To protect and maintain the positivity of self-concept, normal people usually show a self-serving bias  by the motives of self-enhancement and self-protection. Additionally, self-serving assessments predominantly activate the subcortical-cortical midline structures (CMS) in healthy individuals. However, little is known about self-serving bias and its underlying neural correlates among individuals with Internet gaming disorder (IGD).Twenty-four participants with IGD and 25 recreational Internet gaming users (RGUs) were scanned while attributing the causes of positive/negative self- and other-related events that could occur in both the game-world and real-world contexts. Region-of-interest (within CMS regions) and parametric analysis were performed to investigate the neural correlates of self-serving bias in IGD.Behaviorally, the IGD participants attributed more negative and fewer positive events to themselves than RGU participants in both contexts. Neurally, during the attributions of negative events, the IGD participants exhibited increased ventromedial prefrontal cortex (vmPFC) activation in both contexts compared with RGU participants. Higher vmPFC activation was associated with weaker self-protective motivation in the IGD group. Meanwhile, during the attributions of positive events, the IGD participants exhibited decreased precuneus/posterior cingulate cortex activation in the real world compared with RGU participants. Parametric analysis showed a reduced positive correlation between precuneus activation and self-attribution ratings of positive events in the real world in the IGD group relative to the RGU group.These results suggest that individuals with IGD show an attenuated self-serving bias and altered brain activity within CMS regions involved in self-attribution, providing evidence for the negative self-concept and weakened abilities in both self-enhancement and self-protection in IGD. With the rapid development of network and information technology, the Internet has become a ubiquitous tool for the convenience of human life. However, the negative impacts of excessive Internet use have gradually been brought into public view. In particular, some individuals play online games excessively and persistently without considering the severe consequences, such as disrupted relationships, social deficits and poor academic/work performance , 2. ThisImportantly, these cognitive processes cannot be fully separated from the self which is an essential and core prerequisite for individuals to form their own perspectives, characteristics and behaviors . Normal However, it remains unclear how the self-serving bias is manifest in IGD. Notably, research has recently reported that individuals with IGD evaluated both actual self and ideal self more negatively, suggesting a distorted self-concept in IGD . Furtherpcc) regions  and group  and no significant three-way interaction effect among context, valence and group . However, the main effect of valence  and interaction between valence and group  reached the significant level. To explore this interaction, simple-effect analysis for group was conducted. As shown in Fig. F  = 10.56, p = 0.002, partial η2 = 0.19] and lower in positive events  than the RGU participants, indicating the reductions of both self-protection and self-enhancement in IGD.As the dependent variable, the attribution rating difference scores between self and other (self t-tests showed that the IGD group had higher depression scores  and lower self-esteem scores  than the RGU group. We also found negative correlations between the BDI scores and self-esteem scores in these two groups .Independent r = 0.513, p = 0.010; Fig. rIGD = 0.421, p = 0.040; Fig. rRGU = − 0.417, p = 0.038; Fig. Correlation analyses were conducted among the attribution rating difference scores that were averaged across the two contexts, BDI scores and self-esteem scores for each group. Only in the IGD group were the self-esteem scores positively correlated with the attribution rating difference scores in the positive condition  = 4.81, p = 0.033, partial η2 = 0.09]. Subsequent simple-effect analysis performed on the groups showed that the IGD group exhibited increased brain response in the vmPFC than the RGU group in the negative condition , but no group difference was found in the positive condition   across two contexts were positively correlated with the mean attribution rating difference scores across two contexts in the IGD group, indicating that higher vmPFC activation predicted a lower level of self-protection  = 5.19, p = 0.027, partial η2 = 0.10]. Simple-effect analysis showed a significant group × context interaction in the positive condition  but not in the negative condition . Further analysis showed that, in the positive condition, the IGD group exhibited lower precuneuspcc activation in the real world than the RGU group , but no significant group difference was observed in the game world   = 0.35, p = 0.56] or interaction with group .For the ROI of the dmPFC, no main effect was found regarding group [In the positive condition, positive correlations between the attribution rating scores of self-related events and activations in the regions of the precuneus, inferior frontal gyrus and middle temporal gyrus were reduced in the IGD group relative to the RGU group in the real-world context. However, in the negative condition, negative correlations between the attribution rating scores of self-related events and activation in the regions of the inferior