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The area under the receiver‐operating characteristic curve (AUC) has become a popular index not only for measuring the overall prediction capacity of a marker but also the strength of the association between continuous and binary variables. In the current considered study, the AUC was used for comparing the association size of four different interventions involving impulsive decision making, studied through an animal model, in which each animal provides several negative (pretreatment) and positive (posttreatment) measures. The problem of the full comparison of the average AUCs arises therefore in a natural way. We construct an analysis of variance (ANOVA) type test for testing the equality of the impact of these treatments measured through the respective AUCs and considering the random‐effect represented by the animal. The use (and development) of a post hoc Tukey's HSD‐type test is also considered. We explore the finite‐sample behaviour of our proposal via Monte Carlo simulations, and analyse the data generated from the original problem. An R package implementing the procedures is provided in the supporting information. [ABSTRACT FROM AUTHOR]

The generalized receiver-operating characteristic, gROC, curve considers the classification ability of diagnostic tests when both larger and lower values of the marker are associated with higher probabilities of being positive. Its empirical estimation implies to select the best classification subsets among those satisfying particular condition. Both strong and weak consistency have already been proved. However, using the same data for both to select the classification subsets and to calculate its gROC curve leads to an over-optimistic estimate of the real performance of the diagnostic criteria on future samples. In this work, the bias of the empirical gROC curve estimator is explored through Monte Carlo simulations. Besides, two cross-validation based algorithms are proposed for reducing the overfitting. The practical application of the proposed algorithms is illustrated through the analysis of a real-world dataset. Simulation results suggest that the empirical gROC curve estimator returns optimistic approximations, especially, in situations in which the diagnostic capacity of the marker is poor and the sample size is small. The new proposed algorithms improve the estimation of the actual diagnostic test accuracy, and get almost unbiased gAUCs in most of the considered scenarios. However, the cross-validation based algorithms reported larger L 1 -errors than the standard empirical estimators, and increment the computational cost of the procedures. As online supplementary material, this manuscript includes an R function which wraps up the implemented routines. [ABSTRACT FROM AUTHOR]

Background: Tumor mutational burden (TMB) has been demonstrated to predict the response to immune checkpoint inhibitors (ICIs) in various cancers. However, the role of TMB in head and neck squamous cell carcinoma (HNSCC) has not yet been specifically addressed. Since HNSCC patients exhibit a rather limited response to ICIs, there is an unmet need to develop predictive biomarkers to improve patient selection criteria and the clinical benefit of ICI treatment. Methods: We conducted a systematic review and meta-analysis according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines. HNSCC cohort studies were selected when TMB prior to ICI treatment was evaluated, TMB cutoff value was available, and the prognostic value of TMB was evaluated by time-to-event survival analysis. A total of 11 out of 1960 articles were analyzed, including 1200 HNSCC patients. Results: The results showed that those patients harboring high TMB exhibited a significantly superior overall response rate (OR = 2.62; 95% CI 1.74–3.94; p < 0.0001) and a survival advantage (HR = 0.53; 95% CI 0.39–0.71; p < 0.0001) after ICI treatment. Conclusion: This is the first meta-analysis to demonstrate a higher response and clinical benefit from ICI therapy in HNSCC patients with high TMB. [ABSTRACT FROM AUTHOR]

The binary classification problem is a hot topic in Statistics. Its close relationship with the diagnosis and the prognosis of diseases makes it crucial in biomedical research. In this context, it is important to identify biomarkers that may help to classify individuals into different classes, for example, diseased vs. not diseased. The Receiver Operating-Characteristic (ROC) curve is a graphical tool commonly used to assess the accuracy of such classification. Given the diverse nature of diagnosis and prognosis problems, the ROC curve estimation has been tackled from separate perspectives in each setting. The Two-stages Mixed-Subjects (sMS) ROC curve estimator fits both scenarios. Besides, it can handle data with missing or incomplete outcome values. This paper introduces the R package sMSROC which implements the sMS ROC estimator, and includes tools that may support researchers in their decision making. Its practical application is illustrated on three real-world datasets. [ABSTRACT FROM AUTHOR]

Modern science is frequently based on the exploitation of large volumes of information storage in datasets and involving complex computational architectures. The statistical analyses of these datasets have to cope with specific challenges and frequently involve making informed but arbitrary decisions. Epidemiological papers have to be concise and focused on the underlying clinical or epidemiological results, not reporting the details behind relevant methodological decisions. In this work, we used an analysis of the cardiovascular-related measures tracked in 4–8-year-old children, using data from the INMA-Asturias cohort for illustrating how the decision-making process was performed and its potential impact on the obtained results. We focused on two particular aspects of the problem: how to deal with missing data and which regression model to use to evaluate tracking when there are no defined thresholds to categorize variables into risk groups. As a spoiler, we analyzed the impact on our results of using multiple imputation and the advantage of using quantile regression models in this context. [ABSTRACT FROM AUTHOR]

The overlap coefficient (OVL) measures the common area between two or more density functions. It has been used for measuring the similarity between distributions in different research fields including astronomy, economy or sociology, among others. Recently, different authors have studied the use of the OVL coefficient in the binary classification problem. They argue that, in particular settings, it could provide better accuracy measure than other stablished indices. We prove here that the OVL coefficient does not provide additional information to the Youden index and that, the potential advantages previously reported are based on the assumption that the classification rules underlying any classification process always assign more probability of being positive to the larger values of the marker. Particularly, we prove that, for a fixed continuous marker, the OVL coefficient is equivalent to the Youden index associated with the optimal classification rules based on this marker. We illustrate the problem studying the capacity of the white blood cells count to identify the type of disease in patients having either acute viral meningitis or acute bacterial meningitis. [ABSTRACT FROM AUTHOR]

The comet assay in Drosophila has been used in the last few years to study DNA damage responses (DDR) in different repair-mutant strains and to compare them to analyze DNA repair. We have used this approach to study interactions between DNA repair pathways in vivo. Additionally, we have implemented an ex vivo comet assay, in which nucleoids from treated and untreated cells were incubated ex vivo with cell-free protein extracts from individuals with distinct repair capacities. Four strains were used: wild-type OregonK (OK), nucleotide excision repair mutant mus201, dmPolQ protein mutant mus308, and the double mutant mus201;mus308. Methyl methanesulfonate (MMS) was used as a genotoxic agent. Both approaches were performed with neuroblasts from third-instar larvae; they detected the effects of the NER and dmPolQ pathways on the DDR to MMS and that they act additively in this response. Additionally, the ex vivo approach quantified that mus201, mus308, and the double mutant mus201;mus308 strains presented, respectively, 21.5%, 52.9%, and 14.8% of OK strain activity over MMS-induced damage. Considering the homology between mammals and Drosophila in repair pathways, the detected additive effect might be extrapolated even to humans, demonstrating that Drosophila might be an excellent model to study interactions between repair pathways. [ABSTRACT FROM AUTHOR]

