To assess the association between cigarette smoking and ventricular arrhythmias in adolescents. Novel electrocardiographic parameters –Tp-e interval, as well as Tpe/QT and Tpe/QTc ratios – were used to make this assessment. The study population consisted of 87 subjects aged between 16-19 years. Fifty-one adolescent smokers with no risk of arrhythmia comprised the Smoker Group, and 36 adolescents who had never smoked cigarettes comprised the Control Group. Smokers were defined as patients smoking more than three cigarettes per day, for at least 1 year. Body mass index, systolic, diastolic and mean blood pressures were measured, and electrocardiograms were performed on all subjects. Heart rate, PR and Tp-e intervals, and Tpe/QT, Tpe/QTc ratio were digitally measured. Adolescents in Smoker Group had smoked cigarettes for 2.9±1.4 years (range 1 to 6 years). The mean age at starting smoking was 13.8±1.4 years. There were no differences between smokers and Control Group as to baseline clinical variables (p>0.05). The PR, QT and QTc intervals were similar in all groups. Tp-e interval , Tpe/QT , Tpe/QTc  ratios were significantly higher in Smoker Group. There were no correlations between years of smoking, number of cigarettes per day, Tpe interval, Tpe/QT or Tpe/QTc ratios. Cigarette smoking is associated with risk of ventricular arrhytmogenesis with prolonged Tp-e interval and increased Tpe/QT and Tpe/QTc ratios in adolescents. In addition to this, smoking may also lead to ventricular arrhythmias and sudden cardiac death.The effects of cigarette smoking on cardiovascular system have been investigated extensively by several studies in adults.-3 Th(-) Nicotine that is released into the circulation during smoking is clearly known to increase the plasma catecholamines, heart rate, and arterial blood pressure and as a result of all those alterations augmented myocardial work and oxygen demand may contribute to the generation of cardiac arrhythmias. In an experimental study, Mehta et al., determined the dose-dependent arrhythmogenecity of nicotine in dogs, and reported that higher doses, bioequivalent to smoking two standard cigarettes, can lead to supraventricular arrhythmias, atrioventricular junctional arrhythmias and ventricular arrhythmias. Hayashi et al., compared the influences of nicotine in modulating vulnerability to atrial tachycardia and fibrillation (AT/AF) in young and old rats, and reported that nicotine prolonged the interatrial conduction time and effective refractory period in both groups. They also described that nicotine had biphasic effects on inducible AT/AF in young rats, but suppressed them in the old rats by causing high degrees of interatrial conduction block. In a recent clinical study, Conrad et al., also reported that smoking significantly decreased resting respiratory sinus arrhythmia and increased mean heart rate in adolescents that are the indicators of how well the body maintains homeostasis as a response to environmental demands.The influence of smoking on ventricular arrhythmogenesis is explained with ventricular recovery time alterations by limited studies.-8 Ni Moreover, QT interval (QT) and corrected QT interval (QTc) may also show the myocardial repolarization in electrocardiograms (ECG). The Tp-e/QT and Tp-e/QTc ratios are also used as an index of ventricular arrhythmogenesis.In previous studies, the interval between the peak and the end of T wave (Tp-e) was reported as an indicator of the total dispersion of repolarization, and prolongation of Tp-e was associated with ventricular arrhythmias.13 MorTo assess the association between cigarette smoking and ventricular arrhythmias in adolescents.diabetes mellitus, hypertension, abnormal thyroid tests, or who were on medications that could affect ventricular recovery time  were excluded from the study.This observational study was approved by the Research Ethics Committee of a public tertiary hospital (number 2012-KAEK-15/1323). The study population consisted of two groups: 51 adolescent smokers were recruited  for the smoking group (smokers); and 36 healthy subjects who had never smoked before  were included in the Control Group. Both groups enrolled subjects who had been admitted to the pediatric cardiology department for various reasons , between March and June 2016. Smokers were defined as adolescents smoking more than three cigarettes per day, for at least 1 year. Participants were asked not to smoke 30 minutes before the evaluation to avoid the acute nicotine effects. Adolescents who had any risk factors for arrhythmia other than smoking, such as congenital heart disease, rheumatic valvular disease, 2). Blood pressure was taken in triplicate after a 10-minute seated rest using a mercury sphygmomanometer and appropriate cuff size. Korotkoff phases 1 and 5 were used to define systolic and diastolic blood pressure, respectively. The adolescents diagnosed as presenting any clinical alteration in the physical examination, or cardiac disorder in the transthoracic echocardiography, were also excluded from the study.The medical history was obtained, and physical and echocardiographic examinations were performed, together with the evaluation of blood pressure and anthropometric data including weight and height, in all participants. Body mass index (BMI) was defined as weight in kilograms divided by the square of height in meters  set at 25mm/s paper speed and 10mm/mV voltage was used. All ECG were recorded when participants were at rest and on supine position and were transferred to the digital media by scanning with an optical scanner (Hewlett Packard Deskjet 2130). All measurements were digital and taken by one experienced pediatric cardiologist, who was blinded to the smoking status of participants, in order to avoid interobserver variability. The Tp-e /QT and Tp-e/QTc ratios were calculated based on these measurements. Rhythm, speed, QRS axis, ventricular hypertrophy and ST-T changes in electrocardiograms were also evaluated.The PR interval was measured from the first visible upward of the P wave to the QRS complex starting point . The QT 16 Thet test or Mann Whitney U test were used to compare groups. Correlation between variables was assessed by using Spearman’s rank correlation test. A p value of <0.05 was considered significant.All statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) for Windows, version 20.0. Continuous variables were expressed as mean±standard deviation. Kolmogorov-Smirnov test was used to assess the normal distribution suitability of variables. According to distribution, the Student’s Clinical characteristics of both groups are summarized in The baseline PR, QT and QTc values were similar in two groups. In the Smoker Group, Tp-e interval was significantly prolonged compared to the Control Group . A statistically significant difference in Tp-e/QT ratio was also determined between groups . In addition to these results, the Tp-e/QTc ratio was also higher in the Smoker Group as compared to controls  .Adolescents in Smoker Group had been smoking cigarettes for 2.9±1.4 years (range 1 to 6 years). The mean age at starting smoking was 13.8±1.4 years. Twenty-four (47.1%) adolescents in Smoker Group smoked more than ten cigarettes per day. No correlations were determined between years of smoking, number of cigarettes per day and Tp-e interval, or Tpe/QT and Tpe/QTc ratios. Nicotine is the main component of cigarettes, known as a nonspecific blocker of potassium channels. It has several systemic effects, including tachycardia, increased blood pressure, and catecholamine release; and it is therefore defined as arrhythmogenic.Cigarette smoking increases mortality not only due to induction of coronary artery disease, but also to sudden cardiac death, since smoking is associated with increased sympathetic tone and reduced vagal modulation.,,18 In this study, we did not find any differences in the QT and QTc intervals between groups. However, ventricular arrhythmia risk is usually evaluated using QT interval and changes in T wave, and measurements of these changes were also reported as predictor of arrhythmia. The Tp-e interval between the peak and the end of T wave is a reflection of transmural dispersion of repolarization. Prolongation of this interval is a period of potential vulnerability to re-entrant ventricular arrhythmias. In previous studies, an association between increased risk of mortality and prolongation of Tp-e interval was shown in Brugada syndrome, long QT syndrome and hypertrophic cardiomyopathy.(-) A case control study by Panikkath et al., also reported that prolongation of Tp-e interval was associated with sudden cardiac death in general community.Effects of smoking on ECG were shown in studies that found increased QT and QT dispersion in acute and chronic periods of smoking.,8 In Taşolar et al., also found QT prolongation and dispersion, in addition to these parameters.The effects of cigarette smoking on Tp-e interval were evaluated in a limited number of studies, particularly in adults. İlgenli et al., showed prolongation of Tp-e interval and Tp-e /QT, Tp-e/QTc ratios in long term heavy smokers.6 Taşo To the best of our knowledge, this study is the first that has determined alterations of Tp-e interval in adolescent smokers.In this study, the Tp-e interval, as well as the Tp-e/ QT and Tp-e /QTc ratios were found significantly higher in Smoker Group. However, statistically significant difference could not be found by means of QT and QT dispersion. Nonetheless, it seems that this significant difference in Tp-e interval in the Smoker Group could be associated with indirect assessment of repolarization time dispersion. The Tp-e/QT ratio is considered a more sensitive index of arrhythmogenesis, since it is not affected by variations in body weight and heart rate.14 To  Ilgenli et al., found Tp-e interval, and Tp-e/ QT and Tp-e/QTc significantly higher just after smoking. In our study, in accordance with previous studies, cigarette smoking altered ventricular repolarization, regardless of past history. We did not find a statistically significant relation between years of smoking, number of cigarettes per day, and ventricular repolarization parameters. These findings also suggest that cigarette smoking may increase the risk of sudden cardiac death even in adolescents, due to alterations in ventricular repolarization, apart from the cumulative and long-term effects.Akbarzadeh et al., reported that even a single cigarette induces the potential of sudden cardiac death and arrhythmia by QT dispersion prolongation.8 Ilge In the present study, we also determined that the mean heart rate of smokers was higher than the Control Group, but the difference was at significance margin (p=0.053). In this study, we asked participants not to smoke for, at least, half an hour before evaluation in order to avoid the acute effects of nicotine, which may alter heart rate and increase QT and QTc measurements. We did not observe any difference regarding QT and QTc measurements between groups, and the reason may be the increased heart rates that were positive at statistical margin. It is well known that smoking is a worldwide public health problem. Rosewich et al., reported the addiction to nicotine develops mainly before the age of 20 years  in Germany, and the proportion of adolescent smokers has regularly increased in recent years. In the present study, the mean age at starting smoking was found to be as young as 13.8±1.4 years and adolescents in Smoker Group had been smoking for 2.9±1.4 years (range of 1 to 6 years).Cigarette smoking is known to increase the sympathetic outflow and heart rate.25 In This study is a preliminary study and has some limitations. First, it included only a small number of participants, and future larger studies may provide more accurate results. Second, we did not ask and record the interval between the last cigarette smoked and the ECG recording in the Smoker Group. Smoking is known to have some acute effects on cardiac functions and participants were asked not to smoke for half an hour before the evaluation in order to avoid those acute effects. Further studies may investigate the effects of smoking on different time periods. And, third, only short-term records could be obtained. Further prospective follow-up studies should be performed to understand the prognostic importance of Tp-e interval prolongation and increased Tp-e/QT ratio in ventricular arrhythmogenesis and sudden cardiac death.This was the first study in adolescents that has reported prolonged Tp-e interval and increased Tp-e/QT and Tp-e/QTc ratios in smokers. The present study provides evidence that smoking is associated with alterations in ventricular repolarization time that may contribute to ventricular arrhythmogenesis.


