Treating patients with anorexia nervosa (AN) remains a major challenge. The choice between an inpatient or an outpatient care setting is an essential issue for the patients and for their relatives with major health economic implications. However, health services‐related studies are lacking. The present study was a descriptive exploration of time‐trends in treatment modes of patients with free and equal access to health services.The study was based on a nationwide cohort of patients diagnosed for the first time with AN, each followed for 5 years in the registers covering the years 1994–2018. The per patient number of hospital admissions, cumulated number of days of hospitalization and number of outpatient visits during the first 5 years after initial diagnosis were considered.N = 7,505. A clear trend was observed in the per patient five‐year cumulated number of inpatient days, decreasing by 6% per year after adjustment for age at diagnosis, parental mental diagnosis, and family income. The five‐year number of hospital admissions after initial diagnosis decreased by 2% per year, while no trend was observed for outpatient visits.The cohort of patients with AN with at least 5 years of follow‐up amounted to The per patient number of hospitalizations and cumulated days of hospitalization during 5 years after diagnosis were reduced for patients initially diagnosed with AN while there was no change in the number of outpatient visits. The factors contributing to these changes of treatment modes over time are in need of further study. The diagnostic criteria were revised in 2013 for the Fifth Edition of the The severe cases with AN may be regarded as intractable, and there is no evidence that the prognosis generally has improved substantially throughout the 20th century  Committee on Quality Issues (CQI), Although hospitalization for acute medical complications is a necessity to ensure survival, the benefits of further hospitalization remain questionable unless there are suicidal ideations, severe self‐harm, or intolerable family situations with intensive request. Prolonged hospitalization may have numerous profound negative consequences including loss of social networks and interruption of education or job. It is not known whether patients with AN in particular may be vulnerable to the negative effects of hospitalization.n = 75) it was observed that hospital admission was a major predictor of poor outcome   or atypical AN (F50.1) according to the 10) WHO,  in the DEach patient in the study sample was followed for 5 years after diagnosis in the registers covering the years 1994–2018 in total. The first 5 years of treatment were chosen to minimize the effect of enduring long‐standing courses with recoveries and relapses, which change the approach as described in a recent review  or atypical AN F50.1) with at least 5 years follow‐up amounted to  with at 2.2The study was based on the comprehensive Danish system of registries with mandatory recording of each patient visit to the public health service, covering various domains of data including extensive health information on the total national population  were excluded  and previous mental disorders of parents  were computed for all years combined and separately by calendar year. Further, for each calendar year, the mean, standard deviation, median and interquartile range were calculated for each of the three 5‐year treatment outcomes, that is, the per patient number of hospital admissions, per patient cumulated number of days of hospitalization and per patient number of outpatient visits during the first 5 years after initial AN diagnosis. Spearman correlation was used to calculate pairwise correlations between the overall annual means of the three 5‐year treatment outcomes from 1994–2013.Associations in terms of incidence rate ratios (IRRs) between each of the three patient‐level 5‐year treatment outcomes and age (in years), sex, family income  at the time of inclusion, and parental mental disorder prior to inclusion  were analyzed using negative binomial regression with robust standard errors, mutually adjusting for sex, age, income tertile, parental mental disorder prior to inclusion, and calendar year. Negative binomial distributions were used instead of Poisson distributions to address overdispersion in the data. Family income was treated as a categorical variable in the analyses due to residual analyses indicating broken model assumptions on linearity of income as continuous variables in the regression models.The crude and adjusted time trends from 1994 to 2013 of each of the patient‐level 5‐year treatment outcomes were analyzed in terms of IRRs using negative binomial regression with robust standard errors and calendar year of first‐time AN diagnosis as independent variable, adjusting for sex, age, and income tertile at the time of inclusion, and parental mental disorder prior to inclusion. The adjusted time trends of each of the three patient‐level 5‐year treatment outcomes were illustrated graphically as marginal (by year) predicted means with 95% confidence intervals (CIs) based on negative binomial regression with robust standard errors.3N = 7,505 patients with first‐time AN diagnoses in 1994–2013, 94% were females and the average age at diagnosis was 18.0 years. In some 19% of the AN patients one or two parents had received the diagnosis of a mental disorder. The average age of patients with first‐time AN diagnosis decreased from 18.4 to 17.6 years from 1994 to 2013 (p < .001).Sample characteristics are shown in Table  to 2013 . The numThe per patient mean number of hospital admissions, per patient cumulated number of days of hospitalization and per patient number of outpatient visits during the first 5 years after first‐time AN diagnosis are displayed in Table s = 0.91, p < .001), while neither was correlated with per patient number of outpatient visits (both p > .2). The overall annual means of per patient number of hospital admissions and per patient cumulated number of days of hospitalization during the first 5 years after initial AN diagnosis were highly correlated (rAge at diagnosis was associated with per patient five‐year cumulated number of inpatient days (IRR (95%‐CI) per year of age = 0.97 , adjusted for sex, income tertile, parental mental diagnosis, and calendar year), and five‐year number of outpatient visits (IRR (95%‐CI) = 1.04 ), but not with per patient five‐year number of hospital admissions. Female AN patients had more hospital admissions per patient than males during 5 years after initial AN diagnosis (IRR (95%‐CI) = 1.36 , adjusted for age, income, parental mental diagnosis, and calendar year), but no association was observed between sex and per patient five‐year numbers of outpatient visits or per patient five‐year cumulated length of stay in hospital.The family income (in tertiles by year) was associated with per patient number of hospital admissions (reference lower income tertile: IRR (95%‐CI) for middle income tertile = 0.89 , IRR (95%‐CI) for higher income tertile = 0.79 , adjusted for sex, age, parental mental diagnosis, and calendar year), but not with per patient number of outpatient visits or per patient cumulated length of stay in hospital. Parental mental diagnosis at baseline was associated with per patient number of outpatient visits (IRR (95%‐CI) = 1.15 , adjusted for sex, age, income, and calendar year), and per patient number of hospital admissions (IRR (95%‐CI) = 1.31 ), and marginally with per patient cumulated length of stay in hospital (IRR (95%‐CI) = 1.16 .4The major finding of this study was a clear time‐trend at the patient level for patients with first‐time AN diagnosis in 1994–2013 of decreasing cumulative number of inpatient days and decreasing number of hospital admissions during 5 years after initial AN diagnosis. These trends were robust after adjustments for age at diagnosis, sex, family income, and parental mental disorder.During the observation period, inpatient treatment may have covered different therapeutic programs changing over time. However, all treatment had been carried out in public hospitals subjected to guidelines from the national health board ensuring uniformity and an evidence‐based approach (National Board of Health, Notably, the data of the present study do not provide evidence that reduction in length of hospitalizations leads to a higher frequency of admission. This is important since brief stays for stabilization a priori may be associated with less stigma, and improved maintenance of usual social‐ and work activities. Though the present study does not include any information on longterm outcome of AN, it should be considered that controlled trials have not provided evidence of a better outcome in favour of hospitalization (Hay et al., Our findings confirm a considerable increase in incident patients with AN who sought treatment in the mental care system over the 20 years of the observation period. This increasing tendency applies to psychiatric disorders in general, and it has previously been shown in a Danish registry study that the increase in patients seeking treatment for AN is at the same level as for other psychiatric disorders (Steinhausen & Jensen, We found that male AN patients had significantly less hospital admissions per patient than females during 5 years after initial AN diagnosis. This sex difference is in line with a previous study (Striegel‐Moore et al., In parallel to a major trend of economic pressure on the health system, decreased hospitalization might have been driven by a wish to reach sizeable cost savings. Indeed, in a study of a three‐month treatment—which did not include patients with BMI < 15—the largest share of costs resulted from hospitalizations (Stuhldreher et al., The present study is based on a large nationwide sample and long‐term follow‐up in a country that is homogeneous regarding sociodemographics and access to tax‐funded healthcare. The diagnostic classification is based solely on ICD‐10 criteria during the whole period of inclusion 1994–2013 and, thus, is not affected by the essential shift from DSM‐IV to DSM‐5. However, there are limitations to acknowledge. First, the registers only include treatment seeking patients. Thus, less severe cases of AN may have remained undetected, and lack of recognition of symptoms among patients with AN may have contributed to selection bias which is a general challenge in studies of this group of patients. Nevertheless, free access without costs to the public health system and a clearly defined responsibility of care for these patients by specialists rather than general practitioners may have minimized the selection bias. Secondly, the Danish registers do not cover patients who seek private financed treatment. However, since specialized treatment in the public health system is free and accessible, the private care constitutes only a very small part in the Danish health provision system (Ministry of Health, Denmark, In conclusion, the patient‐level cumulated length of hospitalizations during 5 years after initial AN diagnosis has been reduced dramatically along with a decrease in hospital admissions for Danish patients with first‐time AN diagnosis in the period 1994–2013. However, our observational study does not allow for causal conclusions and clinical recommendations regarding treatment modes. For this purpose, we recommend controlled intervention studies to examine outcomes of AN after short versus long‐term hospitalizations.The authors have no conflict of interest.AppendixS1: Supporting informationClick here for additional data file.


