation. Before DAAs were introduced, the number of tests exceeded that of available treatments, leading to an accumulation of diagnosed but untreated people; after their introduction, the number of treated people exceeded that of tested persons, and the number of diagnosed but untreated people gradually approached zero.After the sharp initial increase in diagnostic RNA testing after the introduction of DAAs, the quarterly number of tests increased only slightly; the proportional increase was small compared with that in treatment numbers. This suggests that most treatments were prescribed to people who had been diagnosed for some time. People who have had hepatitis C longer are likely to have more advanced liver disease than those recently infected, and were more likely to be among the backlog of patients in specialist care that accumulated before DAAs became available ; hepatitHepatitis C testing trends indicated by MBS data are largely consistent with other Australian data. The number of hepatitis C diagnoses notified to the Australian National Notifiable Diseases Surveillance System has been stable since 2008–2017, but has declined among people aged 18–25 years.If treatment is predominantly of people who have been diagnosed some time ago, some people with newly acquired infections may be missed. In Australia, people with newly acquired infections are likely to be people who inject drugs, as sharing injecting equipment is the major route for HCV transmission, and low treatment uptake in this group would have consequences for overall viral transmission. While data from the Australian Needle Syringe Program Survey indicate that the prevalence of recent hepatitis C treatment (during the past 12 months) among people who inject drugs has increased from less than 3% before DAAs to 36% in 2017,Our modelling suggests that hepatitis C RNA testing in Australia would need to increase by at least 50% for the WHO elimination targets to be achieved. One approach to increasing the identification of hepatitis C‐exposed people would be to use rapid point‐of‐care antibody tests, reducing the need for multiple appointments to obtain a diagnosis.The cost implications of a large increase in testing must also be considered. On the one hand, treating hepatitis C is cost‐effective in Australia because it averts liver disease and its associated health care costs,We were unable to assess HCV antibody testing, meaning that the RNA testing numbers reflect the success of broader testing programs in identifying exposed people, and not necessarily the scaling up of antibody testing programs in Australia. Second, the entire (unlinked) PBS and MBS datasets were included in our analysis, including repeat tests for some people and tests with negative results. As more people are treated, the pool of antibody‐positive/RNA‐negative individuals is increasing, meaning that the proportion of positive RNA test results will decline over time, and the need for increased testing will be even greater than we have estimated. Third, inconsistencies in how health practitioners use MBS codes when ordering RNA tests for diagnostic purposes, treatment commencement, and treatment outcomes are recognised , table 1To eliminate hepatitis C as a public health threat in Australia by 2030, hepatitis C elimination programs should maintain treatment uptake by focusing on increasing testing and linkage to care.Joseph Doyle, Margaret Hellard, and the Burnet Institute receive investigator‐initiated research funding from Gilead Sciences, Merck, AbbVie, and Bristol‐Myers Squibb. Joseph Doyle is an advisory board member for Gilead Sciences, AbbVie, Merck. Alexander Thompson is an advisory board member for Gilead Sciences, AbbVie, Bristol‐Myers Squibb, Merck, and Roche Diagnostics, and a speaker for Gilead, Merck, Bristol‐Myers Squibb, AbbVie, Roche Diagnostics. Nick Scott has received investigator‐initiated research funding from Gilead Sciences. Amanda Wade has received investigator‐initiated research funding from AbbVie. No pharmaceutical grants were received for this study.Supplementary materialsClick here for additional data file.


Studies from large clinical series indicate that conjunctival congestion or conjunctivitis can be a clinical manifestation of coronavirus 19 (COVID‐19),Cataracts and macular degeneration, two common ophthalmic conditions, are associated with aging. Advanced age is a known risk factor for susceptibility to COVID‐19. Thus, additional increased risk portended to ophthalmic practice relates to the increased age of patients in ophthalmology clinics or undergoing ophthalmic procedures.Patients with acute conjunctivitis present to a variety of providers, from ophthalmologists to internists to family care physicians to emergency room providers. However, only ophthalmologists assume an intimacy that routinely utilizes the slit‐lamp biomicroscope for closely examining patients within a proximate nominal hazard zone. As physicians, ophthalmologists also understand that many systemic viral infections have ocular manifestations; examples include adenovirus,In a 2004 series of 36 patients with suspected SARS‐CoV,Ophthalmologists have long been aware that tears/ocular secretions can contain transmissible, highly contagious pathogens . Patients who present to ophthalmologists with isolated conjunctivitis may already be infected and secreting SARS‐CoV‐2 in tears and elsewhere. Thus, universal precautions that should be programmed into standard ophthalmic practice apply more than ever. One study has raised the possibility of different viral shedding routes and questioned whether evidence of positive RT‐RNA in respiratory specimens is sufficient for diagnosis. It appears that patients can be viremic or have positive anal swabs later in the disease course but have negative respiratory specimens by that time.Although it appears no one studied whether SARS‐CoV was present in convalescent patients or whether tears were infectious, ophthalmologists should probably don protective equipment when examining certain groups of patients in the pandemic caused by SARS‐CoV‐2. One group would be patients with signs and symptoms of upper respiratory infection such as fever and shortness of breath, regardless of conjunctival findings. The other group would be patients with conjunctivitis and possessing risk factor(s) (travel to high‐risk areas or contact with persons who have returned from such areas or persons with known infection). With the SARS outbreak in 2004, Singaporean ophthalmologists wrote, “Stringent barrier methods using the ‘M3G’  should be the gold standard when dealing with suspected SARS patients.”It is better to avoid exposure than try to contain exposures. At the front end of protection of patients and ophthalmology personnel is instituting screening measures prior to patient arrival. Hong Kong provides one example. Starting January 25, 2020, the Emergency Response Level was activated in all public hospitals, and the ophthalmology departments of two hospitals serving the Kowloon peninsula with a catchment area of 1.1 million people adopted even stricter standards to curb disease transmission in ophthalmology patients.As ophthalmologists may be the first medical contact a newly infected patient makes in seeking care for acute conjunctivitis, they have an essential role in guiding infection control measures to prevent unnecessary exposures to other patients, staff, doctors, and the community. Like Dr Li, ophthalmologists may be the first to diagnose a more serious condition than isolated conjunctivitis. And like Dr Li, without the measures described above, ophthalmologists may face a potentially lethal occupational hazard.TPOB is responsible for conception and design of submitted work; acquisition, analysis, and interpretation of data; drafting and revising the work for intellectual content; final approval of submitted version; and agreement to be accountable for all aspects of the work regarding the accuracy and integrity of the work.