Feed represents the major cost of chicken production. Selection for improving feed utilization is a feasible way to reduce feed cost and greenhouse gas emissions. The objectives of this study were to investigate the efficiency of genomic prediction for feed conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG) and average daily feed intake (ADFI) and to assess the impact of selection for feed efficiency traits FCR and RFI on eviscerating percentage (EP), breast muscle percentage (BMP) and leg muscle percentage (LMP) in meat-type chickens. Genomic prediction was assessed using a 4-fold cross-validation for two validation scenarios. The first scenario was a random family sampling validation (CVF), and the second scenario was a random individual sampling validation (CVR). Variance components were estimated based on the genomic relationship built with single nucleotide polymorphism markers. Genomic estimated breeding values (GEBV) were predicted using a genomic best linear unbiased prediction model. The accuracies of GEBV were evaluated in two ways: the correlation between GEBV and corrected phenotypic value divided by the square root of heritability, i.e., the correlation-based accuracy, and model-based theoretical accuracy. Breeding values were also predicted using a conventional pedigree-based best linear unbiased prediction model in order to compare accuracies of genomic and conventional predictions. The heritability estimates of FCR and RFI were 0.29 and 0.50, respectively. The heritability estimates of ADG, ADFI, EP, BMP and LMP ranged from 0.34 to 0.53. In the CVF scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR were slightly higher than those for RFI. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.360, 0.284, 0.574 and 0.520, respectively, and the model-based theoretical accuracies were 0.420, 0.414, 0.401 and 0.382, respectively. In the CVR scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR was lower than RFI, which was different from the CVF scenario. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.449, 0.593, 0.581 and 0.627, respectively, and the model-based theoretical accuracies were 0.577, 0.629, 0.631 and 0.638, respectively. The accuracies of genomic predictions were 0.371 and 0.322 higher than the conventional pedigree-based predictions for the CVF and CVR scenarios, respectively. The genetic correlations of FCR with EP, BMP and LMP were -0.427, -0.156 and -0.338, respectively. The correlations between RFI and the three carcass traits were -0.320, -0.404 and -0.353, respectively. These results indicate that RFI and FCR have a moderate accuracy of genomic prediction. Improving RFI and FCR could be favourable for EP, BMP and LMP. Compared with FCR, which can be improved by selection for ADG in typical meat-type chicken breeding programs, selection for RFI could lead to extra improvement in feed efficiency. Feed represents up to 70 percent of the total cost of chicken production, and the selection of feed utilization traits can reduce feed cost and greenhouse gas emissions. Feed coGenomic selection is revolutionizing animal breeding due to the higher accuracy of selection early in life –13. GenoThe aims of this study were to investigate the accuracy of genomic prediction for FCR and RFI and to assess the impact of improving the two feed efficiency traits on carcass traits in meat-type chickens.The present study was approved by the Animal Care Committee of the Institute of Animal Science, Guangdong Academy of Agricultural Sciences  .0 generation, 51 breeding individuals (8♂ + 43♀) from the F1 generation and 511 individuals from eight half-sib families of the F2 generation. All birds were genotyped, and only F2 birds had phenotypic records. The birds were genotyped using the Illumina Chicken 60K SNP Beadchip [The chicken population was constructed by crossing the “High Quality chicken Line A” (HQLA) with the Huiyang Beard chicken (HB), as described in Sheng et al. . The HQLBeadchip  by DNA Lth week. Thereafter, all birds were housed in single cages and feed was provided in single containers. The chickens had ad libitum access to water and feed. The feed were gradually changed to a grower ration of 2,950 kcal ME/kg and 180 g/kg CP during the 6th week. Body weight and feed intake were measured during the period from the beginning of 7th to the end of 12th week (42 d). Individual body weight gain was calculated based on body weight between the start and end of the period and used to derive ADG. The total feed intake for each bird was calculated by summing the feed consumptions during the period and used to derive ADFI. At the end of the 13th week, the birds were slaughtered to measure carcass traits. Quality control was applied to ADG and ADFI, removing deviant data that were more than 3 standard deviations from mean [Seven traits in the analysis included feed conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG), average daily feed intake (ADFI), eviscerating percentage (EP), breast muscle percentage (BMP) and leg muscle percentage (LMP). The mean and standard deviation of the seven traits are shown in rom mean . Finallyrom mean :ADFI=μ+μ is the intercept, sex and hatch are fixed effects, MBW is the average of the start and end of body weight, β1 and β2 are partial regression coefficients, and e is residual. The estimated e was taken as the measure of RFI.In the model, In this study, a conventional pedigree-based best linear unbiased prediction model (BLUP) and a genomic best linear unbiased prediction model (GBLUP) were used to predict breeding values for each trait, respectively.y is the vector of phenotypic records of the trait , b is the vector of the fixed effect (sex and hatch), X is the incidence matrix linking b to y, a is a vector of additive breeding values to be estimated, Z is the incidence matrices linking a to y and e is the vector of residuals. It was assumed that A is the pedigree-based genetic relationship matrix. For analysis of RFI, the fixed effects were not included in the model because these effects were already corrected when calculating RFI values.The pedigree-based BLUP model isy=Xb+y, b, X, Z and e are the same as those in the BLUP model. g is a vector of genomic breeding values to be estimated. It was assumed that g ∼ N, where G is the genomic relationship matrix based on SNP markers [Vg is the covariance matrix of genomic breeding values.The GBLUP model  isy=Xb+ markers , Vg is tVariance components were estimated with a univariate linear mixed model, the genetic correlations between feed efficiency and each of the carcass traits were estimated with a multivariate linear mixed model, using the average information restricted maximum likelihood (AIREML) approach . The linCross-validation is a robust, non-parametric technique for assessment of predictive ability , 26. Thec) divided by the square root of heritability [c was the phenotypic value corrected for sex and batch effects. For RFI, the yc was RFI itself, as RFI was already corrected for sex and batch effects. The other was the model-based theoretical accuracy of GEBV obtained by inverting the coefficient matrix of the mixed model equations [Accuracies of GEBV were assessed in two ways. One was the correlation between prediction and corrected phenotypic value . For RFI, the top 50 efficient birds were expected to have the same ADG as the bottom 50 inefficient birds because RFI was already adjusted for ADG. The small difference  was due to sampling error. The efficient individuals had lower feed intake than the inefficient individuals for RFI, which is not the same for FCR. The three carcass traits had significantly higher values in the efficient group of birds (P<0.05).P<0.05). The correlations of FCR with EP, BMP and LMP ranged from -0.427 to -0.156. The correlations between RFI and the three carcass traits ranged from -0.404 to -0.320.The genetic correlations between feed efficiency and carcass traits based on full data are presented in Several studies have shown that the heritabilities for FCR and RFI are moderate to high in chickens , 28, 29.Although the size of the dataset was small in the current study, the standard errors of heritability estimates were low. There are at least three possible reasons for the low standard errors. First, the data were collected from a single farm during the same period; thus, the model was rather simple and only a few parameters needed to be estimated. Second, the dataset had a good structure  with regard to estimating genetic parameters. Third, heritabilities in the current study were estimated based on the genomic relationship matrix built using the 60K SNP chip, which described the relationship between birds more accurately than the pedigree-based relationship. Previous studies , 31 repoIn the current study, the accuracies of GEBV were evaluated in two ways: correlation-based and model-based theoretical accuracy. The results showed that in both CVF and CVR scenarios, the model-based theoretical accuracy was higher than correlation-based validation accuracy for all traits, except for ADG and ADFI in the CVF scenario. This phenomenon of high model-based theoretical accuracy was also observed in previous studies, 32, 33,2 population, which was a newly established line with a small effective population size. In a population with small effective population size, the animals shared large chromosome segments; thus, it is expected that genomic predictions will be more accurate [Although the training data set was small, the accuracy of genomic prediction was relatively high in this study. This could be explained as the following. The data were collected from a Faccurate , 36.The accuracy of predicted genomic breeding values for feed efficiency traits has been studied in laying hens. Wolc et al.  investigIn the real breeding scheme of poultry, parents are usually ready to be included in training data to predict the breeding value of progenies, but not sibs. In other words, the relationship between test and training birds would be higher than that in CVF and lower than that in CVR. Therefore, it could be expected that the accuracies of genomic prediction in the real scenario of chicken breeding would be between the accuracies of the CVF and CVR scenarios. In line with the higher heritability for RFI than FCR, the trait had a higher accuracy of GEBV than FCR, indicating an advantage of selection for RFI to improve feed efficiency.The accuracy of genomic prediction for the animals without their own records of FCR and RFI was actually lower than the Previous studies have reported strong genetic correlations between FCR and ADG , 6, 29. Carcass traits are highly important in meat-type chicken breeding because they can greatly affect the economic value of the birds. However, carcass traits are difficult to measure, and direct measurements can be obtained only when the individuals are slaughtered. Due to the limitation of phenotypic records, there are very few studies on the relationship between feed efficiency and carcass traits. N'Dri et al.  investigRFI and FCR are moderately heritable with higher heritability for RFI than FCR. Furthermore, GEBV of RFI was more accurate than FCR. In addition, RFI has a favourable correlation with all three carcass traits and no correlation with ADG. Therefore, RFI could be a good alternative to selection for improving feed efficiency in chicken breeding programs.S1 TableThe accuracies of genomic prediction were evaluated in two ways: the correlation-based accuracy and model-based theoretical accuracy.(XLSX)Click here for additional data file.