Inferential research commonly involves identification of causal factors from within high dimensional data but selection of the ‘correct’ variables can be problematic. One specific problem is that results vary depending on statistical method employed and it has been argued that triangulation of multiple methods is advantageous to safely identify the correct, important variables. To date, no formal method of triangulation has been reported that incorporates both model stability and coefficient estimates; in this paper we develop an adaptable, straightforward method to achieve this. Six methods of variable selection were evaluated using simulated datasets of different dimensions with known underlying relationships. We used a bootstrap methodology to combine stability matrices across methods and estimate aggregated coefficient distributions. Novel graphical approaches provided a transparent route to visualise and compare results between methods. The proposed aggregated method provides a flexible route to formally triangulate results across any chosen number of variable selection methods and provides a combined result that incorporates uncertainty arising from between-method variability. In these simulated datasets, the combined method generally performed as well or better than the individual methods, with low error rates and clearer demarcation of the true causal variables than for the individual methods. Over recent years, methods have been proposed in the statistical literature to improve variable selection for inference in high dimensional data, including modifications to AIC/BIC5, and a variety of regularisation methods based on functions that penalise model coefficients to balance over- and under-fitting (the variance-bias trade off)8. It has been shown, however, that different methods of variable selection can result in considerable differences in covariates selected9 and this poses difficult questions for the researcher about which method to choose, as well as presenting wider concerns around variability of results and therefore the reproducibility of science11.Inferential epidemiological research commonly involves identification of potentially causal factors from within high dimensional data spaces; examples include genetics, sensor-based data capture and large scale questionnaires. The selection of ‘important’ variables from within a high dimensional space is challenging because conventional stepwise selection procedures are known to perform poorly, resulting in inflated coefficients, downward biased errors and over fit models12. Triangulation of multiple methods has been proposed as an aid to identify important variables13; in this context triangulation refers to conducting a variety of analytic methods on one set of data, on the premise that the most important variables will tend to be identified by most methods. Indeed, recent research has indicated this approach is likely to be beneficial9. However, rather than using triangulation to simply compare methods, a route to formally combine results from several statistical approaches would be advantageous to explicitly represent the additional uncertainty arising from variation between methods. Here we propose an approach to synthesise parameter estimates across different methods, to formally triangulate results and to compare, capture and account for between-method variability.To mitigate the issue of results being method-dependent, it has been argued that uncertainty in data are always explored from many angles15. The concept is that covariates most frequently selected under repeated resampling are most likely to be of importance in a target population. Resampling, such as bootstrapping, is effective to evaluate selection stability14 and has the advantage of simultaneously providing an estimate of model coefficient distributions4, both of which can be used to provide a ranking of the relative importance of potential covariates16.Furthermore, it is recognised that robustness in model selection can be improved through use of selection stabilityIn this paper we propose an adaptable method to compare and synthesise results across any chosen number of variable selection techniques to triangulate results between methods. The method incorporates selection stability and coefficient distributions to provide an overall, unified result that encompasses the uncertainty arising between methods. To illustrate the method, we also describe a simple graphical technique to compare individual and synthesised models which readily allows identification and visualisation of the important variables selected by each, and the combination of methods.Seven datasets were used to compare and synthesise results from six methods of automated variable selection. Six datasets were simulated such that underlying relationships were known a priori within the data and one dataset was from a previously conducted field study, to illustrate the proposed methods using real data. Construction of the simulated datasets is described in detail in the “16.The simulated datasets used were of different dimensions. Datasets 1 and 2 were constructed by randomly simulating 1000 observations (rows) for 910 theoretical potential explanatory covariates (columns), ten of which were specified to have a known relationship with an outcome variable. Datasets 3 and 4 were constructed by randomly simulating 200 observations for 10,000 potential explanatory covariates, again only ten of which were specified to have a known relationship with an outcome variable. All covariates were simulated to have a mean = 0 and a standard deviation = 1 (i.e. they represented standardised variables) and each dataset allowed the outcome variable, “y_out”, to be calculated with a known degree of uncertainty. Datasets 5 and 6 comprised 1000 observations by 910 covariates, and 200 observations by 10,000 covariates respectively, but an outcome variable was generated at random . The real dataset was gathered from a study conducted on 408 commercial sheep farms in the UKThe signal from the true variables was set to be stronger in Dataset 2 (the ten true variables explained 73% of variability in the outcome) than Dataset 1 (28% variability explained) but both datasets were considered to be representative of possible realistic field data. The partial coefficient distributions of the variables providing the ‘true’ underlying signal in these datasets are provided in Table Statistical methods implemented to conduct variable selection within these datasets were stepwise linear regression (SLR) based on AIC, elastic net regression (enet), smoothly clipped absolute deviation (SCAD), minimax convex penalty (MCP), SparseStep regression, and stepwise selection based on a modified Bayesian Information Criterion (mBIC). These statistical approaches were employed using standard methodology which is described in detail in the “The coefficient estimates for the variables selected in these models are provided in Table Covariate coefficients and selection stability were estimated for all models using a bootstrap methodology, except for the conventional SLR based on AIC which was deemed to perform too poorly to carry forward. The procedures for bootstrapping and calculating selection stability are described in detail in the “Covariate selection stability and bootstrap P values estimated for Dataset 1, for each statistical method and all covariates, are illustrated in Fig. The coefficient distributions of the combined method are illustrated in Fig. Covariate selection stability and bootstrap P values estimated for Dataset 2 are illustrated in Fig. The coefficient distributions of the combined method for Dataset 2 are illustrated in Fig. The same analytic work flow was conducted on the two larger simulated datasets in which p (number of covariates) was much greater than n (number observations), . Dataset 3 had the stronger signal (ten true variables explained 73% of variability in the outcome) than Dataset 4 (63% variability explained). The partial coefficient distributions of the variables providing the ‘true’ signal in these datasets are illustrated in Fig. Model coefficients distributions derived from the bootstrap sampling are illustrated in Fig. Covariate selection stability and bootstrap P values for Datasets 3 and 4, are illustrated in Figs. For Dataset 4, covariate stability and bootstrap P values are illustrated in Fig. 16 with a normally distributed outcome variable and 340 potential explanatory covariates.Although in these data there were no known ‘gold standard’ covariates , we use a real field dataset to illustrate the concept of comparing and synthesising different methods of variable selection. The real data were gathered from a study conducted on 408 commercial sheep farms in the UKCovariate selection stability and bootstrap P values are displayed in Fig. To evaluate how the proposed combination method of covariate selection would perform when no important explanatory variables were present, we simulated two further datasets with no underlying signal. That is, an outcome variable was generated from a random normal distribution and potential explanatory covariates were randomly simulated independently of the outcome. Dataset 5 was comparable in size and structure to Datasets 1 and 2 with 910 potential explanatory variables, 1000 observations and correlations between explanatory covariates as described for Datasets 1 and 2. Dataset 6 was comparable in size and structure to Datasets 3 and 4 with 10,010 potential explanatory variables, 1000 observations and correlations between explanatory covariates as described for Datasets 3 and 4.An identical analytic pathway was employed as described for Datasets 1–4 and illustrations of the resulting covariate selection stability and bootstrap P values are provided in Figs. 12 and that use of multiple analytic approaches may mitigate problems with scientific reproducibility17, it is rare that more than one technique is reported when conducting high-dimensional data analyses. In this paper we describe an approach to compare and combine results from different statistical methods used on one dataset, firstly to provide a basis to evaluate between-method variability and secondly to provide a means to formally combine and triangulate results between methods. Informal triangulation between statistical methods has been suggested as a route for researchers to confirm truly important variables9 but approaches to numerically combine results from different methods using the same data are lacking. With the approach proposed in this paper, triangulation is given a numeric foundation based on covariate stability and coefficient distributions. A graphical visualisation of selection stability and bootstrap P values was found useful to provide a framework to picture covariate importance of both individual and combined methods. In the combined method, covariates with highest stability and lowest bootstrap P values reflect those identified as most important overall by the individual methods and therefore those most sensible to be inferred of greatest importance in the data.Despite the fact that many views have been expressed suggesting that analysis of individual datasets should be considered from multiple angles18. Whilst it remains a truism that no models are right, but some can be useful19, we believe that using this comparison of individual and synthesised methods adds to the interpretation of high dimensional data analysis through transparently displaying differences between approaches and providing an overall result that incorporates this uncertainty.In these simulated datasets, the combined method generally performed as well or better than the individual methods; it tended to rank the true variables highly  and gave coefficient estimates that, although slightly conservative , produced median values in close proximity to the true partial coefficient values. It should be noted, however, that the performance of the combined method will depend entirely on which individual methods are chosen to comprise the combination and will not necessarily produce an answer nearer