parietal lobule, superior frontal gyrus and precentral gyrus were enhanced in the IGD group compared with those in the RGU group in the game-world context Table . No othepcc activation was found during attributing positive events in the real world, and the positive correlation between precuneus activation and the attribution rating scores of self-related positive events was reduced.To our best knowledge, this is the first study to clarify the behavioral and neural correlates of self-serving bias in individuals with IGD and RGU. A modified self-serving bias task was adopted to examine how they made causal attributions in game-world and real-world contexts. At the behavioral level, the individuals with IGD exhibited an attenuated self-serving bias with relatively diminished self-protection and self-enhancement, a finding that was consistent with our hypotheses. At the neural level, the IGD group demonstrated increased vmPFC activation during attributing negative events compared with the RGU group in both contexts, and the individuals with IGD with higher vmPFC activation showed a lower level of self-protection. However, in the IGD group relative to the RGU group, decreased precuneusIn the development process of human life, individuals spontaneously strive to understand the world through attributing the cause of events and usually build an adaptive attributional pattern of self-serving bias . The RGUAdditionally, the RGU group reported high self-esteem and low depression, and those with relatively higher BDI score exhibited a higher motivation of self-protection. These findings may suggest that the RGUs adopted various self-protective tactics to resist depressive symptoms and preserve positive self-view, further indicating that they could adaptively regulate self-protective behavior. Unlike the RGU group, the IGD group reported low self-esteem and high depression. The behavioral results in our study also demonstrated that people with low self-esteem and high depression showed less self-serving bias, a finding that agrees with previous work , 25. MorNeurobiologically, the individuals with IGD showed increased brain activation in the vmPFC when assigning internal causes of negative valenced events. It is widely acknowledged that the vmPFC is commonly activated when individuals process self-relevant information , 52. Morpcc activation compared with the RGU group in the real world. The precuneuspcc has been proven to be involved in self-referential processing and causal attribution [pcc is engaged during tasks that require specific judgements of self-relevant traits compared with self-irrelevant traits or that attribute causes of social positive or negative events. More precisely, it is clearly evidenced that the anterior precuneuspcc (close to the part in our study) is implicated in internal attribution during the evaluation of positively valenced situations [pcc, indicating the extent to which a value was perceived as an internalized part of a person’s self-concept [pcc activation in facing positive events may be explained by the intrinsic characteristic of assigning less positive subjective value to self-related information on which the individuals with IGD based their actions . Furthermore, we found a reduced positive correlation between activation in the precuneuspcc and attribution ratings of positive events in the IGD group in the real world, revealing that individuals with IGD tended to view themselves in less positive terms and lowered their standing on positive traits.Moreover, during attributing positive events, the IGD group exhibited decreased precuneusribution , 57. Fortuations . What’s -concept . TherefoOverall, the attenuated self-serving bias in IGD was pertinent to the aberrant functioning of CMS regions critically implicated in self-related processing. Considering the negative attitude towards self-concept for individuals with IGD, future studies should adopt strategies to improve and protect their self-esteem and maintain a positive self-concept. Early studies have detected that self-affirmation interventions can bring about an expansive view of the self and sustain the self-integrity, lead to great intentions to reduce alcohol consumption and the number of cigarettes smoked and result in lasting benefits in education, health and relationships –61. Thuspcc. These results may indicate the intrinsic negative self-view and impaired adaptive functions in defending against negative self-concept and elevating positive self-concept in IGD. Clinical treatments from the insights of improving self-esteem and building a positive self-view may be potent for intervention and treatment of IGD.In this study, we examined the behavioral and neural correlates of self-serving bias in individuals with IGD and RGUs, further extending our understanding of the self-related cognition of IGD. The individuals with IGD showed an attenuated self-serving bias with reductions in both self-enhancement and self-protection and exhibited altered brain activations within the CMS regions including the vmPFC and precuneus