Toxoplasma gondii is one of the most successful parasites on Earth, infecting a wide array of mammals including one third of the global human population. The obligate intracellular protozoon is not capable of synthesizing cholesterol (Chl), and thus depends on uptake of host Chl for its own development. To explore the genetic regulation of previously observed lipid metabolism alterations during acute murine T. gondii infection, we here assessed total Chl and its fractions in serum and selected tissues at the pathophysiological and molecular level, and integrated the observed gene expression of selected molecules relevant for Chl metabolism, including its biosynthetic and export KEGG pathways, with the results of published transcriptomes obtained in similar murine models of T. gondii infection. The serum lipid status as well as the transcript levels of relevant genes in the brain and the liver were assessed in experimental models of acute and chronic toxoplasmosis in wild-type mice. The results showed that acute infection was associated with a decrease in Chl content in both the liver and periphery , and a decrease in Chl reverse transport. In contrast, in chronic infection, a return to normal levels of Chl metabolism has been noted. These changes corresponded to the brain and liver gene expression results as well as to data obtained via mining. We propose that the observed changes in Chl metabolism are part of the host defense response. Further insight into the lipid metabolism in T. gondii infection may provide novel targets for therapeutic agents. Toxoplasma gondii is considered one of the most successful parasites on Earth due to its omnipresence and widest array of hosts, including all mammals. The genus comprises a single species infective for all hosts, with limited genetic diversity in Europe and North America where all isolates belong to three clonal genotypes . However, a wider genetic diversity characterized by non-clonal, atypical strains is found in South America and Africa, and is thought to be related to the presence of diverse Felidae as the only definitive host in which sexual reproduction, and consequentially, genetic recombinations, occur. In intermediate hosts, T. gondii occurs in two forms, the metabolically active rapidly proliferating tachyzoite which characterizes acute infection and the presumably metabolically inert encysted bradyzoite, characteristic of chronic infection; the parasite readily converts between the two in response to the hospitality or hostility of the host environment (mostly depending on the immune response) but is never eliminated from the infected host. The encysted bradyzoites generally localize in the host brain and muscles and remain latent under the control of the host adaptive immune response.Human infection is widespread; it has been estimated that one third of the global population is infected ,2. HowevT. gondii is not capable of synthesizing cholesterol (Chl), and thus depends on uptake of host Chl for its own development [T. gondii acquires host Chl via endocytosis mediated by the LDL receptor [A potential novel drug target may be Chl metabolism . It has elopment . Chl metreceptor , 5 or threceptor . On the receptor .T. gondii infection induced a decrease in total Chl and VLDL and LDL levels, attributed to acquisition by T. gondii, while HDL levels remained unaltered. However, these experiments were carried out in the setting of mice genetically modified to contain high levels of LDL.Although Chl apparently has an important role in the pathogenesis of toxoplasmosis, this topic has, interestingly enough, not received sufficient attention. The only data from in vivo models have been obtained in genetically modified animals, i.e. ApoE- and LDL receptor deficient mice (LDLr-/-), respectively, with consequently high VLDL, LDL, and total Chl levels , 8. In bT. gondii infection on Chl metabolism in the normal host, and have shown that T. gondii infection indeed alters the lipid status in wild-type mice [T. gondii.But wild-type mice have very little LDL  and totaype mice . In an iT. gondii, because of its central role in lipid metabolism. In addition, the observed gene expression of selected molecules involved in the Chl metabolic pathway was analyzed in the context of the results of published transcriptomes obtained in similar murine models of T. gondii infection.To further analyze the observed changes, namely, to explore whether the observed lipid metabolism alterations are genetically regulated, we proceeded to compare lipid metabolism in acute and chronic infection in serum and tissues including the brain and the liver, at both the pathophysiological and molecular level. The brain has been chosen as the target organ for encysted parasites, and the liver, although not a primary target of T. gondii type I, II and III strains [The serum lipid status as well as the transcript levels of relevant genes in the brain and the liver have been assessed in experimental models of acute and chronic toxoplasmosis in wild-type mice. To put the obtained results into a wider context, we have mined all published microarray (n = 7) and RNA-seq (n = 1) datasets of murine tissues during acute infection with  strains , 13, 14,For experimental infections, female Swiss-Webster mice  weighing 18–20 g, were used. Mice were housed at five per cage and offered regular mouse feed and drinking water ad libitum.The low virulence BGD-1 strain (human origin type II strain) characterized in our laboratory  was usedT. gondii infection, groups of infected (n = 10) and control (n = 6) mice were euthanized at time of infection (d0), d14 p.i. and d42 p.i. At these points, blood samples were collected for measurement of lipid fractions, and livers and brains were harvested for real time-PCR analysis.To examine the serum lipid changes for each individual animal during Total Chl, HDL, LDL and triglyceride levels were measured on an Olympus AU 400 biochemical analyzer , according to the manufacturer’s recommendations.RNA was extracted from brain and liver tissue with TRIzol reagent  according to the manufacturer's protocol and the products were stored at −80°C until use. Total amount and RNA purity were determined on the Eppendorf ECOM-P4153 spectrophotometer  by absorbance at 260 nm and the 260/280 nm absorbance-ratio, respectively. Total cDNA was produced using the RevertAid First Strand cDNA Synthesis Kit , according to the manufacturer's protocol. The cDNA products were stored −20°C until real time-PCR amplification.T. gondii genomes and RefSeq mRNA databases. Amplicon specificity was verified by gel electrophoresis and melting curve analysis of qPCR products (data not shown). The real-time PCR amplifications were carried out in a Mastercycler ep realplex  PCR machine using the following thermal profile: 10 min at 95°C, followed by 40 cycles of denaturation for 15 sec at 95°C, annealing and extension for 30 sec at 60°C (63°C for ApoA2). To avoid pipetting errors and to ensure that equal amount of cDNA was distributed across all PCR tubes for target and reference genes for each of the three biological replicate samples, cDNA was added to the master mix and mixed thoroughly before it was aliquoted into tubes already containing primer pairs. To control for DNA contamination in the reaction mix, control tubes lacking DNA templates were included, with primers for Gapdh for each biological replicate master mix. The Calqplex algorithm of the realplex software was used to determine the amplification cycle at which product accumulation was above the threshold (Ct).Relative quantification of the transcript levels of the selected genes  was inveData were analyzed using Gnumeric Spreadsheet 1.10.17 software and SPSS for Windows, version 16.0 . Real-time PCR Ct values were analyzed using the 2−ΔΔCt method, where ΔΔCt = ΔCt samples– ΔCt controls. For each biological replicate, the Ct for a given gene was normalized to the Ct value of the reference Gapdh gene. Range of fold change expression was calculated by incorporating standard deviations (SD) of three biological replicates, from 2−(ΔΔCt−SD) to 2−(ΔΔCt+SD). Unpaired t-test analysis or Mann-Whitney U test were used to examine the differences in various variables between control and infected animals. P-values of 0.05 or less were considered statistically significant.T. gondii infection. Literature search revealed three relevant studies, of which two were based on microarray [We mined data obtained from previously published transcriptomics studies in the model of experimental croarray , 13 and croarray .T. gondii. Briefly, Balb/C mice were intraperitoneally (i.p.) infected with 10 tachyzoites per mouse, and brain tissue and peripheral lymphocytes were harvested 8 days p.i. for total RNA isolation. The gene expression profiling was done using Agilent Whole Mouse Genome (4 × 44 K) Microarrays (one-color