The central endocannabinoid system (ECS) and the hypothalamic-pituitary-adrenal-axis mediate individual responses to emotionally salient stimuli. Their altered developmental adjustment may relate to the emergence of emotional disturbances. Although environmental influences regulate the individual phenotype throughout the entire lifespan, their effects may result particularly persistent during plastic developmental stages . Here, we investigated whether prenatal stress – in the form of gestational exposure to corticosterone supplemented in the maternal drinking water (100 mg/l) during the last week of pregnancy – combined with a pharmacological stimulation of the ECS during adolescence , influenced adult mouse emotional behaviour and brain metabolism measured through in vivo quantitative magnetic resonance spectroscopy. Compared to control mice, URB597-treated subjects showed, in the short-term, reduced locomotion and, in the long term, reduced motivation to execute operant responses to obtain palatable rewards paralleled by reduced levels of inositol and taurine in the prefrontal cortex. Adult mice exposed to prenatal corticosterone showed increased behavioural anxiety and reduced locomotion in the elevated zero maze, and altered brain metabolism . Present data further corroborate the view that prenatal stress and pharmacological ECS stimulation during adolescence persistently regulate emotional responses in adulthood. Yet, whilst we hypothesized these factors to be interactive in nature, we observed that the consequences of prenatal corticosterone administration were independent from those of ECS drug-induced stimulation during adolescence. Individual emotional regulations depend on a continuous cross-talk between biological predispositions and environmental challenges Along with the phenotypic description of stress-induced alterations at the neuronal, endocrine and behavioural level, several studies attempted to elucidate their biological determinants. A large proportion of these studies focussed on the hypothalamic-pituitary-adrenocortical (HPA) axis (and its effectors) as a principal mediator of the environmental influences on individual plastic regulations Beside the HPA axis, other biological systems play a fundamental role in the regulation of emotions. Among these, the endocannabinoid system (ECS) has emerged as a fundamental regulator of emotional reactivity to stressful events in vivo non-invasive magnetic resonance imaging-guided spectroscopy (MRI/MRS) in behaviourally-relevant brain areas.Here we attempted to investigate the role of prenatal stress and of an exogenous modulation of the ECS activity during adolescence in the long-term regulation of emotional responses in rodents. Specifically, we hypothesised that prenatal stress and the pharmacological stimulation of the ECS during adolescence may exert additive effects in persistently affecting emotional reactivity throughout development. To address this hypothesis we supplemented mouse dams with corticosterone in their drinking water during the last third of pregnancy – through a well-validated methodology ad libitum water and rodent pellets . They were maintained on a reversed 12∶12 h light:dark cycle (lights on at 1900 h) with temperature at 21±1°C and relative humidity of 60±10%. Dams were inspected daily at 0930 h for delivery and day of birth was designated as postnatal day 1 (P1). Between delivery and weaning (P25) all subjects were kept under standard facility rearing conditions (cage cleaning once a week). Litters were not culled until weaning and dams that delivered less than six pups or litters in which male to female ratio was heavily skewed in one or the other direction (more than 75% of same sex pups) were excluded from the experiment. At weaning, each dam contributed 3–4 male offspring, which were allocated in cages of two unrelated subjects (one AFR and one PNC). Out of these four subjects, two were allocated to the VEH group and two were allocated to the URB597 group. All experimental procedures were performed according to European Communities guidelines (EC Council Directive 86/609), Italian legislation on animal experimentation (Decreto L.vo 116/92) and NIH guide for the care and use of laboratory animals. The study has been approved by the Service for Biotechnology and Animal Welfare of the Istituto Superiore di Sanità and authorized by the Italian Ministry of Health (Decree Nr. 217/2010-B). A general timeline of the study is reported in Twenty pregnant outbred CD-1 mice  were housed in standard polycarbonate cages (33.0×13.0×14.0 cm) with sawdust bedding and Pregnant dams were randomly assigned to one of the following prenatal conditions: (1) Animal Facility Rearing : apart from weekly cage cleaning dams where kept in the animal housing room without being handled or manipulated; (2) prenatal corticosterone administration  between gestational days 14–21 mice had free access to a solution of 100 µg/ml of corticosterone 21-hemisuccinate URB597 (cyclohexyl-carbamic acid 3′-carbamoyl-biphenyl-3-yl ester)  was dissolved in Tween-80 (5%), and 0.9% saline (95%) and administered i.p. at a volume of 0.1 ml/kg body weight. All animals were injected once daily for 10 consecutive days during adolescence (P29–38) with vehicle (VEH) or URB597 (0.4 mg/kg).Spontaneous locomotor activity in the home-cages was monitored continuously for 5 hours the first and last day of URB597 administration, starting 35 minutes after the URB597 injection. Daily spontaneous activity was monitored by means of an automatic device using small passive infrared sensors positioned on the top of each cage . The sensors (20 Hz) detected any movement of mice with a frequency of 20 events per second. Data were recorded by an IBM computer with dedicated software. No movements were detected by the sensors when mice were sleeping, inactive, or performed moderate self-grooming. Scores were obtained during 1-h intervals and expressed as counts per minute (cpm). The position of cages in the rack was such that mice of each group were equally distributed in rows and columns. The access of the authorized personnel to the animal room was not restricted and followed the routine schedule. The floor of the apparatus was cleaned after each mouse was tested, and the test was performed under dim illumination.To evaluate the exploration of an environment imposing on the animal an approach-avoidance conflict, mice were tested on the elevated 0-maze, a paradigm originally described by Approximately two months after the last URB597 injection, general locomotion was measured during a 30-min open-field test session. The testing apparatus consisted of a squared arena (40×40 cm) with a 50 cm-high wall made of black plastic. The session, performed under indirect dim light, started placing the animal in one corner of the arena. The floor of the apparatus was cleaned with a 10% ethanol 90% water solution after each session. Behavioural analysis was performed by a trained observer, using a computer and dedicated software . This allowed a detailed analysis of the following parameters: latency to access, frequency of entries, and time spent in the centre of the arena (20×20 cm); absolute levels of general locomotion.At age 5–6 months, mice were trained and tested in the progressive ratio (PR) schedule for reinforcement. We obtained three measures of reward motivation, namely, the total number of nose-pokes responses, total number of reinforcements obtained and the final ratio achieved/breakpoint. The apparatus, general schedule and food restriction procedures have been described elsewhere Following the acquisition of a stable level of nose poking on the FR3 schedule, the animals were transferred to the PR schedule, which requires the subjects to emit an increasing number of responses in order to obtain each reward. The ratios used in this experiment increased as follows: 3, 3, 6, 6, 10, 15, 21, 28, 36, 45, 55, 66, 78, 91, 105, 120, 136, 153, 171, 190, 210, 231, 253, 276, 300, 325, 351, 378, 406, and 435. The highest ratio achieved by each animal during a session represented its breakpoint value. The testing session began by placing the mouse into the chamber and terminated when subjects failed to reach the next nose-poke criterion within 8 min. Values for these measures correlated highly with each other and the final ratio achieved/breakpoint is presented.2 1 l/min  and maintained to the animal body temperature at 37.0±0.1°C by an integrated heating system. Gradient echo scout images were detected for accurate positioning of the head of the animal inside the magnet and spin echo sagittal anatomical images  were acquired for positioning the voxel for the MRS study. Single voxel localised 1H MR spectra  were collected from relevant brain areas: prefrontal cortex , hippocampus  and hypothalamus  as shown in At adulthood (>P160), the animals underwent MRI/MRS analyses, with the aim to measure any long-term modifications in biochemical parameters in relevant brain areas. All MRI and MRS experiments were conducted on a 4.7 T Varian Inova animal system , equipped with actively shielded gradient system . A 6-cm diameter volume coil was used for transmission in combination with an electronically decoupled receive-only surface coil . The shape of this receiver coil  was designed to optimally fit the dorsal surface of the mice’ head, centered over forebrain regions. Animals were anaesthetised with isofluran 1.5–2.5% in OSpectra were analysed using LCModel (Provencher 1993) that calculates the best fit to the experimental spectrum as a linear combination of model spectra (spectra of metabolite solutions). Seventeen metabolites were included in the basis set: alanine (Ala), aspartate (Asp), creatine (Cr), γ-aminobutyric acid (GABA), glucose (Glc), glutamate (Glu), glutamine (Gln), glycerophosphorylcholine (GPC), guanidoacetate (Gua), phosphorylcholine (PCho), myo-inositol (Ins), lactate (Lac), N-acetylaspartate (NAA), N-acetylaspartylglutamate (NAAG), phosphocreatine (PCr), scyllo-inositol, and taurine (Tau). Spectra of lipids and macromolecules were also included in the basis set. Only those metabolites that were estimated to have Cramer-Rao lower bounds (CRLB) less than 20%, which corresponded to an estimated concentration error <0.2 µmol/g, were included into the quantitative analysis. In some cases, metabolites that have resonance overlapped or very close are also given as their sum.The signals due to inositol, glutamate and glutamine underwent J-coupling modulation with increasing TEs. However, the