Individual characteristics, dietary intake and physical activity influence weight status; however, the contribution of each factor to weight change has not been studied. The objective was to confirm a conceptual framework by simultaneously assessing the relative influence of socioeconomic, psychological and sensory characteristics, physical activity, and dietary intake on five-year weight gain in French adults. Individual characteristics, physical activity, and dietary data were assessed at baseline in 8014 participants in the NutriNet-Santé cohort. Self-reported anthropometric data were collected at baseline and five years later. Structural equation models, stratified by baseline body mass index (BMI), were used to perform analyses. Dietary restraint was a direct predictor of weight gain, with a stronger effect than age or intake of energy-dense foods, both in non-overweight and overweight participants. In non-overweight individuals only, intake of nutrient-dense foods and physical activity were inversely associated with weight gain. Regarding dietary intake, fat liking was the most important predictor of nutrient-dense food intake and was also related to energy-dense food intake. In these models, dietary restraint appears to be a direct predictor of weight gain and fat liking is a strong determinant of dietary intake. The influence of dietary restraint on weight gain, not explained by diet, warrants further investigation. Obesity is a worldwide public health concern ,2, and tIndividual sensory liking, such as liking for fat sensation, sweet and salty tastes, may be associated with body weight. Indeed, previous studies have shown a positive association between liking for fat sensation and body mass index (BMI) ,19 and aAlthough various determinants of weight change have already been identified in the literature, very few studies considered simultaneously a large set of determinants of weight status. In addition, the relative influence of each direct and distant determinant of weight status, compared to others, and their causal paths and complex interactions have not been considered to date. In order to study the complex hierarchical framework of the effect of determinants on weight change, we constructed a theoretical model based on the literature.The aim of the present study was to test an a priori conceptual framework by simultaneously assessing the relative contribution of sensory, psychological and socio-economic characteristics, physical activity and dietary intake on five-year weight change in French adults. In addition, the relative effects of these individual characteristics on dietary intake were evaluated.http://www.etude-nutrinet-sante.fr). Then, multimedia campaigns were repeated every six months. Further information is maintained on a large number of websites . A billboard advertising campaign is regularly updated via professional channels . The study was designed to investigate the relationship between nutrition and health, as well as determinants of dietary behaviour and nutritional status [We used data from the NutriNet-Santé study, a large web-based observational cohort launched in France in 2009 with a scheduled follow-up of 10 years. It was implemented in a general population and targeted internet-using adult volunteers. Briefly, eligible participants were recruited by a variety of means. Initially a vast multimedia campaign  called for volunteers and provided details on the study’s specific website  and the “Commission Nationale Informatique et Libertés” (CNIL No. 908450 and No. 909216). Electronic informed consent was obtained from all subjects. This study is registered in EudraCT (No. 2013-000929-31).This longitudinal analysis focused on participants included in the NutriNet-Santé study between May 2009 and May 2010. Data regarding age, sex, socioeconomic status, smoking, sensory and psychological factors, physical activity, and dietary intake used in this analysis were collected in 2010. Weight and height data were collected in 2010 and five years later in 2015.2 at baseline were classified as non-overweight (including 332 underweight participants in our study) and participants with BMI ≥ 25 kg/m2 were considered overweight (including obese) in accordance with WHO reference values [Height and weight data were collected at baseline and each year thereafter by a self-administered anthropometric questionnaire . BMI wase values . The relAt enrolment, and each year thereafter, participants were invited to provide three random 24 h dietary records during a two-week period (one weekend day and two weekdays) . The diePhysical activity level was assessed using the French short version of the International Physical Activity Questionnaire . Weekly Liking for fat-and-salt and fat-and-sweet sensations was assessed using the PrefQuest  in 2010.Psychological data were collected in 2010 using the Three-Factor Eating Questionnaire, which is a self-assessment instrument of eating behaviour (TFEQ-R21) . It coveSocio-demographic (age and sex: women and men), economic , and lifestyle data  were collected in 2010.t-test and the chi-square test, as appropriate. Correlations between individual characteristics were also investigated using Pearson and Spearman correlations, as appropriate  “liking for fat” measured by indicator variables from the PrefQuest questionnaire, liking for fat-and-salt and liking for fat-and-sweet scores; (ii) “intake of nutrient-dense foods” assessed by the intake of fruits and vegetables, fish, milk and yogurt, whole grain products, and oil; (iii) “intake of energy-dense foods” measured by the intake of starchy foods, meat and processed meat, cheese, salted snacks, appetizers and savory sauces, butter and other added animal fats (cream and lard), fatty-sweet products, sweetened soft drinks , and alcoholic beverages; (iv) “uncontrolled and emotional eating” was assessed by uncontrolled eating and emotional eating scores; and (v) “dietary restraint” was measured by cognitive restraint score and history of dieting.In a third step, the structural equation modelling approach called LISREL   was usedp < 0.05). Standardized parameter estimates were presented to remove scaling effects and to compare parameters in models. They can be interpreted as beta weights in a multiple regression. Standardized residuals were examined to determine if conceptually-appropriate changes could be made to improve model fit . Structural equation modelling was conducted with R v3.1.2 (www.cran.r-project.org), using the package “lavaan” [p < 0.05.Three fit indexes were used to judge the fit of the model: the standardized root mean square residual (SRMR), the root mean square error of approximation (RMSEA), and the comparative fit index (CFI) . To indi“lavaan” . The robAmong the 49,818 participants included in the NutriNet-Santé study in May 2010, 31,407 had available data for height and weight at baseline. We excluded 2961 participants with fewer than three 24 h dietary records or identified as diet under-reporters. Next, 3578 participants with missing data for sensory liking and 1291 individuals with missing data for psychological characteristics at baseline were also excluded. Furthermore, 3704 participants with missing data for physical activity and 1871 subjects with missing data for education or income per consumption unit were excluded. Finally, 9988 participants with missing data for weight five years later were excluded, which left 8014 participants available for analysis (5730 women and 2284 men). Compared with excluded subjects  and a model for overweight participants, including obese persons (BMI ≥ 25 kg/m2), to investigate the contribution of each determinant to weight change.First, we tested the theoretical model , includiCompared to overweight subjects , percentThe fit statistics were, in the model with non-overweight individuals: CFI = 0.91, RMSEA = 0.049 (0.047–0.051), and SRMR = 0.036; and in the model with overweight subjects: CFI = 0.87, RMSEA = 0.053 (0.050–0.056) and SRMR = 0.046.Different pathways were also found between models in non-overweight and overweight individuals. Indeed, in non-overweight participants, intake of nutrient-dense foods and physical activity were inversely associated with weight gain. In addition, age was positively associated with intake of nutrient-dense foods, but was not associated with intake of energy-dense foods in either sample. In overweight participants, smokers and former smokers had higher liking for fat than never-smokers, and dietary restraint was also inversely related to intake of energy-dense foods, with a high estimate.To our knowledge, this study, using structural equation modeling, was the first to simultaneously investigate the relative influence of direct predictors of weight gain and more distant determinants that probably influence weight status through dietary intake. In non-overweight and overweight participants, dietary restraint was the main predictor of weight gain, followed by age and intake of energy-dense foods, according to loadings. In non-overweight individuals, intake of nutrient-dense food and physical activity were inversely associated with weight gain. Regarding indirect determinants that may have an effect through dietary intake, fat liking and sex appear to be the strongest predictors of dietary intake. In addition, high uncontrolled eating was associated with higher intake of energy-dense foods, while higher socioeconomic status, high dietary restraint, and high physical activity were associated with higher intake of nutrient-dense foods.Dietary restraint had a direct influence on weight gain in both samples, and was the most important predictor, followed by other common determinants, such as dietary intake, physical activity, or age. This original finding may be explained by the co-occurrence of uninhibited and restrained eating patterns at an individual level , such asAs found in previous works ,10, bothOther individual characteristics included in the theoretical model were not directly associated with weight gain, highlighting their indirect influence through dietary intake. First, fat liking was the main predictor of dietary intake, followed by socioeconomic status and uncontrolled and emotional eating, well-known determinants of dietary intake. This original finding confirms that sensory liking is the first criterion in food choice . SensoryA socio-economic gradient, through education and income, was observed for intake of nutrient-dense foods but not for energy-dense foods. This is consistent with previous studies showing that higher socioeconomic status is associated with higher intake of nutrients required for a healthy diet ,31. LoweRegarding psychological characteristics, dietary restraint also appears to be a strong determinant of dietary intake, particularly in overweight participants. Higher intake of nutrient-dense foods and lower intake of energy-dense foods were observed in high restrainers, in concordance with a previous study . Nearly Our findings also showed that men had higher intake of energy-dense and nutrient-dense foods, probably due to their greater need for energy compared to women . In addin = 2513), and showed that the reporting bias was reasonably small [Interpretation of the present results must take into account characteristics of the cohort. Subjects were volunteers in the NutriNet-Santé cohort and, thus, were probably more concerned about healthy lifestyle and nutrition than the general population. Furthermore, as the collection and analysis of ethnicity characteristics is not allowed in French studies, it could not be considered in the analyses. In addition, only participants who completed questionnaires used in this analysis and who were not lost to follow-up after five years were included, which limits the representativeness of the samples. Thus, caution is needed when interpreting and generalizing the results. In contrast, the prospective design of the study with a five-year follow-up is a strength and enabling us to explore relationships between various direct and distant predictors of weight gain. Individual characteristics and dietary intake were assessed only at baseline; therefore, the cumulative effect of these behaviors on weight change could not be assessed. However, slight changes of dietary intake and physical activity have been shown between baseline and five years later, highlighting that behaviors were unchanging during five years in this population . Other sly small . Finallyly small ,71; it eThis original study focused on the relative influence of sensory, psychological, socioeconomic, physical activity, and dietary intake on five-year weight change in non-overweight and overweight participants. Our findings confirmed several well-known relationships found in the literature regarding the influence of age, sex, and socio-economic factors on dietary intake, as well as the influence of dietary intake and physical activity on weight change. Specific associations were also found, such as fat liking as a major determinant of dietary intake in both populations. Finally, dietary restraint was a direct predictor of weight gain compared to other determinants, although dietary restraint appeared to be associated with a healthier diet. Further investigation is warranted to understand this complex relationship and to replicate results in other populations. At a clinical level, a sensory based-intervention  may be i