Background: Data about long-term prognosis after hospitalisation of elderly multimorbid patients remains scarce. Objectives: Evaluate medium and long-term prognosis in hospitalised patients older than 75 years of age with multimorbidity. Explore the impact of gender, age, frailty, physical dependence, and chronic diseases on mortality over a seven-year period. Methods: We included prospectively all patients hospitalised for medical reasons over 75 years of age with two or more chronic illnesses in a specialised ward. Data on chronic diseases were collected using the Charlson comorbidity index and a questionnaire for disorders not included in this index. Demographic characteristics, Clinical Frailty Scale, Barthel index, and complications during hospitalisation were collected. Results: 514 patients (46% males) with a mean age of 85 (± 5) years were included. The median follow-up was 755 days (interquartile range 25–75%: 76–1,342). Mortality ranged from 44% to 68%, 82% and 91% at one, three, five, and seven years. At inclusion, men were slightly younger and with lower levels of physical impairment. Nevertheless, in the multivariate analysis, men had higher mortality (p<0.001; H.R.:1.43; 95% C.I.95%:1.16–1.75). Age, Clinical Frailty Scale, Barthel, and Charlson indexes were significant predictors in the univariate and multivariate analysis (all p<0.001). Dementia and neoplastic diseases were statistically significant in the unadjusted but not the adjusted model. In a cluster analysis, three patterns of patients were identified, with increasing significant mortality differences between them (p<0.001; H.R.:1.67; 95% CI: 1.49–1.88). Conclusions: In our cohort, individual diseases had a limited predictive prognostic capacity, while the combination of chronic illness, frailty, and physical dependence were independent predictors of survival. [ABSTRACT FROM AUTHOR]

We believe that the assumed data-generating mechanism under which the marginal Cox model HT ht obtains is not given as much emphasis as it deserves. The general question of whether a marginal causal model, HT ht , versus a conditional causal model, HT ht , is more justified in a given situation is an interesting topic for future research anddiscussion. [Extracted from the article]

A good diagnostic test should show different behavior on both the positive and the negative populations. However, this is not enough for having a good classification system. The binary classification problem is a complex task, which implies to define decision criteria. The knowledge of the level of dissimilarity between the two involved distributions is not enough. We also have to know how to define those decision criteria. The length of the receiver-operating characteristic curve has been proposed as an index of the optimal discriminatory capacity of a biomarker. It is related not with the actual but with the optimal classification capacity of the considered diagnostic test. One particularity of this index is that its estimation should be based on parametric or smoothed models. We explore here the behavior of a kernel density estimator-based approximation for estimating the length of the receiver-operating characteristic curve. We prove the asymptotic distribution of the resulting statistic, propose a parametric bootstrap algorithm for confidence intervals construction, discuss the role that the bandwidth parameter plays in the quality of the provided estimations and, via Monte Carlo simulations, study its finite-sample behavior considering four different criteria for the bandwidth selection. The practical use of the length of the receiver-operating characteristic curve is illustrated through two real-world examples. [ABSTRACT FROM AUTHOR]

The Cox regression model and its associated hazard ratio (HR) are frequently used for summarizing the effect of treatments on time to event outcomes. However, the HR's interpretation strongly depends on the assumed underlying survival model. The challenge of interpreting the HR has been the focus of a number of recent papers. Several alternative measures have been proposed in order to deal with these concerns. The marginal Cox regression models include an identifiable hazard ratio without individual but populational causal interpretation. In this work, we study the properties of one particular marginal Cox regression model and consider its estimation in the presence of omitted confounder from an instrumental variable-based procedure. We prove the large sample consistency of an estimation score which allows non-binary treatments. Our Monte Carlo simulations suggest that finite sample behavior of the procedure is adequate. The studied estimator is more robust than its competitor (Wang et al.) for weak instruments although it is slightly more biased for large effects of the treatment. The practical use of the presented techniques is illustrated through a real practical example using data from the vascular quality initiative registry. The used R code is provided as Supplementary material. [ABSTRACT FROM AUTHOR]

Hazard ratios (HR) associated with the well-known proportional hazard Cox regression models are routinely used for measuring the impact of one factor of interest on a time-to-event outcome. However, if the underlying real model does not fit with the theoretical requirements, the interpretation of those HRs is not clear. We propose a new index, gHR, which generalizes the HR beyond the underlying survival model. We consider the case in which the study factor is a binary variable and we are interested in both the unadjusted and adjusted effect of this factor on a time-to-event variable, potentially, observed in a right-censored scenario. We propose non-parametric estimations for unadjusted gHR and semi-parametric regression-induced techniques for the adjusted case. The behavior of those estimators is studied in both large and finite sample situations. Monte Carlo simulations reveal that both estimators provide good approximations of their respective inferential targets. Data from the Health and Lifestyle Study are used for studying the relationship of the tobacco use and the age of death and illustrate the practical application of the proposed technique. gHR is a promising index which can help facilitate better understanding of the association of one study factor on a time-dependent outcome. [ABSTRACT FROM AUTHOR]