decreases in these signals due to J-coupling at TE = 23 ms were automatically accounted for in the LCModel basis sets.To avoid litter effects, only one offspring per dam was used in each test. Additionally, in order to minimize potential carryover effects due to repeated testing, mice used for the behavioural observations during adolescence were tested either for the progressive ratio or for general locomotion and elevated-0-maze (E0M). This strategy of allocation of subjects to experimental groups was aimed at guaranteeing that at least seven unrelated individuals constituted the statistical unit. Thus, for the behavioural analysis we tested N = 7/8 subjects per prenatal treatment per drug; for the MRI/MRS, we tested N = 7 subjects per prenatal treatment per drug. The total number of subjects was n = 60. An independent group of subjects were tested for MRI/MRS. Data were analysed using general analysis of variance for split-plot designs. Post-hoc comparisons were performed using Tukey’s test. Statistical analysis was performed using Statview II . Data are expressed as mean ± standard error of the mean (SEM). Significance level was set at p<0.05.In particular, for the analysis of fluid intake the general model was 2 prenatal treatment (AFR vs. PNC) x 4 days. The general model for body weight gain was 2 prenatal treatment x 2 drug (VEH vs. URB597) x 9 time points. The general model for general locomotion was 2 prenatal treatment x 2 drug x 2 time points (P29 vs. P38). The general model for progressive ratio schedule was 2 prenatal treatment x 2 drug x 4 session days. Finally, the general model for the E0M, the open field and the different metabolites measured through MRI/MRS was 2 prenatal treatment x 2 drug. Prenatal treatment and drug were between-subject factors while all other variables were within-subject factors.To evaluate whether corticosterone supplementation resulted in variations in overall drinking, and to quantify the corticosterone ingested by each dam, we constantly monitored fluid intake in all dams during pregnancy. Levels of fluid intake were indistinguishable between dams having access to tap water and dams supplemented with corticosterone  = 0.07, NS). The daily average fluid intake shown by PNC dams resulted in 1.32±0.06 mg of corticosterone.Body weight was constantly monitored between infancy and adulthood in order to evaluate whether prenatal corticosterone and URB597 administration during adolescence directly affected offspring body growth. Prenatal corticosterone administration significantly reduced body weight between infancy through adolescence see . Thus, dAcute URB597 administration during adolescence significantly reduced absolute levels of general locomotion  = 8.7, P = 0.006, see Anxiety related behaviour was assessed in a single 5-min E0M experimental session. In the absence of significant differences due to URB597 administration during adolescence  = 0.330, NS), adult mice exposed to corticosterone during gestation predictably showed a significant reduction in the time spent in the open sectors  = 7.306, p = 0.0115, see Additionally, compared to AFR controls, PNC mice showed a reduced number of transitions between the open and the closed sectors of the E0M  = 8.591, p = 0.006). Finally, the administration of URB597 during adolescence also reduced the number of transitions between the open and the closed sectors of the E0M  = 5.023, p = 0.033).Data on absolute levels of locomotion during the 30-min session of the open field revealed that the semi-chronic exposure to URB597 during adolescence had significant long-term effects. Specifically, in the absence of differences due to prenatal treatment  = 0.775, NS), adult mice exposed to URB597 during adolescence displayed reduced levels of locomotion compared to VEH-injected subjects  = 4.226, p = 0.0492). Additionally, this effect was independent of prenatal treatment  = 0.073, NS). Finally, neither PNC nor URB597 administration resulted in variations in the time spent in the centre of the open field  = 2.71, NS; drug F = 0.883, NS; prenatal treatment x drug F = 0.420, NS).In order to evaluate whether prenatal corticosterone exposure and pharmacological stimulation of the ECS during adolescence affected depressive-related behavioural responses, we assessed adult mice on a progressive ratio schedule. Thus, mice were required to nose poke to obtain a palatable reward in an operant task. Following three sessions of fixed FR1 and three sessions of FR3, mice were required to perform an increasing number of operant responses to obtain the reward. During the first sessions of the training phase (FR1), URB597 mice showed a reduced number of nose-pokes compared to VEH individuals  = 4.554, p = 0.045). Yet, such difference disappeared during the FR3 phase  = 3.75, NS), thus indicating that all mice achieved a similar level of operant responding before the beginning of the PR phase. The break point achieved, the number of operant responses and the number of rewards obtained were significantly reduced in URB597 mice  = 4.81, p = 0.040; F = 4.85, p = 0.039; F = 4.80, p = 0.040, respectively). Prenatal corticosterone administration did not influence these differences  = 0.603, p = 0.446 (breakpoint); F = 0.432, p = 0.639 (number of operant responses); F = 1.716, p = 0.204 (number of rewards)).The quantitative results of these analyses are summarised in Whilst prenatal corticosterone exposure had no effect on metabolites’ concentration in the prefrontal cortex, URB597 administration during adolescence resulted in significant long-term reductions in inositol  = 11.97, p = 0.0023; prenatal treatment x drug F = 0.001, NS) and taurine  = 15.070, p = 0.001; prenatal treatment x drug F = 0.007, NS) concentrations.In the absence of differences related to URB597 administration during adolescence, we observed that PNC subjects showed reduced concentrations of inositol  = 4.185, p = 0.016; prenatal treatment x drug F = 1.66, NS) and NAA compared to AFR controls  = 5.041, p = 0.035; prenatal treatment x drug F = 3.51, P = 0.074). The other metabolites were apparently unaffected by prenatal corticosterone administration.In line with what we observed in the hypothalamus, URB597 administration during adolescence did not modify the concentrations of all the metabolites investigated in the hippocampus. Conversely, prenatal corticosterone exposure permanently modified relative concentrations of Glu and taurine in the hippocampus. Specifically, compared to AFR individuals, PNC adult mice showed increased concentrations of Glu  = 4.64, p = 0.04; prenatal treatment x drug F = 0.021, NS) and reduced concentrations of taurine  = 5.992 p = 0.022; prenatal treatment x drug F = 0.73, NS).In the present study, we showed that both prenatal exposure to corticosterone (PNC) and cannabinoid URB597 administration during adolescence significantly modify emotional regulations during development and alter, in the long-term, several indices of brain metabolism. Specifically, PNC individuals showed substantial reductions in body weight during infancy, increases in behavioural indices of anxiety in the E0M, and alterations in inositol and NAA concentrations in the hypothalamus, and taurine in the hippocampus (coupled with increased levels of Glu). URB597 subjects showed significant reductions in general locomotion, operant responding to obtain palatable rewards and in inositol and taurine concentrations in the prefrontal cortex. Thus, both treatments significantly affected long-term regulations in the experimental individuals; yet, whilst we originally hypothesised these effects to be synergistic, they were independent.The consequences of prenatal stress have been addressed, in literature, using a number of different paradigms, ranging from psychological and physical stressors  and observed significant increments in glutamate and significant reductions in taurine concentrations PNC mice also showed significantly increased glutamate and significantly decreased taurine in the hippocampus. With respect to the former, its widely distributed action as a neurotransmitter in the brain confers on it a fundamental role in neuronal excitability. An abnormal increase in glutamate has been associated with excitotoxicity Although these effects are most likely mediated by the direct elevation in corticosterone concentrations in the dams One of the main goals of this study was to demonstrate that exposure to cannabinoid agonists during adolescence may relate to alterations in emotional reactivity in adulthood. This hypothesis has been recently reviewed by Casadio and collaborators To investigate the consequences of ECS modulation during adolescence, rather than using a direct cannabinoid agonist, we used an indirect agonist acting through the inhibition of the fatty acid amide hydrolase, the enzyme catalysing the degradation of anandamide. Compared to direct agonists, the administration of URB597 allowed modulating the ECS within a physiological range. Thus, rather than directly binding CB receptors, URB597 inhibits the degradation of endocannabinoid ligands whenever they are naturally elevated on demand, ultimately protracting their action In line with our expectations, URB597 administration resulted in a short-term reduction of general locomotion. Beside the well-known effects of direct cannabinoid agonists on locomotor activity in rodents The role of cannabinoid receptors in the acquisition and preservation of operant responding for palatable rewards has been detailed by Ward and Dykstra (2005). Specifically, they observed that, compared to wild-type controls, CB1 receptor knockout mice required a longer number of sessions to acquire operant responding and that the break point achieved was significantly reduced Finally, present data do not support the view that cannabinoid exposure during adolescence may result in long-term increase in anxiety-related behaviour e.g. . SpecifiIn contrast with our predictions, rather than being interactive in nature, the effects of the developmental interventions were independent from each other. Specifically, based on the cross-talk between the ECS and the HPA axis Table S1Levels of metabolites (median (interquartile range)) in prefrontal cortex (PFC), hypothalamus and hippocampus of adult mice, measured through MRI/MRS. *p<0.05 significantly different from AFR-VEH in post-hoc tests. AFR = animal facility rearing; PNC = prenatal corticosterone administration; VEH = vehicle; URB597 = URB597 administration between P29–38.(DOC)Click here for additional data file.