Virtual reality (VR) is a computer technique that creates an artificial environment composed of realistic images, sounds, and other sensations. Many researchers have used VR devices to generate various stimuli, and have utilized them to perform experiments or to provide treatment. In this study, the participants performed mental tasks using a VR device while physiological signals were measured: a photoplethysmogram (PPG), electrodermal activity (EDA), and skin temperature (SKT). In general, stress is an important factor that can influence the autonomic nervous system (ANS). Heart-rate variability (HRV) is known to be related to ANS activity, so we used an HRV derived from the PPG peak interval. In addition, the peak characteristics of the skin conductance (SC) from EDA and SKT variation can also reflect ANS activity; we utilized them as well. Then, we applied a kernel-based extreme-learning machine (K-ELM) to correctly classify the stress levels induced by the VR task to reflect five different levels of stress situations: baseline, mild stress, moderate stress, severe stress, and recovery. Twelve healthy subjects voluntarily participated in the study. Three physiological signals were measured in stress environment generated by VR device. As a result, the average classification accuracy was over 95% using K-ELM and the integrated feature (IT = HRV + SC + SKT). In addition, the proposed algorithm can embed a microcontroller chip since K-ELM algorithm have very short computation time. Therefore, a compact wearable device classifying stress levels using physiological signals can be developed. Virtual reality (VR) involves creating and implementing a simulated, realistic, three-dimensional environment . In otheGenerally, stress is induced by physical, mental, or emotional tensions and causes changes in the body’s response. In addition, since VR environment can trigger unfamiliar stimuli for subjects, several phenomena such as the anxiety, mental concentration and nausea are regarded as stress in a broad sense. Especially, physiological changes can be caused by unfamiliar external environments and psychological changes . Among tThe ANS, when affected by stress stimuli, secretes stress hormones such as cortisol and adrenaline within the blood vessels. This causes the activation of sympathetic nerves and the inactivation of parasympathetic nerves . As the Heart activity, skin sweating and skin temperature are known to be regulated by the ANS, so biosignals related to such activities (photoplethysmograms (PPG), electrodermal activity (EDA) and skin temperature (SKT)) could provide insights on ANS activity.The PPG signal is measured from the finger, and reflects the change in blood volume in the peripheral blood vessels . Since tThe heart rate variability (HRV) can be calculated from the intervals between the peaks of the PPG signal . The HRVThe EDA is an electrical signal measuring continuous skin-conductance (SC) changes . In geneLastly, changes in SKT are also associated with changes in the ANS. For instance, it is known that the combined inhibition of dopamine (DA) and norepinephrine (NE) reuptake by the activity of sympathetic nerves improves exercise performance and increases body temperature . Taken tRecently, mobile healthcare system has been studied for detecting stress levels using various wearable sensors measuring the physiological signals ,22. The Automatic classification methods using features extracted from biosignals have been developed ,25,26. ISince the ELM is based on single-hidden–layer feedforward neural networks (SLFNs), and randomly selects the input weights and biases for the hidden layer, it has low computational complexity as well as high classification accuracy . In addiIn this study, a stressful task with cognitive loads was designed to measure stress-related biosignals in a VR environment. The task was composed of five sequential sessions with varying stress levels: baseline, mild stress, moderate stress, severe stress, and recovery. During this task, physiological signals  were measured simultaneously. Then, we classified these five different states by combining K-ELM and the features obtained from the three physiological signals. Additionally, we evaluated whether the calculated features reflect intended stress levels, and compared the performances of the conventional machine learning algorithms with those of the proposed algorithm.Twelve healthy subjects  voluntarily participated in the study. The average age of the subjects was 27.5 years (standard deviation (SD): ±3.18). None of the subjects had any experience with the VR equipment. In addition, they had not taken any medicine, etc. that could affect the result of this study. Before participating in the experiment, the subjects were fully aware of the purpose and procedure of our research. This study was approved by the Institutional Review Board of the Gwangju Institute of Science and Technology (GIST).The participants performed a stress-inducing task with a cognitive load: arithmetic subtraction. The task proceeded in configurable VR environments using the commercial Gear VR device . Our task was composed of five sessions, including three with different levels of stress: mild stress (MIS-S), moderate stress (MOS-S), and severe stress (SES-S). The other two sessions were the baseline session (BA-S) and the recovery session (RE-S).https//www.youtube.com) and initially selected nine VR videos. Then, 22 people (who did not participate in the main experiment) scored the nine VR videos to assess how much stress they triggered; finally, three VR videos were selected. According to the scores, we assigned the three videos to the mild, moderate, and severe stress-inducing sessions.To acquire suitable VR videos, we investigated the YouTube website . In the MOS-S, we provided a relatively dynamic VR environment consisting of a car-racing situation on a rainy day, while similar arithmetic tasks were performed at the same time. Lastly, the SES-S was composed of VR stimuli that could induce a sensation of fear: a dark, dingy room where a guard on patrol appears while a frightening background sound is heard. Again, similar arithmetic tasks were conducted simultaneously. To avoid the familiarity of arithmetic calculation in each stress-inducing session, we randomly changed the numbers in each session .After the experiment, we conducted two questionnaire surveys to evaluate how much stress was induced: the state-trait anxiety inventory (STAI) Y-1 and the visual analogue scale (VAS). The STAI Y-1 questionnaire consists of 20 questions and is often used to assess anxiety states . The VASWe performed a stress-inducing experiment with five sessions: BA-S, MIS-S, MOS-S, SES-S, and RE-S. In BA-S and RE-S, a black cross mark was displayed on a white background on the monitor. The difference between BA-S and RE-S is their order during the experiment. The flow of our experiment is shown in Three physiological signals were measured from each subject during the experiment: a photoplethysmogram (PPG), electrodermal activity (EDA), and skin temperature (SKT). The physiological signals were obtained using the Biopac PPG100C, Biopac EDA100C, and UIM100C systems . The PPG sensor was attached to the index finger, EDA sensors were attached to the middle and ring fingers, and the SKT sensor was attached to the thumb of the non-dominant hand. Each physiological signal was sampled at 400 Hz according to the manufacturer’s instructions, using the Biopac Acknowledge 4.2.0 software . An HRV analysis was performed using the PPG signal of each subject. We assumed that the frequency range of the motion artifact was less than 0.1 Hz, the frequency range of the respiration was 0.15 Hz to 0.4 Hz, and the frequency range of the heartbeat was around 1 Hz . TherefoFrequency-domain measures can be obtained from the power spectrum calculated from the NN tachogram. The NN tachogram can be obtained by interpolating the unevenly sampled NN intervals . The powLastly, nonlinear measures are as follows: approximate entropy (ApEn) , sample However, since SampEn does not include a self-match process, it is relatively trouble-free and stable. Therefore, the result of SampEn analysis is much more robust than the result of ApEn analysis . PoincarThe SC signal indicates the electrodermal activity (EDA) measured by a non-invasive electrode on the skin. The SC signal consists of two types of signals: slow-varying tonic activity and fast-varying phasic activity. They are called skin-conductance level (SCL) and skin-conductance response (SCR), respectively. SCL is a baseline level; it changes in the absence of any environmental events. On the other hand, SCR changes when environmental events occur. Furthermore, it is also known that changes in SCR are associated with activity of the sudomotor nerves related to the sweat glands . In this SCRpeak . The decIt is known that continuous stress can trigger an increase in body temperature by affecting the ANS . FurtherIn general, the weights and hidden-layer biases of traditional neural networks can be obtained using an optimization process based on iterative processes to find optimal parameters . ObtainiL are given, the standard SLFNs is mathematically described as follows:ith hidden node. The hidden-layer output row vector is x, and the superscript T denotes the transpose operator. One of typical hidden-layer component j. In general, the solution for the estimated weight matrix Assuming that M class numbers, N arbitrary distinct input samples H is the hidden-layer output matrix with respect to input data i = 1,…, N) and T is the target value matrix with the number of classes M. To obtain the weight-matrix estimate H. There are many methods to calculate position . In thisI is the identity matrix. The solution of the output function Eventually, the estimated weighted matrix Given that hidden-layer vector In this paper, we used the radial basis function (RBF) kernel  from the participants in each session. Before applying the preprocessing methods, we eliminated the first 35-s period of the measured signal to exclude unstable signals. We divided the processed signals into 6-s intervals (epochs), so that the total number of epochs in the mental tasks was 170 (34 epochs × 5 sessions) per subject. Then, the features in F-value of one-way ANOVA can be calculated by dividing the difference of groups into difference within groups. Therefore, the large F-value can indicate that the groups can be well distinguished. In the opposite case, the distinction between groups is ambiguous.We performed a one-way analysis of variance (ANOVA) with Tukey’s HSD (honestly significant difference) test and post hoc test to compare the features of physiological signals measured in each stress sessions . In general, one-way ANOVA is a statistical tool to analyze whether mean differences between three or more independent groups are statistically significant or not. In this study, since features of three groups were calculated from physiological signals measured in three independent stress experiments, the groups can be independent each other. Furthermore, p < 0.05), it is difficult to select the accurate hypothesis due to multiple comparison problem. Therefore, post hoc test step is essential. In general, Tukey’s HSD is only applicable for pairwise comparison and the number of observation should be same.Tukey’s HSD is an approach dealing with the multiple comparison problem. Although the results of one-way ANOVA are statistically significant , which is unsupervised learning that imitates visual cortex neurons. Using this mapping, we can see the clusters of artificial neurons, which can identify the relationships among the input data .We implemented the K-ELM classifier with four different conditions: HRV + K-ELM, SC + K-ELM, SKT + K-ELM, and integrated features (IT) + K-ELM. IT combines the HRV, SC, and SKT features, as shown in It should be noted that the classification performance depends on the selected parameters. In particular, the K-ELM performance was associated with the regularization coefficient (C) and the kernel size (l in (8) . In thisConsequently, the classification accuracies of IT + K-ELM  were genTo compare the classification performances of the proposed algorithm to the those of other algorithms, we implemented linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), multi-class support vector machine (mSVM), kernel-based multi-class support vector machine (K-mSVM) and extreme learning machine (ELM) ,28,42. ETo investigate the organic relationships between the features calculated from the physiological signals, we applied a self-organizing map (SOM) analysis, which is unsupervised learning, to produce a two-dimensional map consisting of artificial neurons. In SOM analysis, the artificial neurons compete amongst themselves. The winning neurons can be determined by the results of the competition. We call these winning neurons SOM output neurons. These output neurons form the clusters in the map, and each cluster represents a common input pattern .In this study, we trained the SOM using 2040 epochs (12 subjects × 170 epochs) from all subjects. Since the SOM input features are generated from all subjects, the clusters consisting of artificial neurons represent common input-feature patterns, contributing to the stress-level classification. Therefore, to evaluate the contributions of each feature, we made four different SOMs by changing the input features, e.g., HRV, SC, SKT, and IT.k = 5) are described in The results of the k-means clustering . In particular, there were features that showed statistically significant differences in all three different signals: PPG , 0.77 (EDA) and 2.85 (SKT), respectively. Additionally, the averages of p-values were lower than 0.19 (PPG), 0.47 (EDA) and 0.49 (SKT), respectively.According to statistical test, seven features such as As indicated in To evaluate the proposed method combining kernel-based extreme learning machine (K-ELM) with various features, we followed these three steps.F-value: 172.29, p < 0.01) and VAS , respectively.Firstly, we collected data related to the subjective stress levels from the subjects (STAI Y-1 and VAS). From the results, we confirmed that the subjective stress levels were correlated with the intended stress levels in our task. For instance, most of the subjects produced low scores on MIS-S and high scores on SES-S. In other words, our VR experiment could trigger the intended stress levels, and the physiological signals were measured under the intended stress conditions. Furthermore, we performed one-way ANOVA followed by the Tukey’s HSD and post-hoc comparisons to evaluate the difference of STAI-Y1 and VAS scores from three stress stimulation. As a result, the effects of three stress stimulation are significantly different in the STAI-Y1 . In general, the performance of the conventional machine-learning algorithm was governed by the corresponding parameters . Since tIt should be noted that the IT features showed the best results, which implies that the relationships among the features are also important for classifying the stress levels. The low classification results of the SKT feature might be because it is difficult to measure the core body temperature, which depends on the ANS .It is also noteworthy that we successfully discriminated two resting conditions, i.e., BA-S and RE-S, even though we provided the same conditions except for the task sequences. There is evidence that it takes some time for a physiological signal to return to its original state after a stressful condition . Our resk(k − 1)/2 times [/2 times . Therefo/2 times .In addition, computation time of ELM is higher than that of K-ELM since the solution of ELM requires the calculation of the hidden node output matrix H in (8). Therefore, whenever the number of hidden node increases, the computation time also rises. Instead, K-ELM converts from Finally, the organic relationships among the features were evaluated using self-organizing maps (SOMs). The shapes and distributions of the clusters in It is also noteworthy that the SOM results were calculated from all subject data without group distinction. It implies that the clusters trained by the SOM algorithm exhibited characteristics reflecting the common stress levels of all subjects, and uniformed clusters indicate that the input features well represented the stress levels. According to our results, the SC and SKT features had high person-to-person variability, so the classification accuracies might be lower when we used them separately. When we integrated every feature, the classifier showed better performance, suggesting that feature integration partially improved the accuracy.p-value < 0.05). There are features of PPG . As a result, a few features extracted from each physiological signal were significantly different , 0.77 (EDA) and 2.85 (SKT). Especially, the average of F-value in PPG was higher than results of EDA and SKT. The higher F-value is, the more significant difference between the groups is. Therefore, the features calculated by PPG have a close relationship with the stress state. As a result, In statistical analysis, the averages of In this study, we designed a stress-inducing task using a VR device and developed a stress-level classification method using K-ELM and various features. We found that the physiological signals provided enough information about the stress level that we could classify the stress levels with over 95% accuracy. Our results showed the possibility of stress measurement using physiological signals. If we can develop a wearable device, which can more conveniently measure the physiological signals, we may utilize our proposed algorithm for stress monitoring in real-life situations as well.


Bioactive peptides, including biological sources-derived peptides with different biological activities, are protein fragments that influence the functions or conditions of organisms, in particular humans and animals. Conventional methods of identifying bioactive peptides are time-consuming and costly. To quicken the processes, several bioinformatics tools are recently used to facilitate screening of the potential peptides prior their activity assessment in vitro and/or in vivo. In this study, we developed an efficient computational method, SpirPep, which offers many advantages over the currently available tools.http://spirpepapp.sbi.kmutt.ac.th.The SpirPep web application tool is a one-stop analysis and visualization facility to assist bioactive peptide discovery. The tool is equipped with 15 customized enzymes and 1–3 miscleavage options, which allows in silico digestion of protein sequences encoded by protein-coding genes from single, multiple, or genome-wide scaling, and then directly classifies the peptides by bioactivity using an in-house database that contains bioactive peptides collected from 13 public databases. With this tool, the resulting peptides are categorized by each selected enzyme, and shown in a tabular format where the peptide sequences can be tracked back to their original proteins. The developed tool and webpages are coded in PHP and HTML with CSS/JavaScript. Moreover, the tool allows protein-peptide alignment visualization by Generic Genome Browser (GBrowse) to display the region and details of the proteins and peptides within each parameter, while considering digestion design for the desirable bioactivity. SpirPep is efficient; it takes less than 20 min to digest 3000 proteins  with 15 enzymes and three miscleavages for each enzyme, and only a few seconds for single enzyme digestion. Obviously, the tool identified more bioactive peptides than that of the benchmarked tool; an example of validated pentapeptide (FLPIL) from LC-MS/MS was demonstrated. The web and database server are available at SpirPep, a web-based bioactive peptide discovery application, is an in silico-based tool with an overview of the results. The platform is a one-stop analysis and visualization facility; and offers advantages over the currently available tools. This tool may be useful for further bioactivity analysis and the quantitative discovery of desirable peptides.The online version of this article (10.1186/s12859-018-2143-0) contains supplementary material, which is available to authorized users. Spirulina platensis after digestion with alcalase from Bacillus licheniformis [. Although, enzymatic hydrolysis is a preferred method, several peptide-containing food products available in markets are generated from microbial fermentation. For example, sour milk and fermented milk which contain lactotripeptides such as isoleucine-proline-proline (IPP) and valine-proline-proline (VPP) [Bioactive peptides (BP) are protein fragments or peptides that play a significant role in human and animal health  and can niformis , and “YAniformis . Althougne (VPP) , 13, werne (VPP) . Howeverne (VPP) .Conventionally, the discovery of bioactive peptides is a time-consuming and costly process. The classical approach consists of enzyme selection, peptide production from protein hydrolysis, peptide-purification, peptide-identification, and in vivo or in vitro assays. Recently, the use of computational tools offers relief as they shorten time for the screening of peptide candidate prior to purification and the biochemical validation process in the laboratory. Currently available online tools can be classified into three groups: (i) in silico peptide digestion tools, e.g., PeptideCutter, a tool with a single protein sequence input, various enzymes and chemical options, can generate peptide sequences, allowing users