The MAP method applies a network diffusion approach, starting from the known disease genes in a heterogenous network constructed from miRNA-gene associations, protein-protein interactions, and gene-disease associations. Validation using experimental data on miRNA-disease associations demonstrated superior performance to two current state-of-the-art methods, with areas under the ROC curve all over 0.8 for four types of cancer. MAP is successfully applied to predict differential miRNA expression in four cancer types. Most strikingly, disease-disease relationships in terms of shared miRNAs revealed hidden disease subtyping comparable to that of previous work on shared genes between diseases, with applications for multi-omics characterization of disease relationships.With critical roles in regulating gene expression, miRNAs are strongly implicated in the pathophysiology of many complex diseases. Experimental methods to determine disease related miRNAs are time consuming and costly. Computationally predicting miRNA-disease associations has potential applications in finding miRNA therapeutic pathways and in understanding the role of miRNAs in disease-disease relationships. In this study, we propose the MiRNA-disease Association Prediction (MAP) method, an  MiRNAs target the 3′UTR of genes and downregulate or silence their expression. With dysregulation of miRNAs having been reported in many diseases, it is not surprising that identifying and prioritizing disease associated miRNAs is an ongoing focus of research3. The ability of miRNAs to simultaneously target multiple genes makes them an attractive alternative to the ‘one target, one drug’ model, but also decreases their overall disease specificity and increases the likelihood of potential off target effects4. Prioritizing disease specific miRNAs early in the drug development process will be key to developing viable miRNA therapeutics. The current standard for identifying miRNAs involved in a disease process is miRNA expression profiling, a time consuming, labor-intensive and expensive experimental approach. Expression profiling, which compares expression levels of various miRNAs in diseased and healthy tissues, bases miRNA identification upon the magnitude of their differential expression5.MiRNAs are small non-coding RNAs that regulate critical biological processes like cell proliferation, development and metabolismet al. constructed a network between functionally related miRNAs by computing the overlap in gene targets; this was integrated with phenome data, using known miRNA-disease associations to infer new associations6. Several studies have similarly relied on computing miRNA similarity and disease similarity networks to infer new miRNA-disease associations from known ones. Fu and Peng et al. developed a deep ensemble model, DeepMDA, that computed disease similarity by combining lncRNA-disease and gene-disease associations, and miRNA similarity was computed from known miRNA-disease and miRNA-gene associations7. Features extracted from the similarity information with stacked autoencoders were fed into a three-layer neural network to infer miRNA-disease associations. Chen et al. and colleagues developed several methods based on integrating known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases12. These include PBMDA, which applies a depth first search algorithm to a graph of miRNA similarity, disease similarity, and known miRNA-disease associations; MDHGI, which applies matrix decomposition to known miRNA-disease associations, then constructs a heterogenous network with miRNA similarity, disease similarity, and miRNA-disease associations; BNPMDA, which uses a bipartite network recommendation algorithm for assigning transfer weights for links between miRNAs and diseases; and others. Gu et al. integrated data including known miRNA-disease associations, miRNA functional similarity, and disease semantic similarity to construct miRNA-miRNA and disease-disease similarity networks, which are then projected and combined to predict potential miRNA-disease associations13. The M2LFL method developed by Xiao et al. obtains similarity profiles from multiple data sources such as miRNA functional similarity, disease semantic similarity, and known miRNA-disease associations, from which latent geometric features are extracted and used to infer new miRNA-disease associations14. Several recent methods have been developed within the last year that also rely on miRNA and disease similarity networks to infer novel miRNA-disease associations, employing methods such as neural networks, computation of a Laplacian graph score, kernel fusion, and graph convolutional networks18.Prior work has attempted to computationally predict miRNA-disease associations, employing a variety of machine learning and network-based methods. An early model by Jiang 19. Yu et al. constructed a miRNA-disease transition probability matrix by applying a hybrid recommendation algorithm to known miRNA-disease associations20. This is followed by an unbalanced bi-random walk on a heterogenous network comprised of the miRNA-disease transition matrix, disease similarity matrix, and miRNA similarity matrix. Chen et al. applied a random walk with restart on a miRNA-miRNA functional similarity network21. These studies, and many others, rely on experimentally known miRNA-disease associations as input data to infer new miRNA-disease associations. Yet, because miRNA-disease association predictions are at an early stage, experimentally known associations are limited. Thus, predicting miRNA-disease associations without relying on known miRNA-disease associations is of importance.Several studies have adopted network diffusion based methods. He et al. developed a method called CNMDA that applies a random walk with restart to a composite network constructed from known miRNA-disease associations; Gaussian interaction profile kernel similarity for long non coding RNAs (lncRNAs), miRNAs, and