Aims: To determine the spectral dynamics of early spontaneous polymorphic ventricular tachycardia and ventricular fibrillation (PVT/VF) in humans.Methods and Results: Fifty-eight self-terminated and 173 shock-terminated episodes of spontaneously initiated PVT/VF recorded by Medtronic implanted cardiac defibrillators (ICDs) in 87 patients with various cardiac pathologies were analyzed by short fast Fourier transform of shifting segments to determine the dynamics of dominant frequency (DF) and regularity index (RI). The progression in the intensity of DF and RI accumulations further quantified the time course of spectral characteristics of the episodes. Episodes of self-terminated PVT/VF lasted 8.6 s [95% confidence interval (CI): 8.1-9.1] and shock-terminated lasted 13.9 s (13.6-14.3) (P < 0.001). Recordings from patients with primarily electrical pathologies displayed higher DF and RI values than those from patients with primarily structural pathologies (P < 0.05) independently of ventricular function or antiarrhythmic drug therapy. Regardless of the underlying pathology, the average DF and RI intensities were lower in self-terminated than shock-terminated episodes [DF: 3.67 (4.04-4.58) vs. 4.32 (3.46-3.93) Hz, P < 0.001; RI: 0.53 (0.48-0.56) vs. 0.63 (0.60-0.65), P < 0.001]. In a multivariate analysis controlled by the type of pathology and clinical variables, regularity remained an independent predictor of self-termination [hazard ratio: 0.954 (0.928-0.980)]. Receiver operating characteristic (ROC) curve analysis of DF and RI intensities demonstrated increased predictability for self-termination in time with 95% CI above the 0.5 cut-off limit at about t = 8.6 s and t = 6.95 s, respectively.Conclusion: Consistent with the notion that fast organized sources maintain PVT/VF in humans, reduction of frequency and regularity correlates with early self-termination. Our findings might help generate ICD methods aiming to reduce inappropriate shock deliveries. [ABSTRACT FROM AUTHOR]

In biomedicine, binary classification problems are involved in diagnostic but also, for instance, in personalized medicine. The objective is to use information for correctly allocating subjects in groups. Frequently, this information implies high-dimensional data. An adequate classification rule is a trade-off between the sensitivity and the specificity. The ROC curve helps to understand, evaluate and compare the accuracy of classification processes. We propose a procedure for estimating the optimal classification rules based on a penalized estimator of the underlying probability distribution functions. We study its asymptotic properties. Through Monte Carlo simulations, we compare our proposal with a support vector machine-based ROC curve. We illustrate its practical use in a real-world problem. Results suggest that, despite some techniques promise to improve the results provided by traditional methods, in the binary classification problem, the limit is the actual relationship among the density functions. [ABSTRACT FROM AUTHOR]

The receiver operating-characteristic (ROC) curve is a well-known graphical tool routinely used for evaluating the discriminatory ability of continuous markers, referring to a binary characteristic. The area under the curve (AUC) has been proposed as a summarized accuracy index. Higher values of the marker are usually associated with higher probabilities of having the characteristic under study. However, there are other situations where both, higher and lower marker scores, are associated with a positive result. The generalized ROC (gROC) curve has been proposed as a proper extension of the ROC curve to fit these situations. Of course, the corresponding area under the gROC curve, gAUC, has also been introduced as a global measure of the classification capacity. In this paper, we study in deep the gAUC properties. The weak convergence of its empirical estimator is provided while deriving an explicit and useful expression for the asymptotic variance. We also obtain the expression for the asymptotic covariance of related gAUCs and propose a non-parametric procedure to compare them. The finite-samples behavior is studied through Monte Carlo simulations under different scenarios, presenting a real-world problem in order to illustrate its practical application. The R code functions implementing the procedures are provided as Supplementary Material. [ABSTRACT FROM AUTHOR]

Background: The effect of resistance training on gut microbiota composition has not been explored, despite the evidence about endurance exercise. The aim of this study was to compare the effect of resistance and endurance training on gut microbiota composition in mice. Methods: Cecal samples were collected from 26 C57BL/6N mice, divided into three groups: sedentary (CTL), endurance training on a treadmill (END), and resistance training on a vertical ladder (RES). After 2 weeks of adaption, mice were trained for 4 weeks, 5 days/week. Maximal endurance and resistance capacity test were performed before and after training. Genomic DNA was extracted and 16S Ribosomal RNA sequenced for metagenomics analysis. The percentages for each phylum, class, order, family, or genus/species were obtained using an open-source bioinformatics pipeline. Results: END showed higher diversity and evenness. Significant differences among groups in microbiota composition were only observed at genera and species level. END showed a significantly higher relative abundance of Desulfovibrio and Desulfovibrio sp., while Clostridium and C. cocleatum where higher for RES. Trained mice showed significantly lower relative abundance of Ruminococcus gnavus and higher of the genus Parabacteroides compared to CTL. We explored the relationship between relative taxa abundance and maximal endurance and resistance capacities after the training period. Lachnospiraceae and Lactobacillaceae families were negatively associated with endurance performance, while several taxa, including Prevotellaceae family, Prevotella genus, and Akkermansia muciniphila , were positively correlated. About resistance performance, Desulfovibrio sp. was negatively correlated, while Alistipes showed a positive correlation. Conclusion: Resistance and endurance training differentially modify gut microbiota composition in mice, under a high-controlled environment. Interestingly, taxa associated with anti- and proinflammatory responses presented the same pattern after both models of exercise. Furthermore, the abundance of several taxa was differently related to maximal endurance or resistance performance, most of them did not respond to training. [ABSTRACT FROM AUTHOR]

Modern science frequently involves the study of complex relationships among effects and factors. Flexible statistical tools are commonly used to visualize nonlinear associations. When our interest is to study the discrimination capacity of a multivariate marker on a binary outcome, the theoretical transformation leading to the optimal results in terms of sensitivity and specificity has already been settled. It is particularly useful to know this function, not only to allocate items to groups, but also to understand the relationship between the multivariate marker and the outcome. In this paper, we explore the use of the multivariate kernel density estimator in order to approximate such transformation. Large sample properties of the finally derived estimator are outlined, while its finite sample behavior is studied via Monte Carlo simulations. We consider six different bivariate and three additional higher-dimensional scenarios. The performance of the estimator is studied by using four different tuning parameters computed automatically. Besides a cross-validation algorithm is incorporated with the aim of reducing the potential overfitting. The proposed methodology is applied in order to study the capacity of two molecular characteristics to predict the toxicity of some chemical products. Results suggest that smoothing techniques are promising classical and simple statistical tools which can be used for a better understanding of some current scientific problems. However, the incorporation of additional machine learning techniques such as cross-validation is advisable in order to control the frequently over optimistic results, specially in those cases with small sample size. The function implementing the proposed methodology is provided as supplementary material. [ABSTRACT FROM AUTHOR]