to manually search for bioactivities against other databases , (ii) biDue to the advantages of bioinformatics tools, the in silico methodology is used for in silico peptide digestion and predicting bioactive peptides; for example, dipeptidyl peptidase-IV and angiotensin I-converting enzyme inhibitory peptides were identified by combining online in silico tools  as an in-house tool . HoweverIn this work, we, therefore, develop a web-based application tool, SpirPep, which is a one-stop analysis and visualization pipeline for bioactive peptide discovery. Our pipeline is able to rapidly predict putative peptides by the in silico digestion of protein(s) up to a genome-wide level with the choice of 15 restriction enzymes and the number of miscleavages as input parameters. The resulting peptides are then searched against our in-house database collected from 13 public bioactive peptide databases for bioactive peptide identification. All peptides are kept temporarily in the database and are used to visualize protein-peptide alignment and display the region as well as details of the proteins and peptides listed separately for each input parameter by the Generic Genome Browser (GBrowse) . In addiSpirPep was constructed as a web-based application tool for bioactive peptide discovery by the in silico peptide digestion of protein sequences derived from protein-coding genes such as single sequences, multiple sequences, and genome-wide scales. This tool consists of a database server and a web server for bioactive peptide discovery.Four databases were constructed for storing data and putative peptides from each process as well as to integrate the in silico peptide results with bioactive peptide sequences retrieved from 13 public bioactive peptide databases, which contained both naturally synthesized and hydrolysed proteins Table . The secTo manage input and output data, we created both SpirPepApps and GBrowseDB by date and deleted the entire database information within 3 days due to storage space limitation.The SpirPep web-based application tool has been developed to facilitate the use of SpirPep workflow Fig. . It is uFirst, it invokes the in silico peptide digestion module with the given proteins and parameters . Then, all non-redundant digested peptides are compared against the in-house bioactive peptide database, which is gathered from both naturally synthesized and hydrolysed proteins Table . The matThe SpirPep web application contains five parts: Home, SpirPep Tool, Bioactive Peptide Database, User Guide, and Contact Us.Home; presents an introduction to bioactive peptide discovery, motivation of the computational method for bioactive peptide prediction and advantages of the SpirPep web application.SpirPep Tool; here the user can input multiple protein sequences of interest, whole organism genome, or upload files in FASTA format with the required parameters. User replies are sent via email and displayed in the response page. Afterwards, SpirPep Tool predicts the candidate bioactive peptide sequences and then sends the output to the user by email.Bioactive Peptide Database; stores the number and biological function of retrieved bioactive peptides from 13 online bioactive peptide databases and their respective tree maps, grouped by size .User Guide; assists user’s to familiarize themselves with the contents and functionalities embedded in the SpirPep Tool. It demonstrates the processes involved from inputting queries into SpirPep Tool and accessing the results from the email notification. Additional file Contact Us; provides the contact information of the SpirPep Team from KMUTT for consulting, suggestions, or usage problems.In comparison to other available bioactive peptide prediction tools Table , SpirPepFor benchmarking, we compared SpirPep to another tool classified in the same category, BIOPEP Table , using tSpirulina proteins were input into ‘SpirPep’, the enzyme thermolysin was selected, and up to three miscleavage was allowed  and MaXis II Mass Spectrometer Detector ). The identified results from LC-MS/MS contained 3372 proteins  according to protein expression and 7366 peptides (0.53% of in silico peptides). The sample digested by thermolysin prior to LC-MS/MS were 6233 in common with the peptides found in the in silico ‘SpirPep’. Due to the limitation of LC-MS/MS detection, there are the peptides predicted by SpirPep but not present in the results from LC-MS/MS, which consist of only 5–24 amino acids peptide length. Hence, we could find only 76 (5–7 amino acids peptide length), but lost 496  bioactive peptides. Fortunately, a pentapeptide  from LC-MS/MS  was also detected in ‘SpirPep’ , but not in ‘BIOPEP’ , 6108 SpirPep is a one-stop web application that offers rapid identification and efficient analysis of bioactive peptides with six key features: (i) genome-wide scale inputs, (ii) a miscleavage option, (iii) the output of a number of known bioactive peptides for bioactivity identification, (iv) the resulting peptide categorized by enzyme, (v) the original protein tracked, and (vi) a GBrowse-based visualizer. Hence, SpirPep is a promising alternative pipeline for efficient screening and identification of bioactive peptides and their proteins of origin.http://spirpepapp.sbi.kmutt.ac.th. Operation system(s): Web based, Platform independent. Programming language: HTML, CSS, JavaScript, MySQL, PHP.Project name: SpirPep: A web-based database for bioactive peptide discovery. Project home page: Additional file 1:Figure S1. Schematic diagram of miscleavage. a) Example of miscleavage occurrence, b) Example of peptides resulting of thermolysate of molecular chaperone DnaK (SPLC1_S010870) from LC-MS/MS at amino acid 1–26 and 123–134 (Our unpublished data). (TIFF 8492 kb)Additional file 2:Figure S2. SpirPep sequence diagram: SpirPep was designed into a three-tier system . The front-end sends the queries to SpirPepDB (FrontendDB and SpirPepApps) and responds to the users. The queries are queued by the Redis server and sent to Resque worker(s) in the back-end. When the analysis is complete, the system will send an email notification with the link to the results page to users. The results will be stored in the database temporary table and exported to the GFF file format, which can be internally used by the SpirPep visualizer. (JPEG 466 kb)Additional file 3:Figure S3. Example of webpage snapshots from the SpirPep tool: a) query input from user submission  and allowed miscleavage number), b) notification of received query from users as the response page and submission information email when the analysis complete, and c) results pages that contain the list all and summary pages, which show the predicted bioactive peptide sequences derived from input proteins and parameters. This provides a valuable decision to users for their re-designing the digestion system to obtain desirable bioactive peptides and enzymes before validation in a laboratory. (JPEG 2333 kb)Additional file 4:Table S1. Dataset and output comparison between SpirPep and BIOPEP, the identified bioactive peptides obtained from temperature stress – expressed protein [ protein  digested


No significant ON-OFF differences were found for Go trial or No-go trial response times (RTs), or for number of No-go errors. We interpret that dopaminergic therapy induces a more conservative response set, reflected in Go trial Timeouts in PD patients. In this way, dopaminergic therapy decreased motor impulsivity in PD patients. This is in contrast to the widely recognized effects of dopaminergic therapy on cognitive impulsivity leading in some patients to impulse control disorders. Understanding the nuanced effects of dopaminergic treatment in PD on cognitive functions such as impulse control will clarify therapeutic decisions.Parkinson’s disease (PD) is characterized by resting tremor, rigidity and bradykinesia. Dopaminergic medications such as L-dopa treat these motor symptoms, but can have complex effects on cognition. Impulse control is an essential cognitive function. Impulsivity is multifaceted in nature. Motor impulsivity involves the inability to withhold pre-potent, automatic, erroneous responses. In contrast, cognitive impulsivity refers to improper risk-reward assessment guiding behavior. Informed by our previous research, we anticipated that dopaminergic therapy would decrease motor impulsivity though it is well known to enhance cognitive impulsivity. We employed the Go/No-go paradigm to assess motor impulsivity in PD. Patients with PD were tested using a Go/No-go task on and off their normal dopaminergic medication. Participants completed cognitive, mood, and physiological measures. PD patients on medication had a significantly higher proportion of Go trial Timeouts  compared to off medication ( Parkinson’s disease (PD) is the neurodegenerative disease with the second highest prevalence rate, affecting approximately 1% of adults over 60 years of age in industrialized nations  is located adjacent to the SNc. The VTA is another dopamine-producing area in the midbrain  and dopamine agonists  impulsivity in PD, leading to serious ICDs. These findings highlight the importance of: (a) understanding impulsivity as a multi-faceted concept rather than a unitary construct; and (b) fully clarifying effects of dopaminergic therapy across a variety of cognitive functions.In a previous study, we showed that healthy young controls using the same Go/No-go task that was implemented in the current study, evidenced greater Go Timeouts when they were taking the DA pramipexole relative to placebo, revealing more considered and less impulsive responding in the DA condition  lower Go Timeout rate; and/or (b) higher No-go Error rate. In contrast, more cautious and considered responding would be expressed as: (a) higher Go Timeout rate; and/or (b) lower No-go Error rate, as described above.N = 15), or L-dopa in combination with DAs (N = 8). The data of participants who scored below 24 on the Montreal Cognitive Assessment (MoCA) were excluded from analyses. One PD patient was excluded for this reason. Finally, participants were excluded if their mean RTs or error rates in the Go or No-go conditions fell outside 2.5 standard deviations of the Group mean for that Medication Session . Four additional PD patients were excluded for having data that were deemed outliers. Analyses were completed with the data of the remaining 22 PD patients. This study was carried out in accordance with the recommendations of the Health Sciences Research Ethics Boards of the University of Western Ontario with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki  were recruited from the University of Western Ontario and Health Sciences North Hospital in Sudbury, Ontario. Participants were pre-screened for inclusion and exclusion criteria. All PD patients had been previously clinically diagnosed with PD by a licensed neurologist and met the UK Brain Bank criteria for a diagnosis of PD  using the Windows 7 Professional operating system and a 22.0″ monitor (LG Flatron W2242TQ) running on a resolution of 1600 × 900 pixels. Participants were seated approximately 50 cm away from the screen and used a keyboard (Logitech K120) to record their responses.All participants completed two testing sessions on consecutive days at the University of Western Ontario or Health Sciences North Hospital. For the OFF Session, PD patients were instructed to abstain from taking L-dopa/carbidopa and entacapone for 12–18 h before the start of the session, and dopamine agonists  as well as amantadine, rasagiline, and selegiline for 16–20 h before the start of the session. For the ON Session, PD patients were instructed to take all dopaminergic medications