diseases; disease semantic similarity; miRNA functional similarity; known miRNA-lncRNA interactions; and known lncRNA-disease associationset al.22 Indeed, Goh et al. discovered that in a network of disease nodes linked by their shared genes, disease classes such as cancers formed tightly interconnected clusters held together by a small set of shared genes, such as P53 and NF1 in the case of cancers23. Yet, whether the same phenomenon of diseases clustering by their common shared genes extends to miRNAs remains an unanswered question.Many of these prior methods rely on the assumption that functionally similar miRNAs will target phenotypically similar diseases, reflecting a longstanding hypothesis in the field. Yet, robustly validating this assumption remains an ongoing challenge. It is well established that phenotypically similar diseases share genetic origins, first synthesized into the disease module hypothesis of Barabasi 22. Advances in network medicine have enabled the prioritization of traditional drug targets, such as small molecules and proteins, using disease-gene associations and protein-protein interactions25. While gene-disease networks and their emergent properties are well studied23, the mapping of miRNA-disease networks is still in its infancy. As miRNAs are part of a complex regulatory network where each miRNA regulates multiple genes and a gene is regulated by several miRNAs and other genes, it is important to account for both miRNA-gene associations and interactions between genes and their protein products4. In this study, we apply a network diffusion method that exploits the local neighborhood network connectivity among miRNAs and proteins to predict new miRNA-disease associations. Network diffusion methods are based on the small world property of gene networks, where protein products of genes associated with a disease have a strong tendency to interact with each other26. Such methods identify destination nodes, such as miRNAs, that are frequently reached from the seed nodes, such as disease genes. Destination nodes with high scores have redundant paths from the seed nodes, meaning that even if the network is missing edges because of incomplete data, the results would be similar. In contrast, methods that calculate the shortest path between the seed genes and destination nodes are highly sensitive to missing links. Several previous studies have successfully proposed network diffusion methods to analyze molecular interaction networks, called an interactome, even when the interactome data is incomplete. These studies apply the guilt by association principle to the interactome to predict lncRNA-disease associations, prioritize disease genes, predict biochemical perturbation patterns, pinpoint biomarkers, predict cancer mutations, and find network modules for specific diseases like asthma and COPD33. Santolini et al. found a network diffusion model of protein-protein interactions could recover the magnitude and direction of gene expression perturbations33. Sumathipala et al. applied a random walk with restart to subnetworks of the protein-protein interactome to predict lncRNA-disease associations29.Network medicine is an emerging paradigm to better understand the molecular interactions underlying disease and elucidate disease-disease relationshipsHere, we generalize the guilt by association principle to miRNA-related interactome data to reveal the role of miRNAs in complex disease mechanisms and in disease-disease relationships. This is in contrast to many previous studies in miRNA-disease prediction that apply a random walk to miRNA similarity, disease similarity networks, and known miRNA-disease associations, rather than to multi-level molecular interactome data.miRNA-disease association prediction (MAP) method. MAP is a network diffusion method that integrates miRNA-gene, protein-protein, and gene-disease network information into a multi-level complex network to predict and prioritize biologically relevant miRNAs for diseases. We evaluate the performance of MAP using independent experimental miRNA-disease association and differential expression data. Second, we explore the emergent properties of our predicted miRNA-disease network by analyzing disease-disease relationships based on shared miRNAs for disease subtyping.In this study, we take a conceptually different approach to previous studies, proposing the a priori miRNA-disease data. We combine 28,488 gene-disease associations from OMIM and GWAS databases34, 141,296 protein-protein interactions24, and 319,791 miRNA-gene interactions from the miRTar database to form a tripartite network . To generate the receiver operating characteristic (ROC) curves, we plot the false positive rate and true positive rate at different thresholds, using the predicted miRNA-disease edge weights as thresholds. A perfect classifier would yield an AUC of 1 while a random classifier would yield an AUC of 0.5. An AUC above 0.8 is considered good performance and above 0.9 is considered excellent performance.19. MDHGI applies matrix decomposition to an adjacency matrix of known miRNA-disease associations to find the lowest rank matrix, which is used to reconstruct a new adjacency matrix, followed by combination with miRNA similarity and disease similarity to form a heterogenous network10. Unlike MAP, CNMDA and MDHGI infer new miRNA-disease associations from known miRNA-disease associations, relying on the assumption that similar diseases will share functionally similar miRNAs. We generated the