to the truth than an individual method. Nonetheless, a comparison and combination of a variety of methods is still of use; it greatly adds to the transparency of analysis and helps ensure safety of results (by avoiding use of one specific ‘outlying’ method); this supports the view that use of multiple analytic approaches will lead to a better understanding of variability and important relationships within data11, both in terms of variables selected and coefficient estimates9. The simulated datasets used in this study, in which the true underlying relationships were known, were useful to illustrate such differences between methods. Differences between methods occurred in sparsity of solution, the magnitude and relative ranking of variable stability and coefficient estimates. Clear differences were also identified between methods in the analysis of datasets that contained no signal , although it is likely the same principles will apply. Furthermore, additional research would be useful to develop methods to determine the optimal threshold for selection stability, to minimise error rates for any given dataset and model.Similarly, although these principles of model comparison and combination of results could be applied to any suite of methods, our results apply to the specific methods we chose. Our choice was based on methods that were reasonably common and with literature to support their validityIn conclusion, in this paper we have developed an adaptable, straightforward method to compare and synthesise results across any chosen number of variable selection techniques to formally triangulate results between methods. Importantly, the method includes selection stability as well as coefficient estimates and provides a unified result that incorporates uncertainty arising between methods.To evaluate and combine results from six methods of covariate selection, six simulated datasets containing known relationships were generated. Initially, two datasets were constructed, these are described in detail below. Subsequently, two larger datasets were constructed using similar methods and an overview of these is also provided. Finally, the combination of variable selection techniques were evaluated using one real dataset and two datasets containing no signal (i.e. comprising variables generated at random); these datasets are also briefly described.The purpose was to construct realistic datasets in which a ‘true’ set of predictor variables were known and from which an outcome variable was directly calculated. Simulated datasets 1 and 2 both contained 1000 rows and 910 columns. Ten variables were set as being ‘causal’ and used to directly calculate an outcome variable, y. These variables were simulated from the following distributions;23), each variable drawn from a distribution with mean = 0, SD = 1 and covariance matrix specified such that variables were drawn with a correlation between each of 0.6. The purpose of these correlations was to mimic reality; causal variables are often correlated in epidemiological data.Variables X1–X5 were drawn from a multivariate normal .Dataset 1: v = N.Dataset 2: An outcome variable, y, was generated from these 10 variables as follows;24 using solely these ten simulated variables. The coefficient distributions from these models were taken as the ‘true’ underlying distributions for comparison in subsequent analysis. The true coefficients for the covariates derived from Datasets 1 and 2 are provided in Table The ten variables used to calculate y, we refer to as the ‘true’ explanatory covariates throughout the manuscript. For each dataset a ‘true’ model was estimated using conventional linear regression in RAn additional 900 ‘noise’ variables were simulated from distributions with mean = 0 and SD = 1; these had no dependence or relation to the outcome variable y, i.e. they were drawn from independent random distributions. These were deemed ‘false’ variables, not causally related to y. Since in epidemiological data it is common that such non-causal variables may also contain correlations, 400 of the 900 variables were drawn from multivariate Normal distributions as follows. Four sets of 50 variables were drawn from a MVN distribution such that each variable was drawn with a mean = 0 and SD = 1 and the correlation between individual variables within sets of 50 variables was 0.7. Therefore, this resulted in four sets of 50 variables correlated at ~ 0.7. This procedure was repeated for another four sets of 50 variables but for these the correlation was 0.8. A final group of 500 variables were drawn independently from random Normal distributions with mean = 0 and SD = 1.Therefore, the resulting simulated dataset contained an outcome variable that was calculated from ten ‘true’ variables  and an additional 900 ‘false’ (noise) variables, simulated at random and independent of the outcome.v = N.Dataset 3: v = N.Dataset 4: Two further simulated datasets were generated, Datasets 3 and 4, using the same principles but of different dimensions; with 200 rows and 10,010 columns . The datasets again contained 10 ‘true’ variables that were simulated as described for Datasets 1 and 2 (Eq. (2)), except the following random terms were used to reflect additional unknown but casual effects;As with datasets 1 and 2, a ‘true’ model was estimated using conventional linear regression from these ten simulated variables and coefficient distributions from these models are illustrated in Figs. Datasets 5 and 6 comprised 1000 rows by 910 covariates and 200 rows by 10,000 covariates respectively, but an outcome variable was generated at random .16, had an outcome variable that was approximately normally distributed and a set of 340 potential explanatory variables available for selection. Explanatory variables were standardised for analysis; details of the data, pre-processing and variable descriptions have been reported previously16.Finally a set of field data were used to illustrate the proposed modelling approaches. This real dataset was gathered from 408 commercial farms in the UK25, elastic net regression (Enet)8; smoothly clipped absolute deviation (SCAD)26, minimax convex penalty (MCP)27, Sparsestep (S_step)22 and a modified Bayesian Information Criterion (mBIC)5. The basis for model selection for each of these methods is outlined below.To evaluate the effectiveness of retrieval of the ten ‘true’ variables from within the simulated datasets, six methods were employed and compared. These were; a conventional stepwise selection method based on AIC Stepwise linear regression based on AIC (SLR).28 in R24. The regression equation took the form;A conventional linear regression model was implemented using the ‘stepwise’ function in the Bigstep packagei and ŷi were respectively the simulated and model predicted outcome for the ith observation, j denoted a predictor variable with p the number of predictor variables selected in the model through minimisation of the loss function (some variable coefficients are set to zero meaning theElastic net is a form of regularised regression that incorporates a mixture of the lasso (L1) and ridge (L2) penalties and can be described as;E) and the relative proportion of penalisation on either the sum of the square of the coefficients or the unsquared coefficients (α) were optimised by 10 × tenfold cross validation to minimise mean absolute error (MAE).y are not selected in a final model), and |β| represented absolute values of the regression coefficients. The hyperparameters that represent the penalty 27 are also forms of regularised regression. A feature of these methods is that the size of the penalty function varies with the size of variable coefficient, β. Both methods can be described by the general framework;scad/mcp represents the SCAD or MCP loss function to be minimised, i, yi, ŷi, j, p and n are as defined in Eq. (Smoothly clipped absolute deviation (SCAD)d in Eq.  and \docFor SCAD:31.For MCP:22. The SparseStep loss function can be described as;sp_step represents the SparseStep loss function to be minimised, i, yi, ŷi, j, p and n are as defined in Eq. , pr decreases and A modified Bayesian Information Criterion was used for model selection implemented in the R package bigstep32 was used to estimate covariate stability for all analytic approaches, according to methods previously described9. In brief, selection stability20 was evaluated for each model as the percentage of times that each covariate was selected in the model across bootstrap samples. The distributions of variable coefficients were calculated from all non-zero values of the coefficient in the bootstrap samples; this allowed comparisons between methods of variable selection stability as well as coefficient estimates. A further evaluation of the importance of each variable in the final bootstrapped model was made from all non-zero values (i.e. when the variable was selected in the model), by estimating what we term the Bootstrap P value. The Bootstrap P value was calculated as the smallest proportion of (non-zero) coefficient values to one side of zero. That is, if a covariate was selected in the model on 80 occasions  and 70 of these were either greater or less than zero, then the Bootstrap P value would be (80–70)/80 = 0.125. For all methods, variable selection and importance were visualised by plotting selection stability against Bootstrap P value for all variables in the dataset.Conventional bootstrappingCoefficient distributions and stabilities were synthesised across methods (with the exception of Stepwise Linear Regression based on AIC which performed poorly) by aggregating the bootstrap matrix by row from each individual method; matrices were aligned by variable. Therefore, an enlarged ‘combined’ matrix containing an equal number of bootstrap samples for each method was produced comprising variables and coefficient estimates aggregated across all methods; this we term the combined model. Since an equal number of bootstrap samples were used for each method , an equal weighting was given to each method when calculating synthesised coefficient estimates and Bootstrap P values. From this combined matrix, an overall selection stability and Bootstrap P value were calculated as described above. Coefficient distributions for each covariate were also calculated from all non-zero values of the combined matrix. Therefore the combined model resulted in estimates for selection stability, coefficient distributions and Bootstrap P values derived for all covariates across all five methods of covariate selection.FPER = Number of false positive covariates selected/Total number true negative covariates in data.not selected/Total number true positive covariates in data.FNER = Number true positive covariates Model performance was assessed using false positive and false negative rates (FPER and FNER respectively). For each model, these were defined as;For bootstrapped models, since both stability and Bootstrap P value were key for evaluating variable importance, we visualised and compared these parameters graphically. For the combined method, covariates were ranked and plotted in order of descending stability to identify the region where stability changed from being relatively high to low; the same principle as used in a classical scree plot. To evaluate stability objectively in the combined method, the rate of change in stability between consecutive covariates ranked in descending order was determined and a smoothed mean calculated for the rate of change in stability using 15 consecutive values. A sensitivity analysis was conducted on the number of values to include in calculation of the smoothed mean and for values between 10 and 20, there were negligible differences in results. A value under 10 gave insufficient smoothing and over 20 started to show a lack of discrimination, therefore a value of 15 was chosen. Once the rolling mean rate of change reached a value ≤ 1 (i.e. the rate of change moved from being > 1 to ≤ 1), this was deemed to signify the change point in stability for that model and used as a threshold to calculate FPER and FNER (see Fig. Supplementary Information.