for PD as prescribed by their treating neurologist. ON-OFF order was randomly assigned and counterbalanced. After the exclusion of five PD patients as previously described, twelve participants had an ON-OFF medication order and the remaining ten participants had an OFF-ON order. All participants were debriefed about the details of the study once they completed the second session. Participants were compensated for their time and participation.Demographic and clinical data  were collected from all participants. PD duration refers to the number of years since a diagnosis of PD. LED is a calculation of the daily dose of dopaminergic therapy in units of L-dopa equivalents. Calculation of LED (mg) for each PD patient was based on the theoretical L-dopa equivalence , systolic blood pressure (BP) and diastolic BP were measured using an automated BP monitor (Omron model BP785N) at the beginning and end of each testing session. Participants were also given a self-reported visual analog scale (VAS) at these two time-points to assess subjective alertness , MoCA and Controlled Oral Word Association Test (COWAT). The ANART is a measure of verbal intelligence that has been adapted for use in North America  in both sessions. The BDI, BAI and SAS are commonly used assessments of depression  Go RT, comprising the mean RT for responses that occurred prior to the 750 ms deadline; (b) No-go RT, consisting of the mean RT for erroneous responses provided in the No-go condition; (c) Go Timeout Rate, reflecting the percentage of trials on which participants failed to respond prior to the 750 ms deadline; and (d) No-go Error Rate, denoting the percentage of trials on which participants erroneously made a keypress response in the No-go condition. RTs were calculated as the time in ms between the onset of the visual stimuli and the keypress responses. Data values for Go RTs were trimmed if they fell more than 2.5 standard deviations from the mean Go RTs in each medication state for each participant. The same process was used to trim No-go RT values. Lower Go Timeout rates and higher No-go Error rates were indicative of greater motor impulsivity whereas higher Go Timeout rates and lower No-go Error rates indexed less impulsive responding. Go RTs and No-go RTs were analyzed using non-parametric two-tailed Wilcoxon Signed Ranks Tests, and Go Timeout Rate and No-go Error Rate were analyzed using paired-sample two-tailed t-tests, with the Bonferroni correction for multiple comparisons. Analyses were performed using Excel (Version 2016), IBM SPSS Statistics (Version 21), and GraphPad Prism (Version 6). Data were considered significant if p < 0.05.Physiological measures  were compared using 2 × 2 analysis of variances (ANOVAs), with Medication (ON vs. OFF) and Time (Pre-Task vs. Post-Task) as within-subject variables. Affective measures  and dependent measures derived from the Go/No-go task were compared between ON and OFF Medication states using paired-samples two-tailed t-test. PD patients showed significantly higher UPDRS scores off dopaminergic medication compared to on, which was expected (t(21) = 10.139, p < 0.001).Demographic and cognitive measures are presented for PD patients Table . Full deF = 24.569, MSe = 31.507, p ≤ 0.001). Additionally, Systolic BP was significantly higher OFF compared to ON  = 15.647, MSe = 88.459, p = 0.001). Diastolic BP showed a similar significant effect of Medication, with significantly higher Diastolic BP OFF compared to ON dopaminergic therapy for PD patients  = 11.743, MSe = 36.046, p = 0.003). For VAS Alertness .Physiological measures, including HR, Systolic BP, Diastolic BP and VAS Alertness were analyzed using 2 × 2 ANOVAs, with Medication (ON vs. OFF) and Time (Pre-Task vs. Post-Task) as within-subject variables. HR was significantly higher Pre-Task compared to Post-Task .Affective measures  were compared between ON and OFF medication states using paired-samples two-tailed s Figure . For allt-tests in the Go/No-go task using the Bonferroni correction. Mean Go RT was not significantly different for PD patients ON and OFF dopaminergic medication . No significant difference was found between ON and OFF No-go mean RT . PD patients ON had a significantly higher Go Timeout Rate compared to OFF dopaminergic therapy  = 2.851, p = 0.010) even after applying the Bonferonni correction . Examining No-go Error Rate, there were no significant effects of Medication .We investigated the effect of Medication (ON vs. OFF) on the dependent measures of mean Go RT and No-go RT using two-tailed Wilcoxon Signed Ranks Tests, and Go Timeout Rate and No-go Error Rate using paired-samples two-tailed reducing motor impulsivity in contradistinction to its widely-recognized enhancement of cognitive/motivational impulsivity producing ICDs in PD. We did not see a concomitant decrease in No-go errors for patients on relative to off dopaminergic treatment, however. In the No-go condition, a higher No-go Error rate in the ON state would also have signaled reduced motor impulsivity to parallel adoption of a more considered and conservative response strategy in the Go condition leading to more Go Timeouts. To engender a pre-potent Go response, there were far fewer No-go trials relative to Go trials. Consequently, it is possible that the No-go condition did not have the statistical power to reveal No-go error differences between ON and OFF medication states. Neither Go RT nor No-go RT were affected by medication status.We found that dopaminergic medication increased the Go Timeout rate in PD patients compared to their performance off medication. This suggests that dopaminergic therapy induced a more conservative response pattern for PD patients, Comparisons of physiological measures showed that PD patients had lower HR post- relative to pre-Go/No-go Task. This trend of lower HR was fully expected because participants were sitting and inactive for the study period and had acclimatized to the novelty of the setting. PD patients also had increased systolic and diastolic BP off relative to on dopaminergic medication. This was anticipated as L-dopa is known to lower BP  in the PD patients in our study. This was not our aim, though, as detailed below, reviews of this literature confirm that PD patients consistently exhibit deficits in inhibition of pre-potent responses and motor impulsivity  and without (N = 13) dyskinesia, and healthy controls (N = 13), with both patient groups being tested ON and OFF dopaminergic medication. Herz et al. (N = 9) ON and OFF medication relative to healthy controls (N = 9) who performed the task only once. They did not find behavioral differences between patients ON vs. OFF medication, or relative to performance of healthy older controls on Go/No-go accuracy or RT. However, with only nine participants in each group, the study likely was underpowered statistically to detect true differences if they occurred. Further, medication order was not counterbalanced. PD patients were always assessed in the OFF-ON order. In this way, and because healthy controls only performed the task once, order effects were confounded with medication effects. Antonelli et al. (N = 7) ON and OFF the DA pramipexole. They found that administration of pramipexole increased impulsive choices on a delayed discounting task—their measure of cognitive impulsivity. However, no ON-OFF differences were observed on Go/No-go performance–their measure of motor impulsivity. This study was important in providing evidence that dopaminergic treatment affects distinct forms of impulsivity dissimilarly, supporting the idea that impulsivity is not a unitary concept, but rather is multifaceted. These results must be viewed with caution, however, considering that due to a sample size of only seven PD patients, this study was likely underpowered. Further, the authors’ rendition of the Go/No-go task involved presenting Go signals at 60%, and No-go signals at 40%, limiting the pre-potency of the Go response.There are few studies in the PD literature that have investigated motor impulsivity using the Go/No-go in PD patients. Fewer still have investigated the effect of dopaminergic therapy on performance though an important and concerning side effect of dopaminergic therapy is disordered impulse control. Herz et al.  comparedz et al.  used a vz et al.  comparedi et al.  contrastGeffe et al.  used a vIn summary, we provide the first demonstration that dopaminergic therapy affects performance in a straightforward Go/No-go task in PD patients. The limited number of previous studies investigating the effects of dopaminergic therapy on Go/No-go performance in PD had small sample sizes and were impacted by other methodological issues, outlined above, that might reduce sensitivity to detect medication effects, predisposing them to null findings. We sought to redress these concerns by testing a sufficiently large number of PD patients  and setting the Go/No-go parameters at 75% Go trials, with a single Go stimulus, relative to 25% No-go trials, establishing a strong pre-potent Go response. We suspect that these factors explain discrepancies between our findings and the results of previous investigations of dopaminergic therapy on motor impulsivity in the Go/No-go task in PD patients.Our finding of increased Go Timeouts for PD patients when tested ON relative to OFF dopaminergic therapy is entirely in keeping with our previous investigations of pramipexole vs. placebo on Go/No-go performance in young, healthy controls (Yang et al., increases motor impulse control, Hiebert et al. (reduces motor impulsivity in contrast to the more widely-understood effect of increasing cognitive/motivational impulsivity producing ICDs in PD patients. This understanding is important for the clinical approach to PD and decisions regarding titration of dopaminergic therapy considering motor as well as cognitive symptoms.Consistent with the notion advanced here that dopaminergic treatment in fact t et al.  found thOur observations in this study are in accordance with previous research on response inhibition and/or response withholding in PD generally. In a meta-analysis of the effects of dopaminergic medication and PD disease duration on measures of response inhibition, Manza et al.  found thIt is now understood that dopaminergic treatment in PD leads to improvements in some aspects of cognition, but impairments in others (Cools et al., reduced motor impulsivity due to dopaminergic therapy in PD. These effects co-exist with the recognition of ICDs arising with dopaminergic treatment (Pontone et al., enhanced cognitive/motivational impulsivity in PD. Though presenting opposite effects of dopaminergic therapy on cognition, these patterns are understood through the same framework provided above. DS has been implicated in limiting motor impulsivity by ensuring more considered and less habitual responding (Hood et al., Our finding in the Go/No-go task along with the results of the meta-analysis conducted by Manza et al. , are comOverall, we provide support for the role of dopaminergic therapy in decreasing motor impulsivity in PD. Our findings illustrate the importance of recognizing impulsivity as a multi-faceted construct. These findings enhance our understanding of the effect of dopaminergic therapy on cognition in PD. This knowledge will ultimately inform clinical decisions regarding dosing of dopaminergic therapy in PD, taking into account different cognitive as well as motor symptoms.XQY, BL, KNS and PAM designed the experiment and edited the manuscript. XQY and BL conducted the experiment. XQY and PAM performed the analysis. XQY, BL and PAM wrote the 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.