Proportional hazard Cox regression models are frequently used to analyze the impact of different factors on time-to-event outcomes. Most practitioners are familiar with and interpret research results in terms of hazard ratios. Direct differences in survival curves are, however, easier to understand for the general population of users and to visualize graphically. Analyzing the difference among the survival curves for the population at risk allows easy interpretation of the impact of a therapy over the follow-up. When the available information is obtained from observational studies, the observed results are potentially subject to a plethora of measured and unmeasured confounders. Although there are procedures to adjust survival curves for measured covariates, the case of unmeasured confounders has not yet been considered in the literature. In this article we provide a semi-parametric procedure for adjusting survival curves for measured and unmeasured confounders. The method augments our novel instrumental variable estimation method for survival time data in the presence of unmeasured confounding with a procedure for mapping estimates onto the survival probability and the expected survival time scales. [ABSTRACT FROM AUTHOR]

We introduce a protocol for measuring the construct validity of competing willingness-to-pay (WTP) distributions derived from mixed logit models. The protocol is based on a two-round survey. Round 1 consists in a standard discrete choice experiment (DCE) sample survey, at the end of which competing models are estimated. Round 2 introduces additional respondents who, in addition to taking part in the DCE, are asked to identify the money value interval that better reflects their preferences using a set of values drawn from the estimated range of WTPs obtained from competing models estimated on the previous sample. (JEL Q51) [ABSTRACT FROM AUTHOR]

The receiver operating-characteristic (ROC) curve is a graphical statistical tool routinely used for studying the classification accuracy in both, diagnostic and prognosis problems. Given the different nature of these situations, ROC curve estimation has been separately considered for binary (diagnostic) and time-to-event (prognosis) outcomes, even for data coming from the same study design. In this work, the authors propose a two-stage ROC curve estimator which allows to link both contexts through a general prediction model (first-stage) and the empirical cumulative estimator of the distribution function (second-stage) of the considered test (marker) on the total population. The so-called two-stage Mixed-Subject (sMS) approach proves its behavior on both, large-samples (theoretically) and finite-samples (via Monte Carlo simulations). Besides, a useful asymptotic distribution for the concomitant area under the curve is also computed. Results show the ability of the proposed estimator to fit non-standard situations by considering flexible predictive models. Two real-world examples, one with binary and one with time-dependent outcomes, help us to a better understanding of the proposed methodology on usual practical circumstances. The R code used for the practical implementation of the proposed methodology and its documentation is provided as supplementary material. [ABSTRACT FROM AUTHOR]

Objectives: To explore the diagnostic performance of circulating microRNAs (miRNAs) as biomarkers in patients with suspected stable coronary artery disease (CAD).Methods: Plasma samples were collected from 237 consecutive patients referred for coronary computed tomography angiography (CCTA). Presence, extension and severity of coronary stenosis were evaluated using the indexes: presence of diameter stenosis ≥ 50%, segment involvement score (SIS), segment stenosis score (SSS) and 3-vessel plaque score. A panel of 10 miRNAs previously associated with CAD was analysed using RT-qPCR. Multivariate analyses were used to analyse the associations between biomarkers and indexes. Discrimination was evaluated using the area under the ROC curve (AUC). Decision trees were generated using chi-squared Automatic Interaction Detector (CHAID) prediction models.Results: After comprehensive adjustment including cardiovascular risk factors, medication use, confounding factors and protein-based biomarkers (hs-TnT and hs-CRP), several circulating miRNAs were inversely associated with coronary atherosclerosis extension (SIS and 3-vessel plaque score) and severity (SSS). In the whole population, circulating miRNAs showed a poor discrimination value for all indexes (AUC = 0.539-0.644) and did not increase the discrimination capacity of a clinical model of coronary stenosis presence, extension and severity based on conventional cardiovascular risk factors. Conversely, the inclusion of circulating miRNAs in decision trees produces models that improve the classification of cases and controls in specific patient subgroups.Conclusions: This study identifies a group of circulating miRNAs that failed to improve the discrimination capacity of cardiovascular risk factors but that has the potential to define specific subpopulations of patients with suspected stable CAD. [ABSTRACT FROM AUTHOR]

The receiver operating characteristic (ROC) curve is a graphical method commonly used to study the capacity of continuous variables (markers) to properly classify subjects into one of two groups. The decision made is ultimately endorsed by a classification subset on the space where the marker is defined. In this paper, we study graphical representations and propose visual forms to reflect those classification rules giving rise to the construction of the ROC curve. On the one hand, we use static pictures for displaying the classification regions for univariate markers, which are specially convenient when there is not a monotone relationship between the marker and the likelihood of belonging to one group. In those cases, there are two options to improve the classification accuracy: to allow for more flexibility in the classification rules (for example considering two cutoff points instead of one) or to transform the marker by using a function whose resulting ROC curve is optimal. On the other hand, we propose to build videos for visualizing the collection of subsets when several markers are considered simultaneously. A compilation of techniques for finding a rule that maximizes the area under the ROC curve is included, with a focus on linear combinations. We present a tool for the R software which generates those graphics, and we apply it to one real dataset. The R code is provided as Supplementary Material. [ABSTRACT FROM AUTHOR]

The systemic response to exercise is dose‐dependent and involves a complex gene expression regulation and cross‐talk between tissues. This context ARISES the need for analyzing the influence of exercise dose on the profile of circulating microRNAs (c‐miRNAs), as emerging posttranscriptional regulators and intercellular communicators. Thus, we hypothesized that different exercise doses will determine specific c‐miRNA signatures that will highlight its potential as exercise dose biomarker. Nine active middle‐aged males completed a 10‐km race (10K), a half‐marathon (HM), and a marathon (M). Blood samples were collected immediately before and after races. Plasma RNA was extracted, and a global screening of 752 microRNAs was analyzed using RT‐qPCR. Three different c‐miRNA profiles were defined according to the three doses. In 10K, 14 c‐miRNAs were found to be differentially expressed between pre‐ and post‐exercise, 13 upregulated and 1 downregulated. Regarding HM, 13 c‐miRNAs were found to be differentially modulated, in all the cases upregulated. A total of 28 c‐miRNAs were found to be differentially expressed in M, 21 overexpressed and 7 repressed after this race. We had also found 3 common c‐miRNAs between 10K and M and 2 common c‐miRNAs between 10K and HM. In silico analysis supported a close association between exercise dose c‐miRNA profiles and cellular pathways linked to energy metabolism and cell cycle. In conclusion, we have observed that different exercise doses induced specific c‐miRNA profiles. So, our results point to c‐miRNAs as emerging exercise dose biomarkers and as one of regulatory mechanisms modulating the response to endurance exercise. [ABSTRACT FROM AUTHOR]