Oncorhynchus kisutch) populations in which males adopt one of two age‐invariant, heritable life history tactics: most mature as large three‐year‐old “hooknose” and typically fight for spawning opportunities, while some mature as small two‐year‐old “jacks” and fertilize eggs through sneaking. The closure of a fishery targeting three‐year‐old fish provided an experimental test of the prediction that fishery‐imposed selection against hooknose males drives an evolutionary increase in the proportion of males adopting the jack tactic. The data support the prediction: 43 of 46 populations had higher jack proportions during than after the fishery. The data further suggest that changes in jack proportion were not solely the result of demographic adjustments to harvest. We suggest that systems where fisheries differentially exploit phenotypically discrete, age‐invariant life histories provide excellent opportunities for detecting FIE.Fisheries‐induced evolution (FIE) can result when harvest imposes artificial selection on variation in heritable phenotypic traits. While there is evidence for FIE, it remains difficult to disentangle the contributions of within‐generation demographic adjustment, phenotypic plasticity, and genetic adaption to observed changes in life history traits. We present evidence for FIE using dozens of Coho salmon ( The first was that he is up on the latest trends. The evidence for this came from my correspondence with Louis, always via email. In my email software program, the sign‐offs from Louis (on good news days) were always “Louis J”; this led me to think that he had a secret middle name that was kept off of the countless papers that he had authored. In retrospect, I’m glad that I never asked him about this—I first thought it was a weird recurring typo and attributed this to his remarkable ability to field emails at a rapid pace. Years  later, I realized that the “J” was an early version of a poorly translate happy face emoticon, and neither my luddite brain nor my email program was equipped to make that translation. Bottom line: Louis was using emojis before it was something that became integral to virtual communication. The second lesson that I learned from Louis was that no matter how successful or driven a person may be, it is always okay to be human. Witnessing, and being a part of, Louis’ interactions has influenced how I interact with students and colleagues—Louis could be tough on people at times, but he was always compassionate in the end. I think about this every time I am a bit frustrated, and I remember how he always encouraged us in these weird moments, even if he was probably feeling a bit annoyed himself. To this day, over a decade after our short academic relationship, he still notices the littlest things about what I  am up to; this is a testament to his humanity, and the care he puts into his trainees. That is the kind of mentor I strive to be. ‐Laura K. Weir.While it is reasonable to assume that FIE can occur, for a number of reasons it is difficult to unequivocally conclude that observed changes in life history traits are the result of FIE  from the Oregon coast (USA) meet these criteria reasonably well. All females and most males mature as three‐year‐olds following an 18‐month ocean phase, while some males mature precociously as smaller two‐year‐olds following six months in the ocean  and CS at age AS (y‐axis) creates an ellipse in a life history phase space , which extends from the minimum to maximum CS values of the individual life history reaction norms  and after (1994–2003) the commercial fishery. We considered only wild‐born fish, and the data were corrected for observer efficiency because during spawning ground surveys large adults are easier to see than smaller jacks; observer efficiency is corrected by multiplying the number of jacks by 2 and the number of adults by 1.3 Solazzi, . Assumin−2), we used peak count data divided by the product of survey length and bankfull width, which we estimated according to Faustini, Kaufmann, and Herlihy  area under the curve (AUC) estimates. The AUC method uses data from the multiple surveys and the lifespan of spawning fish to calculate estimates of total escapement Young, . Jack pr Herlihy .t) rather than brood year  for three reasons  of missing data eliminates three years  of jack proportion estimates. However, we repeated our main analyses using brood year jack proportions, and our results were qualitatively unchanged  and hooknose (t−1) and compared these ratios before and after the fishery closure in 1994.Because the relative abundance of jacks and hooknose males may be influenced by temporal variation in environmental conditions, we compiled data on three factors known to affect survival and recruitment in Oregon coast Coho salmon: marine upwelling, sea surface temperature, and streamflow . Monthly volumes were obtained from March to September at three locations used by Oregon Coho ; these data were used as a proxy for food availability during the spring and summer of ocean entry. We summed marine upwelling volumes for the seven‐month period each year between 1949 and 2003. Beginning at 1950, we calculated the ratio of seasonal volumes experienced by jack and hooknose males during their first ocean year at each of the three locations as:3m represents upwelling volume for a particular month, and t is a given year. In addition to analysis of upwelling ratios at individual stations, we calculated the mean of these three ratios as an overall metric for the area of the ocean used by Oregon Coho salmon. Because demographic data were collected for 30 populations beginning in 1950, with another 16 added in 1981 (see details below), we compared upwelling data before and after the fishery closure using all data as well as data only from 1981 onward, when all 46 populations could be used for other analyses. We found no strong evidence for differences between the mean upwelling ratios during and after the fishery . Monthly mean estimates for areas inside 42°N–45°N and 125°W–126°W from 1949–2003 were used in our analyses. Means between March and September were calculated for each year to reflect conditions during the spring and summer. As for the marine upwelling data, we took the ratio of SST means experienced by two‐year‐old jacks (µSSTt) and three‐year‐old hooknose (µSSTt‐1) males. Again, means during and after the fishery did not differ considerably for this variable (Table Sea surface temperature (SST) data were collected by the University Corporation for Atmospheric Research database (le Table .http://waterdata.usgs.gov). We summed the mean monthly flow rate from five Oregon coast rivers between 1948 and 2003 and calculated the total flow experience by jack and hooknose males during their 18‐month freshwater residency as:t is a given year. These values were compared as above and did not differ between the periods during and after the fishery . Second, noting that exploitation rate did not increase systematically during the fishery  to determine whether there was a tendency for jack proportions to increase through time, a pattern that is consistent with an evolutionary response to selection against hooknose males. Third, we quantified the relationship between population‐level temporal changes in jack proportion and adult density during the fishery to test whether the rate of increase in jack proportion was related to the rate of decline in adult escapement across populations. Such a relationship would implicate demographic adjustment as an important mechanism in driving temporal changes in jack proportion. For this analysis, we calculated the slope of change in jack proportion and adult density for each population from 1950 to 1993 and tested the correlation between these values (n = 30).First, we tested whether fishery exploitation rate significantly affected interannual variation in jack proportion. To this end, we calculated the correlation coefficient between exploitation rate and jack proportion for each of the 30 populations; positive correlations would suggest demographic adjustment to harvest was important. We then tested whether exploitation rate affected interannual variation in jack proportion by regressing jack proportion on exploitation rate using a linear mixed‐effects model with jack proportion as the dependent variable, exploitation rate as a fixed effect, and population as a random effect , mean jack escapement, and mean jack proportion during (1981–1993) and after (1994–2003) the fishery. We first used a linear mixed‐effects model with mean jack proportion (untransformed) as the response variable, period as a fixed‐effect class variable, mean three‐year‐old breeding density as a continuous variable, and population as a random effect to account for the paired nature of the analysis. We asked whether declines in jack proportion following the fishery closure were due simply to increases in three‐year‐old density (same slope and intercept) or to a change in the relationship between three‐year‐old density and the proportion of males adopting the jack tactic (different slopes and/or intercepts). Second, we compared mean adult and jack escapement across populations during and after the fishery using linear mixed‐effects models with population as a random effect (n = 46). If declines in jack proportion reflected an evolutionary response to the cessation of the fishery targeting adults, we predict that jack escapement would decline despite increases in adult escapement.Finally, using data for all 46 populations between 1981 and 2003, we explored how the fishery affected the relationship between three‐year‐old breeding density (escapement) and the proportion of males adopting the jack tactic. If postfishery declines in jack proportions were due simply to demographic adjustment, we expect both adult and jack escapement to increase, but jack escapement to increase by less. If such declines reflect evolutionary change, we expect the relationship between adult escapement  and jack escapement to change; there should be fewer jacks per spawning female. We explored these complementary predictions two ways. For each of the 46 populations in the 1981 to 2003 dataset, we calculated mean adult escapement  population revealed the same temporal increase in jack proportion observed across Oregon coast Coho populations (DeFilippo et al., We conclude that the data from Oregon coast Coho salmon populations offer compelling evidence for FIE. Identifying the specific evolutionary mechanism and quantifying the relative importance of FIE and ecological processes remain open challenges. Such uncertainty underscores the challenge of studying FIE in nature. Notwithstanding these caveats, our study highlights the value of using the discrete male life history tactics common in anadromous salmonids to study FIE (Fleming & Reynolds, Appendix S1Click here for additional data file.