To examine variability and effectiveness of interventions provided to comparator (control) groups in smoking cessation trials.Systematic review with meta‐analysis of randomized controlled trials (RCTs) of behavioral interventions for smoking cessation, with or without stop‐smoking medication. We searched the Cochrane Tobacco Addiction Group Specialized Register for RCTs with objective outcomes measured at ≥ 6 months. Study authors were contacted to obtain comprehensive descriptions of their comparator interventions. Meta‐regression analyses examined the relationships of smoking cessation rates with stop‐smoking medication and behavior change techniques.n = 23 706) had complete data and were included in analyses. There was considerable variability in the number of behavior change techniques delivered  and the provision of smoking cessation medication (43% of groups received medication) throughout and within categories of comparator groups . Higher smoking cessation rates were predicted by provision of medication  and number of behavior change techniques included . Modelled cessation rates in comparator groups that received the most intensive support were 15 percentage points higher than those that received the least (23 versus 8%).One hundred and four of 142 eligible comparator groups (Interventions delivered to comparator groups in smoking cessation randomized controlled trials vary considerably in content, and cessation rates are strongly predicted by stop‐smoking medication and number of behavior change techniques delivered. Tobacco smoking is a leading cause of premature mortality, disease and health‐care expenditures 1We use the term ‘comparator group’ in recognition of the fact that many of these groups receive (comparator) interventions. as well as of other variables. Study aims vary from trial to trial, and the type of comparator employed should covary with these aims The effectiveness of smoking cessation interventions is commonly determined by comparing them with an active control. As such, the observed effect sizes will be a function of the effectiveness of the intervention provided to the experimental group, but also the intervention provided to the corresponding control group, or ‘comparator group’,https://osf.io/24pzj/) primary hypotheses were that provision of pharmacological support (i.e. smoking cessation medication) and behavioral support [i.e. higher number of smoking cessation behavior change techniques (BCTs)] The present aim was to examine the variability and effectiveness of comparator intervention content in a much larger and more heterogeneous body of randomized controlled trials (RCTs) of behavioral interventions for smoking cessation among adults https://osf.io/23hfv/). The completed Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) checklist is in Supporting information, This study is part of a larger systematic review of behavioral smoking cessation trials IC‐SMOKE; The Cochrane Tobacco Addiction Group Specialized Register was searched on 1 November 2015 for RCTs assessing the impact of behavioral interventions (with or without smoking cessation medication) on biochemically verified smoking cessation at 6 months or longer. Trials without biochemically verified outcomes were excluded to protect against multiple sources of bias https://osf.io/e834t/) was a purpose‐built questionnaire capturing smoking cessation activities . We developed it based on international stop smoking treatment manuals, input from advisory board members , expertise within the study team and smoking cessation examples provided in previous BCT taxonomies. A similar approach was shown to be reliable and valid in the previous HIV medication adherence systematic review Data were first extracted from published materials . A comprehensive procedure of contacting authors of all included trials was then executed to retrieve additional, unpublished materials The active content provided to comparator groups  was extracted from the above‐described materials. Two researchers independently and reliably https://osf.io/23hfv/). Analyses were conducted using the metafor package in R https://osf.io/gk56j/) are also available on the Open Science Framework. Multi‐level, mixed‐effects meta‐regression models were used to examine the association between comparator intervention content and (logit‐transformed) smoking cessation rates. Outcome time points were all those at 6 months (± 1 month)2To allow for variation between studies in the exact time at which follow‐up assessments were conducted, we allowed all time‐points at 5 months or later to be included. post‐randomization or later (i.e. multiple time‐points per study were permitted). Multiple outcome time‐points were included, as this provides a more thorough synthesis of the available evidence than would be afforded by omitting all data other than those of a single time‐point. The model included random intercepts for studies (to account for between‐study heterogeneity),3As each study contributed a single comparator group to the analyses, study and group are synonymous in this context. correlated random effects for multiple observations (i.e. logit rates) within studies with a continuous time autoregressive structure (to account for heterogeneity in multiple observations corresponding to the same group) and correlated sampling errors for multiple observations within studies (to account for the dependency between multiple observations corresponding to the same group). For the sampling errors, we conservatively assumed an autocorrelation coefficient of ρ = 0.9 for a lag of 1 month. The smoking cessation rates extracted from the studies were based on intent‐to‐treat analyses with missing responses treated as smokers  and the provision of medication . In the second primary model, smoking cessation rates were regressed on the number of personalized BCTs, the number of non‐personalized BCTs and the provision of medication (model 2).In the secondary analyses, we first examined (models 3 and 4) whether the effect of BCTs on smoking cessation depends on the mode of delivery [i.e. whether comparator interventions were primarily delivered in writing (= 0) or by a person (= 1)] by adding a mode of delivery main effect and its interaction with smoking cessation BCTs to models 1 and 2. To investigate the additional value of supporting the adjuvant behaviors (models 5 and 6), we added number of treatment engagement BCTs, number of medication adherence BCTs and the interaction between medication provision and number of medication adherence BCTs (as adherence BCTs should primarily benefit people who were provided with medication) to models 1 and 2, respectively. Exploratory analyses were also conducted; namely, to investigate potential differences over time, differences by quitting versus abstinence BCTs, and interactions between smoking cessation BCTs and medication in determining smoking cessation. The results of these are described in Supporting information, All analyses were controlled for potential confounding variables that we identified through literature review and input from our advisory board panel https://osf.io/khm8u/). Further, as the link between comparator interventions and outcomes has now been