platform). For the purpose of our study, we used four datasets of microarray hybridization results of up- and down- regulated genes in a) mouse brain after infection with parasites of the ME49 strain of T. gondii; b) mouse peripheral lymphocytes after infection with T. gondii parasites of the ME49 strain; c) mouse brain after infection by the T. gondii RH strain; and d) mouse peripheral lymphocytes after infection by the T. gondii RH strain.The study of Jia B et al. from 2013  involvedT. gondii strains GT1 (type I), PTG (type II), and CTG (type III). Briefly, CD-1 mice were infected intraperitoneally (i.p.) with 500 tachyzoites and total RNA from peritoneal cells was extracted 5 days p.i. Gene expression profiling was done using Affymetrix Mouse 1.0 ST array containing 28,853 genes. For our study we used data sets originating from infection with all three T. gondii strains.The study of Hill et al. from 2012  involvedT. gondii PYS (Toxo DB#9) strain, which is of the I/III type. Mice were sacrificed 6 days p.i. and extracted RNA samples from liver were analysed using Illumina HiSeq 2000 system. Results were expressed as fold-change of differentially expressed genes in infected and uninfected mice for three biological replicates. We used these data to calculate averages of ratios of RPKM values. Ratio values between 0 and 1 were taken to indicate down regulation, while values above 1 were considered as up regulation.The study of He et al. from 2016  involvedThe study protocol was approved by a local  Ethics Committee, approval no. 0091-2/10, and the State Ethics Committee . All experiments were conducted concordant to procedures described in the National Institutes of Health Guide for Care and Use of Laboratory Animals .T. gondii infection at two different time points: d14 p.i. and d42 p.i., as time points respectively representing acute and chronic infection. Chl metabolism was assessed at the pathophysiological and molecular levels, i.e. by measuring Chl content in the serum, and by examining the expression of selected genes involved in the regulation of Chl metabolism, respectively. The analyzed genes were selected based on their role and position in the physiological and metabolic pathways, in such a manner to have representatives at all critical points, while bearing in mind the cost-effectiveness of the experiment.Chl metabolism was analyzed in a murine model of Analysis of individual changes in serum lipids on d14 p. i. revealed some significant changes in the lipid fractions as compared to pre-infection levels . Both toExpression analysis of the selected genes involved in Chl metabolism showed that acute infection was associated with a decrease in the transcription of genes involved in Chl biosynthesis in both the liver and the brain.T. gondii infection was associated with a significant increase in the expression of Abca1 gene. In contrast, there was a decrease in the expression of all other analyzed genes, including Ldlr and SR-B1 genes. Significant down-regulation was observed in the expression of apolipoprotein genes including ApoA1, ApoA2, ApoB100, ApoC4 and ApoE , few changes vs. control mice were registered in the serum . No diffAt the gene level, chronic infection in the liver was associated with a significant increase in the expression of only ApoB100 . The expIn the brain, significant up-regulation in the expression of only Abca1 was observed, along with a trend of up-regulation of genes including ApoA2, ApoE, ApoF, Hmgcr and SRB-I .A compilation of all published transcriptome data obtained in murine toxoplasmosis pertaining to Chl metabolism is presented in In the brain of mice infected with type II parasites in acute infection (day 8 p.i.), the data revealed down-regulation of most of the 13 genes from the Chl biosynthetic pathway starting from farnesyl-PP, and of two transcriptional activators of this pathway, Srebf1 and Srebf2 . On the Peripheral lymphocytes seemed to show a similar expression profile. Down-regulation of several genes from the Chl biosynthesis pathway and up-regulation of genes involved in its esterification were observed during infection with both type I and type II parasites. Furthermore, in infection with type II parasites, up-regulation of Abcg4, a Chl export protein, and of liver X receptor alpha (Nr1h3), was revealed.T. gondii infection (day 6 p.i.), several genes from the Chl biosynthetic pathway were down-regulated. Furthermore, Cyp7b1 and Abcg5, involved in bile acid synthesis and export, were down-regulated. On the other hand, Abcg1, involved in Chl export, and Soat 1, involved in Chl esterification, were up-regulated.In the liver of mice in acute T. gondii type I, II and III parasites, mining for differentially expressed genes involved in Chl biosynthesis and transport revealed an over two-fold increase in several genes, including Hmgcr, Fdft1, Sqle and Ldlr, while the expression of ApoE was reduced by more than six-fold (data not shown).In peritoneal cells of mice infected with T. gondii infection (day 42 p.i.) was characterized by a return back to normal values of Chl metabolism, which was manifested at the molecular level by few differences, including upregulation of ApoB100 in the liver and of Abca1 in the brain.Integration of our experimental data with those obtained by mining is schematically represented in T. gondii infection in murine models induced by type II parasites at both the biological and molecular level. Combining own experimental data with those mined from published transcriptomes of T. gondii infected mice, we showed that acute infection was associated with a decrease in Chl content in both the liver and periphery , and a decrease in Chl reverse transport. In contrast, in chronic infection, at the time of an established balance between the parasite and its host, these metabolic changes were not as prominent.In this study, we analyzed Chl metabolism during acute and chronic T. gondii infection was associated with a significant increase in the expression of Abca1 and with down-regulation (or a trend of) of a number of apolipoprotein genes as well as of the Ldlr and SR-B1 genes. Since Abca1 plays a key role in hepatic Chl efflux [T. gondii into hepatocytes, as has been shown for the closely related Apicomplexan Plasmodium sporozoites [We have specifically analyzed the liver and the brain. In the liver, acute l efflux , its incl efflux , 18. In rozoites . Taken trozoites .In the brain during acute infection, we observed a reduction in Chl levels. At the level of transcription, significant up-regulation of ApoF was observed, along with a trend of down-regulation of a number of analyzed genes involved in Chl intake, trafficking and biosynthesis. Normally, ApoF forms complexes with lipoproteins and may be involved in transport and/or esterification of Chl and has also been shown to act as a stimulator of Chl transport from HDL to VLDL and LDL, causing a decrease in serum level of HDL . On the The reduction of brain Chl may be part of the host defensive reaction to the parasite. The brain is the major site of parasite replication and development . We haveT. gondii infection may contribute per se to impaired lipid metabolism and decreased Chl content.Analysis of the published transcriptomes showed significant changes in the lymphocytes as well. We did not verify this in our experimental model, but it is interesting to note that mining showed a decrease in the level of transcription of genes involved in Chl biosynthesis, and up-regulation of genes involved in its esterification, as well as an increased level of liver X receptor alpha, which may all lead to a decrease in Chl accumulation and increased Chl efflux from the lymphocytes. Chl content in the plasma membrane of immune cells causes alteration in membrane viscosity, influencing functions such as maturation , antigenT. gondii in the brain [T. gondii infected mice on d42 p.i. supports increased export and transport of Chl to the neurons, possibly in response to increased Chl needs of parasitized neurons due to developing cysts, and/or as a neuron repair mechanism. Increase in Chl metabolism components is the normal brain response to injury or disease [T. gondii infection, when the infection is patent, as compared to d42 which is an early moment of established chronicity, still associated with cyst development and growth. In addition, the impact of chronic infection on lipid metabolism should be examined in other cyst predilection sites such as the heart and skeleton muscles.At the time of established chronicity (d42 p.i.), a return to normal levels of Chl metabolism has been noted. Total serum Chl and HDL levels, both decreased in acute infection, were both comparable to controls in chronic infection. This has also been supported by fewer differential gene expression findings at this time; the only upregulated gene in the liver was ApoB100, which may suggest increased transport of LDL from the liver to the periphery. More importantly, changes in the brain on day 42 included increased Abca1 gene expression along with (a trend of) upregulation of ApoE. Neurons have been shown to be the primary target cells of he brain . Howeverhe brain . Accordi disease . It woulTrypanosoma cruzi and Schistosoma mansoni were found to have decreased levels of total blood Chl. In the trypanosome infection model, Chl fractions have not been analyzed [S. mansoni is a trematode completely unrelated to Toxoplasma, schistosomes do not synthesize Chl but incorporate host LDL via inducible LDL receptors. In S. mansoni infected outbred mice, a reduction in total Chl and HDL and an insignificant rise in LDL was shown at a time at which infection became patent [Chlamydia pneumoniae has been shown to cause significant changes in liver lipid metabolism, but, as opposed to the above organisms, which result in increased serum Chl and triglyceride levels [In its proliferative stage, the parasite needs Chl for development. It has been shown that Chl is uptaken in the form of LDL, through the Ldlr . Howeveranalyzed . On the e patent . In conte levels , 33.T. gondii infecting strain, route of infection) but they used inbred mice and collected the samples d8 p.i. as opposed to d14 in our model. On the other