By deleting E. coli’s phosphoglycerate mutases, we divide its central metabolism into an ‘upper’ and ’lower’ metabolism, each requiring its own carbon source for the bacterium to grow. Glycerate can serve to replace the upper or lower carbon source depending on the product of glycerate kinase. Using this strategy we show that while glycerate kinase from Arabidopsis thaliana produces 3-phosphoglycerate, both E. coli’s enzymes generate 2-phosphoglycerate. This strategy represents a general approach to decipher enzyme specificity under physiological conditions.Apart from addressing humanity’s growing demand for fuels, pharmaceuticals, plastics and other value added chemicals, metabolic engineering of microbes can serve as a powerful tool to address questions concerning the characteristics of cellular metabolism. Along these lines, we developed an  In fact, numerous enzymes, most belonging to secondary metabolism, were assigned a new catalytic role after further studies shed light on their actual role in vivo, e.g., [E. coli’s glycerate kinase enzymes present such an interesting case in central carbon metabolism. During the last decades several studies on the bacterium’s two glycerate kinases, encoded by garK and glxK, were published. Earlier in vitro assays claimed that these enzymes generate 3-phosphoglycerate [glxK resulted in no clear phenotype when glycolate, glyoxylate or glycerate are provided as sole carbon sources. On the other hand, deletion of garK led to a severe retardation of growth using these substrates.The identification of the o, e.g., –4. More lycerate ,6 while lycerate ,8. In falycerate  we foundin vitro experiments supporting 2-phosphoglycerate as being the product of E. coli’s glycerate kinase enzymes are quite compelling , we reain vivo metabolic assay that identifies the enzyme product via a simple growth selection experiment. We started by deleting the two endogenous phosphoglycerate mutase enzymes . As far as we know, this is the first time such a double knockout mutant was generated [e.g., glycerol, and a ‘lower’ one, e.g., pyruvate . The present ; alterna present .E. coli’s protein products of garK (Ec garK) and glxK (Ec glxK), as well as Arabidopsis thaliana’s glycerate kinase (At glyK), which is known to produce 3-phosphoglycerate [gpmA, ΔgpmM strains, which were about half of the WT strain ; these i.e., having higher Gibbs energy of formation). In fact, the conversion of 3-phosphoglycerate to 2-phosphoglycerate has ΔrG’° > +6 kJ/mol under E. coli’s physiological conditions of pH 7.5 and ionic strength of 0.25 M [rG’° < -18 kJ/mol, under the same conditions), it makes perfect sense for the cell to produce the more energetic compound that can be converted to its counterpart favorably. If 3-phosphoglycerate was the kinase’s product, its conversion to 2-phosphoglycerate and its downstream metabolites will suffer from a reduced thermodynamic driving force due to the energetic barrier [What is the physiological difference between phosphorylating glycerate at the alpha position and the beta position? We speculate that the main difference is energetic. The proximity of the negatively charged phosphate to the negatively charged carboxyl in 2-phosphoglycerate makes it more energetic than 3-phosphoglycerate . The strogenase , one canrogenase  and the rogenase  if and oE. coli’s garK and glxK genes were amplified directly from an E. coli K12 Mg1665 strain. The gene encoding for At glyK was amplified from Arabidopsis thaliana’s cDNA library. The primers used are given below. Cloning was done using the “no background” cloning method we previously developed [glxK and Arabidopsis‘s glyK genes were removed using a silent point mutation via the overhang extension procedure (primers used are given below) [eveloped ; the glyeveloped . The Econ below) .Gene deletions were performed using a standard P1 phage transduction ; all donPrimers:9 cells for 1 min at 9,500 g. The pellets were re-suspended in 1 ml of M9 medium, from which 10 μl were dispensed into a 96-multiwell plate. Each well also contained 200 μl of M9 medium supplemented with 0.2% appropriate carbon sources, as well as antibiotics (kanamycin 50mg/L and chloramphenicol 30mg/L). Notably, by taking ≈109 cells for the initial centrifuge, we made sure that the initial OD in each well is ≈0.05.The different strains were grown overnight in 5 ml LB media containing Kanamycin (50mg/L) and chloramphenicol (30mg/L). Following OD measurements, we centrifuged ~10The plates were cultivated within a LiCONiC incubator shaker at 37°c, 100% humidity and ambient gas composition. Every 90 minutes plates were automatically carried by a robotic arm  to a plate reader , in which OD (600nm) measurements were taken.We picked several clones of each transformation for growth experiments. Different clones of the same strains were found to share the same qualitative growth phenotype, although with somewhat different growth yield and dynamics.


Multiple Sclerosis International has retracted the article titled “Consensus Guidelines for CSF and Blood Biobanking for CNS Biomarker Studies” [ Neurology December 1, 2009; 73(22): 1914–1922. doi: 10.1212/WNL.0b013e3181c47cc2 [ Multiple Sclerosis International and the authors, but an arrangement with the American Academy of Neurology and its publisher, Wolters Kluwer, to republish these guidelines did not include permission to publish in this journal.Studies”  for dupl


Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers  are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability  than a traditional RRS approach  that does not employ cross-validation sampling for all three datasets. A growing amount of clinical research employs computerized classification of medical imaging data to develop quantitative and reproducible decision support tools –3. A keyAnother issue in utilizing computerized image analysis for clinical research is the need to select the best classifier at the onset of a large-scale clinical trial . The selIn this paper, we aim to overcome the major constraints on classifier selection in clinical trials that employ medical imaging data, namely (1) the selection of an optimal classifier using only a small subset of the full cohort and (2) the prediction of long-term performance in a clinical trial as data becomes available sequentially over time.Given a small pilot dataset, can we predict the error rates associated with a classifier assuming that a larger data cohort will become available in the future?Will the relative performance between multiple classifiers hold true as data cohorts grow larger?To this end, we aim to address crucial questions that arise early in the development in a classification system, namely:a priori prediction of classifier performance using only a small dataset.Traditional power calculations aim to determine confidence in an error estimate using repeated classification experiments , but do K-fold cross-validation framework to ensure that all samples are used for both classifier training and testing  that characterize the behavior of error rate as a function of training set size.Due to the heterogeneity present in biomedical imaging data, we extend the RRS-based approach originally used to model gene microarray data  by incor testing . First, Application of the RRS model to patient-level medical imaging data, where each patient or image is described by a single set of features, is relatively well-understood. Yet disease classification in radiological data (e.g. MRI) occurs at the pixel-level, in which each patient has pixels from both classes (e.g. diseased and non-diseased states) and each pixel is characterized by a set of features . In thisk-nearest neighbor (kNN) classifier [This paper focuses on comparing the performance of three exemplar classifiers: (1) the non-parametric assifier , (2) theassifier , and (3)assifier , 18. Allassifier , (2) graassifier , and (3)assifier .The novel contributions of this work include (1) more stable learning curves due to the incorporation of cross-validation into the RRS scheme, (2) a comparison of performance across multiple classifiers as dataset size increases, and (3) enabling a power analysis of classifiers operating on the pixel/voxel level (as opposed to patient/sample level), which cannot be currently done via standard sample power calculators.Methods section presents a description of the methodology used in this work. Experimental Design includes a description of the datasets and experimental parameters used for evaluation. Results and Discussion are subsequently presented for all experiments, followed by Concluding Remarks.The remainder of the paper is organized as follows. First, the x ∈ 𝓓 is denoted by yt ∈ {ω1, ω2}. A set of training set sizes N = {n1, n2, …, nN}, where 1 ≤ n ≤ |𝓝| and |·| denotes set cardinality.For all experiments, a dataset 𝓓 is divided into independent training 𝓝 ⊂ 𝓓 and a testing 𝓣 ⊂ 𝓓 pools, where 𝓝∩𝓣 = ∅. The class label of a sample n ∈ N, a total of T1 subsets 𝓢(n) ⊂ 𝓝, each containing n samples, are created by randomly sampling the training pool 𝓝. For each n ∈ N and i ∈ {1, 2, …, T1}, the subset Si(n) ∈ 𝓢 is used to train a corresponding classifier Hi(n). Each Hi(n) is evaluated on the entire testing set 𝓣 to produce an error rate ei(n). The mean error rate for each n ∈ N is calculated asThe estimation of classifier performance first requires the construction of multiple classifiers trained on repeated subsampling of the limited dataset. For each training set size Permutation tests are a well-established, non-parametric approach for implicitly determining the null distribution of a test statistic and are primarily employed in situations involving small training sets that contain insufficient data to make assumptions about the underlying data distribution , 20. In Si(n) is compared against the performance of randomly labeled training data. For each Si(n) ∈ 𝓢(n), a total of T2 subsets n samples, are created. For each n ∈ N, i ∈ {1, 2, …, T1}, and j ∈ {1, 2, …, T2}, the subset yr ∈ {ω1, ω2} and used to train a corresponding classifier n, a p-valueθ(z) = 1 if z ≥ 0 and 0 otherwise. Pn is calculated as the fraction of randomly-labeled classifiers Pn < 0.05, i.e. there is a statistically significant difference between M = {n:n ∈ N, Pn < 0.05} includes only those n ∈ N that have passed the significance test.To ensure the statistical significance of the mean error rates K-fold cross-validation strategy. In this paper, the dataset 𝓓 is partitioned into K = 4 pools in which one pool is used for evaluation while the remaining K − 1 pools are used for training to produce mean error rates k ∈ {1, 2, …, K}. The pools are then rotated and the subsampling and permutation tests are repeated until all pools have been evaluated exactly once. This process is repeated over R cross-validation trials, yielding mean error rates r ∈ {1, 2, …, R}. For all training set sizes that have passed the significance test, i.e. ∀n ∈ M, learning curves are generated from a comprehensive mean error ratek ∈ {1, 2, …, K} and iterations r ∈ {1, 2, …, R}.The selection of training 𝓝 and testing 