established in both the HIV medication adherence Addiction Journal's Paper Authoring Tool might facilitate this Smoking is a leading cause of premature mortality, disease and health‐care expenditures https://osf.io/m5vea/). Finally, it will be important to investigate how BCTs can be best translated to be delivered in writing , as we did not find evidence of an association with smoking cessation when delivered through these mediums. At present, such interventions produce small increases in smoking cessation The results of this study highlight avenues for research into improving usual care for smoking cessation. Researchers could investigate whether adding additional BCTs—especially BCTs delivered by a person—into existing programmes leads to higher rates of smoking cessation. Further, to operationalize comparator group support we took a pragmatic approach using a simple sum score of BCTs. To provide better guidance on which support is most effective, it will be important to investigate which combinations of BCTs are associated with the highest smoking cessation rates .Initially, 5992 records were identified Fig. . FollowiThere was considerable variability between the 104 comparator groups in the number of smoking cessation BCTs delivered , number of personalized smoking cessation BCTs delivered  and provision of medication (43%). The most commonly delivered smoking cessation BCTs were unspecified social support, tell to act and information about health consequences . The most commonly delivered personalized smoking cessation BCTs were unspecified social support, behavioral goal‐setting and reducing exposure to cues .k = 21), but considerable variability remained within the other categories: ‘self‐help’ , ‘brief advice’ , ‘extended counselling’  and ‘usual care’ . Hence, even within these typical categories, some comparator groups receive little to no support, whereas others receive extensive support.Further, when grouping the comparators in categories typically used in systematic reviews of smoking cessation trials, variability in the number of BCTs delivered to ‘no behavioral support’ comparator groups was low . Given that these two predictors were correlated , we re‐ran the models with each predictor separately, which gave slightly larger differences in effect sizes and smaller P‐values , suggesting that both variables were competing for the same variance. These results suggest that delivering more personalized and non‐personalized BCTs, as well as smoking cessation medication, predicts higher smoking cessation rates in these comparator groups.Provision of smoking cessation medication and more smoking cessation BCTs predicted higher smoking cessation rates model 1, Table , Table 1B = 0.022, P = 0.001), but not those delivered in writing ; although the direct interaction test did not suggest that these association differed (model 3).Whether smoking cessation BCTs are more strongly predictive of smoking cessation when delivered by a person than when delivered in writing  was tested in models 3 and 4. The association between smoking cessation rates and number of BCTs delivered was evident for those BCTs delivered by a person , but not when delivered in writing , and these associations were significantly different (model 4). There was no evidence that non‐personalized BCTs delivered in writing  or by a person  predicted smoking cessation. Together, these results show that personalized and person‐delivered BCTs are associated with higher smoking cessation, with no support found for non‐personalized BCTs or those delivered in writing.When BCTs were personalized, the association with higher smoking cessation was again evident when they were delivered by a person . The association between number of treatment engagement BCTs and smoking cessation was attenuated by the removal of one influential case . The comparator group in this study The associations between BCTs targeting adjuvant behaviors and smoking cessation rates were tested in models 5 and 6. Higher smoking cessation rates were predicted by provision of more BCTs to engage the participant in the treatment (models 5 and 6), but not by more BCTs to aid medication adherence  provided to comparator groups in RCTs of behavioral interventions for smoking cessation. The active content varied considerably among all comparator groups and within typical categories of comparator groups . Further, delivery of smoking cessation medication and more behavioral support predicted higher smoking cessation rates in these comparator groups. The predicted effect of this variability was a 15%‐point absolute difference in cessation rates between comparator groups, with the least and most intensive comparator interventions predicting 8 and 23% cessation, respectively. This difference between comparator groups is greater than the typical differences seen between experimental and comparator groups in smoking cessation trials This study replicates and extends earlier work, in which the authors raised the issue of variability in interventions delivered to comparator groups of behavioral trials and its implications for interpreting and comparing effect sizes This study also found support for the potential role of BCTs in increasing smoking cessation rates, particularly when personalized and delivered by a person, and did not find evidence that the effect of these BCTs on cessation declines over time  predicted differences in smoking cessation between comparator groups that were associated with the level of support received. This variability could make some experimental interventions appear much stronger than others whereas, in fact, this might have nothing to do with the experimental interventions; instead, it might be due to one trial having a minimal and another trial an intensive comparator intervention. Currently used methods for accounting for comparator group variability  may go some distance, but as variability in active content within commonly used categories is substantial this is unlikely to fully address the issue. Hence, we recommend that researchers, policymakers and practitioners ensure that full information on the comparator interventions is available and considered when interpreting, comparing and generalizing intervention effects and when making decisions on which services to fund and implement.Interventions provided to comparator groups in smoking cessation trials vary substantially and predict cessation rates in these groups. This variability should be considered when synthesizing, interpreting, comparing or generalizing intervention effects.https://osf.io/24pzj/).PROSPERO (CRD42015025251) and the Open Science Framework . R.W. is an unpaid adviser to the UK's National Centre for Smoking Cessation and Training. R.W.’s salary is funded by Cancer Research UK. N.B. and M.C.E.’s salaries were funded by Cancer Research UK. J.H.B. receives funding from the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. All other authors declare that they have no conflicts of interest. The National Drug and Alcohol Research Centre is supported by funding from the Australian Government Department of Health under the Drug and Alcohol Program.Data S1. Supporting informationClick here for additional data file.