hand, the model used by He et al. [T. gondii strain (type I/III as opposed to type II in our model) and in the route of infection as they used the intraperitoneal route. A limitation intrinsic to our experimental model involves the variations among individual animals obtained both at the physiological and molecular level, as visualized by occasionally wide standard deviations, the reason for which is likely to be our use of outbred mice for experimental infections. Wild-type mice have been shown to widely differ in their lipid content [Limitations to our study and the presented results include variations in the experimental models between ours and those in the mined data complicating direct comparisons; the model used by Jia et al.  is large content . It is pT. gondii infection on Chl metabolism may have a significance beyond this immediate effect, as Chl influences various key physiological processes such as role in the metabolism of lipid soluble vitamins, synthesis of sex hormones etc.We propose that the observed changes in Chl metabolism are part of the host defense response. In acute infection, the host responds by an attempt to deprive the parasite of Chl, necessary for tachyzoite proliferation and development. The influence of T. gondii infection has opened exciting new therapeutic approaches. Coppens et al. [T. gondii on exogenous Chl may be used for the delivery to the parasite of lipid analogues with parasitostatic effect. It is hoped that further insight may lead to additional novel targets for therapeutic agents.The variety of lipid metabolism alterations induced by s et al.  have pro


This study aimed to evaluate the prevalence of comorbidities in patients with rheumatoid arthritis (RA) compared with the non-RA population. The 2010–2012 Korea National Health and Nutrition Examination Survey (KNHANES), which assesses the general health status of populations in South Korea using interviews and basic health assessment, was analyzed retrospectively. Weighted prevalence and odds ratio (OR) of comorbidities were analyzed in patients with RA compared with the non-RA population. The overall weighted  prevalence of RA was 1.5%. Patients with RA were older and more female predominant than subjects without RA. The prevalence of living in an urban area, college graduation, alcohol consumption and smoking was lower in patients with RA than non-RA. Patients with RA had more comorbidities including hypertension, dyslipidemia, myocardial infarction (MI) or angina, stoke, osteoarthritis, lung cancer, colon cancer, pulmonary tuberculosis, asthma, diabetes, depression, thyroid disease and chronic kidney disease. After adjusting socioeconomic and lifestyle characteristics, RA was associated with an increased prevalence of MI or angina , pulmonary TB , asthma , thyroid disease , depression  and hepatitis B  compared with the non-RA population. Prevalence of solid cancer was not significantly associated with RA after adjustment. Rheumatoid arthritis (RA) is a chronic inflammatory disease and with dominant features of joint inflammation and damage. RA is associated with progressive disability, systemic complications, and socioeconomic costs . MoreoveThe increased prevalence of comorbidities, as well as RA itself, is associated with an economic burden on patients, their families, and society . The eldSeveral studies addressing comorbidities in RA have been published; however, some studies lack a comparator group without RA and other studies were performed adjusting only for age and gender. Moreover, the data regarding medical comorbidities associated with RA in previous studies were mainly from western populations. The aim of this study was to determine the prevalence of comorbidities in Korean adult population with RA compared with the non-RA population, with adjusting for socioeconomic and lifestyle characteristics using data from the 2010–2012 Korea National Health and Nutrition Examination Survey (KNHANES).The KNHANES is a nation-wide survey that has been conducted periodically by the Korea Centers for Disease Control and Prevention to investigate the health and nutritional status of the Korean population. It assesses the general health and nutrition status of populations in South Korea using interviews about health and nutrition, and a basic health assessment. Participants were selected using proportional allocation-systemic sampling with multistage stratification to derive a representative Korean population. Although individual participants were not equally representative of the Korean population, this survey provides representative estimates of the noninstitutionalized Korean civilian population by using the power of sample weight. Every year, 10,000 to 12,000 individuals in about 3,800 households are selected from a panel based on the National Census Data. The participation rate of selected households in the past several cycles of KNHANES has been high, ranging from 79% to 84%. Written informed consent was obtained from all participants before completing the survey. This study was approved by the interstitial review board of Samsung Medical Center.2).The KNHANES was conducted by four special research teams, each composed of eight experts including nurses, nutritionists, and persons who major in public health. The selected professional investigator was placed at the investigation site after completing one month of education and practice. Subsequently, the ability to conduct research was verified through regular education and on-site quality management. A standardized interview was performed with an established questionnaire in the homes of the participants to collect information on demographic variables. The established questionnaire consisted of the demographic and socioeconomic characteristics of the subjects. Data on age, gender, income, region, education, marital status, alcohol consumption, and smoking status were collected. Alcohol consumption was divided into 4 groups based on the frequency of alcohol consumption during the last one year; never, 1 ≤ week, 2–3 /week, and 4 ≥ week. Income level was divided by quartile for average of individual monthly income. Urban and rural areas were classified by administrative district. RA was defined in the questionnaire as “RA diagnosed by a physician” through a standardized interview. The question was, “Was your RA diagnosed by a physician?” The questionnaire consists of three responses . Participants who choose 1 (Yes) were classified as RA. The interview was conducted individually by a trained professional investigator. Classification criteria for RA were not applied in defining RA in this study. Medical comorbidities were defined in the same way as the RA diagnosis. Information was collected on cardiovascular comorbidities including hypertension, dyslipidemia, diabetes, stroke, myocardial infarction or angina that were diagnosed by a physician. Information was also collected on malignancies including stomach cancer, colon cancer, breast cancer, cervical cancer, and lung cancer and other medical comorbidities including osteoarthritis, pulmonary tuberculosis, thyroid disease, depression, atopic dermatitis, depression, chronic kidney disease, hepatitis B, hepatitis C, and liver cirrhosis that were diagnosed by a physician. Classification criteria for osteoarthritis was not applied in defining osteoarthritis. Definition of osteoarthritis and other medical comorbidities were dependent on the information provided by the participants in the interview. Height and weight were assessed using standardized techniques and equipment. Height was measured to the nearest 0.1 cm using a portable stadiometer . Weight was measured to the nearest 0.1 kg using a Giant-150N calibrated balance-beam scale . Body mass index (BMI) was calculated by dividing weight by the square of height , were applied in all of the analyses. Univariable logistic regression analysis was performed to compare clinical characteristics between subjects with RA and subjects without RA. Multivariable logistic regression models were computed with comorbidities as a dependent variable, and RA as an independent variable, adjusting for socioeconomic and lifestyle characteristics . Odds ratios (ORs) with 95% confidence intervals (CIs) of RA for each of the comorbidities were calculated. P-values were corrected by Bonferroni’s method for multiple testing. Statistical analyses were performed using SAS . All 2. The prevalence of RA was 1.5%.Among 25,534 participants in the 2010–2012 KNHANES, 19,599 participants who were 19 years of age or older were selected. Then, 1,712 participants who had missing data for diagnosis of RA were excluded. In total, 17,887 participants were selected for analysis. The frequency of missing data for each item was very low, with a maximum of about 1%. The weighted  demographics and clinical characteristics of study population are presented in Clinical characteristics were analyzed according to the presence of RA . SubjectOn adjusting socioeconomic and lifestyle characteristics , RA was associated with an increased prevalence of myocardial infarction or angina . Angina was significantly related to RA rather than myocardial infarction. Prevalence of solid cancer was not significantly associated with RA after adjustment. Among other medical comorbidities, RA was associated with an increased prevalence of pulmonary tuberculosis , asthma , thyroid disease , depression  and hepatitis B  compared with the non-RA population .We evaluated the comorbidities of patients with RA using a nationwide survey conducted by the Korean government. The most frequently associated comorbidities in patients with RA included hypertension (30.3%), osteoarthritis 22.6%), dyslipidemia (14.1%), diabetes (12.9%), depression (11.2%), pulmonary tuberculosis (8.6%), thyroid disease (8.0%), asthma (7.3%), and myocardial infarction or angina (5.7%). These data partly correspond with COMORA, an earlier international, cross-sectional study, which reported that depression (15%), asthma (6.6%), and cardiovascular events (6%) were the most frequently associated diseases with RA [%, dyslipPTPN22) gene has been associated with RA in Caucasians, it does not play a role in Korean and Asian populations with RA [PADI4), solute carrier family 22 member 4 (SLC22A4), and Fc receptor like 3 (FCRL3) genes have been well documented among Asian populations, but not among the western Europeans [The results of our study indicated that Korean RA patients showed characteristics of a healthier lifestyle than the general population. The percentage of ex-smokers in the RA and non-RA groups was similar. However, the percentage of current smokers was significantly lower and never-smoking was significantly higher in subjects with RA, as compared with the non-RA population. Previous studies reported that the prevalence of cigarette smoking was higher in RA compared with the non-RA population; in addition, RA patients had a higher prevalence of cardiovascular disease than non-RA patients ,16–19. H with RA –41. In curopeans –46. ConsIn the current study, alcohol consumption was lower in subjects with RA. This is consistent with the recent meta-analysis that reported that low-to-moderate alcohol consumption was inversely associated with the development of RA, suggesting a protective effect ,48. AlcoIt is well known that cardiovascular disease is increased among RA patients, as compared with the general population ,51. The In the current study, patients with RA had an increased prevalence of lung cancer and colon cancer, as compared with the non-RA population. However, there was no significant relationship between cancer and RA after adjustment. A recent meta-analysis study showed that patients with RA were at an increased risk of lung cancer and lymphoma compared with the general population . ColorecAmong other medical comorbidities, RA was significantly associated with pulmonary tuberculosis, asthma, thyroid disease, depression and hepatitis B. The prevalence of pulmonary tuberculosis in this study was 4.2%. WHO reports that the incidence rates of active tuberculosis in South Korea in 2015 are 80 per 100,000 population . ConsideIn the current study, the prevalence of asthma was significantly increased in patients with RA. Several studies reported that the prevalence of asthma was lower in RA patients than in control groups –67. HoweWe found that RA was associated with an increased risk of thyroid disease. In the KANHNES survey, the type of thyroid disease was not investigated. The most common thyroid dysfunction in RA patients is of an autoimmune nature accompanied by elevated thyroid autoantibody titers. In addition, the concurrent affection of joint and thyroid gland is probably related to a genetic predisposition, most often HLA-DR . Chan etRA is a chronic illness that has physical as well as psychological effects. Comorbid depression is common in patients with RA, with a prevalence of 15%–42% ,74,75. TIn the current study, RA was associated with an increased prevalence of hepatitis B. This corresponds well with the recent nationwide data in Taiwan, which reported that RA patients had an increased risk of hepatitis B infection after adjustment for potential risk factors . ExtraheThe present study has some limitations. First, the different diagnoses were dependent on the information provided by the participants in the interview. Patients may misunderstand the information given by physicians. In this study, classification criteria were not applied for the definition of RA. This is a major limitation of this type of nationwide survey. The prevalence of RA in this study was 1.5%. Considering that the prevalence of RA is approximately 1% of the worldwide population ,81, the In conclusion, the overall prevalence of RA was 1.5% and patients with RA had healthier lifestyle characteristics than the non-RA population in Korea. Our study showed that RA was associated with an increased risk of cardiovascular disease, pulmonary tuberculosis, asthma, thyroid disease, hepatitis B, and depression after adjustment of socioeconomic and lifestyle characteristics. In addition, the prevalence of solid cancer was not significantly associated with RA. These findings collectively suggest that individuals with RA might be at risk of several medical comorbidities; therefore, clinicians need to pay attention to emerging comorbidities in patients with RA. Further study is required to verify the prevalence of medical comorbidities in patients with RA based on specific classification criteria of RA. We believe that the present study will provide important information for further research on the association of comorbidities and RA.


Background/Aims: Short tests to early detection of the cognitive impairment are necessary in primary care setting, particularly in populations with low educational level. The aim of this study was to assess the performance of Memory Alteration Test (M@T) to discriminate controls, patients with amnestic Mild Cognitive Impairment (aMCI) and patients with early Alzheimer’s Dementia (AD) in a sample of individuals with low level of education.Methods: Cross-sectional study to assess the performance of the M@T (study test), compared to the neuropsychological evaluation (gold standard test) scores in 247 elderly subjects with low education level from Lima-Peru. The cognitive evaluation included three sequential stages: (1) screening (to detect cases with cognitive impairment); (2) nosological diagnosis (to determinate specific disease); and (3) classification (to differentiate disease subtypes). The subjects with negative results for all stages were considered as cognitively normal (controls). The test performance was assessed by means of area under the receiver operating characteristic (ROC) curve. We calculated validity measures , the internal consistency , and concurrent validity  scores).Results: The Cronbach’s alpha coefficient was 0.79 and Pearson’s ratio coefficient was 0.79 (p < 0.01). The AUC of M@T to discriminate between early AD and aMCI was 99.60%  and to discriminate between aMCI and controls was 99.56% .Conclusions: The M@T is a short test with a good performance to discriminate controls, aMCI and early AD in individuals with low level of education from urban settings. Mild cognitive impairment (MCI) is a well recognized risk factor for Alzheimer’s disease (AD), and for the predemential phase of this and other dementias —Mano’s version , and patients with early AD , compared to the neuropsychological evaluation (gold standard test).The M@T is a valid screening test that assess the temporal orientation and different types of memory  and discriminates between healthy elderly subjects, patients with aMCI and patients with early AD. This is a cognitive test with high internal consistency and validity, short application (5–10 min), easy to perform and to interpret, developed in Spain , short term memory (10), semantic memory (15), free recall (10) and facilitated recall (10). Thus, the maximum score of this test is 50 points.The neuropsychological assessment is the detailed evaluation of the cognitive functions, by means of a neuropsychological battery adapted to Peruvian population. The battery included the following tests: Rey Auditory Verbal Learning Test  between March and September of 2015. We included subjects older than 60 years, Spanish speakers with low educational level , excluding those with any condition that might cause cognitive impairment non-related to neurodegenerative etiology , etc.) or that could affect their performance to realize the cognitive tests .Additionally, we excluded to patients that consumed any of following drugs: opioid analgesics, decongestants, anti-spasmodics, anti-cholinergics, anti-depressants, antiarrhythmics, antipsychotics, anti-emetics, anxiolytics and valproate.We requested the list of regular users  of the elderly care home centers. By means of simple random sampling (table of random numbers), the potential participants were selected until completing a quota of half of available population (sample size = 0.5 N), consented to participate, and provided information necessary to assess compliance with eligibility criteria. The evaluation of cognitive impairment was performed in three successive stages: (1) screening (to detect cases with cognitive impairment); (2) nosological diagnosis (to determinate specific disease that is the cause of cognitive impairment); and (3) final classification (to differentiate disease subtypes).In the screening phase, an integral clinical evaluation was performed, including measurement of anthropometry and blood pressure, application of Pfeffer Functional Activities Questionnaire (PFAQ) and cognitive screening tests (MMSE and CDT). If any cognitive test was positive for impairment, it was repeated by a different evaluator. The confirmed cases were considered as patients with cognitive impairment (PCI). According to educational level, the cutoff score used was 23 for subjects with 4 years of education, 21 for subjects with 1–3 years of education, and 18 for subjects with less than 1 year of education , urea, creatinine, vitamin B12, folic acid, free T3 and T4, and ultra-sensitive TSH), images studies (brain tomography and/or magnetic resonance imaging), and Beck Depression Inventory II (BDI-II) for discarding non-neurodegenerative causes of cognitive impairment. We applied the DSM-IV . The M@T was applied to study subjects in first stage and the evaluators were blinded to the results of this psychometric. The results of M@T were not used as part of the neuropsychological battery for diagnosis. The team of evaluators of the second and third phases (expert neurologists and neuropsichologists) was different from the team of the first phase (students of medicine and psychology supervised by expert neurologists).T tests (for quantitative variables) and Chi Square . We assessed the internal consistency  and the concurrent validity (Pearson’s ratio coefficient between the M@T and CDR scores).The corresponding descriptive statistics were performed. The analysis was performed comparing the cognitive groups  by pairs. For this purpose we applied We performed a logistic regression (logit) for each pair of study groups , using a model of two variables: final diagnosis as dependent variable and test as independent variable. We applied postestimation analysis to compute area under receiver operating characteristics (ROC) curve and graph ROC curve, and