Bilateral deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a treatment option for patients with advanced idiopathic PD successful at alleviating disabling motor symptoms. Nevertheless, the effects of STN-DBS on cognitive functions remain controversial and few studies have investigated modification of social cognitive abilities in patients with PD treated with STN-DBS. Here we expanded the typically-investigated spectrum of these abilities by simultaneously examining emotion recognition, and both affective and cognitive Theory of Mind (ToM). By means of a cross-sectional study, 20 patients with PD under dopaminergic replacement therapy, 18 patients with PD treated with STN-DBS, and 20 healthy controls performed the Ekman 60-Faces test, the full version of the Reading the Mind in the Eyes test, and the Protocol for the Attribution of Communicative Intentions. There were no differences between the PD groups (treated and not treated with STN-DBS) on any of the social cognitive tests. Our results suggest that patients with PD who are treated with STN-DBS do not experience detrimental effects on their social cognitive abilities. The present study, the first one examining a wide spectrum of social cognitive abilities after DBS of the STN, suggests that this surgical procedure can be considered safe from this standpoint. Cognitive deficits in patients with PD result from dopamine depletion and are attributed to disruption of the reciprocal loops between the striatum and structures in the prefrontal cortex. Therefore, individuals with PD may experience impairments in various cognitive domains that primarily depend on the frontal lobes. Furthermore, patients with PD have impaired performance in social cognitive tasks, such as recognition of emotional facial expression and those requiring Theory of Mind (ToM), i.e. the ability to explain and predict other people’s mental states5.Parkinson’s Disease (PD) is a neurodegenerative disease associated with clinical symptoms that include bradykinesia, rigidity, resting tremor, and postural instability. Although PD is characterized primarily by motor symptoms, cognitive impairment and psychiatric disorders can be also observed even in the earliest stages of the disease6. Nevertheless, the effects of STN-DBS on cognitive functions, mood, and behaviour remain controversial, and experimental findings in this domain are sometimes contradictory11. Although cognitive difficulties such as moderate declines in executive functions, verbal memory, and fluency tasks have been reported, studies with large patient cohorts indicate that STN-DBS does not produce general cognitive declines13. However, clinical and psychiatric issues, such as depression14 and (hypo)mania15 have been reported following STN-DBS. Furthermore, DBS may also result in social maladjustment and aggravation of other pre-existing psychiatric disorders17.Chronic bilateral deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a treatment option for patients with advanced idiopathic PD. STN-DBS is specifically indicated in patients for whom long-term pharmacological treatment has failed, and is remarkably successful at alleviating disabling motor symptoms17 . The effect of STN-DBS on emotion processing and its reported effects on cognition and mood are likely related to the role of the STN in the basal ganglia network. The DBS modifies dopaminergic transmission in the basal ganglia, affecting the limbic, associative, and motor networks via highly organised circuits19. These basal ganglia circuits are comprised of both cortical  and subcortical regions , and correspond with structures that also mediate social cognition21. Furthermore, the basal ganglia networks also directly target the prefrontal cortex, including the dorsolateral prefrontal cortex and the anterior cingulate cortex.Previous research has examined emotional processing following STN-DBS, focusing primarily on subjective emotions and arousal, as well as emotion recognition23.Several studies have investigated social cognitive domains in patients with PD, whereas very few studies have tested modification of these domains by directly comparing patients with PD who are either treated or not treated with STN-DBS. In particular, no study has simultaneously investigated the effects of STN-DBS on (1) emotion recognition, i.e. reading facial expressions of emotion, (2) affective ToM (emotion representation), i.e., the ability to represent others’ emotions, and (3) cognitive ToM, i.e. the ability to represent others’ beliefs and intentionsThe aim of the present study was to examine the effects of STN-DBS on social cognitive abilities, particularly on emotion recognition, affective ToM, and cognitive ToM. This study aim at expanding knowledge regarding social cognitive abilities by testing patients’ abilities in both emotion recognition and representation, as well as by assessing both affective ToM and cognitive ToM. The rationale for investigating these features was to increase understanding regarding the complex clinical outcomes following STN-DBS, especially in relation to the non-motor symptoms of PD.(2) = 8.221, p = 0.016). Pairwise Mann–Whitney tests revealed significant differences between DRT-PD and STN-DBS-PD  groups, as well as STN-DBS-PD and HC  groups, with participants in the STN-DBS-PD group performing worse than participants in the DRT-PD and HC groups.The cognitive functioning assessment and demographic data for the DRT-PD, STN-DBS-PD, and HC groups are presented in Table (2) = 6.824, p = 0.033), and for anger (H(2) = 7.094, p = 0.029) and surprise (H(2) = 11.129, p = 0.004), but not for sadness, happiness, disgust, or fear. Moreover, significant group differences occurred for both the CInt (H(2) = 19.469, p < 0.001) and PhC (H(2) = 7.275, p = 0.026) conditions of the PACI, and for the RME experimental score (H(2) = 11.861, p = 0.003), but not for the RME control score.The difference in emotion recognition performance between DRT-PD, STN-DBS-PD and HC groups , affective ToM, and cognitive ToM are presented in Table Pairwise Mann–Whitney tests revealed no statistically significant differences between the DRT-PD and STN-DBS-PD groups for social cognitive dimensions, with the exception of anger , for which the STN-DBS-PD group performed better than the DRT-PD group. However, this difference disappeared after correction for multiple comparisons, suggesting a spurious effect.η2 = 0.395), and the RME experimental score . There were also significant differences between the STN-DBS-PD and HC groups for Ekman-surprise , CInt condition , and RME experimental scores .Pairwise Mann–Whitney tests corrected for multiple comparisons revealed significant differences between the DRT-PD and HC groups for the CInt condition , for which patients in the DRT-PD group performed better than patients in the STN-DBS-PD group.The differences in performance between the two groups of patients with PD for the neuropsychological, psychiatric and, clinical measures are presented in Tables η2 = 0.137), for which patients in the DRT-PD group took a higher dosage compared to patients in the STN-DBS-PD group.Furthermore, the patient groups were well-matched based on the clinical variables, with the exceptions of the LEDD  and psychiatric  measures, we analysed bivariate correlations in the two groups of patients with PD, between each dimension , dopaminergic pharmacological treatment (LEDD), duration of illness, and severity of the disease (UPDRS III Off) , and cognitive ToM. There were no differences between the PD groups on any of the social cognitive tasks. DRT-PD, STN-DBS-PD and HC groups were matched for age, gender, and education, as well as for general cognitive functioning, as assessed by MMSE scores. In addition, the two PD groups were well-matched for duration of illness, and severity of the disease. No significant differences between the two PD groups occurred for psychiatric  or neuropsychological scales, with the exception of phonemic fluency. The phonemic fluency score indicated impaired ability in the STN-DBS-PD group compared to the DRT-PD and HC groups, which is a well-documented outcome of STN-DBSOur results show that STN-DBS, when combined with optimal medical treatment, does not result in impairment of emotion recognition in patients with PD. We found no differences between the PD groups nor on Ekman 60-Faces total score nor on any of Ekman basic emotion, whereas only Ekman-surprise differed between the STN-DBS-PD and HC groups, with participants in the STN-DBS-PD group performing worse than participants in the HC groups.25. Impaired recognition of disgust has been described in both un-medicated and medicated patients with PD26. Other authors have investigated the relationships with neuropsychiatric and neuropsychological symptoms, again yielding mainly inconclusive results27.Previous research suggests that patients with PD experience impaired facial emotion expression recognition; however, the results are inconclusive regarding the degree and selectivity of emotion recognition impairments28, and disgust30, as well as fear and sadness33. In contrast, Mondillon, et al.34 found no impairment in emotion recognition in patients with STN-DBS-PD who were evaluated while receiving both dopaminergic and DBS therapies (Stim On-Med On condition). Moreover, Albuquerque, et al.35 reported no differences in emotion recognition in patients with advanced PD after STN-DBS, based on comparisons of the ability to recognize emotions before and one year after surgery. It is important to note that previous studies have administered an emotion recognition task that uses pictures of the Ekman 60-Faces Test36, showing the pictures for 3 sec. In the present study, we chose not to use a specified presentation duration, in order to reduce the demand on attention and working memory. Moreover, our version of the Ekman 60-Faces Test is comprised of 10 stimuli for each basic emotion, whereas previous studies have used a modified version of the test that consists of fewer stimuli for each emotion.The effects of STN-DBS on recognition of facial emotion expressions have yielded contrasting findings, likely due to different study methods. However, the overall results suggest that STN-DBS may affect the ability to recognise negative facial expressions, in particular angeret al.34, who found no impairment of facial expression recognition in patients with STN-DBS-PD in an optimal medication and stimulation condition, compared to an HC group. The authors of this study did not assess the patients for facial expression recognition pre-STN-DBS and did not have a control group of patients with PD, therefore, we cannot make comparisons for all aspects of the study.Our results confirm the findings by