Streptococcus mutans (MS) and Lactobacilli (LB) counts and caries among older adults.There is little knowledge about factors which may affect oral health among older adults. The objective of this study was to determine the relationship between Streptococcus mutans (MS) and Lactobacilli (LB) counts, as well as to record salivary flow rate and buffering capacity of saliva.In this community-based observation study, 141 participants aged 60 years and above were recruited from the west district of Singapore. Alongside the clinical examination, saliva samples were collected to determine 5 while LB counts were ≥ 105 for 48.2%. 83.7% had normal salivary flow or hypersalivation (> = 1 mL/min), the buffering capacity of the saliva was alkaline in 61% of the participants. Multivariate analysis showed that participants who had high MS counts were less likely to have a DMFT < 12  whereas participants who had high LB counts were less likely to have a DMFT ≤ 14 .Of the 141 participants, 63.8% were female and 94.3% were of Chinese ethnicity. The mean DMFT was 11.08 (s.d. 8.27). 9.9% of participants had at least one decayed tooth, 52.5% had minimum one missing tooth and 86.5% had at least one filled tooth. 67.4% had MS counts of ≥ 10Our study showed a positive correlation between MS and LB counts and caries experience in older adults. The mean DMFT was on the low side in our sample despite having a relatively high MS count. This suggests that there are many other factors which vary according to host environment, physiological and biological conditions that may affect MS and LB counts in the oral cavity.Our study supports the knowledge that the aetiology of dental caries among older adults is a complex process and it would be wrong to consider caries as a same problem with the same solution for all age groups. Population growth and aging has led to a dramatic increase in the burden of untreated oral conditions throughout the world in older adults . UntreatStreptococcus mutans (MS) is one of the well-established etiologic agents for caries in both healthy and medically compromised individuals  whereas study participants who had high LB count were less likely to have root DMFT ≤ 14 (Table To our knowledge, this is the first study of dental caries conducted within a community-based research network in Singapore. The findings of our study emphasise that aetiology of dental caries is a complex process and it would be wrong to consider caries as a same problem with the same solution for all age groups . Oral miOur study population had low DT scores and still MS count was found to be significantly associated with DMFT score. MS is considered to be a specific micro-organism that causes dental caries , there iOur study showed a significant association between root caries and LB count. This relationship should be interpreted with caution. The codes used for recording root status of study population did not include ‘missing due to caries’ which means that teeth which were missing were considered as missing whereas codes used for recording coronal status included ‘missing due to caries’ meaning teeth which were missing due to caries were considered as missing. Relationship between LB count and caries is still debatable, future studies should be done to explore and compare association between microbial host factors and coronal and root caries.Bacteria in dental biofilms are regarded as important in initiation and progression of dental caries . PreviouIt is interesting to note that the present study population showed a shift in DMFT scores from higher number of missing teeth to higher number of filled teeth in older adults. This could possibly be due to their relatively higher SES level, and thus more likely to have lower untreated decay and higher levels on restored teeth. These findings corroborates with previous longitudinal research in which there was a reduction in MT scores compared to FT scores in 65–74 year old senior citizens . FurtherThis study characterised stimulated salivary flow and buffering capacity in a group of older Singaporeans. It has been documented that salivary flow varies according to the different parts of the mouth where it is measured and also between persons and across different biological situations . MajoritLittle is known about other factors apart from physiologic and biological changes responsible for saliva flow and buffering capacity. Salivary flow rate and buffering capacity are influenced by SES . As prevThis study found that MS and LB are an independent predictor of coronal and root caries respectively in older adults, but no significant association between caries and saliva flow and saliva buffering capacity was noted. Thus, one should interpret with caution an assessment of microbial and host factors to determine caries activity in older adults. Our study provides useful baseline data to inform future studies to look at potential factors which will yield more predictive value in determining caries risk in the older adults. Future longitudinal studies should be undertaken to explore this further, including saliva sample collection at different time points to explore determinants of caries experience in older adults.