calculate validity measures .Additionally, we calculated the diagnostic accuracy  for M@T, MMSE and CDT. The maximum values of this measure were the standard for the cut-off scores selection of sensitivity, specificity and predictive values. Finally, we compared the AUC of this tests using the method of Hanley and McNeil. The tests were performed at 95% confidence using the STATA software (version 12.0).Universidad de San Martin de Porres.This study was carried out in accordance with the recommendations of the Council for International Organizations and Medical Sciences (CIOMS). A written informed consent was obtained from all participants or their carers in accordance with the Declaration of Helsinki. The protocol was approved by the Ethics Committee of the The first stage started with 346 participants, but 41 were missed . In the second stage, 22 of 305 participants were missed .Finally, 283 participants completed the third stage. However, 36 participants were not included in the present analysis because they were classified as non-amnesic MCI (16), vascular dementia (6), Frontotemporal dementia (4), dementia associated with Parkinson’s disease (2) and other unspecified dementias (8).Statistical analysis of the sociodemographic data, MMSE scores and M@T scores were performed according to the comparison groups. In patients with AD, compared to those with aMCI, age was significantly higher and test scores  were significantly lower. On the other hand, in the patients with aMCI the age was significantly higher and the M@T and CDT scores were significantly lower, compared to control subjects Table . The M@Tr = 0.79; p < 0.01) were good. In relation to the M@T cutoff, a score of 26 allows to discriminate between early AD and aMCI (sensitivity = 100.00% and specificity = 97.53%), with an accuracy of 98.41%. Similarly, a score of 35 allows discriminating between aMCI and controls (sensitivity = 99.17% and specificity = 91.11%), with an accuracy of 96.99%  and concurrent validity (p = 0.000) for all combinations of analyzed group pairs. Furthermore, the performance of M@T was significantly higher than CDT to discriminate between patients with aMCI from controls , which was slightly lower than that reported previously  on the performance of several cognitive tests  and orientation well, but no other types of cognitive impairment nor functionality. Therefore, the simultaneous use of brief functional tests to compensate for this deficiency is required.Recent studies in European populations have evaluated the ability of M@T to discriminate between aMCI and subjective memory complaints (SMC), showing an optimal performance in subjects with medium (Rami et al., Additionally, the future studies should include population with a broad variability of educational level and higher sample size. Thus, multivariate models could be applied to assess the factors that is statistically associated with clinical diagnosis, which includes the years of education.The M@T constitutes a brief, non-invasive and reliable cognitive test, which could be applicable for non-specialist physicians to support the discrimination between aMCI and early AD in primary care centers.NC performed the conception of the study. NC, DL, RM and EH-P designed the study. NC, DL, RM, SC-S, JC-A and LV-L collected the data. NC and EH-P analyzed and interpreted the data of the work. EH-P and NC drafted the first draft of the article. All authors critically revised the manuscript and approved the version to be published.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We conclude that the union rather than the intersection of the aforementioned tools is the best strategy for maximizing performance while minimizing the loss of time and resources in subsequent in vivo and in vitro experiments for functional validation of miRNA-target interactions.Target prediction is generally the first step toward recognition of bona fide microRNA (miRNA)-target interactions in living cells. Several target prediction tools are now available, which use distinct criteria and stringency to provide the best set of candidate targets for a single miRNA or a subset of miRNAs. However, there are many false-negative predictions, and consensus about the optimum strategy to select and use the output information provided by the target prediction tools is lacking. We compared the performance of four tools cited in literature—TargetScan (TS), miRanda-mirSVR (MR), Pita, and RNA22 (R22), and we determined the most effective approach for analyzing target prediction data . For this purpose, we calculated the sensitivity, specificity, precision, and correlation of these approaches using 10 miRNAs  and 1,400 genes  as targets of these miRNAs. The four tools provided a subset of high-quality predictions and returned few false-positive predictions; however, they could not identify several known true targets. We demonstrate that union of TS/MR and TS/MR/R22 enhanced the quality of  Caenorhabditis elegans development (MicroRNAs (miRNAs) are a large class of small non-coding RNAs [∼22 nucleotides (nts)] that post-transcriptionally regulate gene expression. They were first identified in the context of elopment , and theelopment . Their felopment . TherefoIn animals, a sequence of approximately seven nts in the 5′ region of the miRNA (ranging from nts 2 to 8), known as the seed region, guides the miRNA to its target mRNA. Five types of perfect Watson–Crick pairing of seed matches have been described so far, namely, 8-mer, 7-mer-m8, 7-mer-A1, 6-mer, and offset-6-mer in the descending order of the strength of their matches . The 8-mIrrespective of seed or non-seed match, miRNA pairing is largely prevalent with elements at the 3′ untranslated region (UTR) of target genes. However, studies have identified miRNA pairing to sites outside the 3′UTR, both in the coding region  and in tin silico tools are available for identifying putative miRNA targets. The main parameters used by these tools can be gathered and divided into three groups: duplex features, local context features, and global context features  and presence of flanking AU. SA evaluates the capacity of the mRNA to unfold into a potential secondary structure in the region containing the miRNA cognate sequence, which is known as the miRNA recognition element MRE; . The flaGlobal context features aggregate mRNA sequence properties with indirect influence on target recognition, such as whole transcript length, 3′UTR length, transcriptome abundance, pairing position at the 3′UTR, and sequence conservation. Sequence length evaluates the total length of the string analyzed, since the chances of false prediction increases with target length . The 3′UDespite the availability of several target prediction tools that use distinct parameters and strategies to search for putative targets, consensus about the best tool is lacking. In fact, experimental validation  has revealed many false-negative predictions, implying that further improvement of prediction tools is required. To circumvent this caveat, researchers use diverse strategies for determining putative miRNA targets, including intersection and union of predictions. However, this approach is being used indiscriminately, without well-defined criteria and rigorous comparative tests to assess the performance of the prediction strategies. Thus, whether union or intersection of results obtained from multiple tools improves the overall quality of target prediction is yet unknown.Here, we compared the performance of four widely used target prediction tools to identify the strategy that best predicts miRNA targets. Our results would assist researchers in selecting the correct candidates for subsequent experimental validation of miRNA-target interactions.We used TargetScan (TS), miRanda-mirSVR (MR), Pita (PT), and RNA22 (R22) pre-computed predictions, which are freely available online. TS, MR, and PT consider seed-based interactions in the 3′UTR, whereas R22 also considers non-seed based interactions (full-length matches) in the whole transcript.Table 1). In detail, the best predictions were those with conserved sites for TS. TS considers different cutoffs for conservation, according to seed match; for example, it is ≥0.8 for site 8-mer, ≥1.3 for site 7-mer-m8, and ≥1.6 for site 7-mer-A1, whereas sites 6-mer and offset 6-mer are always classified as non-conserved1. Best predictions of MR present good mirSVR score (≤ -0.1) and conserved sites . We exclusively used the best predictions from each database to maximize the quality of predictions :in silico target prediction tools are binary classifiers, these two functions can be used to evaluate the quality of each tool. Sensitivity measures a tool’s ability to identify bona fide miRNA targets, while specificity measures the capacity of the tool to correctly exclude a gene target that is not regulated by the miRNA (where TP (true positive) is the number of validated targets predicted, FN  is the number of validated targets not predicted, FP  is the number of predicted targets that were not validated, and TN (true negative) is the number of genes that were neither predicted nor validated. Sensitivity and specificity are mathematical functions that measure the quality of binary classifications. Since he miRNA .Knowledge about the proportion of true predictions within the total number of miRNA targets predicted is also important. Therefore, precision is calculated to evaluate the number of true targets among all predicted targets . FinallyN = 5). Finally, the average of the five replicates was calculated and subjected to statistical analysis  for each replicate (Table 1).To confirm whether the specificity values of the tools were biased due to the lack of false predictions in literature, we performed a control test by predicting putative targets in a random strings analysis. Toward this objective, we generated four different groups with 1,000 random strings each, totaling to 4,000 random strings. Groups of variable length  were tested because length highly influences the chances of false prediction . Then, wp-value < 0.05) since the data presented a Gaussian distribution.To compare the performance of each tool and the combinatorial method, we used one-way