𝓣 pools from the limited dataset 𝓓 is governed by a n, a is the learning rate, and α is the decay rate. The Bayes error rate b is defined as the lowest possible error given an infinite amount of training data [a, α, and b are calculated by solving the constrained non-linear minimization problema, α, b ≥ 0.The power-law model  describeror rate  for traiing data . The modN at the patient-level. Definition of the K training and testing pools as well as creation of each subsampled training set Si(n) ∈ 𝓢 are also performed at the patient-level. Training of the corresponding classifier Hi(n), however, is performed at the pixel-level by aggregating pixels for all patients in Si(n). A similar aggregation is done for all patients in the testing pool 𝓣. By ensuring that all pixels from a given patient remain together, we are able to perform pixel-level calculations while avoiding the sampling bias that occurs when pixels from a single patient span both training and testing sets.The methodology presented in this work can be extended to such pixel- or voxel-level data by first selecting training set sizes T1 = 50 subsampling trials, T2 = 50 permutation trials, and R = 10 independent trials of K = 4 fold cross-validation. In addition, all experiments employ the k-nearest neighbor (kNN), naive Bayes (NB), and Support Vector Machine (SVM) classifiers. A more detailed description of each classifier is presented in Our methodology is evaluated on a synthetic dataset and 3 actual classification tasks traditionally affected by limitations in the availability of imaging data . All expThe three different datasets used in this study were retrospectively acquired from independent patient cohorts, where the data was initially acquired under written informed consent at each collecting institution. All 3 datasets comprised de-identified medical image data and provided to the authors through the IRB protocol # E09-481 titled “Computer-Aided Diagnosis of Cancer” and approved by the Rutgers University Office of Research and Sponsored Programs. Further written informed consent was waived by the IRB board, as all data was being analyzed retrospectively, after de-identification.1 comprises anonymized hematoxylin and eosin (H & E) stained needle-core biopsies of prostate tissue digitized at 20x optical magnification on a whole-slide digital scanner. Regions corresponding to PCa were manually delineated by a pathologist and used as ground truth. Slides were divided into non-overlapping 30 × 30-pixel tissue regions and converted to a grayscale representation. A total of 927 features including first-order statistical, Haralick co-occurrence [N = {25, 30, 35, 40, 45, 50, 55}, was used to extrapolate error rates  a triage mechanism to help pathologists spend less time analyzing samples without cancer and (2) an initial step for decision support systems that aim to quantify disease aggressiveness via automated Gleason grading . Datasetcurrence , and stecurrence   or high (mBR > 7) grade. First, boundaries of 30–40 representative epithelial nuclei were manually segmented in each image region , grade is routinely used to characterize tumor differentiation in breast cancer (BCa) histopathology ; yet, ite region . Using te region . A singl regions .3 comprises 34 anonymized 1.5 Tesla T2-weighted MRI and MRS studies obtained prior to radical prostatectomy, where the ground truth was defined (as cancer and benign metavoxels) via visual inspection of MRI and MRS by an expert radiologist [N = {2, 4, 6, 8, 10, 12}, followed by LOO cross-validation on a larger dataset of 34 patients (Magnetic resonance spectroscopy (MRS), a metabolic non-imaging modality that obtains the metabolic concentrations of specific molecular markers and biochemicals in the prostate, has previously been shown to supplement magnetic resonance imaging (MRI) in the detection of PCa , 26. Theiologist  Fig 4)..3 compripatients .1 and corresponding experimental parameters from Experiment 1. However, since traditional RRS does not use cross-validation, a total of This experiment compares the results of Experiment 1 with the traditional RRS approach, using both dataset 𝓓N = {25, 30, 35, 40, 45, 50, 55} in conjunction with a kNN classifier. Validation is subsequently performed on a larger dataset containing 500 samples . This suggests that the classifier yielding the best results for the smaller dataset may not necessarily be the optimal classifier as the dataset increases in size.Learning curves from kNN and NB classifiers yield predicted error rates similar to their LOO cross-validation errors (0.1552 for both classifiers) as shown in Fig ncreases . However1, the LOO error for both the NB and SVM classifiers  fall within the range of the predicted error rates  predict long-term error rates when only small pilot studies may be available and (2) select the classifier that will yield the lowest error rates when large datasets are available in the future. Predicting classifier performance from small datasets is difficult because the resulting classifiers often produce unstable decisions and yield high error rates. In these scenarios, traditional RRS approaches have previously been used to extrapolate classifier performance (e.g. for gene expression data). Due to the heterogeneity present in biomedical imaging data, we employ an extension of RRS in this work that uses cross-validation sampling to ensure that all samples are used for both training and testing the classifiers. In addition, we apply RRS to voxel-level studies where data from both classes is found within each patient study, a concept that has previously been unexplored in this regard. Evaluation was performed on three classification tasks, including cancer detection in prostate histopathology, cancer grading in breast histopathology, and cancer detection in prostate MRS.We demonstrated the ability to calculate error rates with relatively low variance from three distinct classifiers . A direct comparison of the learning curves showed that the more robust NB and SVM classifiers yielded lower error rates than the kNN classifier for both small and large datasets. A limitation of this work is that all datasets comprise an equal number of samples from each class in order to reduce classifier bias from a machine learning standpoint. However, future work will focus on application to imbalanced datasets where class distribution is based on the overall population . In addition, we will incorporate additional improvements to the RRS method (e.g. subsampling of testing set as in RIDT) while maintaining a robust cross-validation sampling strategy. Additional directions for future research include analyzing the effect of (a) noisy data on different classifiers  and (b) S1 AppendixkNN), Naive Bayes (NB), and Support Vector Machine (SVM). For ease of the reader we provide a metholodogical summary for each of these classifiers with appropriate descriptions and equations.Each experiment in this paper employs three classifiers: k-nearest neighbor ((PDF)Click here for additional data file.


Following stimulation in each trial, participants actively chose whether the caress they would receive in the next trial would be the same speed (“repeat”) or different (“change”). Since preferred stroking speeds should be sought with greater frequency than non-preferred speeds, this paradigm provided a measure of such preferences in the form of active choices. The stimulation velocities were implemented with respect to the differential subjective pleasantness ratings they elicit in healthy subjects, with intermediate velocities  considered more pleasant than very slow or very fast ones. Such pleasantness ratings linearly correlate with changes in mean firing rates of unmyelinated low-threshold C-tactile (CT) afferent nerves in the skin. Here, gentle, dynamic stimulation optimal for activating CT-afferents not only affected behavioral choices, but engaged brain regions involved in reward-related behavior and decision-making. This was the case for both hairy skin of the arm, where CTs are abundant, and glabrous skin of the palm, where CTs are absent. These findings provide insights on central and behavioral mechanisms underlying the perception of affective touch, and indicate that seeking affective touch involves value-based neural processing that is ultimately reflected in behavioral preferences.Affective touch is a dynamic process. In this fMRI study we investigated affective touch by exploring its effects on overt behavior. Arm and palm skin were stroked with a soft brush at five different velocities (0.3, 1, 10, 3, and 30 cm s Affective touch is an emotional form of social interaction, and the only one that relies on bodily contact. Its importance for social connection and well-being is essential from infancy onward  afferent nerve fibers in human skin convey signals related to the hedonic value of a soft caress  and on different skin types (hairy and glabrous) would differentially affect their hedonic evaluation and preferences, and give rise to different patterns of behavioral and neural activation. These speed and location conditions were therefore incorporated in a novel paradigm designed to characterize hedonic evaluation in terms of its impact on behavioral choices for both hairy and glabrous skin. The feedback-based paradigm allowed participants to choose the speed of gentle brushing stimulation they received. In each trial, the subject received brush strokes on the forearm (hairy skin) or palm (glabrous skin) at one of the five different speeds. After each trial, participants indicated by button-press whether they would rather receive the same stroking speed again (“repeat”) or change to another one, randomly selected by the computer (“change”). This was designed to approximate everyday interpersonal touch behavior, in which individuals decide to seek a specific touch stimulus, perhaps based on experience or past evaluations.With this candidate pathway as a starting point, we therefore hypothesized that stroking participants’ skin at different speeds . The procedures were approved by the ethics committee of the University of Gothenburg, in accordance with the Declaration of Helsinki. Participants gave informed consent and were compensated at 200 Swedish crowns (22 Euro) per session.