This study investigates the dynamic interplay between bullying relationships and friendships in a sample of 481 students in 19 elementary school classrooms . Based on a relational framework, it is to be expected that friendships would be formed when two children bullied the same person and that children would start to bully the victims of their friends. Similarly, it is to be expected that friendships would be formed when two children were victimized by the same bully and that children would become victimized by the bullies of their friends. Longitudinal bivariate social network analysis supported the first two hypotheses but not the latter two. This study provides evidence for group processes in bullying networks in childhood. School bullying affects the lives of many children. Victims of school bullying often experience weak social support, rejection, and social isolation that can be detrimental to their socioemotional development, such as weak social bonding, poor academic performance, and elevated levels of anxiety and depression . In other words, when two individuals bully the same person they become friends. We call this the bully selection hypothesis, indicating that bullies select each other as friends. This selection effect may be explained by that similarity between two individuals breeds attraction . In other words, when a friend bullies someone, the person will bully that same person as well. This effect may be seen as an influence effect . This selection effect may be explained by bonding over shared negative experiences . In other words, when a friend is bullied by someone, the person will be bullied by that same person as well. A friendship with a victim may weaken one’s own position in the group, thus putting oneself at risk to become the next target. This effect may be seen as an influence effect, as it indicates that there is a contagion of victimization. Accordingly, we call this the victim influence hypothesis.The fourth configuration describes a situation where two individuals are friends, and one of them is victimized by someone else Figure : b → c. We investigate bullying relationships together with friendships and use a so‐called multiplex social network approach. We expected that bullies start to become friends over time (H1) and that bullies influence their friends to start bullying the same victim (H2). In addition, we expected that victims start to become friends over time (H3) and that bullies start to victimize the friends of their victims (H4). We tested our hypotheses using longitudinal bivariate social network analysis using SIENA  in the Netherlands , six Grade 3 classrooms (n = 184), three Grade 4 classrooms (n = 72), and five Grade 5 classrooms (n = 129). The average classroom size was 25 . Changes in student classroom composition were minimal . The final sample consisted of 19 classrooms  with 481 students, of which, at the first wave, consisted of five Grade 2 classrooms , schools sent information letters to students’ parents. A passive consent procedure allowed students or parents to opt out of student participation. At the start of data collection (2012), universities in the Netherlands did not require institutional review board permission for this type of research. All procedures performed in our study were in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. A few students did not want to participate, and also a few parents objected to their child’s participation. Overall, the participation rate was high (98.5% at Wave 1).Bullying is when some children repeatedly harass another child. The child who gets bullied has problems defending itself against this. Bullying is not the same as having a fight between two people who are equally strong. Bullying should also not be confused with joking around. Bullying is treating someone repeatedly in a mean way.In an instructional movie, a professional actress explained to students what bullying means, using the following text:Several examples of bullying were given to students, including physical and material forms  and relational and verbal forms .Bullying was measured with network nominations for peer bullying. At each time point, students were asked to indicate if they were victimized by classmates, and if so, were presented with a roster showing the names of all classmates with the accompanying text: “The following two questions are about who starts the bullying. Often, certain classmates initiate the bullying and others join them.” For bullying initiation, they were asked: “Who starts bullying you?” For bullying assisting, they were asked: “Who always joins the bully or is there when the bullying takes place?”i considers j as his or her bully, this translates into j bullying i (from the victim’s perspective). The perception and experience of victims are important in bullying research. For that reason, we look at bullying from the point of view of the victim. Bullying nominations were measured as present (1) or absent (0). Students who indicated not being victimized by classmates did not fill out the nomination questions. Their “answers” were considered as absent nominations. Based on these nominations, bullying networks were obtained containing all directed bully nominations for each classroom.The two items were collapsed into one measure. The overlap between nominations for initiation and assisting was on average 22.7% over the waves, indicating that there is some overlap at the dyadic level between initiating and assisting in bullying. Nominations for the one measure of bullying were reversed so that when Friendship was measured with network nominations. In all waves, students were presented with a roster showing the names of all classmates and asked whom of their classmates they considered as their best friends (“Who are your best friends?”). Students could choose as many same‐sex and other‐sex classmates as they wished. Friendship nominations were coded 1 and non‐nominations were coded 0, resulting in friendship networks consisting of directed nominations for each classroom.Analyses were conducted using longitudinal bivariate social network analysis , and were then meta‐analyzed in R , selection parameters for each type of network, and between‐networks parameters, reflecting the interplay between the two networks. This model specification was largely derived from Huitsing et al.  and RambFor the structural part of the model for friendship dynamics and bullying dynamics, we included the basic effects , network closure effects (actors at Distance 2 and transitive triplets), and subgroup effects (four cycles). For the structural part of the model for bullying dynamics, we also included effects that inversely represent network isolates (indegrees and outdegrees of at least 1). A dyadic covariate effect was included to control for same‐sex friendship and bullying.shared victim to friendship effect indicates that “agreeing” upon shared victims will result in the formation of a new friendship tie : They did not become friends with or bullied everyone (as indicated by the victims). Some students were uninvolved in bullying relationships: They were neither victimized (negative indegree isolate effect) nor bullied others (negative outdegree isolate effect). Nominations received were likely to be reciprocated (positive reciprocity effects). Furthermore, participants tended to nominate friends of friends as friends (positive transitive triplets effect) and to avoid having indirect connections (negative Distance 2 effects). Friendships were often same sex, whereas bullying was directed to same‐ as well as cross‐sex peers.shared victim to friendship effects were positive in all classrooms . In line with the bully influence hypothesis, the friendship agreement to bullying effects were positive in all classrooms .These were tested by two between‐networks effects (see Table shared bully to friendship effect was close to zero and nonsignificant (see Table p = .53). There was on average no indication that two children victimized by the same bully were likely to become friends . There was no indication that children were likely to become victimized by the bullies of their friends  and that children would start to bully the victims of their friends (bully influence hypothesis). Similarly, it was expected that friendships would be formed when two children were victimized by the same bully (victim selection hypothesis) and that children would become victimized by the bullies of their friends (victim influence hypothesis). Longitudinal bivariate social network analysis provided evidence for the first two hypotheses but not for the latter two.The finding that two children are likely to become friends when they bully the same classmate suggests that sharing a “common target” promotes friendship. From a developmental perspective, friendships serve different functions and have different meanings for children across different stages of child development  group processes in bullying and victimization, but many classrooms were left out from schools that combined one or more classes or grades. Most school children across the world are traditionally organized in same‐age/grade classrooms  bully their classmates, however. Similarly, we do not know precisely why bullies with the same victims befriended each other or why victims with the same bullies did not befriend each other. More detailed information about the specific reasons resulting in friendship selection and influence would allow for a better understanding of the mechanisms behind the group process of bullying.By incorporating a relational perspective to bullying, In sum, our study provides evidence for group processes in bullying networks in childhood. On the one hand, bullies are likely to select each other as friends; on the other hand, bullies are likely to influence their friends to start bullying. These novel findings highlight how group processes in bullying operate through children’s peer networks in the classroom.Figure S1. Visualization of Goodness of Fit Statistics for Models Summarized in Table S3Figure S2. Distribution of Parameter Estimates and Standard Errors for Models Summarized in Table 2Table S1. Overview of Individual and Classroom Information for Descriptive Statistics Summarized in Table 1Table S2. Effects Included for Models Summarized in Table 2Table S3. Goodness of Fit Statistics for Models Summarized in Table 2Table S4. Time Heterogeneity Tests of Selection Effects and Influence EffectsTable S5. Sensitivity Analysis Excluding the Five Classrooms With No Optimal FitClick here for additional data file.