Mondillon, Our findings show that patients with PD who are treated with STN-DBS do not experience detrimental effects on affective ToM. No differences between the PD groups on the RME were found, whereas the performance on the RME differed between both the PD groups and HC, with participants in the STN-DBS-PD and DRT-PD groups performing worse than participants in the HC group.37. These declines may be mediated by dopaminergic depletion-induced hypostimulation of the ventromedial prefrontal cortex by the frontostriatal loops, since the ventromedial prefrontal cortex contributes to emotion representation.Consistent with most previous studies, we observed impairments in emotion representation in patients with PD. We have previously suggested that the decline in this ability depends on the spatiotemporal progression of dopamine depletion in patients with PD, indicating that emotion representation abilities may be impaired at more advanced stages of PD39, however, these studies yielded contrasting results. McIntosh, et al.39 administered the RME to patients with early PD, who were randomised to receive either DRT (n = 7) or STN-DBS surgery (n = 9). Due to small sample size, the authors did not compare the two groups directly and did not suggest any conclusions regarding the comparison. Investigating the effect of treatment type (DRT vs. STN-DBS) and treatment state (ON/OFF) tasks, these authors found no significant effects of therapy on RME performance. Peron, et al.38 correlated RME performance to changes in glucose metabolism by conducting fludeoxyglucose positron emission tomography scans of 13 patients with PD at both three months prior to and three months post-STN-DBS surgery. The authors examined RME performance using a reduced 17 item version of the test, and compared the performance of the PD group pre- and post-surgery with that of an HC group (n = 13). They found no significant difference between preoperative PD and HC results, whereas postoperative patients performed significantly worse than either preoperative PD or HC groups.Two previous studies have assessed affective ToM in patients with STN-DBS-PD, using the RMEet al.38 may have resulted from different study methods. As previously discussed, most studies that have used the RME in patients with PD have found impaired emotion representation. Peron, et al.38 found that the pre-surgery performance of patients with PD did not differ from that of participants in the HC group, indicating that these abilities were preserved in their PD group. Furthermore, there are methodological differences regarding the versions of the RME administered, with Peron, et al.38 using a shorter 17 item test, whereas we used the full version comprised of 36 items. It has been suggested that using short versions of the RME is problematic, as mild or sub-threshold impairments in affective ToM may not be detected37.The discrepancies between our results and those of Peron, The present study is the first examination of cognitive components of ToM after DBS of the STN in patients with PD, and our results show that STN-DBS has no detrimental effects on cognitive ToM. No differences between the PD groups on PACI tasks were found, whereas the performance on CInt differed between both the PD groups and HC, with participants in the STN-DBS-PD and DRT-PD groups performing worse than participants in the HC group.45. The results of these studies strongly implicate the prefrontal cortices in the ability to understand the intentions of people involved in communicative interactions (i.e. cognitive ToM). The prefrontal cortices are critical to the neuropathology of PD, as well as in STN networks, and are also implicated in cognitive ToM; therefore, effects of STN-DBS on this latter ability cannot be excluded. Furthermore, numerous reports support impairment of cognitive ToM in patients with PD46, leading to the question of whether an existing impairment changes as a result of stimulation. Thus, the present findings corroborate the hypothesis that tasks requiring the comprehension of others’ intention may be impaired in both PD groups and, more importantly, suggest that STN-DBS has no detrimental effects on cognitive ToM.The experimental protocol that we used to study intention attribution was initially developed for a series of fMRI studies aimed at exploring the theoretical classification of intentionsSTN-DBS surgery is commonly used to treat the motor symptoms of patients with PD. Research has begun to investigate the effects of STN-DBS on mood and cognition, and factors such as subtle differences in electrode placement, stimulation parameters, patient vulnerability prior to STN-DBS, and dopamine dose changes may influence the outcomes of STN-DBS in patients with PD. Since non-motor effects of STN-DBS have the potential for large impacts on patient quality of life, it is essential to assess possible effects of STN-DBS on every aspect of cognition.38. Most previous studies focus on emotion recognition following STN-DBS, however, the present study investigated different facets of social cognition, namely emotion recognition, affective ToM, and cognitive ToM.STN-DBS may also provide a useful model for studying the role of the STN in emotional processes and ToMThe present study has two main limitations. Firstly, the sample size is relatively small, and future studies should aim to replicate our findings using larger samples. Secondly, we used only the MMSE and FAB as neuropsychological measures to compare the two groups of PD with the HC group. Despite these limitations, the present study is the first investigation of the effects of STN-DBS on social cognition, using measures to assess emotion recognition and representation, as well as both cognitive and affective ToM. We found no specific effects of STN-DBS on emotion recognition, cognitive ToM and affective ToM. The results of the present study contribute to knowledge regarding the cognitive safety of STN-DBS treatment. Our results support that STN-DBS is safe in relation to the social cognitive domain and does not have detrimental effects on the social cognitive abilities investigated.Written informed consent was obtained from all participants. The study was approved by the San Giovanni Battista University Hospital’s ethics committee and was conducted in accordance with the Declaration of Helsinki.47, which is a validated measure for depression symptoms in patients with PD48.By means of a cross-sectional study, 20 consecutive patients with PD under dopaminergic replacement therapy (DRT), 18 patients with PD treated with STN-DBS, and 20 healthy controls (HC) with negative neurological and psychiatric history were enrolled in the study. All patients were consecutively referred to the neurology unit of Turin University Hospital for standard care visits. Inclusion criteria included a diagnosis of idiopathic PD, and age ≥40 years. Exclusion criteria for all groups of participants were the presence of dementia or severe cognitive impairment , and presence of other neurological or psychiatric disorders, such as severe depression as assessed using a psychological interview and the Beck Depression Inventory (BDI)All patients received their daily optimal dopamine replacement therapies .49. Scores were measured at baseline  and after administration of a loading dose of L-dopa .Motor evaluations were performed according to the Unified Parkinson’s Disease Rating Scale, Part III (UPDRS-III)50 scale was administered in order to assess the progression of motor symptoms in both On and Off conditions. L-dopa equivalent daily doses (LEDD) were calculated according to commonly established standard conversions51.In the first clinical group (DRT-PD), UPDRS scores were measured during two conditions: on-medication (Med On) and off-medication (Med Off). The second group (STN-DBS-PD) was assessed during four different conditions: with stimulation switched on after overnight medication withdrawal (Stim On-Med Off); 60 min after switching off stimulation, after overnight medication withdrawal (Stim Off-Med Off); with stimulation switched off and about 40 minutes after the administration of L-dopa therapy, at the same dose used for the preoperative test (Stim Off-Med On); and undergoing both stimulation and L-dopa therapy condition (Stim On-Med On). In the present study we analysed the UPDRS Off conditions, i.e., the Med Off condition for DRT-PD group and Stim Off-Med Off for STN-DBS-PD, in order to compare the severity of the disease between the two PD groups. Furthermore, Hoehn and Yahr (H&Y)52 involved bilateral electrode implantation in the STN, which was accomplished during two independent surgeries. Anatomical targeting was performed using magnetic resonance imaging (MRI)/computed tomography image fusion. During surgery, clinical effects were evaluated using electro-physiological recordings and microstimulation, in order to achieve the best possible lead placement. Postoperative MRI was performed in order to confirm electrode positioning and to exclude surgical complications.The surgical procedure53.Eligibility criteria for DBS included diagnosis of idiopathic PD, presence of severe motor fluctuations and/or drug-related dyskinesias, and clinical response to L-dopa treatment for motor symptoms. Moreover, patients were <70 years of age, did not have dementia and/or relevant psychiatric comorbidities, and did not have relevant atrophy or focal abnormalities on brain MRI. The patient selection was performed based on the Core Assessment Program for Surgical Interventional Therapies in Parkinson’s Disease (CAPSIT-PD)54 and the Frontal Assessment Battery (FAB)56 were administered to all participants prior to social cognitive tasks, as a preliminary screening for global cognitive and executive functioning. In addition, a standardised neuropsychological battery of tests10 was administered to both DRT-PD and STN-DBS-PD groups, in order to evaluate reasoning, memory, attention, and executive functions. Reasoning was assessed using Raven’s Coloured Progressive Matrices57. Short term memory for verbal (bi-syllabic word repetition test) and visuospatial components (Corsi’s Block Tapping Test)58 were assessed, and long term memory was assessed using the Wechsler memory scale subtest, which is a paired-associate learning test59. Attention and executive functions were assessed using attentional matrices58, the Trail Making Test, Part B60, the Nelson Modified Card Sorting test62, as well as phonemic58 and category verbal fluency tasks59.The MMSE63, the State Trait Anxiety Inventory (STAI)64, in particular (STAI-X1) and (STAI-X2), and the BDI47.Psychiatric assessments included evaluation of apathy anxiety and depression using the Apathy Evaluation ScaleThe DRT-PD, STN-DBS-PD, and