The receiver operating characteristic (ROC) curve has become one of the most used tools for analyzing the diagnostic capacity of continuous biomarkers. When the studied outcome is a time-dependent variable two main generalizations have been proposed, based on properly extensions of the sensitivity and the specificity. Different procedures have been suggested for their estimation mainly under the presence of right censorship. Most of them have been implemented, as well, in diverse types of software, including R packages. This work focuses on the R implementation for the smooth estimation of time-dependent ROC curves. The theoretical connection between them through the joint distribution function of the biomarker and time-to-event variables prompts an approximation method: considered estimators are based on the bivariate kernel density estimator for the joint density function of the bidimensional variable (Marker, Time-to-event). The use of the package is illustrated with two real-world examples. [ABSTRACT FROM AUTHOR]

Background: The trigemino‐vascular system (TVS) plays a key role in migraine pathophysiology. Glial cells are abundant in the TVS system and mainly in the trigeminal ganglion. S100B protein is a calcium‐binding protein, found in the cytoplasm of glial cells in the central nervous system, which is released in response to inflammatory stimuli. Previous works analyzing S100B in migraineurs have offered contradictory results. Objective: In this case‐control study, we analyzed serum levels of S100B as a possible biomarker of the glial TVS activation in chronic migraine (CM). Patients and Methods: The study group consisted of patients attending our clinic with CM and, as control groups, patients with episodic migraine (EM), cluster headache (CH) outside of a bout and healthy volunteers (HV) with no headache history. S100B levels were determined interictally in peripheral blood samples by ELISA. Results: We assessed serum samples from 43 patients with CM, 19 with EM, 29 HV (mostly women), and 22 with (CH). S100B levels in CM (mean 22.9 ± 9.8 pg/mL) were not different (P =.727) when compared to EM patients (21.2 ± 9.3 pg/mL), difference of 1.7 (95% CI −5.7 to 8.9), CH patients (22.4 ± 7.8 pg/mL), difference of 0.5 (−5.7 to 6.7), and HV (20.6 ± 8.3 pg/mL), difference of 2.3 (−3.7 to 8.3). Conclusion: In contrast to other neuropeptides such as calcitonin gene‐related‐peptide and vasoactive intestinal peptide, which are increased in CM, interictal serum S100B levels are not elevated in these patients. According to our results, S100B levels do not seem to be a useful peripheral biomarker of the glial TVS activation in CM. [ABSTRACT FROM AUTHOR]

The receiver operating characteristic (ROC) curve is a graphical tool commonly used to assess the discriminatory ability of continuous markers in binary classification problems. Different extensions of the ROC curve have been proposed in the prognosis context, where the characteristics in the study are time-dependent events. Perhaps the most direct generalization is the so-called cumulative/dynamic (C/D) ROC curve. The main particularity when dealing with the C/D ROC curve estimation is the presence of incomplete information. Several approximation methods addressing this censoring problem have been suggested in the statistical literature, most of them focused on the right-censored case. Interval censorship arises naturally from those studies where subjects undergo periodical follow-ups. They may miss a scheduled appointment and the exact event times are only known to fall in a certain range. A new approach for estimating the C/D ROC curve under the particular scheme of interval censorship is presented in this work. Its finite-sample behaviour is studied via Monte Carlo simulations on two different scenarios. Results suggest that the proposed approximation is better than the existing one in terms of absolute error. Its direct application is illustrated in the real-world data set which motivated this research. The uniform strong consistency and a suitable R function for its practical implementation are provided as Appendices. [ABSTRACT FROM AUTHOR]

In applications, it is common to classify life distributions according to different ageing properties based on different reliability measures. Those based on the failure rate function or the mean residual life are very popular in the literature. However, the increasing or decreasing failure rate ageing notion is very restrictive and, on the other hand, the mean residual life function has some drawbacks which make convenient to prevent its use: it may not exist and, even when it does, sometimes the empirical mean residual life function cannot be calculated or a single long-term survivor can have a marked effect upon it which will tend to be unstable due to its strong dependence on very long durations. The median, or other percentiles, of the residual life of a random variable, are, in general, useful alternatives to the mean residual life. In this paper, we propose a test statistic to check the ageing notion decreasing percentile residual life. This estimator is based on the difference of the empirical percentile residual life function and the monotone estimator. We study the asymptotic and the bootstrap distributions of the proposed statistic. A Monte Carlo simulation study has been carried out to see the performance of the statistic in finite sample situations. Finally, we apply our procedure to a real data set. [ABSTRACT FROM AUTHOR]

Summary: Two‐stage instrumental variable methods are commonly used for estimating average causal effects in the presence of an unmeasured confounder. In the context of the proportional hazard Cox regression models, this problem has recently received attention with several methods being proposed. Previously, we developed an improved estimator under the incumbent two‐stage residual inclusion procedure called '2SRI' by adding a Gaussian frailty in the second stage. We now consider the more complex situation in which the treatment and the unmeasured confounders can have time varying effects, illustrating the method with the case of a step function with one prespecified change point. We prove that, in situations where the effects of the unmeasured confounder or the treatment change during the follow‐up, the first stage of the 2SRI algorithm induces a frailty with time varying coefficients in the second stage, which enables incumbent methods and our previously developed procedure to be improved on. A Monte Carlo simulation study demonstrates the superior performance of the proposed extension of 2SRI that we develop. We apply the new procedure to estimate the effect of endarterectomy versus carotid artery stenting on the time to death of patients suffering from carotid artery disease by using linked vascular quality initiative registry–Medicare data. [ABSTRACT FROM AUTHOR]

Diagnostic procedures are based on establishing certain conditions and then checking if those conditions are satisfied by a given individual. When the diagnostic procedure is based on a continuous marker, this is equivalent to fix a region or classification subset and then check if the observed value of the marker belongs to that region. Receiver operating characteristic curve is a valuable and popular tool to study and compare the diagnostic ability of a given marker. Besides, the area under the receiver operating characteristic curve is frequently used as an index of the global discrimination ability. This paper revises and widens the scope of the receiver operating characteristic curve definition by setting the classification subsets in which the final decision is based in the spotlight of the analysis. We revise the definition of the receiver operating characteristic curve in terms of particular classes of classification subsets and then focus on a receiver operating characteristic curve generalization for situations in which both low and high values of the marker are associated with more probability of having the studied characteristic. Parametric and non-parametric estimators of the receiver operating characteristic curve generalization are investigated. Monte Carlo studies and real data examples illustrate their practical performance. [ABSTRACT FROM AUTHOR]