Today, approximately one in five United States adolescents age 12 to 19 years is obese and just over a third are either overweight or obese. This study examines how parents and peers influence diet and physical activity behaviors of older adolescents (14–18 years) with overweight or obesity to inform weight management interventions.Adolescent participants included 14 to 18-year-olds with a Body Mass Index (BMI) greater than the 85th percentile for their age and sex who were receiving care in a large healthcare system in Northern California. Adolescents and their parents participated in separate focus groups and interviews (if not able to attend focus groups) that were held at the same time in the same location. We used qualitative thematic analysis to identify common themes discussed in the adolescent and parent focus groups as well as paired analysis of adolescent-parent dyads.Participants included 26 adolescents and 27 parents. Adolescent participants were 14 to 18 years old. Half were female and the participants were almost evenly distributed across year in school. The majority self-identified as White (56%) and Asian (36%).Three themes were identified which included 1) parents overestimated how supportive they were compared to adolescents’ perception 2) parents and adolescents had different views regarding parental influence on adolescent diet and physical activity behaviors 3) parents and adolescents held similar views on peers’ influential role on lifestyle behaviors.Parents’ and adolescents’ differing views suggest that alignment of parent and adolescent expectations and behaviors for supporting effective weight management could be incorporated into interventions. The prevalence of obesity among adolescents age 12 to 19 years in the United States (US) has doubled over the past two decades. Today, approximately one in five US adolescents age 12 to 19 years is obese and just over a third are either overweight or obese  and psycDespite the urgent public health need for treating obesity in adolescence, there is a paucity of evidence-based interventions, especially for older adolescents age 14–18. Adolescence is a distinct development period in which children transition to adulthood and increasingly gain autonomy , includiIn younger children direct involvement of parents is a key factor for success –24. Whil> the 85th percentile for age and sex.To fill a critical gap in our understanding regarding successful weight management strategies for this age group, we engaged adolescents and their parents using separate focus groups to examine how parents and peers influence diet and physical activity behaviors of older adolescents (14–18 years) with overweight and obesity. The ultimate goal of this study was to directly inform the development of a primary care-based weight management intervention for adolescents age 14 to 18 with a BMI Participants included 14 to 18-year-old adolescents with a Body Mass Index (BMI) greater than the 85th percentile for their age and sex who were receiving care within a large multispecialty healthcare system in Northern California. The Sutter Health Institutional Review Board approved all study procedures (PAMF# 14–03-302EXP). Electronic Health Records (EHR) were used to identify potentially eligible current patients based on age and BMI percentile. We identified individuals with an encounter or clinic visit including a measurement of weight and height in the past 6 months and used this to calculate BMI for eligibility criteria. Exclusion criteria included serious physical or mental health conditions listed in the EHR and per physician discretion. Physicians involved in the study were consulted regarding participant eligibility and were asked to review the list of potential participants from their patient panel and indicated any patients they viewed as inappropriate for the study  as well as referred additional patients to be recruited. There were no specific inclusion or exclusion criteria applied to parent participants. Once approval was obtained, 377 invitation letters were mailed to parents/guardians [referred to as “parents” in the remaining text] from their adolescent’s pediatrician or family medicine doctor explaining the study and inviting their adolescent to participate with an option to opt out of the study. Parents who did not opt out through phone or reply card received a call from study staff to assess interest in participation and screen for eligibility. After parents consented for their adolescent to participate, they were invited to participate in a separate parent focus group. All participating parents provided written informed consent and adolescents provided written assent.n = 5) and with their parents (n = 4). Adolescents and their parents participated in separate focus groups and interviews that were held at the same time in the same building. The focus group/interview guides for both adolescents and parents covered five main topic areas: 1) causes and consequences of overweight, 2) experiences or attempts with diet and weight loss, 3) diet, 4) physical activity, and 5) family and peer influences. Questions were open-ended and attempted to foster discussion. Probing questions were asked when necessary to deepen the discussion. All focus groups and interviews were audio recorded and transcribed verbatim.Study staff trained in qualitative methods facilitated three focus groups with adolescents and three with their parents in summer 2015. For participants who were not able to attend the focus groups, trained study staff conducted individual in-depth interviews with adolescents  came to consensus on an initial set of codes and their definitions based on reviewing the first 2 focus group and 2 interview transcripts. Through discussion among all study team members, the codebook was refined and finalized. KG and LL conducted the coding of all the focus group and interview transcripts according to the codebook using Dedoose (Version 7.0.23), a web-based software for qualitative and mixed-methods analysis. All respondents were assigned a study identifier with a linking identifier for adolescent-parent dyads, which was applied as a code to corresponding excerpts throughout all transcripts. This method allowed adolescents’ data that were coded with a linking identifier to match responses from their parent in order to facilitate comparisons between adolescent and parent responses within the same family.The data were then reviewed by code to identify common themes both within and across the focus groups/interviews, with data summarized for adolescents and parents