−1, 1 cm s−1, 3 cm s−1, 10 cm s−1, and 30 cm s−1. Presentation of the initial velocity was random, and after the first trial subjects could choose the velocity presented in the next trial by pressing one of two buttons with their right hand, indicating whether to repeat the previous velocity (“repeat”) or to change to a new velocity randomly selected by the computer program (“change”).Stimuli consisted of single brush strokes over 5 cm of left forearm or palm skin using a soft 70 mm-wide goat hair artist’s brush, brushing in a distal to proximal direction. Brush strokes were delivered manually at 5 different velocities: 0.3 cm sEach trial began with a 2 s inter-trial interval during which subjects fixated their gaze on a dot presented centrally on the screen. There followed a stimulation interval lasting between 2 and 16 s, depending on the velocity delivered. A 1 s interval occurred between stimulation and the onset of the response cue. The response cue consisted of the sentence “Repeat or Change?” remaining on the screen for 3 s as the subjects pressed the button indicating their choice . Data were collected in two runs during one scanning session, with arm and palm stimuli presented in separate runs to avoid movement artifacts associated with turning the arm to and from a pronated position. Run order was counterbalanced across subjects. Importantly, since the design was feedback-based, the number of trials and volumes per run varied between subjects. To counteract “shift” biases while ensuring a minimum number of stimulations per velocity for all participants, each velocity was presented a baseline minimum of 6 times over the whole run. To counteract “stay” biases, the program automatically changed to a different randomly selected velocity after two consecutive repeats. The subjects were aware that they would receive a maximum of three trials of the same velocity in a row.The experimental design was a 2 × 5 factorial with the factors location  and velocity . The experimenter (I.P.) was trained in the delivery of the stimuli, and during the experiment was guided by a visual meter with a moving stripe representing the velocity and distance in each trial. Visual cues to the participant were projected onto a screen positioned near the participants’ feet, visible through mirrors affixed to the head coil. Occluders were placed on the mirror to ensure that participants’ field of view was limited to the screen and did not include the stimulated limb or the experimenter. Each run began and ended with a fixation period of 45 s to allow a return to baseline.A 1.5 T Philips Intera magnetic resonance imaging (MRI) scanner with a SENSE head coil was used. For functional imaging, a single-shot echo-planar imaging sequence was used  = 3000, echo time = 50 ms, flip angle = 90º, field-of-view (FOV) 230 mm). The scanned area included 30 axial slices, 5 mm thick, with no gap, at 64 × 64 voxel in-plane resolution, which covered the whole cerebral cortex and the cerebellum. To minimize head movement, participants’ heads were stabilized with a vacuum hood filled with polystyrene balls .p < 0.05) was imposed based on the binomial distribution of the responses. Therefore velocities for which the subjects showed an above-chance mean of >45% selection of “repeat” over “change” responses were taken to indicate a positive preference for brush stroking stimulation at that velocity; those <45% were taken to indicate no preference or negative preference.Behavioral data were analyzed using Statistical Package for the Social Sciences . To obtain a measure of preference per velocity, the mean ratios of the number of repeats for all active choices  for each velocity were converted to percentage values. A chance cutoff of 45% (−1). Further analysis was conducted to assess the curve of the percentage value pattern across velocities to and address potential similarities with the pleasantness’ rating pattern across velocities  with factors location  and velocity  and exploratory ROIs analysis . For each participant, three different general linear models (GLMs) were created for each of the 2 runs: “Stimulation interval” capturing activation during the brush stroking on the skin; “Evaluation interval” capturing post-stimulus processing; skin stimulation and “Preference ratio” capturing behavioral preference across velocities for arm and palm.Two predictors modeled the intervals corresponding to the tactile stimulation period in each trial. One predictor (“repeat”) included all trials in which the participant selected “repeat.” The other predictor (“change”) included all trials in which the participant selected “change”. To investigate BOLD signal changes following tactile stimulation, a whole-brain contrast compared all stimulation intervals vs. a fixation baseline  for both arm and palm. Repeat vs. baseline and change vs. baseline contrasts were also used to address potential differences between arm and palm tactile processing. Whole-brain random effects contrasts were corrected for multiple comparisons using BrainVoyager’s cluster threshold estimator plug-in, which uses a Monte Carlo simulation procedure  to establish the critical cluster size threshold corresponding to a family-wise alpha of 0.05 corrected for the whole brain  included all trials in which the participant chose “repeat”. The other predictor for this interval (“change”) included all trials in which the participant chose “change”. Whole brain contrasts between repeat and fixation baseline and a contrast between repeat and change were also performed. Whole-brain random effects contrasts were corrected for multiple comparisons using BrainVoyager’s cluster threshold estimator plug-in, corresponding to a family-wise alpha of 0.05 corrected for the whole brain.−1 for arm; 0.3, 1, 3, 10, and 30 cm s−1 for palm). Because the number of trials per condition was variable across subjects due to the feedback-based nature of the design, a baseline of the first 6 trials per condition was used to define the predictors for the whole-brain, group average analysis. Using the first trials for each velocity also avoids potential confounds associated with repetition of the same trial type . Whole-brain random effect contrasts were performed at an uncorrected threshold of p < 0.005. We performed a whole-brain search for areas showing higher hemodynamic responses following velocities in which the proportion of “repeats” to “changes” was above a “preference” cutoff, based on the group’s binomial distribution of ”repeat” choices, in which chance was at 45% with a p of 0.05; see Section Behavioral data analysis above). Velocities with above-chance percentages were weighted positively and those below chance weighted negatively. Based on the behavioral responses we used the contrast [(1 + 3 + 10) cm s−1 > (0.3 + 30) cm s−1] for the arm trials and [(3 cm s−1 ) > (0.3 + 1 + 10 + 30) cm s−1] for the palm trials.To reveal which areas represented the behavioral preferences across all five velocities, thus likely reflecting any differential weighting across velocities, a whole-brain search was conducted based on the participants’ individual behavioral ratios of repeat-to-change choices . As in the “Evaluation” GLM, each predictor modeled the 1 s interval between stimulus and choice. However in this model, ten predictors were created for each of the 10 conditions : 36.1 ± 31.4, 56.1 ± 29.1, 57.4 ± 23.5, 45.9 ± 23.7, 30.7 ± 30.2. For palm were : 32.4 ± 28.7, 40.6 ± 26.3, 58.8 ± 23.3, 37.9 ± 26.9, 29.8 ± 34.1.The total number of trials varied from 36 to 83 for the different subjects. For the arm, the binomial distribution of responses  exceeded chance (45%) for 1, 3, and 10 cm s1 Figure . For the−1). A significant main effect of velocity was seen, F = 5.482, p = 0.006 (Greenhouse-Geisser). A negative quadratic term in the regression provided a significantly better fit compared to a linear term both for arm p = 0.001 and palm p = 0.005 repeat-change ratio data.The mean repeat vs. total choice percentages were submitted to a 2 × 5 repeated-measures ANOVA with 2 within-subject factors: location  and velocity  primary and secondary somatosensory cortices (SI and SII) and posterior insula (PI) Figure , Table 11For all trials in which subjects chose to repeat the stimulation in arm conditions, repeated trials vs. fixation baseline revealed activation in contralateral PI. All repeated trials in palm conditions activated contralateral primary and bilateral secondary somatosensory cortices Figure , Table 1The 1 s interval following stimulation and preceding subjects’ choice of “repeat” or “change”, compared to fixation baseline, revealed activation in bilateral anterior insula, prefrontal cortex, occipital cortex, cerebellum, thalamus and striatum. All ”repeat” vs. “change” trials revealed activity in the head of the caudate, the dorsolateral prefrontal cortex (dlPFC) and precentral gyrus Figure , Table 2F = 29.312, p < 0.001, with higher values for arm (mean beta 0.737 (SE = 0.324)) than palm (mean beta −0.990 (SE = 0.315)).Comparing the “above-chance” (preference) to “below-chance” (no preference) velocities during the 1 s post-stimulation “Evaluation” interval preceding button-press revealed peak activations in right dlPFC and right PI Figure , Table 3Gentle, dynamic stimulation optimal for activating CT afferents influenced behavioral preferences and engaged brain regions involved in reward-related evaluation and decision-making. This was the case for both hairy skin of the arm, where CTs are abundant, and glabrous skin of the palm, where CTs are absent. The experiment’s novel feedback-based paradigm was designed not only to capture relationships between tactile processing and behavioral preferences, but also to disambiguate the key hedonic components of “liking” and “wanting”  with above-chance frequency for arm stimulation, but not the very slow or very fast speeds less likely to activate CTs (0.3 and 30 cm s−1). This corroborates the previous VAS evaluations, but using an orthogonal measure that does not rely on explicit semantic labeling of the stimulus as “pleasant.” In the palm skin, which lacks CT afferents, only 3 cm s−1 stroking was selected with above-chance frequency, suggesting a narrower range of hedonic preference in glabrous skin. The behavioral results supported the hypothesis that the positive valence of affective touch also carries motivational value.Previous affective touch experiments have relied on visual analog scales (VASs) to provide subjective measures of hedonic evaluation. Such rating measures correspond to “liking” the stimulus. Choosing to repeat a stimulus more closely taps into “wanting”. Consistent with VAS ratings across stroking speeds for hairy forearm skin  activated contralateral PI and SII for both arm and palm, compared to a fixation baseline. PI activation is consistent with previous studies of selective CT stimulation  and glabrous skin, innervated by Aßs . The repeat-to-change ratios for both arm and palm followed an inverse U-shaped pattern, best described by a negative quadratic regressor (Löken et al., Arm and palm stimulation and post-stimulus evaluation also activated PI and somatosensory cortices. These regions receive mixed input from both unmyelinated CT afferent pathways and discriminative tactile pathways, with predominant discriminative inputs to somatosensory cortices. Speed of stroking is a crucial feature of touch pleasantness when it comes to tactile stimulation on the hairy skin, where CT fibers are present. However, we found no significant BOLD differences for preferred speeds for arm and palm, which suggests that CT optimal stroking speeds are processed nonspecifically for both glabrous and hairy skin. So far, the CT afferent pathway has been the only afferent system observed to have a unique relationship with both stroking speed and perceived pleasantness (Löken et al., 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.