HC groups performed three social cognitive tasks: the Ekman 60-Faces Test for emotion recognition, the 36-item full version of the Reading the Mind in the Eyes (RME) test for affective ToM, and the Protocol for the Attribution of Communicative Intentions (PACI) for cognitive ToM.66. This task assesses both overall emotion recognition and basic emotion detection. It consists of 60 black and white pictures that portray the faces of 10 actors, each displaying the six basic emotions . A computerised sequence of slides containing a facial emotion picture was administered to participants, requiring them to select the label that best described the facial expression in the picture. The labels were visible throughout testing and participants were allowed as much time as they needed to verbally make their selection. No feedback was given throughout testing.The ability to recognise basic facial emotions was evaluated using the Italian version of the Ekman 60-Faces Test67 is an advanced test of affective ToM (emotion representation) that consists of 36 partial pictures of a face depicting only the eye region. The task was presented on a computer screen. For each photograph, participants were asked to choose one of four words that best described what the character was thinking or feeling. One correct and three distractor words were presented for each item. Explanations of the labels were given when requested by participants, using the definitions provided by the Italian validation of the task68. The score was calculated by counting the total number of correct choices (maximum score 36). In order to exclude visual impairment, we administered the RME gender-recognition control task, which required participants to identify the gender of the character in the photograph.The RME70 that distinguishes between social and physical interactions, and was administered as a measure of cognitive ToM. A series of 18 comic strips was presented to participants, representing two different conceptual categories: communicative intentions (CInt), and physical causality (PhC). The CInt condition consisted of nine stories that each portrayed two characters involved in a communicative interaction (e.g. a person asking for a glass of water and obtaining it from another person). The PhC condition consisted of nine stories that each depicted a physical interaction between objects , and did not require ToM abilities. Each story consisted of three pictures (development phase) that appeared consecutively on the screen, followed by a set of four options (response phase), and participants selected the most appropriate and logical story ending. The correct picture represented a probable and congruent outcome resulting from the development phase, while the incorrect pictures represented an improbable or incongruent effect. In order to avoid working memory overload and inaccuracies due to slowed information processing speed, patients were allowed an unlimited duration of time to respond, and all pictures remained on the screen while participants completed the story. Examples of the stories can be found at the following web address: www.psych.unito.it/csc/pers/adenzato/pdf/stn_dbs_pd.pdf.The PACI is an intention attribution task71, with a mean follow up of 1.72 (±1.18) years.The neuropsychological assessment and the experimental tasks were performed under optimal clinical conditions for all participants. In particular, patients in the DRT-PD group, who were not treated with STN-DBS, were assessed in the Med On condition, while patients in the STN-DBS group were assessed in the Stim On-Med On condition. Patients in the STN-DBS-PD group were evaluated at least six months after surgery, in order to minimise microlesion effectsη2).Statistical analyses were conducted using SPSS version 24.0 for Windows. There were a limited number of participants in each group and significant deviations from normality of variable distribution, as assessed by Shapiro-Wilk’s tests (p < 0.05); therefore, non-parametric methods were used . Comparisons of the three independent groups were performed using the Kruskal-Wallis H-test. When the Kruskal–Wallis test yielded a significant difference, pairwise Mann–Whitney tests were conducted in order to determine specific differences with a significance level of 0.05, Bonferroni-corrected for multiple comparisons. The effect size was determined by calculating Eta squared (Tables 5, 6 and 7


The Ob-X group showed a noticeable reduction in visceral fat of 20.5% after the 12-week treatment as compared to baseline measured by computed tomography. The change in visceral fat in the Ob-X group was statistically significant as compared to the placebo group (p = 0.0495) and 1.9 times higher than in the placebo group. Therefore, angiogenesis inhibitor Ob-X has the potential to improve obesity-related metabolic syndrome by reducing dangerous visceral fat.Adipose tissue growth is angiogenesis-dependent, and angiogenesis inhibitors can regulate adipose tissue mass by cutting off the blood supply. We examined whether antiangiogenic herbal composition Ob-X can reduce fast-growing abdominal fat, especially visceral fat in humans by inhibiting angiogenesis. Eighty abdominally obese subjects (body mass index: 25-29.9 kg/m Obesity, particularly abdominal obesity, plays an important role in the pathogenesis of metabolic disorders and cardiovascular diseases, including type 2 diabetes , 2, hypeAngiogenesis is the process of new blood vessel formation from preexisting vasculature. Similar to tumor tissue, adipose tissue growth is angiogenesis-dependent, and adipose tissue mass can be regulated by its vasculature –13. It h Morus alba L. , Melissa officinalis L. , and Artemisia capillaris Thunb.  showed antiangiogenic and MMP-inhibitory activities . ex vivo rat aortic ring assay, indicating that Ob-X can inhibit VEGF-induced angiogenesis [It is reported that vascular endothelial growth factor (VEGF) and its system account for most of the angiogenic activity in adipose tissue, making it an attractive target to reduce obesity –30. Ob-Xogenesis . Furtherogenesis . In vitro and animal studies showed that Ob-X can regulate the growth and development of adipose tissue by inhibiting MMP activities [MMPs play major roles in the extracellular matrix remodeling events associated with angiogenesis , 32 and tivities . Thus, tThe Ob-X reduced adipose tissue mass in nutritionally obese mice and the size of adipocytes in Ob-X treated mice were markedly smaller than those in control mice. The size of visceral adipocytes was decreased by 63%, while the size of subcutaneous adipocytes was decreased by 32% in Ob-X-treated mice relative to control mice. In addition, in Ob-X-treated mice, the blood vessel density in visceral adipose tissue was lower than that of the controls, which shows that the decrease in visceral adipose tissue mass by Ob-X is the result of inhibition of angiogenesis .Adipose tissue is one of the few adult tissues that can grow and regress throughout adulthood. This characteristic relies heavily on active angiogenesis, since the expansion of capillary beds is necessary for tissue growth. Accordingly, adipose tissue contains extensive capillary networks surrounding the adipocytes, which are probably induced by adipocyte-secreted growth factors and hormones . AdiposeIn a randomized placebo-controlled double-blind human study, Ob-X reduced total abdominal fat, subcutaneous fat, and visceral fat. However, a statistically significant reduction was shown only in visceral fat compared to placebo. The percentage of reduction in visceral fat by Ob-X was 20.5% as compared to baseline (10.7%), which was 1.9 times as much as placebo group. The selective effect of Ob-X in reducing visceral fat may be attributed to the different properties of visceral fat and subcutaneous fat. Specifically, visceral fat is more sensitive to angiogenesis inhibitor, which is consistent with animal studies of Ob-X .The effects of Ob-X treatment in reducing visceral fat, subcutaneous fat, and total abdominal fat were negatively correlated with the initial areas of fat before intervention. The significantly negative correlation was the strongest in visceral fat. This implies that Ob-X may have a strong effect in reducing visceral fat in individuals with high visceral fat.Although Ob-X reduced visceral fat in a statistically significant manner compared to the placebo group, we observed no effect on the other measurements related to visceral fat loss. The reduction in body fat mass and percentage body fat measured by BIA were not statistically significant compared to placebo. This may because the percentage of visceral fat in the total body fat is small, and 85% of the subjects were women, who have a larger proportion of fat in their body mass than men and are more likely to deposit fat subcutaneously . In addiNo side effects were observed during the study period, and there were no statistically significant differences between the two groups. The adverse events reported in the Ob-X group were common cold, heartburn, constipation, dry mouth, stomachache, menstrual irregularity, and meningitis, which were considered not to be related to Ob-X. The results for the safety analysis showed no significant differences between the placebo and Ob-X-treated groups in the clinical laboratory blood test, biochemical test, blood pressure, and pulse. All the results were within normal ranges. The safety analysis suggested that Ob-X is safe. Moreover, the good compliance and low dropout rates in the Ob-X group indicate that Ob-X can be administered for long-term use.γ) related to lipid turnover in visceral fat increases with fat feeding in visceral fat relative to subcutaneous fat. This can enhance the flow of free fatty acids (FFA) through the portal vein to the liver, as well as to other tissues [Many studies indicate that visceral adipose tissue, independent of obesity, is a major determinant of insulin resistance and contributes to variations in insulin sensitivity, even in healthy nonobese subjects . Abdomin tissues , 44. FFA tissues , 46. Med tissues .Metabolic syndrome is a group of metabolic risk factors, including abdominal obesity, high blood pressure, high triglyceride levels, low levels of high density lipoprotein (HDL) cholesterol, and high levels of fasting plasma glucose . MetabolHayashi et al. showed that visceral adiposity increases the odds of hypertension in Japanese Americans, independent of other adipose depots and fasting plasma insulin . In addiThe results of the present human study demonstrate that the angiogenesis inhibitor Ob-X reduces visceral fat safely in humans. Therefore, it is expected that Ob-X would reduce the risk of metabolic syndrome by reducing dangerous visceral fat.