separately for comparison to identify overarching themes for each group. In addition, paired parent-adolescent responses were examined by code to identify similarities and discrepancies in accounts of adolescents’ diet and physical activity routines and their attempts to improve their diet or increase physical activity. KG and LL summarized coding reports and created an overall summary of key themes for parents and for teens separately, and of key areas of inconsistency between parent and teen reporting. Not all teen-parent dyads had a quote for every theme. Adolescent and parents’ demographic and lifestyle surveys were quantitatively summarized using means and proportions using SPSS (Version 14.0).A total of 26 adolescents and 27 parents participated in the focus groups and interviews. Adolescent participants ranged in age from 14 to 18 years old  of parents reported aiding their adolescent in their change attempts for healthy diet; but only 15% (n = 4) of adolescents reported receiving aid from their parent(s) in their change attempts. There were also substantial discrepancies between adolescents’ accounts of their parents’ assistance and parents’ own accounts of their assistance. Among one parent-adolescent pair, the parent reported helping her adolescent with portion control and planning meals  on adolescent behaviors. Finally, patients of this healthcare system may be different from the general population. Future research, with a larger, representable sample of adolescents and parents would be helpful to verify the study’s results, including the comparison between views of adolescents and those of parents. Given that the prevalence of overweight/obesity are higher among individuals/families of low income/social advantage, compared with high income/social , 51 advaThis study provides perspectives of both parents and their older adolescents on a pressing public health issue. The findings of this study affirm the need for interventions that aim at initiating and supporting effective communication between parents and older adolescents in order to promote effective weight management efforts for adolescents at risk for adulthood obesity. More work is needed to explore barriers and facilitators to effective communication as well as methods to overcome barriers and improve communication specifically regarding older adolescent weight management.


Dear Editor,We have read with great interest the manuscript recently published in your journal by Malek and Bucciarelli‐Ducci entitled “Myocardial fibrosis in athletes—Current perspective.”isolated arrythmogenic left ventricle dysplasia. Predominant left ventricular involvement with fibro‐fatty infiltration and replacement and without affecting right ventricle is rare, and there is a lack of specific criteria for it which can make its diagnosis sometimes difficult. Magnetic Resonance Imaging findings include fibrosis with left ventricle late gadolinium enhancement in a subepicardial/midwall distribution and then differential diagnosis include previous myocarditis A high level of suspicion of this under‐recognized cardiomyopathy should be present and again and, due to the high risk of sudden death, other factors such as familial disease, systolic dysfunction, and Electrocardiogram changes must be always considered.Otherwise, another entity we would like to mention and that we think should be included separately by Malek and Bucciarelli‐Ducci is The authors declare no potential conflict of interest.


The increased interest in (enzymatic) transformations between nucleosides and nucleobases has demanded the development of efficient analytical tools. In this report, we present an update and extension of our recently described method for monitoring these reactions by spectral unmixing. The presented method uses differences in the UV absorption spectra of nucleosides and nucleobases after alkaline quenching to derive their ratio based on spectral shape by fitting normalized reference spectra. It is applicable to a broad compound spectrum comprising more than 35 examples, offers HPLC‐like accuracy, ease of handling and significant reductions in both cost and data acquisition time compared to other methods. This contribution details the principle of monitoring reactions by spectral unmixing, gives recommendations regarding solutions to common problems and applications that necessitate special sample treatment. We provide software, workflows and reference spectra that facilitate the straightforward and versatile application of the method. The bases of life and their analogues display UV absorption spectra that differ from those of their respective nucleosides under alkaline conditions. This enables robust, versatile and fast analysis of reactions between these species. Nucleoside‐altering enzymes harbor significant potential for the synthesis of nucleoside analogues. Nucleoside phosphorylases (NPs), for instance, catalyze the reversible phosphorolytic cleavage of nucleosides into the corresponding free nucleobase and pentose‐1‐phosphate  nucleobase cleavage reactions and will prove helpful to all current and future users of our previously published method.Spectral unmixing in this case describes the concept of linear combination of absorption spectra that can be traced back to its individual components. In this sense, any mixture of two (or more) compounds with known absorption spectra can be deconvoluted into its constituents if appropriate reference spectra are available.1 and 3).Our method for monitoring of nucleobase cleavage reactions employs this concept by deriving nucleoside/nucleobase ratios from experimental spectra recorded after alkaline dilution of samples from a reaction mixture.In this workflow, alkaline dilution of the sample serves a threefold purpose. This step simultaneously terminates the reaction by denaturing the enzyme, adjusts the concentration of the analytes and regulates the pH value of the sample to achieve deprotonation and spectral shifting of the UV absorption spectra. The suitable degree of alkaline dilution as well as the concentration of the base used for dilution (in our case aqueous NaOH) varies between different nucleoside‐nucleobase pairs, since the extinction coefficients and the pH range for stable and reproducible spectra needed for analysis differ. For example, purine nucleosides generally display a stronger UV absorption than pyrimidine nucleosides, which requires smaller sampling volumes to achieve the same peak signal intensity for these substrates.Although we mainly discuss nucleobase cleavage  in this report, spectral