Antiplatelet therapy is widely used for the primary or secondary prevention of stroke. Drugs like clopidogrel have emerged as alternatives for traditional antiplatelet therapy, and dual therapy with clopidogrel and aspirin is of particular interest. We conducted this meta-analysis to systematically review studies about dual therapy comparing monotherapy with aspirin alone.2 test was significant  or the I2 > 50.00%. Subgroup analyses were carried out on the long and short time periods, the race and region.Randomized controlled trials were searched in PubMed , EMBASE , the Cochrane Central Register of Controlled Trials (CENTRAL) , WHO International Clinical Trial (ICTRP) , China Biology Medicine disc (CBM disc)  and were included into the final analysis according to the definite inclusion criteria mentioned in the study selection section. Risk ratio (RR) was pooled with 95% confidence interval (CI) for dichotomous data. The heterogeneity was considered significant if the χWe included 5 studies involving 24,084 patients. A pooled analysis showed that dual therapy with clopidogrel and aspirin had a lower stroke incidence than monotherapy in both the short term and long term . With regard to safety, dual therapy had a higher risk of bleeding than monotherapy for both periods .Dual therapy with clopidogrel and aspirin could be a preferable choice to prevent stroke in patients who have had a previous stroke or transient ischemic attack, as well as those who are at high risk for stroke. And the effect of dual therapy seems to be more obvious for short-term. However, it is associated with a higher risk of bleeding. Stroke has high mortality, disability, and recurrence rates and is one of the leading causes of death worldwide ,2,3. SubAspirin is now the first-line antiplatelet drug, and its effectiveness in reducing stroke recurrence has been demonstrated in a large number of clinical trials . As an aSeveral studies have been published describing the combination of clopidogrel and aspirin versus aspirin alone for ischemic stroke prevention, but the results were variable. Here, we analyzed eligible randomized studies comparing the effects and safety of clopidogrel and aspirin versus aspirin alone to clarify the effects and possibly guide clinical use.We identified eligible trials by performing electronic searches of PubMed , EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL) , WHO International Clinical Trial (ICTRP)  and China Biology Medicine disc (CBMdisc)  using the search items related to aspirin, clopidogrel and stroke benzoic Acid” or “Acylpyrin” or “Colfarit” or “Ecotrin” or “Endosprin” or “Magnecyl” or “Micristin” or “Polopirin” or “Polopiryna” or “Solupsan” or “Zorprin” or “Acetysal” or “clopidogrel” or “clopidogrel napadisilate” or “Iscover” or “clopidogrel hydrochloride” or “clopidogrel-Mepha” or “Plavix” or “SR 25989” or “clopidogrel besylate” or “clopidogrel Sandoz” or “clopidogrel bisulfate” or “PCR 4099” or “PCR-4099” or “clopidogrel hydrogensulfate” or “Plavix” AND "Stroke" or "Hypoxia-Ischemia, Brain" or "Intracranial Arterial Diseases" or "Cerebral Arterial Diseases" or "Intracranial Embolism and Thrombosis" or "Carotid Artery Diseases" or "Carotid Artery Thrombosis" or "Ischemic Attack, Transient" or “stroke*” or “apoplexy*” or “cerebral vasc*” or “cerebrovasc*” or “CVA” or “(transient ischemic attack*)” or “(transient ischaemic attack)” or “TIA*”). We limited the languages to English and Chinese and got ready to retrieve potentially relevant studies from the references lists to identify studies that may have missed. And no more studies met our standards finally. The time period included was from the establishment of the databases to May, 2015.Studies were included if (1) patients were older than 18 years old and had either had (a) a noncardiogenic stroke within 3 days, (b) TIA, (c) minor stroke; or (d) a high risk of stroke due to atherosclerosis or hyperlipidemia. (2) Only randomized controlled trials (RCTs) were considered. (3) The included studies primarily compared a combination of clopidogrel and aspirin versus aspirin alone, but we also included studies that compared other drugs but also had groups using clopidogrel and aspirin or aspirin alone.The participant exclusion criteria were mainly based on the included studies, but we also excluded studies that (1) focused on strokes caused by cardiogenic thrombus, (2) did not assess any of the outcomes, and (3) lacked primary data.www.cochrane.org/training/cochrane-handbook) using Review Manager 5.2 [All data from eligible studies were independently abstracted by two investigators (Hanyu Ma and Zhaohui Zhang) according to the aforementioned inclusion criteria. Discrepancies were resolved by discussion with a third investigator (Jiangbo Chen) and by referencing the original report. Recorded data variables were the author, publication year, sample size, baseline participant characteristics, treatment dosage, and treatment duration. Then we  extracted the data assessed the quality of included studies according to the Cochrane risk of bias tools  primary outcomes: stroke incidence, safety index-any bleeding without intracranial hemorrhage; (2) secondary outcomes: all-cause death, ischemic stroke, cerebral/intracranial hemorrhage, and TIA.2 test was significant  or the I2 > 50.00%.Then If we fond that the heterogeneity was significant, we further continued to analyze the clinical heterogeneity using the subgroup analysis. Among the five included trials, some followed the patients for relatively long time periods in clinical practice (>90 days follow-up), while others for a short time with certain days reported (≤90 days). After consulting the clinicians on how to report our results more precisely and reduce the heterogeneity, we analyzed them as short term [We calculated the relative risk (RR) for dichotomous data. Firstly, the analysis was performed with a fixed-effect model and the heterogeneity was considered significant if the χort term , 17, 18 ort term , 20. AndA total of 2292 studies were identified through electronic searches, and 263 were excluded because of duplicates removed. Then 1908 studies were also excluded after reading the title and abstract. The remaining 121 studies were assessed by reading the full texts. The 116 full-text articles excluded, with reasons: 1.No “dual therapy with clopidogrel and aspirin VS monotherapy with aspirin alone” (62 articles); 2.No “The patients were diagnosed as stroke, transient ischemic attack, or at high risk for stroke” (28 articles); 3.No “proper outcomes” (26 articles). And there were 5 studies included in qualitative synthesis and meta-analysis finally. : 0.69–0.86, P<0.05,which indicated a statistical significant difference between mono- and dual therapy. Clopidogrel and aspirin dual therapy is better than aspirin alone.All included trials reported this outcome. The statistical values for heterogeneity were P = 0.46, I2 value of 65.10%, which indicated high heterogeneity. We obtained the following RR = 1.07, 95% CI: 0.95–1.21, P = 0.29, which indicated no statistical significance between these two groups.Three RCTs  reported this outcome. The test of heterogeneity yielded a p value of 0.06 and an I2 = 0.00%. After analysis, the results were RR = 0.75, 95% CI: 0.66–0.84, P <0.05, which indicated statistically significant difference between mono- and dual therapy.Four trials  reported outcomes for ischemic stroke. The heterogeneity results were P = 0.55, I2 = 0.00%). The results were RR = 1.14, 95% CI: 0.78–1.68, P = 0.50, indicating no statistical differences.Three studies reported outcomes for cerebral/intracranial hemorrhage. No heterogeneity was found among these trials , and the result  was not statistically significant.Three trials with data from 8,288 participants were pooled for this outcome. The results indicated no heterogeneity . However, the pooled results indicated that the addition of clopidogrel to aspirin may higher the risk of hemorrhage compared with aspirin alone .Four of the trials provided data for this outcome, and no heterogeneity was found , while others only assessed the subjects for a short time with certain days reported (≤90 days). After consulting the clinicians on how to report our results more precisely, we analyzed them as short term (≤90 days) , 17, 18 The analysis of short-term is depicted in 2 = 0.00%, P = 0.83).All three short-term studies reported this outcome and included data from 5,461 patients. The result showed that the patients who took both aspirin and clopidogrel had lower risk for stroke than those who took aspirin alone . No heterogeneity was found . The data showed a significant difference regarding the outcome of ischemic stroke .Two studies with 5,268 patients were included, and no heterogeneity was found . The data  showed no significant differences between the two groups in the short-term follow-up.2 = 0.00%, P = 0.38). The data  showed a significant difference between the two groups. Dual therapy had a higher risk of bleeding than monotherapy.Two studies with 5,268 patients were included, and no heterogeneity was found . An analysis of the data revealed a significant difference between the two groups in the long-term follow-up. Dual therapy was associated with a low incidence of stroke .Two studies of 18,623 patients were included, and no heterogeneity was found for stroke incidence ; however, the data  showed no significant difference between the two groups for the long-term follow-up.Two studies with 18,623 patients were included, and significant heterogeneity was found . The data  showed significant difference between the two groups in the long-term follow-up.The same study and patients were included as for the former outcomes. No heterogeneity was found . The data  showed no significant difference between the two groups in the long-term follow-up.Two studies with 18,623 patients were included, and no heterogeneity was found . The data was assessed, which showed significant differences between the two groups with regard to long-term follow-up .Two studies that assessed 18,623 patients were included, and heterogeneity was found .2 = 0.00%, P = 0.98). But the data  showed no significant difference. (2) The result of non-Asian group [2 = 82.50%, P = 0.02). The data  showed no significant difference. (3) All-cause death and safety index: any bleeding without intracranial hemorrhage on the analysis of long-term these two indexes only included two studies respectively, so they could not be further analyzed in this way. Above all, the difference of race and region might be one of the reasons for high heterogeneity. Further to say, the participants’ hypertension, diabetes ratio and the dosages of clopidogrel and aspirin in the five studies varied to some degree among the studies, which could also affect the incidence of stroke.The differences of race and region might lead to the clinical heterogeneity and these patients were from non-Asian (Canada), Asia: a randomised controlled pilot trial” has a ClinicalTrials.gov number, NCT00109382 ). There In conclusion, dual therapy could be a good alternative for stroke patients and high-risk populations, and the effect of dual therapy seems to be more obvious for short-term stroke prevention. However, we should consider the cost and patient status while weighing the benefits and adverse effects of this dual therapy.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.