Altered capacity for self‐renewal and differentiation is a hallmark of cancer, and many tumors are composed of cells with a developmentally immature phenotype. Among the malignancies where processes that govern cell fate decisions have been studied most extensively is acute myeloid leukemia (AML), a disease characterized by the presence of large numbers of “blasts” that resemble myeloid progenitors. Classically, the defining properties of AML cells were said to be aberrant self‐renewal and a block of differentiation, and the term “differentiation therapy” was coined to describe drugs that promote the maturation of leukemic blasts. Notionally however, the simplistic view that such agents “unblock” differentiation is at odds with the cancer stem cell (CSC) hypothesis that posits that tumors are hierarchically organized and that CSCs, which underpin cancer growth, retain the capacity to progress to a developmentally more mature state. Herein, we will review recent developments that are providing unprecedented insights into non‐genetic heterogeneity both at steady state and in response to treatment, and propose a new conceptual framework for therapies that aim to alter cell fate decisions in cancer. This review by A. Lewis and L. Kats summarizes the recent advances in our understanding of "differentiation therapy". The authors share new developments into non‐genetic heterogeneity, and propose a conceptual framework for therapies that aim to alter cell fate decisions in cancer. Cellular plasticityThe capacity of a cell to reprogram its transcriptional and epigenetic state and adopt a new phenotype.Cellular hierarchyIn some tissues, cells are hierarchically organized. Cells at the top levels  can give rise to cells at lower levels , but not vice versa.Cancer stem cell (CSC)A type of malignant cell is capable of limitless self‐renewal and is thought to be responsible for tumor maintenance and propagation. CSCs share many features with normal stem cells. In the context of leukemia, CSCs are referred to as leukemic stem cells (LSCs).De‐differentiationConversion of a mature or terminally differentiated cell to a stem‐like cell.DifferentiationThe process by which the features, function, or potential of a cell is altered, e.g., conversion of a multi‐potent stem cell to a lineage‐committed progenitor. Differentiation is underpinned by alterations in the chromatin and transcriptional states of the cell.Differentiation arrestA phenomenon whereby cells are not able to follow their normal maturation to become a terminally differentiated and fully functional effector cell.Differentiation therapyA therapeutic strategy that induces the conversion of cellular states. In the context of cancer, differentiation therapy is intended to convert cells from a self‐renewing to a non‐self‐renewing phenotype.Differentiation trajectoryThe development and maturation of a cell over time.Epigenetic memoryA phenomenon where daughter cells inherit information from the mother cell following cell division. Epigenetic memory is controlled by chromatin regulators that enable and enforce selective gene expression.ExhaustionLoss of self‐renewal capacity due to constant proliferation and/or differentiation.HematopoiesisThe process of normal blood cell development.Lineage biasA phenomenon where a cell with multi‐potent capabilities preferences a particular fate over others, e.g., in the hematopoietic system, some hematopoietic stem cell subsets generate reduced numbers of lymphoid cells (myeloid biased) whereas others generate fewer myeloid cells (lymphoid biased).Non‐genetic heterogeneityAlso known as epigenetic heterogeneity. Genetically clonal cells can exhibit variable phenotypes, functions, and fates. The variation is controlled by both cell intrinsic  and cell extrinsic  factors.Self‐renewalA type of mitotic event where the two daughter cells have the same features, function, and potential as the mother cell.Stem cellin vivo, typically using lineage tracing of transplantation. In the hematopoietic system, stem cells are referred to as hematopoietic stem cells (HSCs).A cell that is essential for the maintenance of a particular tissue. In some tissues, such as the hematopoietic system, stem cells are rare specialized cells capable of self‐renewal and multi‐lineage differentiation . In other contexts, many cells can behave as a stem cell and can contribute to tissue regeneration. Stem cell activity is assessed During normal development and under homeostatic conditions, mammalian tissues are hierarchically organized with long‐lived stem cells maintaining a repertoire of effector cells through a carefully calibrated balance between self‐renewal and differentiation. Cells follow prescribed trajectories involving proliferation, specification, and death, ensuring that appropriate numbers and ratios of the various cell types are preserved. At any given juncture, the phenotype and fate of an individual cell is dictated by its epigenetic state, enabling the generation of the myriad specialized cells required for the existence of a multi‐cellular organism from a single genome.in vitro not only to arrest the growth of cancer cells, but indeed to convert them to what appeared to be “normal” non‐malignant cells  as model systems where knowledge of molecular programs that dictate cell phenotype and fate is most advanced. We summarize recent data that demonstrate that altered cell fate and epigenetic reprogramming underpin response and resistance to a growing number of therapies. We discuss the interplay between epigenetic heterogeneity, genetic heterogeneity, and the immune system. Finally, we speculate how increased knowledge of the mechanistic basis of existing drugs can be leveraged to develop next‐generation anti‐cancer modalities.in vitro and in vivo, and identified various factors that are linked with either self‐renewal or differentiation of the daughter cells , or is acquired shortly after but independent of mitosis , has not been fully resolved.Before embarking on a discussion of malignant development, it is worth considering normal developmental trajectories. One of the best studied developmental systems is hematopoiesis, which has long served as a model for hierarchically organized tissues , drives senescence that is partially dependent on the TP53 axis , a sub‐type of AML commonly driven by the t chromosomal translocation encoding the PML‐RARα fusion protein. APL presents clinically as a massive and rapid expansion of promyelocytes in the bone marrow, with their characteristic appearance as a halfway stage between an undifferentiated “blast” and a mature granulocyte. APL cells are highly responsive to retinoic‐acid (ATRA) and arsenic trioxide (ATO), and the combination of the two compounds represents one of the true success stories of oncology, converting a once rapidly fatal disease to one that is highly curable  found in an additional 15–20% of AML patients have entered clinical use  cells. Lack of NKG2D expression protects LSCs from NK‐mediated killing and contributes to their ability to maintain disease in an immune‐competent host   influence cellular behavior in specific contexts requires further investigation. Until recently, analyses have been limited by the number of features that could be simultaneously measured at single‐cell level. Advances in single‐cell omics have greatly increased our ability to differentiate between relative changes in cell frequency within a population and changes within specific cell types that could not be achieved by measurements of population averages. Moreover, our capacity to multiplex different types of measurements is providing powerful insights into the interplay between genetic and epigenetic heterogeneity.A greater understanding of spatial heterogeneity is required. Emergence of spatial transcriptomics will greatly advance our knowledge of the interaction between different malignant and non‐malignant cells.As promising non‐cytotoxic therapies that modulate differentiation and self‐renewal  and LSC‐directed strategies specifically targeting the subset of cells required to maintain AML  enter clinical use, we must continue to learn from both successes and failures. Correlative observations in patients can be used to generate hypotheses that can be tested in model systems, with the ultimate goal of expanding the success of differentiation therapy beyond APL to other cancer contexts.LMK has received research funding and consultancy payments from Agios Pharmaceuticals and Celgene Corporation.


Temporal and spatial control over polydopamine formation on the nanoscale can be achieved by installing an irradiation‐sensitive polymerization system on DNA origami. Precisely distributed G‐quadruplex structures on the DNA template serve as anchors for embedding the photosensitizer protoporphyrin IX, which—upon irradiation with visible light—induces the multistep oxidation of dopamine to polydopamine, producing polymeric structures on designated areas within the origami framework. The photochemical polymerization process allows exclusive control over polydopamine layer formation through the simple on/off switching of the light source. The obtained polymer–DNA hybrid material shows significantly enhanced stability, paving the way for biomedical and chemical applications that are typically not possible owing to the sensitivity of DNA. When dopamine is photopolymerized on DNA origami, precisely patterned nanoobjects are obtained with unprecedented spatiotemporal control and nanometer resolution. This approach combines the advantages of visible light as an external stimulus and the unique addressability of the DNA origami methodology. The creation of functional materials with high control and precision during the synthesis process to provide a shape‐customized product is one of the major objectives in the field of nanoscience. Inspired by Nature, bottom‐up strategies often exploit the self‐assembly capacity of various building blocks to produce ordered structures such as liposomes, polymeric nanoparticles, and viral protein mimics.Rationally designed DNA origami objects originate from the sequence‐specific binding properties of DNA: a long, single‐stranded DNA scaffold is folded into a distinct architecture guided by a set of short staple strands, which hybridize at programmed positions along this scaffold. Each staple strand can serve as an attachment point for modifications, either directly attached to the staple strand or through a complementary oligonucleotide, furnishing an unprecedented combinatorial platform for designing versatile nanoobjects.However, the prospects of DNA origami methodology go beyond simply positioning objects and may be expanded to perform chemical reactions or even the bottom‐up synthesis of macromolecules. In this respect, in situ atom‐transfer radical polymerization (ATRP) on DNA origami is suitable for the fabrication of polymers with precisely designed nanopatterns.Herein, we have developed a photopolymerization reaction using a guanine‐rich quadruplex (G4) with the embedded photosensitizer protoporphyrin IX (PPIX). The production of reactive oxygen species (ROS) upon irradiation with white light was used to initiate the oxidative polymerization of dopamine with spatiotemporal control Figure  A. TheseThe DNA origami platforms—tube I and tube II with one and two rings of reaction centers, respectively—were synthesized from scaffold DNA, staples, and G4‐extended staple strands in a one‐step process, and their integrity was confirmed by atomic force microscopy (AFM), whereby the moderately bulky structure of the G4 sequences appeared as slight dots and lines on the tubes . Steric hindrance due to the curvature guided the folding process towards tubes that present G4 strands on the outside. These extended structures were then exploited as anchors for retaining the photosensitizer protoporphyrin IX at distinct locations within the DNA framework. Upon binding to the G‐quadruplexes, the fluorescence of PPIX significantly increased, indicating successful host–guest binding .n,n‐dimethyl‐4‐nitrosoaniline (RNO) through oxidation, while no reaction occurs in the absence of light.Protoporphyrin IX is utilized in photodynamic therapyUpon the light‐triggered ROS generation by PPIX and subsequent dopamine oxidation, reaction progress was monitored by recording UV/Vis spectra, tracking the increase of oxidized species (dopaminochrome), precursor oligomers, and polydopamine Figure  B.13b InAs a first indication of successful polymerization, polydopamine‐coated structures did not show any migration during agarose gel electrophoresis in contrast to unmodified DNA origami Figure S12). To directly confirm the presence of the polymer and verify its position, a topographical map of the obtained structures was recorded via AFM measurements  can interfere with certain applications.DNA origami is emerging as a precise and functional nanomaterial for various biomedical and chemical applications. However, it would be highly desirable to circumvent the intrinsic stability issues and push the limits towards a robust but still versatile nanomaterial in various environments.In conclusion, we have reported the first phototriggered polydopamine polymerization on 3D DNA origami templates to create precise nanostructures. The system benefits from both the unique customization of the DNA origami methodology and the adjustability of utilizing visible light as an external stimulus to achieve unprecedented spatiotemporal control on the nanoscale. Various DNA origami templates were equipped with patterns of G‐quadruplex secondary structures, which—along with the embedded photosensitizer protoporphyrin IX—dictated the locally restricted formation of polydopamine. By suppressing the self‐polymerization of dopamine at slightly acidic pH and simply switching the light on and off, the characteristics, e.g., density and height of the polymer were tailored. Despite the sophisticated nature of the polydopamine‐decorated DNA origami objects, the setup was straightforward, and all materials were biologically derived. The combination of DNA origami technology and polymerizations initiated by visible light allowed the creation of precise photopatterned 3D nanostructures with dimensions far below 100 nm and much smaller than the wavelength of light. Furthermore, the deposition of polydopamine enhanced DNA origami stability, so that it can withstand even pure water conditions, broadening its application scope. Hence, the technique serves as an advanced toolkit for developing DNA‐based devices with nanometer resolution, which exhibit customized properties and stability features.The authors declare no conflict of interest.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.