Background: Focal adhesion kinase (FAK) and cortactin overexpression is frequently detected in a variety of cancers, and has been associated with poor clinical outcome. However, there are no data in cutaneous squamous cell carcinoma (cSCC). Objective: To investigate the relationship of FAK and cortactin expression with the clinicopathologic features and the impact on the prognosis of cSCC patients. Methods: FAK and cortactin expression was analyzed by immunohistochemistry on paraffin‐embedded tissue samples from 100 patients with cSCC, and correlated with the clinical data. Results: FAK overexpression was a significant risk factor for nodal metastasis with crude and adjusted ratios (HRs) of 2.04, (95% CI [1.08‐3.86], [P = 0.029]) and 2.23 (95% CI [1.01‐4.91], [P = 0.047]), respectively. Cortactin expression was not a significant risk factor for nodal metastasis. Conclusion: These findings demonstrate that FAK overexpression is an independent predictor of nodal metastasis that might be helpful for risk stratification and management of patients with cSCC. [ABSTRACT FROM AUTHOR]

The receiver operating characteristic (ROC) curve and their associated summary indices, such as the Youden index, are statistical tools commonly used to analyze the discrimination ability of a (bio)marker to distinguish between two populations. This paper presents the concept of Youden index in the context of the generalized ROC (gROC) curve for non-monotone relationships. The interval estimation of the Youden index and the associated cutoff points in a parametric (binormal) and a non-parametric setting is considered. Monte Carlo simulations and a real-world application illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

Background. Serum phosphate is a key parameter in the management of chronic kidney disease-mineral and bone disorder (CKD-MBD). The timing of phosphate measurement is not standardized in the current guidelines. Since the optimal range of these biomarkers may vary depending on the duration of the interdialytic interval, in this analysis of the Current management of secondary hyperparathyroidism: a multicentre observational study (COSMOS), we assessed the influence of a 2- (midweek) or 3-day (post-weekend) dialysis interval for blood withdrawal on serum levels of CKD-MBD biomarkers and their association with mortality risk. Methods. The COSMOS cohort (6797 patients, CKD Stage 5D) was divided into two groups depending upon midweek or post-weekend blood collection. Univariate and multivariate Cox's models adjusted hazard ratios (HRs) by demographics and comorbidities, treatments and biochemical parameters from a patient/centre database collected at baseline and every 6 months for 3 years. Results. There were no differences in serum calcium or parathyroid hormone levels between midweek and post-weekend patients. However, in post-weekend patients, the mean serum phosphate levels were higher compared with midweek patients (5.5 ± 1.4 versus 5.2 ± 1.4 mg/dL, P < 0.001). Also, the range of serum phosphate with the lowest mortality risk [HR ≤ 1.1; midweek: 3.5–4.9 mg/dL (95% confidence interval, CI: 2.9–5.2 mg/dL); post-weekend: 3.8–5.7 mg/dL (95% CI: 3.0–6.4 mg/dL)] showed significant differences in the upper limit (P = 0.021). Conclusion. Midweek and post-weekend serum phosphate levels and their target ranges associated with the lowest mortality risk differ. Thus, clinical guidelines should consider the timing of blood withdrawal when recommending optimal target ranges for serum phosphate and therapeutic strategies for phosphate control. [ABSTRACT FROM AUTHOR]

The CODEX index was developed and validated in patients hospitalized for COPD exacerbation to predict the risk of death and readmission within one year after discharge. Our study aimed to validate the CODEX index in a large external population of COPD patients with variable durations of follow-up. Additionally, we aimed to recalculate the thresholds of the CODEX index using the cutoffs of variables previously suggested in the 3CIA study (mCODEX). Individual data on 2,755 patients included in the COPD Cohorts Collaborative International Assessment Plus (3CIA+) were explored. A further two cohorts (ESMI AND EGARPOC-2) were added. To validate the CODEX index, the relationship between mortality and the CODEX index was assessed using cumulative/dynamic ROC curves at different follow-up periods, ranging from 3 months up to 10 years. Calibration was performed using univariate and multivariate Cox proportional hazard models and Hosmer-Lemeshow test. A total of 3,321 (87.8% males) patients were included with a mean ± SD age of 66.9 ± 10.5 years, and a median follow-up of 1,064 days (IQR 25–75% 426–1643), totaling 11,190 person-years. The CODEX index was statistically associated with mortality in the short- (≤3 months), medium- (≤1 year) and long-term (10 years), with an area under the curve of 0.72, 0.70 and 0.76, respectively. The mCODEX index performed better in the medium-term (<1 year) than the original CODEX, and similarly in the long-term. In conclusion, CODEX and mCODEX index are good predictors of mortality in patients with COPD, regardless of disease severity or duration of follow-up. [ABSTRACT FROM AUTHOR]

Instrumental variable (IV) methods are widely used for estimating average treatment effects in the presence of unmeasured confounders. However, the capability of existing IV procedures, and most notably the two-stage residual inclusion (2SRI) algorithm recommended for use in non-linear contexts, to account for unmeasured confounders in the Cox proportional hazard model is unclear. We show that instrumenting an endogenous treatment induces an unmeasured covariate, referred to as an individual frailty in survival analysis parlance, which if not accounted for leads to bias. We propose a new procedure that augments 2SRI with an individual frailty and prove that it is consistent under certain conditions. The finite sample-size behavior is studied across a broad set of conditions via Monte Carlo simulations. Finally, the proposed methodology is used to estimate the average effect of carotid endarterectomy versus carotid stenting on the mortality of patients suffering from carotid artery disease. Results suggest that the 2SRI-frailty estimator generally reduces the bias of both point and interval estimators compared to traditional 2SRI. [ABSTRACT FROM AUTHOR]

The receiver operating characteristic (ROC) curve is a graphical method which has become standard in the analysis of diagnostic markers, that is, in the study of the classification ability of a numerical variable. Most of the commercial statistical software provide routines for the standard ROC curve analysis. Of course, there are also many R packages dealing with the ROC estimation as well as other related problems. In this work we introduce the nsROC package which incorporates some new ROC curve procedures. Particularly: ROC curve comparison based on general distances among functions for both paired and unpaired designs; efficient confidence bands construction; a generalization of the curve considering different classification subsets than the one involved in the classical definition of the ROC curve; a procedure to deal with censored data in cumulative-dynamic ROC curve estimation for time-to-event outcomes; and a non-parametric ROC curve method for meta-analysis. This is the only R package which implements these particular procedures. [ABSTRACT FROM AUTHOR]