Callithrix jacchus) using a PDT. By nose- or hand-poking, subjects learnt to prefer a large  over a small  reward and, subsequently, the probability of occurrence of large-reward delivery was decreased progressively to very low levels (from 100% to 17% and 14%). As probability decreased, subjects showed a great versus little shift in preference from LLL to SS reinforcer. Hence, two distinct subpopulations (“non-gambler” versus “gambler”) were differentiated within each species. A proof of the model validity comes from marmosets' reaction to reward-delivery omission. Namely, depending on individual temperament (“gambler” versus “non-gambler”), they showed either persistence  or restlessness , respectively. In conclusion, the marmoset could be a suitable model for preclinical gambling studies. Implementation of the PDT to species other than rats may be relevant for determining its external validity/generalizability and improving its face/construct validity.Interest is rising for animal modeling of pathological gambling. Using the operant probabilistic-delivery task (PDT), gambling proneness can be evaluated in laboratory animals. Drawing a comparison with rats, this study evaluated the common marmoset ( The emerging field of neuroeconomics is focused—by interdisciplinary approaches , 2—on thBetting money represents a recreational activity for the majority of people, but it may become a serious, clinically relevant, behavioural disorder for others . PatCallithrix jacchus, a species of New World monkeys) as a model for experiments in neuroscience. They have been used in different areas of biomedicine, including neurobiology [There has been increasing interest in the common marmoset , an operant protocol classically performed in rats –38. The p = 33% to p = 20%. This situation is depicted in P values before the indifferent point  when LLL was always the optimal choice. Rats were then tested far beyond the indifferent point  when LLL became a suboptimal option and the economic benefit is attained unequivocally by choosing repeatedly the small-reward option.A landmark in the PDT protocol is the “indifferent” point, that is, the specific level of uncertainty at which the animals can choose either option freely with no effect on the overall economic convenience . As an eThis study aims (i) to evaluate marmosets as possible model for gambling proneness, using the PDT and drawing a comparison with rats, and (ii) to investigate interindividual differences as an approach to study the psychobiological bases and evolutionary roots of human gambling behaviour. Besides, the implementation of the PDT to species other than rats may be relevant for determining its external validity/generalizability and for improving its face/construct validity. in-vivo techniques, if available.All experimental procedures were approved by Institutional Animal Survey Board on behalf of the Italian Ministry of Health (licence to GL for rats and to AV for marmosets). Procedures were in close agreement with the European Communities Council Directive (86/609/EEC) as well as with Italian law . As for marmosets, they were housed and cared for following the guidelines of both the Italian Association of Primatology and the International Primatological Society. All efforts were made to minimize animal suffering, to reduce the number of animals used, and to utilize alternatives to ad libitum, whereas food  was available ad libitum until the start of the experimental protocol. Food restriction, imposed by the experimenter through a limited quantity of extra-food given at the end of each experimental session, was applied to increase the animal's motivation to work for food delivery (see below for details).Twelve adult (mean bodyweight 381.3 ± 9.5 g) Wistar male rats  were housed in pairs inside Makrolon-type III cages with sawdust bedding, kept in an air-conditioned room , on a 12 h reversed light-dark cycle (lights off at 7.00 a.m.). Water was available ad libitum.Fifteen adult male and female common marmosets were involved in the present study. Each of the five family groups was housed in a home-cage measuring 80 × 130 × 220 cm. The floors of the cages were covered in wood shavings and each cage contained various forms of enrichment  which were periodically changed. All families had auditory, olfactory, and partial visual contact with each other. One family at a time (on a daily rotation basis) was given access via tunnels to two other cages  of the same size as the home-cages, in an adjacent room. All rooms had a controlled temperature of 22 ± 1°C, a humidity of 50 ± 5%, and a 12-h light-dark cycle, with lights on at 6.00 a.m., which included exposure to UV-B lights. The diet consisted of specific commercial pellets for marmosets , plus a portion of various fruits and vegetables. The monkeys were usually fed on a daily basis at approximately 9.00 a.m. whilst water and pellets were availableComputer-controlled operant chambers, made of aluminium and Plexiglas with grid floor , were placed in an experimental room, adjacent to the animal room. The operant chambers were provided  with two nose-poking holes, two chamber lights (placed over each nose-poking hole), two feeder devices, and two food-magazines  where precision pellets  were delivered. Nose-poking in either hole was detected by a photocell and was recorded by a computer (with custom-made software), which also controlled food delivery.Two computer-controlled operant panels  were plAfter four weeks of habituation to the housing conditions and handling by the experimenters, rats were tested in the probabilistic-delivery (PD) task for gambling proneness, in the middle of the dark phase of their light-dark cycle (between 11.00 and 15.00 a.m.). Four chambers were used; each rat was tested daily in the same chamber at the same hour, five days a week. The total number of completed trials and the intertrial interval were not fixed, since rats were free to express nose-poking for food at their own, individually variable rate during the 25 min session.After each daily session, rats were returned to their home-cage, where they were given an appropriate amount of standard food (approximately 4.5 g each) to keep their body weight at 90% of their free feeding body weight.The experimental sessions were performed between 9.00 and 13.00 a.m., five days a week. The total number of completed trials and the intertrial interval were not fixed, since marmosets were free to express hand-poking for food at their own, individually variable rate during the session. After the experimental session, in order to increase their motivation to work for food delivery on the following day, monkeys were fed at approximately 13.00 a.m., namely, after the experimental sessions had been completed, and received only small portions of fruit.During the pretraining, training, and testing phases, two monkeys from the same family were tested simultaneously in the two opposite experimental cages, with their backs to each other. This was aimed to reduce the effects of social isolation. Before each session, panels remained covered with a wooden box until both subjects (selected to be tested) entered the experimental cages. At that point, the software was started and the wooden covers were removed. This was intended to prevent monkeys from interacting with the panels before they were turned on. Every day, at the end of the experimental sessions, the panels were again covered so that monkeys were free to move around the experimental cages without interacting with them.Each session lasted 5 min, because the common marmoset has a relatively short attention span; attempts to use a longer testing time often made them loose interest in the apparatus.Training. During the training phase (3 days), nose-poking in one of the two holes resulted in the delivery of 1 to 2 pellets in the corresponding magazine, whereas nose-poking in the other hole resulted in the delivery of 5 to 6 pellets in the other magazine. After nose-poking and before food delivery, the chamber light above the nose-poked hole was turned on for 4 s. Following food delivery, the corresponding magazine light was turned on for 15 s , during which additional nose-poking was recorded but was without scheduled consequences . The magazine light was then turned off, and the system was ready for the next trial. These training sessions allowed all subjects to reach a significant preference for the large reward.Testing. During the testing phase (7 days) a probabilistic dimension was associated with the delivery of the large reward. The chamber lights were switched on after nose-poking following the usual schedule. However, delivery of the large reward was sometimes omitted, according to a given level of probability . The small-reward delivery was unchanged. Hence, animals had a choice between a “Large & Luck-Linked” (LLL) or a “Small & Sure” (SS) reward. The probability level was kept fixed for each daily session and was decreased progressively over days .Training. Following familiarization and pretraining (see [ing (see ), each mAll monkeys but 2 (who had to be excluded from further testing for lack of interest in the apparatus) developed and displayed a preference for the large reward in this phase.Testing. During the testing phase (21 days) a probabilistic dimension was associated with the delivery of the large reward. The purple hole lights were switched on after hand-poking following the usual schedule. However, delivery of the large reward was sometimes omitted, according to a given level of probability . The small-reward delivery was unchanged. We intended to implement in monkeys the same rarefaction of large-reward delivery as in rats [p.” in rats , with exThree monkeys and one rat were excluded from data analysis as they failed to reach the inclusion criterion. The inclusion criterion (for both marmosets and rats) was defined as a preference for the large reward of more than 60% during the two sessions before the indifferent point –40, 42.As a measure of gambling proneness, the dependent variable was the choice preference (%) for the LLL reward over total choices expressed. A sustained preference for the LLL reward may be an indication of gambling-prone behaviour –40. As ap” level and odds value is mathematical: odds = (1/p) − 1 or p = 1/(odds + 1). The present protocol employed a fully probabilistic generation of reward delivery versus omission, thus resulting in a totally random sequence of “lucky” versus “unlucky” trials. Therefore, a discrepancy likely appears between the “set” level of probability and the actually experienced rate of reinforcement, due to the stochastic fluctuations. Thus, for each session of the testing phase, we calculated the actually experienced “p” values for individual rats and marmosets , and turned them into the corresponding “experienced odds” values  − 1). We plotted (on y-axis) the percent LLL preference, shown by rats and marmosets at each session, against (on x-axis) either (a) the set “p” values, selected by the experimenter, or (b) the “experienced odds,” once calculated. The latter was thus used to normalize percent LLL preference against an index of subjective impact of uncertainty. A logarithmic fit was also performed. Specifically, for each experimental rat or monkey, the slope of the preference-odds curve was calculated using Microsoft Excel functions, with Log (odds + 1) as x-axis and percent LLL choice as y-axis values.Odds are defined as the mean number of omitted large-reward deliveries (because of “unlucky” events in the PDT) before a successful