random network by shuffling edge weights on the miRNA-disease network predicted by MAP. The shuffling effectively randomizes the miRNA rankings, creating a null model as a control for comparison with MAP’s prediction.To assess the relative performance of MAP, we computed ROC curves for four methods: (1) MAP method, (2) a current state-of-the-art method called CNMDA, (3) another classical method called MDHGI and (4) a randomized network model as a negative control. CNMDA applies a random walk with restart to a composite network formed from known miRNA-disease associations, miRNA functional similarity data, disease semantic similarity data, and data on lncRNAs, another type of non-coding RNA, to predict new miRNA-disease associationsEvaluated on four cancers, MAP yielded AUCs of 0.81, 0.82, 0.84, and 0.81 for lymphoma, breast cancer, kidney cancer and lung cancer, respectively , and has comparable performance in lymphoma . MDHGI had AUCs of 0.67, 0.61, 0.82, and 0.63 for lymphoma, breast cancer, kidney cancer, and lung cancer, respectively. MAP outperforms MDHGI in lymphoma, breast cancer, and lung cancer , and has comparable performance in kidney cancer .a priori experimental miRNA-disease data.These findings confirm MAP’s ability to make predictions with equivalent or improved accuracy compared to CNMDA and MDHGI, current state-of-the-art methods. Of note, MAP does so without relying on Having validated the performance of MAP to predict miRNA-disease associations, we next assessed whether MAP could predict the strength of miRNA-disease associations. To do so, we assess if MAP can predict differential miRNA expression in cancer. We compared the edge weights predicted by MAP against the average fold change in miRNA expression, determined via clinical studies on patient cohorts with and without the cancer.We found strong, positive correlations between the experimental differential miRNA expression and MAP’s predicted edge weights for lymphoma, breast cancer, kidney cancer, and lung cancer Fig. . Spearmaet al. generated a disease projection in gene space, where nodes are diseases and two diseases are connected if they share one or more genes23. Because similar diseases have a strong tendency to share genes with each another, phenotypically similar diseases formed tightly interconnected communities in the disease interactome, enabling disease subtyping.Having validated our miRNA-disease network with experimental data, and directly compared it to state-of-the art methods, next we utilize our miRNA-disease network to study disease-disease relationships based on shared miRNAs. Prior work has analyzed disease-disease relationships based on shared genes. Goh We investigated whether a disease projection in miRNA space would exhibit similar structural communities of closely related diseases. We created a disease projection in miRNA space (DPM) from the bipartite miRNA-disease network predicted by MAP, as described in Methods. For this, we first thresholded the bipartite miRNA-disease network using the edge weights. Then we created a disease projection, which was thresholded again to yield the final DPM, in which two diseases are connected if they share one or more miRNAs. As a point of comparison for our DPM, we generate an analogous disease projection in gene space (DPG) to determine if the previously described clustering of similar diseases occurs in miRNA space.We classified all disease nodes in the DPM and DPG into one of 14 broad disease classes, using the ICD9 standard medical code hierarchy, and visualized the two networks. To assess how structural communities of disease nodes correspond to the ICD9 disease classes, network visualizations are laid out based only on topology. The location of a disease node on the network visualization is determined by its connectivity to other disease nodes and is independent of its ICD9 class: enabling visual assessment of disease clustering by type.et al.  extends to more specific disease subtypes: for instance, differentiating communities of specific cancer types, such as bone and bladder cancers. For this, we generated subnetworks of the DPM and DPG. Each subnetwork is comprised of all diseases in a particular broad class: in each, nodes are colored by disease subtype, such as blood cancers, as determined by ICD9 medical code hierarchy. We generated subnetworks for cancers, neurological diseases and cardiovascular diseases.Similar to the full disease projections in Fig. While in gene space the cancers are more densely connected than in miRNA space, many of the communities in miRNA space are highly separable, homogenous islands. These islands are groups of closely related cancers that share miRNAs with each other, but not pathologically distinct cancers. For example, there is a clique of seven bladder cancers (green) that don’t share high edge weight miRNAs with other cancers, yielding an island cluster Fig. . As can Next, we show that our miRNA-disease network can be applied to distinguish disease subtypes other than cancer. We create disease projection subnetworks for neurological diseases and cardiovascular diseases and find similar results Fig. . For allThe high degree of clustering by disease type in the DPM holds true on both the full network with broad disease classification Fig.  and on sIn this work we build on the success of prior work applying network methods to interactome data to develop MAP, a network medicine framework to predict miRNA-disease associations and elucidate disease-disease