analysis of variance (ANOVA) and the Tukey test for multiple comparisons . MR itself predicted the highest number of targets among all the tools, of which 433 were validated and 33 were non-validated. PT predicted the lowest number , TS predicted 366 validated and 11 non-validated targets, and R22 predicted 325 validated and 60 non-validated. R22 predicted the highest number of validated targets not identified by any other tool , followed by MR , TS , and PT . Interestingly, of the 81 validated targets predicted exclusively by R22, 56 possessed non-canonical sites, 34 of which had sites only outside the 3′UTR (either seed-based or full-length), and 22 targets with sites inside the 3′UTR but with a mismatch in the seed region.Each target prediction tool noticeably generated different results. TS and MR had the highest number of mutual targets . The number of targets predicted by TS, MR, and PT  was equivalent to the number predicted by all tools together  , except for TS + PT , which predicted less targets than MR and majority vote.The majority of intersections consistently returned a lower number of predicted targets than any other approach, with the exception of the intersection of TS + MR  and majority vote , which predicted more targets than PT. All the unions predicted more targets than any other approach . However, approximately 18% of the validated targets (123) were not predicted by any tool. Supplementary Table Table 2 summarizes the sensitivity, specificity, and precision of all tools and methods. All methods showed striking specificity (>0.85) and precision (>0.80), but variable sensitivity. Considering the four tools individually, MR showed the highest sensitivity (0.62) and R22 showed the lowest specificity (0.89) and precision (0.81). TS and PT showed similar values of specificity and precision, but PT showed a significantly lower sensitivity.The union of the four tools undoubtedly returned the best sensitivity, with TS + MR + PT + R22 and TS + MR + R22 returning values above 0.80. Interestingly, the increase in sensitivity had no negative impact in the specificity and precision indexes. By contrast, the intersection of tools resulted in low levels of sensitivity, except for the intersection of TS + MR and majority vote that showed higher sensitivity than PT alone. Overall, there was no improvement in specificity and precision upon using the intersection approach, with values closely resembling to those obtained by PT or TS alone.Table 3), which corroborates the data from For the random strings control analysis, the specificity of all tools decreased with increase in string length . Majority vote showed the best performance among all intersections, which were similar to TS and MR. MR showed slightly higher performance than TS, whereas R22 showed the lowest performance among the individual tools, followed by PT. The unions TS + MR + PT + R22, TS + MR + PT, TS + MR + R22, MR + PT + R22, and TS + MR achieved the highest performances with no statistical differences between them  was not recovered in the outputs of any tool, indicating the existence of biological and/or methodological aspects that have not yet been addressed by prediction strategies and algorithms. These results also demonstrated that prediction tools have a tendency of identifying certain interactions between miRNA and target genes, but lack the ability to predict other putative interactions.Comparison of the strategies showed that intersection of TS, MR, and PT predicted twice the number of targets than the intersection of the four tools, whereas R22 predicted the highest number of exclusive interactions. These findings are related to the similarity in features of the three seed-based and 3′UTR-specific algorithms for target prediction, especially between TS and MR than with R22. Moreover, most of the targets predicted exclusively by R22 were predicted either outside the 3′UTR or they referred to non-seed based interactions, showing that the use of approaches with distinct search strategies may provide valuable information about miRNA-target interactions.Similarly, the low number of targets predicted by PT may be due to the low number of features used by its algorithm. Also, TS, MR, and R22 use input from the human genome version hg19 (released in 02/2009), whereas PT uses an older version . This may also explain the lower sensitivity of PT and demonstrates the importance of regularly updating the database.Table 4 for more details); however, we noticed that for TS, MR, and PT (that focus on the 3′UTR) the missing features in one tool appeared after an update. For instance, the most recent update of TS  uses 16 features that are considered important for miRNA target recognition, which generates a score called the “Weighted Context ++ Score” (WCS). The mirSVR score  is a new ranking system that scores targets predicted by miRanda using seven features to improve the MR approach. PT was last updated in 2008 (version 6) and considers only five parameters to perform the target prediction. R22 , which focuses on the entire transcript and full-length matches, uses a completely different subset of features, which may explain the differences in the targets identified by R22 and the other tools.Each tool has a unique set of learning attributes . For instance, TS has a practical and user-friendly online database, containing the highest number of species that can be analyzed among all tools. However, TS assigns the same targets for miRNAs with similar seed (miRNAs of the same family), which is a drawback considering that the 3′ region of the miRNA has an important impact on target recognition . The lowest performance was obtained by intersections of PT and R22. This may be due to the low sensitivity level of PT  in addition to the differences in the true targets identified by R22 and the other tools. Thus, intersections involving these tools exclusively identify few overlapping targets. Conversely, the intersections of TS + MR and majority vote, which do not depend on PT and R22, showed better performance, although they were inferior to those of TS and MR alone.Our analysis revealed that the intersection strategy showed the lowest performance. All intersections showed results that were inferior to the predictions of the individual tools . Interestingly, all validated targets predicted by PT (with the exception of two targets) were also predicted by one of the other tools . Thus, inclusion of PT is not required for the union approach. The main difference between the unions of TS + MR + R22 and TS + MR was in the balance of sensitivity and specificity/precision. The union of TS + MR + R22 has high sensitivity (0.82) but lower specificity and precision (0.86 for both), whereas the union of TS + MR has lower sensitivity (0.71) but high specificity and precision . Therefore, the choice of the best approach depends on the intended use of the target prediction output.The methods with the best performance were the unions of TS + MR + PT + R22, TS + MR + PT, TS + MR + R22, MR + PT + R22, and TS + MR, with no significant difference between them  and is also recommended for exploratory analysis or when most of the targets of the studied miRNA have been validated . The only disadvantage of using the union of two or more tools is that the scores of these tools  are composed of different parameters and do not correlate with each other. Therefore, this approach cannot be used if the final predictions require ranking. In such cases, a single tool should be selected according to the experimental design. For most cases, MR offers the best performance.Researchers perform target prediction analysis for two main reasons. First, to support the subsequent experimental validation of the miRNA–mRNA interaction predicted The poor performance of the intersection approach demonstrates the importance of sensitivity in miRNA target prediction. Until recently, target prediction tools provided outputs with hundreds of false-positive targets per miRNA, which fuelled efforts for enhancing the overall quality of predictions. However, our data shows that the last available updates of the tools have high specificity and precision levels, independent of the method used to combine the data. Thus, the new challenge is to improve the sensitivity of the analysis without decreasing specificity and precision. Since all the parameters governing miRNA-target interaction are not known, the tools use severe cutoffs in the existing parameters  to eliminate false positive predictions, which results in the exclusion of several correct targets. Identification of new features involving miRNA-target recognition may allow these tools to attenuate these cutoffs and increase the range of putative true targets. For example, it is well known that the 3′UTR undergoes alternative polyadenylation (aPa), resulting in transcripts with distinct 3′UTR length in different tissues , which mCurrent versions of the miRNA target prediction tools evaluated in this study possess high specificity and precision, generating results with negligible false positive rate. This shows that further use of the intersection strategy to obtain high quality predictions is not required. We also found that several true targets were not identified by these tools, necessitating the union of several tools for improving sensitivity. Thus, improvement of sensitivity should be the objective of the next updates.Overall, the unions of TS + MR, as well as that of TS + MR + R22 provided better results in miRNA target prediction in terms of higher specificity and precision, whereas the latter offers remarkable sensitivity. Therefore, we recommend using these approaches prior to designing target validation experiments. However, the union approach should be avoided when ranking of the output is required. In this scenario, MR provided the best performance.AO, LB, NL, and DP designed the experiment. AO analyzed the data. AO, LB, PN, and MH involved in data interpretation and results discussion. AO, LB, PN, and MH wrote the first draft of the manuscript. AO, NL, and DP critically reviewed and wrote the final manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer EL and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.