Epileptic seizure detection and prediction by using noninvasive measurements such as scalp EEG signals or invasive, intracranial recordings, has been at the heart of epilepsy studies for at least three decades. To this end, the most common approach has been to consider short‐length recordings  around a seizure, aiming to identify significant changes that occur before or during seizures. An inherent assumption in this approach is the presence of a relatively constant EEG activity in the interictal period, which is interrupted by seizure occurrence. Here, we examine this assumption by using long‐duration scalp EEG data (21–94 hr) in nine patients with epilepsy, based on which we construct functional brain networks. Our results reveal that these networks vary over time in a periodic fashion, exhibiting multiple peaks at periods ranging between 1 and 24 hr. The effects of seizure onset on the functional brain network properties were found to be considerably smaller in magnitude compared to the changes due to these inherent periodic cycles. Importantly, the properties of the identified network periodic components (instantaneous phase) were found to be strongly correlated to seizure onset, especially for the periodicities around 3 and 5 hr. These correlations were found to be largely absent between EEG signal periodicities and seizure onset, suggesting that higher specificity may be achieved by using network‐based metrics. In turn, this implies that more robust seizure detection and prediction can be achieved if longer term underlying functional brain network periodic variations are taken into account. These sA number of studies have also demonstrated the effect of the circadian rhythm on EEG signal patterns in patients with epilepsy. In a study of one patient suffering from epilepsy, circadian patterns were clearly observable in certain intracranial EEG channel combinations . This suggests that quantifying the characteristics of long‐term network periodic fluctuations and their phase in particular may facilitate reliable detection/prediction of seizure onset. Finally, we show that correlations to seizure onset are much stronger for measures related from functional networks compared to periodicities in the EEG signals 22.1Long‐term video‐EEG recordings were collected from nine patients with epilepsy at the Neurology Ward of the Cyprus Institute of Neurology and Genetics. The study was approved by the Cyprus National Bioethics Committee. All subjects gave written informed consent in accordance with the Declaration of Helsinki. Five patients were monitored using an XLTek  scalp EEG recording system Patients 1–5), while the remaining four were monitored with a Nicolet  system (Patients 6–9). Table –5, whileTwenty‐one electrodes were placed according to the 10–20 international system with two additional anterotemporal electrodes. In addition, four electrodes were used to record the electrooculogram (EOG) and electrocardiogram (ECG) signals, respectively. The data were recorded at a sampling rate of 200 and 500 Hz for the XLTek and Nicolet systems, respectively, using a cephalic reference, Cz, that was not part of the scalp derivations used to display the recorded channels. The EEG and EOG signals were band‐pass filtered between 1 and 45 Hz to remove line noise and muscle artifacts. The Lagged Auto‐Mutual Information Clustering (LAMIC) algorithm  between nodes by quantifying time‐ and frequency‐domain correlations between the corresponding EEG time series. To track network‐related changes over time, we used 5‐s non‐overlapping windows, as this window length was previously found to yield a good compromise between the amount of data needed for accurate calculation of correlation metrics and temporal resolution for identifying seizure‐related changes in the network measures  network, we applied thresholding to identify strongly correlated nodes, which were subsequently assigned edges with a weight of 1. Binarization by thresholding is a common practice for the construction of brain networks. Its implementation is simple and quick, which is important in cases where real‐time implementation is needed  and it also provides a way to compare brain networks between different subject populations/cohorts by selecting the threshold value such that a desired percentage of the total network nodes is included in the binary network. In this context, the threshold value was selected such that networks with similar average degrees were obtained for different correlation measures. For the three examined correlation measures  path length 1dij, was proposed. The global network efficiency is subsequently defined as the average efficiency over all pairs of nodes  functional brain networks corresponding to different times may be compared. If these measure values  corresponding to two different networks differ substantially, it is reasonable to assume that the corresponding networks differ in their topology. However, comparing networks with similar network summative measure values can be inconclusive. Therefore, to quantify topological difference between graphs in more detail, we compared every pair of graphs directly in terms of their structure using the graph edit distance  periodogram to estimate their power spectral density (PSD) P=e1p‐values were computed by applying the Rayleigh test (Equation To account for the fact that we had multiple seizures for some subjects and thus the statistics could be biased toward patients with multiple seizures (mainly Patients 8 and 9), we also calculated corrected hr) Zar, . For theEquation . All theEquation  in Matla3Representative individual results are provided for Patient 4, from which the longest recording (94 hr) was obtained.3.13.1.1The effect of the threshold value on the time‐resolved average network degree is shown in 3.1.2Figure 3.1.3The average network degree obtained using coherence within the six frequency bands of interest is shown in the Supporting Information . Global 3.1.4In addition to the main 24 hr periodicity observed for all network measures, periodicities at shorter time scales were also observed Figures . Figure 3.1.5In addition to examining the evolution of summative brain network measures over time, we investigated the evolution of their topology using the average graph edit distance Equation . Note th3.1.6To complement the results related to functional brain networks, we investigated the long‐term periodicities in the recorded scalp EEG signals. Specifically, we calculate the power of the average EEG signal over all electrodes within the six frequency bands of interest  within the same 5 s sliding windows and show the obtained results in the Supplementary material for Patient 4 . Results3.2Figure 3.3The instantaneous phases of the main identified periodicities  both for the average network degree and the EEG signal power, are shown in Figure A mean resultant vector with a larger length To examine whether the aforementioned correlations were present at an individual level, we repeated the same analysis for Patients 8 and 9, from which multiple seizures were recorded . The results are presented in Figures 4We have examined the time evolution of functional brain networks in patients with epilepsy using long‐duration scalp EEG measurements (between 22 and 94 hr). The brain networks were constructed using three different correlation measures: cross‐correlation, corrected cross‐correlation, and coherence. Network evolution over time was monitored using three widely used summative network measures: average node degree, global efficiency, and clustering coefficient, as well as a novel measure based on the graph edit distance. For all examined correlation measures, it was found that network properties exhibited a main 24 hr periodicity, as well as additional periodicities at shorter time scales. Importantly, a strong correlation between the average degree periodic components and seizure onset was revealed by examining the distribution of the component instantaneous phases at seizure onset, particularly for the shorter periodicities. These correlations were much weaker or totally absent the EEG signal power periodic components. Overall, these findings suggest that functional network properties (average degree) are a more specific marker of the probability of seizure onset and that they could be taken into account for designing more robust seizure detection and prediction algorithms. For instance, patient‐specific correlation patterns between the instantaneous phase of long‐term, network‐based periodicities, and seizure onset could be used as priors in such algorithms, along with other EEG‐based measures calculated around an occurring or impeding seizure, to yield improved performance. This includes both, for example, more accurate detection of seizure events with respect to timing, as well as more reliable prediction of seizures with higher sensitivity and specificity .The long‐term patterns  of EEG signal and functional brain network properties have been investigated in previous studies; however, this has been done mainly using intracranial recordings . Furthermore, scalp EEG typically yields a more complete coverage of the brain, which is more suitable for performing network‐based analysis. The fact that the circadian rhythm is correlated to seizure onset suggests that seizures tend to occur at specific times for different subjects  were recorded to ascertain that these seizures were identical. Thus, the number of seizures per patient was limited by these clinical considerations. One could increase the sample of patients but the problem of collecting a high number of seizures in the same patient is inherent to the clinical situation. Therefore, when calculating circular statistics, we corrected for the fact that we had multiple seizures for some subjects to avoid biasing the results by these subjects. Note that previous related studies were based on similar subject sizes, for example,  and comparing it to alternative approaches. We aim to investigate this in future studies using experimental data from larger patient cohorts. This could also allow investigating the effects of different factors (such as seizure frequency and focus) on the correlations between connectivity and seizure onset.Appendix S1. Supporting Information.Click here for additional data file.