relationships. We combine massive genomic and transcriptomic datasets into a miRNA-gene-disease tripartite network and apply a network diffusion algorithm to rank miRNA candidates for a disease, producing a miRNA-disease bipartite network. MAP powerfully predicts miRNA-disease associations with its unique method of applying network diffusion to multi-omics data. This is in contrast to many prior studies that used a single omics type of data, such as the protein-protein interactome, and sets a precedent for further work in extracting information from multi-level networks constructed by integrating varied omics data.Many current methods for predicting miRNA-disease associations utilize experimentally known associations to infer novel miRNA-disease links. In contrast, MAP exploits the local neighborhood property of the complex multilevel network we construct to rank miRNAs based on their proximity to known disease genes. As a result, MAP can be applied to predict and prioritize miRNAs for any disease using its known disease genes.a priori knowledge of miRNA-disease associations, MAP retains high performance that is comparable or superior to current state-of-the-art methods such as CNMDA and MDHGI19. When compared to experimental miRNA-cancer association data, MAP accurately predicted miRNA-disease associations for lymphoma, breast cancer, kidney cancer, and lung cancer, with AUCs all over 0.8. When compared with the MDHGI method, MAP had improved performance for breast cancer, lung cancer, and lymphoma, and comparable performance for kidney cancer. Moreover, when compared with the performance of the CNMDA method, MAP had improved performance in predicting miRNAs for breast cancer, lung cancer, and kidney cancer and comparable performance for lymphoma. While both CNMDA and MAP utilize a random walk with restart, the starting networks vary greatly. CNMDA relies on using known miRNA-disease associations to infer new ones, and does not utilize protein or gene interaction data. In contrast, MAP’s predictions are independent of known miRNA-disease associations, instead relying on the combined topology of the protein interactome and miRNA-gene associations. As MAP only requires a set of known genes, it is more easily generalized to new contexts, such as diseases with no known miRNAs or predicting patient-specific miRNAs.Despite not using 38, we compare MAP’s predicted miRNA-disease edge weights to experimental data on differential miRNA expression in cancers. Strong positive correlations demonstrate MAP accurately predicts differential miRNA expression using only topology of the protein and the miRNA interactome: to our knowledge, MAP is the first such method to predict differential miRNA expression. As miRNA drug targets are traditionally found via differential expression profiling, which are costly and time consuming, network based prediction of differential expression has the potential to streamline miRNA drug target discovery.Previous methods, such as CNMDA and MDHGI, have predicted binary disease association for miRNAs, but to date, no studies have predicted differential miRNA expression. Motivated by the success of prior work predicting gene expression using network topologyIn addition to differential expression prediction, the miRNA-disease bipartite network predicted by MAP can uncover disease-disease relationships and disease subtyping. For this, we generated a network of disease-disease relationships based on MAP’s predictions of shared miRNAs. We find diseases naturally separate into clusters of phenotypically similar diseases that share top ranking miRNAs. When compared to an analogous disease-disease network based on shared genes, we find a comparable level of clustering, quantified with similar values of mutual information. For example, in both the miRNA space disease network and gene space disease network, cancers aggregate into tightly connected, distinguishable clusters.In further fine-grained analysis of this disease subtyping through a subnetwork of all cancers, we find our miRNA-disease network can not only cluster diseases by broad types like cancer, but by subtypes of cancer, such as bone cancers and genitourinary cancers. Representing disease-disease relationships with miRNAs separates cancers into distinguishable, homogenous clusters of phenotypically similar cancers.This miRNA-based framework for disease relationships is not limited to cancers. When applied to cardiovascular and neurological diseases, diseases are distinguished by subtype. In addition to visual evidence of disease subtyping in the form of distinct, homogenous clusters in disease projection and subnetwork graphs, we find quantitative evidence for miRNA-based disease subtyping: mutual information between disease class and community was significantly higher than in a random network and higher than in gene space. Our findings of disease clustering suggest that the top ranked miRNAs are specific to a disease subtype. Further study of subtype-specific miRNAs may allow for deeper understanding of the molecular underpinnings of disease and could lead to more precise diagnoses and treatments.et al. that in a disease-disease network based on shared genes the diseases will cluster by type23. However, to our knowledge, this study is the first to suggest that diseases can also cluster by their shared miRNAs. Importantly, this finding extends an underlying principle in network medicine—phenotypically similar diseases share close or overlapping disease modules in the protein