unmixing‐based reaction monitoring can also be applied to observe the reverse reaction. The same principles, strategies and challenges considered below for phosphorolysis reactions also pertain to the corresponding transformations in the glycosylation direction.22–27 and 33–36) and highly modified pyrimidines such as 5‐trifluoromethyluridine (15). Notably, we also characterized some 5’‐phosphorlyated nucleotides and found their spectral properties to be essentially identical to their respective non‐phosphorylated counterparts, conveniently allowing the reaction monitoring for these substrates without the need for additional reference spectra or method development.Extending our previously reported list of 20 nucleosides,1, Figure The most common obstacle with the presented method is background absorption. Different types of background absorption are typically observed and need to be addressed individually Figure . Please An occasional and unavoidable type of background signal is atypical UV absorption of the multiwell plate or of particles. A typical background signal is in the range of 0.03 absorption units (AU) at 300 nm and curves up towards slightly higher intensities at 250 nm . We even found success when using especially UV‐active reaction components, such as dithiothreitol (DTT), that proved problematic in some instances, by selecting appropriate substrates and selectively limiting the fitting range .Background absorption by UV‐active components in the reaction mixture needs to be addressed on a case‐to‐case basis. Generally, all common buffers, protein stabilizing agents or even artifacts from protein purification such as imidazole do not represent any challenge to the method and allow straightforward use of the standard procedure without any additional background correction. When using some organic cosolvents, however, we noticed significant background absorption in the lower wavelength region. Whereas alcohols including methanol, ethanol, isopropanol, ethylene glycol and glycerol may be used without alterations to the method (see Supporting Information for details), solvents like dimethyl sulfoxide (DMSO) and dimethyl formamide (DMF) create background signals that need to be accounted for Figure E. In theIn rare cases, we observed elevated baselines when using very concentrated buffers for dilution and spectral measurement. Again, appropriate background spectra to correct for this shift succeeded in resolving this issue.Nonetheless, it should be stressed that background correction does not always lead to accurate and reliable data. We experienced serious difficulties to correct for noise in instances where the background signal intensity is comparable to the signal intensity of the nucleoside‐nucleobase pair under investigation and directly and/or completely overlaps with this signal . In principle, background subtraction from the experimental spectrum is still possible and yields a processed spectrum that can be fitted, but fit quality and, consequently, accuracy of this approach suffered tremendously. We ascribed this to the fact that the signal intensity of the dynamic analytes (substrate and product) is largely irrelevant as those values are normalized to the isosbestic point and considered only in relation to one another, but background signals from most sources are absolute quantities and thus vary with and are very sensitive to pipetting accuracy. As a rule of thumb, we recommend our method for cases where background absorption does not exceed 20 % of the relevant signals .22 and 23 were found to be quite sensitive to alkaline conditions as these nucleosides underwent a temperature‐ and base‐promoted side reaction , presumably by 5’−OH attack at the purine ring. We were able to bypass this issue by quick sample processing avoiding any unnecessary storage. At room temperature and pH 13, compounds 22 and 23 remained stable enough for analysis for at least 10 min.A critical factor for any method is stability and detectability of the analytes. While all nucleosides and nucleobases in our original report displayed excellent stability towards the quenching and analysis conditions, we noted some issues within the extended substrate range. Fluorinated purine nucleosides While the original protocol has proven to be a robust and versatile method, we have used spectral unmixing‐based reaction monitoring in instances that deviated from the original conditions. Some applications that necessitated adjusted sample treatment are worth mentioning.In our earlier report we have used purified protein in all reactions.33–36, Table Conveniently, spectral shifting of the UV absorption spectra of the free nucleobases doesn't always require application of a strong base. Selected nucleoside‐nucleobase pairs feature a marked spectral shift and stable spectra in a pH region easily accessible by established buffer systems  and we were unable to obtain reproducible spectral fits using alkaline quenching. Fortunately, this issue could be resolved by employing organic solvents like methanol as an alternative quenching medium and subsequently adjusting the pH value to 9 for analysis (see the Supporting Information for details). A similar methodology featuring a different buffer system succeeded for the trifluorinated pyrimidine 15  to a volume of 500 μL or quenched in an equal volume of MeOH or iPrOH before dilution in 50 mM Tris/NaOH buffer to give a final volume of 500 μL. Similarly, the fluorinated pyrimidine 15 was sampled by quenching in iPrOH or MeOH followed by dilution with 100 mM glycine/NaOH buffer (pH 10) to a final volume of 500 μL. Subsequently, 200 μL of the diluted alkaline samples were transferred to wells of a UV/Vis‐transparent 96‐well plate to record the UV absorption spectra. All spectra were recorded from 250–350 nm in steps of 1 nm. For exact sampling procedures and example reactions, please see our earlier reportsReference spectra for pure compounds were prepared from 2 mM solutions by 10‐ to 20‐fold dilution in aqueous NaOH. Reactions with purine or pyrimidine nucleosides were typically performed with either 1 or 2 mM of UV‐active compounds. From the 2 mM reactions, 20 μL (purines) or 30 μL (pyrimidines) were withdrawn and quenched in NaOH to give a final volume of 500 μL. From the 1 mM reactions, twice as much sample volume was withdrawn and treated analogously. Note that exact adherence to these volumes is not necessary, as normalization to the isosbestic point accounts for differences in signal intensity. For halogenated nucleosides As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.