delivery . The relation between “On the basis of the median value of steepness of this preference-odds curve, we differentiated two distinct subpopulations , 47 in b versus marmoset) and strategy (the two subpopulations of gamblers versus non-gamblers) were a between-subject factor and session (the set probability per daily session) was a within-subject factor.Data were analyzed using repeated-measures analysis of variance (ANOVA). The general model was 2-level species × 2-level strategy × 7-level session, where species  test.Statistical analysis was performed using Statview II . Data are expressed as mean ± SEM. Significance level was set at P < 0.001), confirming a progressive reduction of LLL preference when moving from p = 100% to p = 14% in probability of reward delivery.On the whole, all animals showed a shift in preference towards the SS reward as the level of uncertainty increased. The ANOVA yielded a main effect of session  = 14.31, P < 0.001): one with a nearly horizontal curve  and another with a very steep slope . For both rats and marmosets, the “non-gamblers” showed a clear shift in preference from LLL to SS as the level of probability decreased. On the contrary, the “gamblers” maintained a significant attraction for LLL, even beyond the indifferent point, when LLL became a suboptimal option  = 7.47, P < 0.001; As expected, individual differences in the preference for LLL versus SS rewards emerged in both rats and monkeys, with the identification of two distinct subpopulations  = 33.29, p = 20%, 17%, 14%) and between “gambler” and “non-gambler” rats on the last 2 sessions .Multiple post hoc analyses revealed a significant difference between “gambler” and “non-gambler” marmosets on the last 3 sessions , confirming a progressive increase of odds values when moving from p = 100% to p = 14%. There were no main effects neither for strategy  = 0.16, P = 0.690 ns) nor for species  = 2.22, P = 0.154 ns) and no interactions as well (Ps > 0.484). No difference emerged as well in multiple post hoc comparisons. This profile confirms no differences in odds values actually experienced by the four experimental groups.The ANOVA yielded a main effect of session  = 4.66, The absence of any difference implies that a similar proportion of “lucky” versus “unlucky” pokes was experienced by subjects of the four groups. In other terms, neither group was “luckier” than the other. Thus, “gambler” individuals (both for marmosets and for rats) did not choose the LLL hole because simply they were “luckier,” but likely because of attraction to binge reward and/or insensitivity to its uncertainty. Vice versa, the “non-gamblers” preferred to shift towards the SS hole not because they were “unlucky” with LLL pokes, but likely because of sensitivity and hence aversion to uncertainty.The inadequate responding  measures the reaction of subjects to punishment (consisting in reward-delivery omission) and can be considered an index of frustration.P = 0.001; species: F = 20.39, P < 0.001; strategy × species: F = 5.80, P = 0.028; strategy × session: F = 5.21, P < 0.001; species × session: F = 2.43, P = 0.031; species × strategy × session: F = 4.09, P = 0.001, At very low probability values, the “gambler” marmosets (but not rats) showed a significant increase in persistence, suggesting that they were still seeking for LLL during TO durations. This was reflected by significant interaction terms in the ANOVA  = 14.55, p = 17%, 14%), that the “gambler marmosets” were showing the highest levels of persistence. In fact, a significant difference was found between “gambler marmosets,” on one side, and “non-gambler marmosets” as well as “gambler rats,” on the other hand.Multiple post hoc comparisons evidenced, on the last 2 sessions .Both rats and marmosets became overall more restless in the last sessions compared to the initial ones. Data are suggesting that, with progressive reward rarefaction, animals were increasingly disturbed by reward-delivery omission  = 19.14, P = 0.048; strategy × session: F = 3.33, P = 0.005; species × strategy × session: F = 2.84, P = 0.014, This profile was particularly evident in “non-gambler” marmosets, who showed a progressive increase of uncertainty-induced restlessness, with decreasing levels of probability. By contrast, such inadequate responding was remarkably low in “gambler” marmosets, suggesting that these animals are less sensitive to uncertainty and relatively unaffected by reward loss. This was reflected by significant interaction terms in the ANOVA  = 4.52, p = 17%, 14%) and, among the non-gambler subpopulation, a significant tendency (0.05 < P < 0.1) toward a difference between marmosets and rats on the penultimate session .Multiple post hoc comparisons evidenced the “non-gambler marmosets” as showing the highest levels of uncertainty-induced restlessness. In fact, we found a significant difference between “non-gambler” and “gambler” marmosets on the last 2 sessions , of species  = 5.69, P = 0.029), of session  = 13.83, P < 0.001), and their interactions  = 11.90, P = 0.003; strategy × session: F = 4.74, P < 0.001; species × session: F = 3.94, P = 0.001; species × strategy × session: F = 3.81, P = 0.002, Interestingly, the number of trials completed per minute increased markedly as the level of probability decreased, but only in uncertainty-aversive marmosets. By contrast, “gambler” marmosets apparently did not adapt their reward-seeking pokes to balance for increasing reward omission. Specifically, the ANOVA yielded a main effect of strategy  = 15.96, p = 33%, 25%, 20%, 17%, and 14%) and between marmosets and rats of the “non-gambler” subpopulation on all of the same sessions but the third .Multiple post hoc comparisons confirmed that the “non-gambler marmosets” were completing the highest number of trials per minute. In fact, we evidenced a significant difference between “non-gambler” and “gambler” marmosets on nearly all sessions  = 9.61, P = 0.006; strategy × species: F = 10.40, P = 0.005). Pellets earned on average by “non-gambler marmosets” were 7.03 ± 0.47, compared with 3.57 ± 0.48 versus 3.62 ± 0.30 versus 4.17 ± 0.22 among “gambler marmosets,” “gambler rats,” and “non-gambler rats,” respectively. The amount of pellets obtained by “non-gambler marmosets” was particularly higher at very low probability values  = 41.40, P < 0.001; strategy × session: F = 3.05, P = 0.009; species × session: F = 5.80, P < 0.001, The total number of pellets delivered per minute was significantly higher in “non-gambler marmosets” than in the remaining groups  = 19.80, p = 20%, 17%, 14%).Multiple post hoc comparisons evidenced, on final sessions, a significant difference between “non-gambler marmosets” on one side and “gambler marmosets” as well as “non-gambler rats” on the other hand  –58 and sThe disruption of DA pathways significantly contributes to the propensity to gamble maladaptively . With reIn the present study, as classically observed in previous studies on rats –40, all Many factors may explain the development of such a suboptimal preference for a binge but largely uncertain reward. One factor is hyposensitivity to risk, whereby the subjects are unable to foresee (as they should) an uncertainty in the outcome  or to perceive the punishment of “losses” (represented by a randomly and frequently omitted delivery of reward). A second factor is habit-induced rigidity, whereby subjects seem to behave according to a strongly consolidated choice strategy. Such form of inflexibility may be due to a failure of negative reinforcement, namely, a lack of feedback-reaction to the uncertainty-induced aversion and/or to the omission-related punishment, just described , 46.Another set of factors is hypersensitivity to rewards: the binge size of the reward has an excessive motivational impact over the subjects and monopolizes their attention, regardless of any other characteristic of the reward itself. There is also the possibility that the internal states, elicited by the risk of “loss” and experienced under conditions of uncertainty, become attractive as a secondary, conditioned feature. This is because the large reward  may well be generating an overwhelming peak of positive reinforcement. Similarly, all the surrounding signals and cues, that accompany and predict the features of uncertainty, may themselves become secondary rewarding stimuli. Regardless of which of these factors prevails in the PDT, the suboptimal preference for a large, rarefied reward is considered an index of “gambling-proneness” , 46.The inadequate responding  allows to evaluate the reaction of subjects to the punishment (consisting in reward-delivery omission). It should be noted that inadequate pokes are mainly performed during the postchoice TO interval that follows an “unlucky” poke into the LLL hole  and can be considered an index of frustration , 72. ComThe lack of omission-induced frustration in “gambler” marmosets and rats may be related to the effectiveness of magazine lights as a secondary reinforcer. In fact, these lights were turned on even when delivery of the large reward was omitted. It may be proposed that reward omission was not properly perceived as punishment by these animals, in that the light cue alone could sustain choice behaviour. Magazine lights turned on in the absence of food could have a much higher, secondary reinforcing value, triggering an anticipated drive for bingeing and persistent LLL seeking. Like in second-order schedules , this cuThe “non-gambler” marmosets were able to obtain a considerably higher amount of pellets compared to “gambler” marmosets and to both “gambler” and “non-gambler” rats. This interesting finding is consequent to two independent phenomena: (i) they were able to make the “optimal choice”  and (ii) they progressively increased the number of completed trials, which compensated for the diminished gain associated with SS choice. Such a combination of these two phenomena was never evidenced in “non-gambler” rats during previous studies. Since the marmosets are primates and have well-developed prefrontal cortical areas in comparison with rodents , future The results we obtained indicate that the common marmoset can be a suitable model for studies on decision-making under conditions of high uncertainty. A proof of the model validity comes from marmosets' individual reaction to reward-delivery omission. We report a clear-cut dissociation in inadequate responding depending on individual temperament (“gambler” versus “non-gambler”). This seems to resemble what has been reported in the clinical literature: while normal people are likely to modify their own seeking behaviour depending on reward outcome, human pathological gamblers are relatively unaffected by losses, hence persisting in this payoff-seeking activity despite repeated losses –78.Nonhuman primate species differ markedly in their risk preferences: chimpanzees and orangutans are risk-seeking whilst bonobos and lemurs are risk-averse , 54, 55.The setting used in the present study  has the potential to be adapted and used in more extensive ways, for permanent monitoring of subjects' operant-choices and spontaneous  behaviour. Such an automated social home-cage system would allow long-term, continuous data collection, which may provide a larger, more accurate picture of gambling-prone behaviour in these species. These systems, in which animals can freely move, interact with each other, and voluntarily access the operant panels, are promising for developing tasks in a more naturalistic environment, with increased ecological validity , 86.