Abstract: Lamin A/C gene (LMNA)-related familial dilated cardiomyopathy (fDCM) is an aggressive heart disease that often leads to transplantation and sudden death. The aim of our study was to evaluate the circulating microRNA (miRNA) profiles of patients with LMNA pathogenic mutations. The study population (N = 75) included (i) patients with pathogenic LMNA mutations responsible for fDCM (LMNAMUT), (ii) age- and sex-matched LMNA wild-type controls (LMNAWT control), and (iii) LMNA wild-type idiopathic DCM (iDCM) patients (LMNAWT iDCM). Detailed clinical information was obtained from each participant. A panel of 179 plasma miRNAs was evaluated using RT-qPCR. An initial screening study was performed in LMNAMUT carriers and age-matched LMNAWT controls (N = 16). Forty-four miRNAs were specifically deregulated in LMNAMUT carriers. Ten miRNA candidates were selected for subsequent validation after coexpression analyses and filtered for expression levels and statistical significance. Among the candidates, let-7a-5p, miR-142-3p, miR-145-5p and miR-454-3p levels were significantly increased in LMNAMUT carriers compared to LMNAWT controls and iDCM patients (P < 0.050). These circulating miRNAs, and their combination, were also associated with the presence of pathogenic mutations in regression and ROC analyses. This signature also discriminates between LMNAWT healthy subjects and LMNAMUT carriers who are phenotypically negative for DCM and between LMNAWT iDCM and LMNA-related DCM patients. Correlation and functional enrichment analyses supported their association with the pathophysiology of the disease. We demonstrated for the first time that a specific miRNA signature could serve as a novel non-invasive tool to assist in the diagnosis of patients with fDCM caused by LMNA pathogenic mutations.Key messages: Let-7a-5p, miR-142-3p, miR-145-5p and miR-454-3p are differentially expressed in LMNAMUT carriers.A composite score based on these miRNAs is a biomarker of mutations in the LMNA gene.This miRNA signature can be associated with the pathophysiology of familial DCM.The circulating miRNA profile can assist in the diagnosis of familial DCM. [ABSTRACT FROM AUTHOR]

Receiver operating-characteristic curve is a popular graphical method frequently used in order to study the diagnostic capacity of continuous (bio)markers. In spite of the existence of a huge number of papers devoted to both theoretical and practical aspects of this topic, the construction of confidence bands has had little impact in the specialized literature. As far as the authors know, in the CRAN there are only three R packages providing receiver operating-characteristic curve confidence regions: plotROC, pROC and fbroc. This work tries to fill this gap studying and proposing a new nonparametric method to build confidence bands for both the standard receiver operating-characteristic curve and its generalization for nonmonotone relationships. The behavior of the proposed procedure is studied via Monte Carlo simulations and the methodology is applied on two real-world biomedical problems. In addition, an R function to compute the proposed and some of the previously existing methodologies is provided as online supplementary material. [ABSTRACT FROM AUTHOR]

Anti-CMV (cytomegalovirus) antibody titers are related to immune alterations and increased risk of mortality. To test whether they represent a marker of infection history, we analyzed the effect of viral reactivations on the production of specific antibodies in kidney transplant patients. We quantified CMV-DNAemia and antibody titers in 58 kidney transplant patients before transplantation and during a follow-up of 315 days (standard deviation, SD: 134.5 days). In order to calculate the intensity of the infection, we plotted the follow-up time of the infection on the x-axis and the number of DNA-CMV copies on the y-axis and calculated the area under the curve (CMV-AUC). The degree of T-lymphocyte differentiation was analyzed with flow cytometry, the cells were labelled with different monoclonal antibodies in order to distinguish their differentiation state, from naive T-cells to senescent T-cells. Peak viremia was significantly higher in patients experiencing a primary infection (VI) compared to patients experiencing viral reactivation (VR). Our data indicate that the overall CMV viral load over the course of a primary infection is significantly higher than in a reactivation of a previously established infection. Whereas patients who experienced an episode of CMV reactivation during the course of our observation showed increased levels of CMV-specific antibodies, patients who did not experience CMV reactivation (WVR) showed a drop in CMV antibody levels that corresponds to an overall drop in antibody levels, probably due to the continuing immunosuppression after the renal transplant. We found a positive correlation between the CMV viremia over the course of the infection or reactivation and the CMV-specific antibody titers in the examined patients. We also observed a positive correlation between anti-CMV titers and T-cell differentiation. In conclusion, our data show that anti-CMV antibody titers are related to the course of CMV infection in kidney transplant patients. [ABSTRACT FROM AUTHOR]

The receiver operating characteristic curve is a popular graphical method often used to study the diagnostic capacity of continuous (bio)markers. When the considered outcome is a time-dependent variable, two main extensions have been proposed: the cumulative/dynamic receiver operating characteristic curve and the incident/dynamic receiver operating characteristic curve. In both cases, the main problem for developing appropriate estimators is the estimation of the joint distribution of the variables time-to-event and marker. As usual, different approximations lead to different estimators. In this article, the authors explore the use of a bivariate kernel density estimator which accounts for censored observations in the sample and produces smooth estimators of the time-dependent receiver operating characteristic curves. The performance of the resulting cumulative/dynamic and incident/dynamic receiver operating characteristic curves is studied by means of Monte Carlo simulations. Additionally, the influence of the choice of the required smoothing parameters is explored. Finally, two real-applications are considered. An R package is also provided as a complement to this article. [ABSTRACT FROM AUTHOR]

The receiver operating characteristic curve is a popular graphical method frequently used in order to study the diagnostic capacity of continuous markers. It represents in a plot true-positive rates against the false-positive ones. Both the practical and theoretical aspects of the receiver operating characteristic curve have been extensively studied. Conventionally, it is assumed that the considered marker has a monotone relationship with the studied characteristic; i.e., the upper (lower) values of the (bio)marker are associated with a higher probability of a positive result. However, there exist real situations where both the lower and the upper values of the marker are associated with higher probability of a positive result. We propose a receiver operating characteristic curve generalization, [Formula: see text], useful in this context. All pairs of possible cut-off points, one for the lower and another one for the upper marker values, are taken into account and the best of them are selected. The natural empirical estimator for the [Formula: see text] curve is considered and its uniform consistency and asymptotic distribution are derived. Finally, two real-world applications are studied. [ABSTRACT FROM AUTHOR]