interactome—to multi-omics data. The results of our disease-disease network analysis suggest that similar diseases share not only proteins, but also miRNAs, warranting further investigation into how non-coding RNAs, proteins, genes, and other biological components interact in a multi-level, complex network to influence disease-disease relationships.It has been previously established by Goh et al. demonstrated that accurate disease-disease relationships can be uncovered even in an incomplete protein-protein interactome24. The miRNA-gene association dataset may also be incomplete, especially given that miRNAs were discovered recently, and much work remains in systematically characterizing their regulatory roles. Moreover, the miRNA-gene association dataset, which is curated from the literature, may be subject to literature biases that could skew the dataset towards well studied miRNAs and genes. Another limitation is that the protein-protein interactome is not tissue specific; integrating data on tissue specific expression could improve MAP’s predictions. A third limitation is that the gene-disease associations for determining the random walker’s starting positions are unweighted; prioritizing crucial disease genes using differential gene expression could improve MAP’s predictions.Despite its promise, the limitations of MAP’s predictions include incompleteness of the protein-protein interactome. Previous work by Menche With growing evidence implicating miRNAs in complex disease, predicting novel miRNAs for diseases has significant biomedical applications, from studying the molecular underpinnings of disease, identifying new biomarkers, developing new therapeutics, and elucidating disease-disease relationships. Our MAP method not only accurately predicts miRNA-disease relationships, but also is successfully applied to uncover disease subtyping.The heterogeneous tripartite network was constructed by combining data from three sources:39. The miRNA-gene interactions are extracted and manually curated from experimental studies in the literature. It contains the largest amount of validated miRNA-gene interactions, compared to similar miRNA-gene datasets.We downloaded the miRTarBase dataset, which contains 422,517 experimentally validated miRNA-gene interactions between 4,076 miRNAs and 23,054 target genes24.We use a protein-protein interaction dataset that combines interactions from sources such as kinase-substrate pairs, signaling interactions, literature curated interactions, and yeast two-hybrid datasets, to yield a network of 15,949 proteins with 217,140 interactions, as previously describedwww.disgenet.org) a publicly available dataset of gene-disease associations, and selected associations sourced from OMIM and GWAS datasets40. The dataset contained 28,488 associations between 6,464 protein-coding genes and 4,454 diseases.We downloaded from DisGenNET  and moves by a series of random steps to other genes and miRNAs in the multi-level network, traveling via the protein-protein interaction and miRNA-gene edges. With each step from the current node to a neighboring node, a distribution pP for a random walk with restart, using the previously described equation for network propagation26:W is the column normalized graph adjacency matrix, a is the restart probability, and I is the identity matrix.Due to the large size of the tripartite network, simulating the random walk with restart for each of the 4,454 diseases would be highly computationally intensive. Instead, we adopt an analytical approach. We pre-calculate the converged matrix x that represents the starting positions of the walker at the known disease genes. The walker has an equal probability of starting at any of the disease genes. We next multiply x by P to calculate an approximation of the steady state probability value, p∞. The i-th element of the steady state probability vector represents the probability of the walker moving from the seed genes to node i. The 1777 miRNAs are ranked for each disease according to their values in p∞.For each disease, we create a vector x. The Spearman correlation between the analytical method’s and the iterative method’s rankings was 0.98, confirming that the analytical method is indeed a very accurate approximation.To validate our direct calculation of the converged matrix on an example disease, we calculated miRNA rankings for breast cancer using the more computationally intensive iterative method, using the same vector p∞. Pseudocode for the network diffusion algorithm is included in the Supplementary Information.We repeat the matrix multiplication for all 4,464 diseases in the tripartite network, yielding the final weighted miRNA-disease bipartite network (MDN) where each disease has a set of 1777 ranked miRNA candidates. Each link in the network represents a predicted association between a miRNA and a disease, where the edge weight is the probability value from To validate the miRNA-disease associations predicted by MAP, we use experimental miRNA-disease associations in the database on differentially expressed miRNAs in cancer (dbDEMC). The dbDEMC database has 34,592 experimentally validated miRNA-disease associations between 1,467 miRNAs and 34 cancers.The miRNA-disease predictions were validated against the experimental dataset dbDEMC on four select cancers: lymphoma, breast cancer, lung cancer, and kidney cancer. For each cancer, we generate a receiver operating characteristic (ROC) curve and compute area under the ROC curve (AUC) using the dbDEMC