Chylothorax is an uncommon cause of pleural effusion. Recurrent chylous effusions are often resistant to conservative treatment and many need surgical intervention. We report a 69‐year‐old woman with refractory idiopathic chylothorax resistant to medium‐chain triglyceride diet and intermittent thoracentesis. Lymphangiography and lymphoscintigraphy failed to identify the site of leakage. We initiated continuous positive airway pressure (CPAP) 12 h before and 48 hours after talc pleurodesis. Chest drain was removed at day 3 and she was discharged at day 5. To our knowledge, this is the first case of successful resolution of idiopathic refractory chylothorax with CPAP ventilation used in tandem with talc pleurodesis. We describe a 69‐year‐old woman with underlying diffuse large B‐cell lymphoma of the colon 16 years ago in remission, who presented with a new problem of refractory chylothorax. It was successfully treated with continuous positive airway pressure (CPAP) ventilation in tandem with talc pleurodesis. Chylothorax is a rare and debilitating disease. It can be congenital or result from obstruction or neoplasm. The treatment varies from conservative dietary modifications, treatment of the underlying cause, pleurodesis, or surgical ligation of the thoracic duct. Continuous drainage of chyle into the pleural cavity causes serious nutritional deficiency and respiratory complications. Refractory chylothorax is often difficult to treat, and its diagnosis and management often presents a significant problem to clinicians.A 69‐year‐old woman initially presented to a gastroenterologist with a vague history of abdominal discomfort and constipation. She was found to have an incidental right‐sided pleural effusion on chest radiograph Fig. . ClinicaShe had a normal colonoscopy examination and colonic biopsy. Two separate lymphangiography sessions, one via the right inguinal and the other via the right axillary Fig. , failed Despite total parenteral nutrition (TPN) of medium‐chain triglyceride (MCT) with a low‐fat diet, she required therapeutic thoracentesis of 1.5 L of chyle every two weeks. There was persistent drainage of the chylothorax of up to 300 mL daily for a further four weeks. We then consulted the cardiothoracic team for a possible thoracic duct ligation. A video‐assisted thoracoscopy with methylene blue injection into the right axillary lymph nodes failed to identify the site of leakage.2O with fraction of inspired oxygen (FiO2) of 40%. We confirmed the absence of residual effusion with a bedside thoracic sonography the following day and proceeded with talc slurry pleurodesis using 5 g of talcum followed by a further 48 h of CPAP at the same pressure. We removed the chest drain at day 3, after confirming the resolution of the effusion by a bedside thoracic sonography. She was discharged at day 5. There was no evidence of reaccumulating effusion at week 2, week 6, and three months.We then performed talc pleurodesis. We aspirated the chyle completely through a 32‐F chest drain and initiated her on 12 h of continuous positive airways pressure (CPAP) at 10 cm HChylothorax is an abnormal collection of chyle in the pleural cavity which results from an anatomical disruption of the thoracic duct or one of its main tributaries. The thoracic duct drains the lymphatics from lumbar, lower extremities, intestine, and liver. When the duct is injured, the chyle leaks into the mediastinum and pleural space. The causes of chylothorax can be divided into surgical and medical. The most common one is the medical cause lymphoma. The workout from our patient did not suggest a relapse of lymphoma.The diagnosis of chylothorax is confirmed when the pleural fluid triglyceride concentration is >110 mg/dL with cholesterol level <200 mg/dL or the presence of chylomicron by lipoprotein electrophoresis. Persistent leakage can result in nutritional deterioration and an immune deficiency state. Conservative management includes MCT diet, TPN, and somatostatin analogue octreotide. Both lymphoscintigraphy and lymphangiography are lymphatic imaging studies which can play a therapeutic role [Standard treatment for refractory idiopathic chylothorax has not been established. Pleurodesis is well validated in malignant pleural effusion with reported success rate up to 76% . There i2O and 200 mg of minocycline were shown to be effective in post oesophagectomy chylothorax with drainage ceasing at 30 h post combined treatment [The success of pleurodesis requires re‐expansion of the lung and apposition of the visceral and parietal pleura. CPAP has been shown to increase the intrapleural pressure which promotes drainage and reabsorption of pleural fluid leading to better lung expansion . CPAP ofreatment . We usedA combination of talc pleurodesis and CPAP for adequate lung expansion appears effective, minimally invasive, and well tolerated. Our method may be a treatment option for patients with idiopathic refractory chylothorax not responding to conservative treatment and in whom surgery is not an option due to failure to locate the site of leakage. Further studies are needed to validate the use of CPAP and talc pleurodesis in the management of refractory chylothorax.Appropriate written informed consent was obtained for publication of this case report and accompanying images.


Immune checkpoint inhibitor (ICI)‐related massive hemoptysis with cavitation has rarely been identified. Here, we report a case of advanced lung adenocarcinoma with lethal bleeding after eight cycles of pembrolizumab. A 55‐year‐old male was diagnosed with stage IV non‐small cell lung cancer (NSCLC). Following confirmation of high programmed death‐ligand 1 (PD‐L1) expression of 60% cancer cells, he subsequently received pembrolizumab monotherapy. His symptoms and chest images significantly improved after four cycles of therapy. However, after eight cycles of immunotherapy, he presented with recurrence of bloody sputum and shortness of breath. Pembrolizumab was discontinued and a diagnosis of checkpoint inhibitor‐associated pneumonitis (CIP) was made. When the CIP was absorbed after glucocorticoid therapy, the patient died of sudden massive hemoptysis with cavitation in the lesion.Although checkpoint inhibitor associated pneumonitis was the leading cause of ICI‐related death, clinicians should be alerted to the finding that more attention should be given to hemoptysis attributed to ICI therapy in advanced lung cancer. Though checkpoint inhibitor associated pneumonitis was the leading cause of ICIs‐related death, clinicians should be alerted that hemoptysis attributed to ICIs in advanced lung cancer deserves more concern. Pembrolinzumab is an ICI which is recommended as first‐line treatment for advanced NSCLC showing ≥50% expression of programmed death‐ligand 1 (PD‐L1), or treatment of patients with NSCLC whose tumors express PD‐L1 ≥ 1%A 55‐year‐old male with a 20 pack‐year smoking status was admitted to our hospital with symptoms of dyspnea and bloody sputum in July 2019. He also complained of dysphagia and was unable to lie flat. He was diagnosed with stage IV lung adenocarcinoma Fig  in AprilHemoptysis is one of the most frequent symptoms of lung cancer and its pathophysiology remains unclear. Pulmonary hemorrhage or hemoptysis events related to tumor treatment are often reported in clinical trials of NSCLC as the most common adverse events of antiangiogenic drugs, such as bevacizumab.Pneumonitis is the most common serious AE associated with pembrolizumab in lung cancer trials and the leading AE that causes pembrolizumab discontinuation.et al. in which a lung mass turned into an abscess together with the patient suffering from fever, infection indicated by laboratory findings and effective antibiotic treatment. Reviewing the studies of hemoptysis related to tumor treatment together with the findings in our case, we believe that the severe bleeding event was caused by the giant central location of the tumor that was significantly PD‐L1 positive, shrinkage of the mass and large area necrosis with cavity formation after immunotherapy, as well as possible vascular involvement. However, in our case, vascular involvement was unclear as it was impacted by the lack of enhanced CT as a result of MRI contrast medium allergy in the patient; in addition, patient autopsy was refused. In conclusion, this study alerts clinicians to place more emphasis on hemoptysis which could be associated with the use of ICIs in NSCLC.In the present study, we report a case of lesion shrinkage after four cycles of pembrolizumab monotherapy and recurrent hemoptysis accompanied by CIP after eight cycles. The body temperature of the patient in our study was consistently normal, laboratory findings were not indicative of bacteria or fungal infection, and corticosteroids were not used before the appearance of the low density shadow and subsequent transparent area following ICI therapy. Accordingly, the cavity formation and hemoptysis could not have been associated with corticosteroid therapy, nor were they consistent with aspergillus infection. The case in our study is inconsistent with that reported by ZarogoulidisNo authors report any conflict of interest.