Meta-analyses, broadly defined as the quantitative review and synthesis of the results of related but independent comparable studies, allow to know the state of the art of one considered topic. Since the amount of available bibliography has enhanced in almost all fields and, specifically, in biomedical research, its popularity has drastically increased during the last decades. In particular, different methodologies have been developed in order to perform meta-analytic studies of diagnostic tests for both fixed- and random-effects models. From a parametric point of view, these techniques often compute a bivariate estimation for the sensitivity and the specificity by using only one threshold per included study. Frequently, an overall receiver operating characteristic curve based on a bivariate normal distribution is also provided. In this work, the author deals with the problem of estimating an overall receiver operating characteristic curve from a fully non-parametric approach when the data come from a meta-analysis study i.e. only certain information about the diagnostic capacity is available. Both fixed- and random-effects models are considered. In addition, the proposed methodology lets to use the information of all cut-off points available (not only one of them) in the selected original studies. The performance of the method is explored through Monte Carlo simulations. The observed results suggest that the proposed estimator is better than the reference one when the reported information is related to a threshold based on the Youden index and when information for two or more points are provided. Real data illustrations are included. [ABSTRACT FROM AUTHOR]

Receiver operating-characteristic (ROC) curve is a popular graphical method frequently used in order to study the diagnostic capacity of continuous (bio)markers. When the considered outcome is a time-dependent variable, the direct generalization is known ascumulative/dynamicROC curve. For a fixed point of time,t, one subject is allocated into thepositivegroup if the event happens beforetand into thenegativegroup if the event is not happened att. The presence ofcensoredsubject, which can not be directly assigned into a group, is the main handicap of this approach. The proposed cumulative/dynamic ROC curve estimator assigns a probability to belong to the negative (positive) group to the subjects censored previously tot. The performance of the resulting estimator is studied from Monte Carlo simulations. Some real-world applications are reported. Results suggest that the new estimators provide a good approximation to the real cumulative/dynamic ROC curve. [ABSTRACT FROM AUTHOR]

Background Pathophysiology of migraine is not fully known. A link has been proposed between migraine and patent foramen ovale (PFO). However, there are conflicting data regarding the causal relationship between PFO and migraine. Objective To test a potential association between migraine frequency and PFO by way of an observational, single-center, case-controlled study. Methods We studied a total of 130 chronic migraine (CM) and 53 episodic migraine (EM) patients. Transcranial Doppler with agitated saline injection was used to evaluate the presence and degree of PFO. PFO was judged to be present if any signal was detected. The degree of PFO during rest and Valsalva was quantified as follows: small (1-10 microbubbles [MB]), medium (10-25 MB), or large (>25 MB with shower or curtain pattern). PFO detected at rest were considered permanent, while those detected during Valsalva maneuver were classified as latent. Results The prevalence of PFO was similar in CM and EM patients (53.1% [44.1-62.2] vs 54.7% [40.3-69.1], P = .871). PFO size was significantly larger in the EM group compared to the CM group (35.8% vs 20.3%, P = .037). The presence of permanent PFO was also significantly higher in EM compared to CM (37.7% vs 22.7%, P = .044). No differences were found according to the presence of aura. Conclusion This study indicates that PFO is not more common or larger in CM than in EM patients. These findings do not support a relationship between PFO and migraine frequency. [ABSTRACT FROM AUTHOR]

Objective To determine total pituitary adenylate cyclase activating polypeptide (PACAP) in peripheral blood as a potential marker of the activation of the parasympathetic arm of the trigemino-vascular system in chronic migraine (CM) in a case-control study. Methods Women older than 17 and diagnosed as CM were recruited. Healthy women with no headache history and women with episodic migraine (EM) served as control groups. Total PACAP and vasoactive intestinal peptide (VIP) levels were determined in blood samples obtained from the right antecubital vein by ELISA outside a migraine attack and having taken no symptomatic medication the day before. Results We assessed serum samples from 86 women with CM, 32 healthy women, and 35 women with EM. There were no differences in PACAP levels in CM patients (109.8 ± 43.8, 97.4 [32.5-253.1] pg/mL), controls (108.7 ± 43.0, 98.7 [50.7-197.3] pg/mL), or EM patients (98.8 ± 34.3, 94.2 [52.0-190.7] pg/mL). VIP levels were significantly increased ( P = .027) in CM as compared to control healthy women (136.0 ± 111.5 pg/mL; 103.1 [20.5-534.0] pg/mL vs 88.6 ± 61.0 pg/mL; 66.0 [21.1-256.1]) and EM patients (103.0 ± 56.7 pg/mL; 103.5 [15.2-263.0] pg/mL). In the range of this study variables such as age, CM duration, the presence of aura, analgesic overuse, depression, fibromyalgia, vascular risk factors, history of triptan consumption or kind of preventative treatment did not significantly influence PACAP or VIP levels. Conclusion In contrast to VIP, interictal PACAP level measured in peripheral blood does not seem to be a biomarker reflecting parasympathetic activation in CM. [ABSTRACT FROM AUTHOR]

Exopolysaccharide (EPS)-producing bifidobacteria, particularly Bifidobacterium animalis subsp. lactis strains, are used in the functional food industry as promising probiotics with purported beneficial effects. We used three isogenic strains of B. animalis subsp. lactis, with different EPS producing phenotypes (mucoid-ropy and non-ropy), in order to determine their capability to survive the murine gastrointestinal tract transit, as well as to evaluate their role in improving clinical outcomes in a chemically-induced colitis model. The three strains were able to survive in the intestinal tract of C57BL/6J mice during the course of the intervention study. Furthermore, the disease activity index (DAI) of the animal group treated with the ropy strain was significantly lower than of the DAI of the placebo group at the end of the treatment. However, no significant differences were found among the three strains. The analysis of several immune parameters, such as TNFa and IL-10 quantified in blood plasma and lymphocyte populations enumerated in mesenteric nodes, showed some significant variations among the four experimental animal groups. Remarkably, a higher capability of the ropy strain to increase regulatory T-cells in mesenteric lymphoid nodes was demonstrated, suggesting a higher ability of this strain to regulate inflammatory responses at mucosal level. Our data indicate that strains of B. animalis subsp. lactis producing EPS that confer a mucoid-ropy phenotype could represent promising candidates to perform further studies targeting intestinal inflammatory processes. [ABSTRACT FROM AUTHOR]