RNA‐based sequencing is considered ideal for detecting pathogenic fusion‐genes compared to DNA‐based assays and provides valuable information about the relative expression of driver genes. However, RNA from formalin‐fixed paraffin‐embedded tissue has issues with both quantity and quality, making RNA‐based sequencing difficult in clinical practice. Analyzing stamp‐derived RNA with next‐generation sequencing (NGS) can address the above‐mentioned obstacles. In this study, we validated the analytical specifications and clinical performance of our custom panel for RNA‐based assays on the Ion Torrent platform.To evaluate our custom RNA lung panel, we first examined the gene sequences of RNA derived from 35 NSCLC tissues with diverse backgrounds by conventional methods and NGS. Next, we moved to the clinical phase, where clinical samples  were used to examine variants. In the clinical phase we conducted an NGS analysis while simultaneously applying conventional approaches to assess the feasibility and validity of the panel in clinical practice.In the prerun phase, all of the variants confirmed with conventional methods were detected by NGS. In the clinical phase, a total of 80 patients were enrolled and 80 tumor specimens were sequenced from February 2018 to December 2018. There were 66 cases in which the RNA concentration was too low to be measured, but sequencing was successful in the vast majority of cases. The concordance between NGS and conventional methods was 95.0%.RNA extraction using stamp specimens and panel sequencing by NGS were considered applicable in clinical settings.Significant findings of the studyNext‐generation sequencing using RNA from stamp specimens was able to detect driver gene changes in non‐small cell lung cancer including fusion genes with the same accuracy as conventional methods.What this study addsUsing RNA from stamp specimens obtained from biopsy increases the number of candidate cases for RNA sequencing in clinical settings. ALK, ROS1 and NTRK) is now recommended as a standard test for the evaluation of advanced NSCLC.EGFR mutations), RNA‐based sequencing provides valuable information concerning the relative expression of driver genes.Various targeted agents have been developed for non‐small cell lung cancer (NSCLC), and fusion gene evaluation  and Sanger sequencing. Furthermore, a highly sensitive PCR method (the cycleave PCR method and the fragment method) was used to verify the results obtained by both conventional PCR and the Ion Torrent platform.This study was approved by the Aichi Cancer Center Hospital's Institutional Review Board. All patients provided their written, informed consent. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.Tumor samples were collected from Aichi Cancer Center Hospital, Japan. The current study consisted of two phases; a prerun phase and a clinical phase. The aim of the first stage (prerun phase) was to evaluate the accuracy of the custom panels. For the validation of the custom RNA lung panel, we examined the gene sequences of RNA derived from 35 NSCLC tissues with diverse backgrounds by conventional methods and NGS. Next, we moved to the next clinical phase, where clinical samples  were used to examine variants. In the clinical phase we conducted an NGS analysis while simultaneously applying conventional approaches to assess the feasibility and validity of the panel in clinical practice. A total of 80 patients were enrolled in the clinical phase of the study from February 2018 to December 2018.RNA used in the prerun phase was derived from various samples, including formalin‐fixed paraffin‐embedded (FFPE) sections and stamp‐slides. Normal tissue was not analyzed. For FFPE sections, tumor sections  were microscopically dissected, deparaffinized with xylene and washed twice in ethanol. After drying at 37°C, the samples were digested with proteinase K at 56°C. A total of three stamp slides were used. RNA extraction was performed applying the RNeasy Mini Kit (Qiagen), as per the manufacturer's instructions. RNA was quantified with the Qubit 2.0 using the RNA HS Assay Kit (Thermo Fisher Scientific). The lowest detectable concentration was 4 ng/μL.In the clinical phase, a total of four stamp‐slides were made using the direct smear method, and one slide was subjected to May‐Giemsa staining to confirm the presence of cancer cells Fig . When apEGFR were amplified by PCR and analyzed. The primers and cycling conditions for PCR amplification were modified from methods published previously.After the synthesis of cDNA, exons 18 to 21 of We used a chimeric DNA‐RNA‐DNA probe labeled with a fluorescent dye and quencher at each end.To detect the deletion in exon 19 of the EGFR gene, a common fragment analysis was used. Sample DNA was amplified with a FAM‐labeled primer set, and PCR products were electrophoresed. PCR amplified the shorter segment of DNA, creating a new peak in an electropherogram when a deletion mutation was present.MET exon 14 skipping. To confirm the presence of a sufficient number of reads in the NGS assay, primers for five housekeeping genes  were included in the primer mix, and the resultant reads were mapped. Even if no fusion genes were found, the assay itself was considered successful when a sufficient number of reads originating from these housekeeping genes was mapped.A custom panel for lung cancer (Ion Ampliseq 84 328 675–1) was designed by a research team at Thermo Fisher Scientific and manufactured as single primer pools by Thermo Fisher Scientific. This panel covers somatic mutation hot spots in five cancer‐associated genes and is capable of detecting gene translocation events . In the clinical phase, when the analysis was successfully performed by the conventional method, we conducted an NGS assay. An Ion Torrent adapter‐ligated library was generated using the custom panel described above. The concentration and size of the library were determined using the Applied Biosystems StepOne Real‐Time PCR system and Ion Library TaqMan Quantitation kit (both from Thermo Fisher Scientific). Sample emulsion PCR, emulsion breaking and enrichment were performed using the Ion Chef system (Thermo Fisher Scientific), according to the manufacturer's instructions. An input concentration of one DNA template copy per ion sphere particle (ISP) was added to the emulsion PCR master mix, and the emulsion was generated using the Ion Chef System (Thermo Fisher Scientific). Template‐positive ISPs were enriched, and sequencing was performed using semiconductor chips on the Ion Torrent S5 (Thermo Fisher Scientific). In this study, we used semiconductor chips of Ion 530 Chip Kit, which was later we changed to the Ion 540 Chip Kit, in order to collect an adequate number of sequenced reads.Data from the S5 runs were initially processed using the Ion Torrent platform‐specific pipeline software program Torrent Suite v4.0 (Thermo Fisher Scientific) to generate sequence reads, trim adapter sequences and filter and remove poor signal‐profile reads. We prepared a reference sequence to match the custom primer and mapped reads to the reference using Torrent Suite. The total number of reads mapped is an indicator of whether or not the NGS run was successful. The total mapped panel reads were counted, and 20 000 reads was considered sufficient for a further analysis; however, with a small sample size, clinical samples sometimes fail to achieve that level. In such cases, we proceeded with the analysis and searched for variants.P < 0.01. The second filter was employed by visually examining mutations using the CLC Genomics Workbench software program version 10.0.0 (Qiagen), as well as by filtering out possible strand‐specific errors.Initial variant calling from the Ion AmpliSeq sequencing data was generated with a plug‐in “variant caller” program. To eliminate erroneous base calling, three filtering steps were used to generate final variant calling. The first filter was set at an average depth of total coverage of >50, each variant coverage of >15 and In the detection of driver gene alteration other than gene rearrangement, DNA‐based NGS was performed only when discrepancies occurred in the results with conventional methods and RNA‐based sequencing. We used AmpliSeq Cancer Hotspot panel to validate the genetic alterations, described previously.ROS1 rearranged, eight ALK rearranged, and four were RET rearranged NSCLC  and immunohistochemistry; 11 cases were The clinical characteristics of the evaluated cases are shown in Table In the prerun phase, the median number of leads was 595 910, ranging from 3183 to 1 710 173. All of the variants confirmed with conventional methods were detected by NGS.Next, we started the clinical phase. From February 2018 to December 2018, a total of 80 patients were enrolled, and 80 tumor specimens were sequenced. The clinical characteristics are shown in Table EGFR). The deletion was not detectable by RNA‐based NGS and was only detected by DNA‐based NGS. Although this case had the EGFR‐activating mutation, the maximum effect of afatinib was stable disease, lasting as short as three months, so some biological mechanisms other than the EGFR pathway may have contributed to the cancer progression. This failure of RNA‐based NGS may be related to the paucity of EGFR mRNA with the activating mutation, considering that the EGFR‐tyrosine kinase inhibitor afatinib showed a minor effect on tumors in the patient. In the second case, although a very weak signal was detected by the cycleave method, the KRAS status was determined to be wild‐type. On NGS (both RNA‐based and DNA‐based), KRAS Gly13Glu was detected. In the third case, KRAS Gly12Cys was also negative by the conventional cycleave method but was detected by NGS (both RNA‐based and DNA‐based). From the stamp‐derived RNA in the fourth case, the CD74‐NRG1 fusion gene was detected only by NGS (RNA‐based) and later was confirmed using cDNA‐based Sanger sequencing.The concordance between NGS and conventional methods was 95.0%. Of the four inconsistencies Table , a reanaALK or ROS1) have proven effective against fusion gene‐derived tumors and been approved. Given their ability to detect structural rearrangements and provide information about the expression, most groups have favored the use of RNA‐based approaches for fusion gene detection. However, one major concern associated with the efficient examination of gene rearrangement is the technical difficulty of obtaining high‐quality RNA.Fusion gene detection is mandatory for the initial evaluation of advanced NSCLC, as various agents that target fusion gene kinase , RNA could be reverse‐transcribed into cDNA, and the resultant cDNA was successfully tested with NGS. Previously, Miki In some cases, DNA‐based sequencing is still required to assess the tumor mutational burden (TMB). The TMB was recently shown to contribute to the immune recognition of cancer and was suggested to be a predictor of the response to cancer immunotherapy.When applying the method described above, it is necessary to carefully examine the handling flow of biomaterial at the facility. For example, a rapid on‐site cytologic examination is a critical step that needs to be performed before NGS. At the NGS stage, library preparation and chip loading are both conducted by the Ion Chef semi‐automatically, which helps smooth the transition to the resultant bioinformatic tasks.ALK, ROS1, RET and NRG1 fusion genes based on the ratio of the 3′ end and 5′ end expression. A disproportionally high expression of the 3′ end of the gene compared to the 5′ end implies an oncogenic fusion event involving the gene.One major limitation associated with our system is that previous knowledge of all possible fusion patterns is necessary in order to prepare an ideal panel for examination. To overcome this limitation, this assay includes a method for detecting ROS1 and RET). NTRK oncogenic fusion is an emerging target for the subset of solid malignancies including NSCLC. Several clinical trials have shown the safety and efficacy of TRK fusion kinase inhibitors, and highly potent pan‐TRK inhibitors have already been approved by regulatory agencies.NTRK fusion gene.Another limitation associated with our study is that we were unable to evaluate all rearrangements using NGS with RNA from stamp cytology because of the rarity of some gene rearrangements  is unknown from the study. Moreover, assuming that a touch imprint sample is used, it is unknown how many slides are best used for RNA extraction. The larger the number, the more RNA can be extracted, but the handling on site becomes complicated.The vast majority of current diagnostic genomic practices are being challenged by massive parallel sequencing technology. Panel‐based RNA assays as a tool for the initial genomic evaluation of known driver gene alteration can deliver useful results that compete with existing traditional methods in both feasibility and accuracy.The authors report no conflict of interest.