associations as ground truths and the predicted edge weights from MAP. We compare MAP’s performance to the CNMDA and the MDHGI method to assess if MAP performs as well as current methods. With the same dbDEMC dataset, we also generate ROC curves and compute AUCs for the CNMDA method, the MDHGI method, and for the randomized null model network. We generate the randomized network by shuffling node labels on the bipartite miRNA-disease network.Each miRNA-disease association in the dbDEMC database has a differential expression value that is calculated by comparing miRNA expression in healthy and diseased patient cohorts. Typically, miRNA drug targets are discovered by such expression profiling studies and selecting the miRNAs which are most differentially expressed (either over- or under-expression) for further drug development. Motivated by the high costs of expression profiling studies, we used the experimental differential expression data in dbDEMC to investigate if MAP could be used for in silico prediction of miRNA differential expression in cancer. We compared the edge weights predicted by MAP to experimental differential expression values from dbDEMC and used a Spearman correlation test to assess significance of the observed positive correlation. To assess the significance of the linear regression model fitted to the data, we performed a t-test on the slope of the linear regression analysis.Our network diffusion approach ranks all 1777 miRNA candidates for every disease, but this yields a fully connected miRNA-disease bipartite network (MDN) with a graph density of 1. To shed light on the hidden network topology and elucidate disease-disease relationships, we must first threshold the MDN to filter important miRNA-disease associations. As we are interested in comparing disease projections of the MDN to that of the gene-disease network (GDN), we identify an edge weight threshold by comparing graph density in the MDN and GDN. We find a threshold of 0.001, where only edges with a weight greater than 0.001 are retained, reduces the graph density of the MDN to 0.0049. This is comparable to the graph density of the GDN, which is 0.00048.41. The weight of each edge is the number of shared miRNAs. Similarly, using the gene-disease association dataset from DisGenNet, we create an analogous disease projection in gene space (DPG), where two disease nodes are connected if they are linked to the same gene.To examine disease-disease relationships at a network level, we create a disease projection from the thresholded MDN; nodes are diseases and two diseases are connected if they have a predicted association with the same miRNA. The weight of each edge represents the number of miRNAs the two disease nodes share. For this, we use the igraph R packageTo threshold the disease projection in miRNA space, we again identify an edge weight cutoff that will reduce the graph density to a value comparable to the disease projection in gene space. We find a threshold of 7 reduces DPM’s graph density to 0.03, close to the DPG’s graph density of 0.005. We remove all nodes in the disease projections with a degree of 0.42. Using these medical codes, we first group all 4,464 diseases into one of 14 broad categories in the ICD-9, such as cancers, endocrine diseases, and neurological diseases. We next sub-group these categories using the next level of the ICD-9 hierarchy: for example, cancers are further classified into brain, bone, lung, etc. cancers.To analyze if closely related diseases form clusters, disease nodes in the DPM and DPG were classified into categories using standardized International Classification of Disease 9 (ICD-9) medical codes. For converting disease names from the string format in the DisGenNet data source to ICD-9 medical codes, we use a classification file43. These forces are dependent upon the degree of the nodes; nodes with higher degrees (stronger attractive forces) are placed closer together and vice versa. The width of an edge on the visualization is proportional to its weight and the diameter of a node is proportional to its degree. Node color corresponds to the ICD-9 class.All network visualizations were generated using Gephi software (version 0.9.2). Networks were laid out using the Fruchterman Reingold Algorithm, a force directed layout algorithm in which nodes are treated as ‘atomic particles’ that exert attractive and repulsive forces from one another to better visualize clusteringI, measures how much information the two variables share. We assign disease nodes to a community using the Louvain community detection algorithm and we assign ICD-9 codes as described above. The mutual information of structural community (C) and ICD-9 disease class (D) is defined as follows:p is the joint probability distribution of c and d, and p(C) and p(D) are the marginal probability distributions of C and D, respectively. To compute p, p(C), and p(D), we construct a matrix A where each index A represents the number of diseases that are both in structural community ci and disease class di.The disease projections in miRNA space and gene space were analyzed to quantify the extent to which diseases of the same ICD-9 class (e.g. cancers) occupy the same local network neighborhood. To achieve this, we calculate mutual information between structural communities and ICD-9 class. Mutual information, Mutual information values are computed for disease projections in miRNA space and in gene space. As a negative control, we computed the average mutual information in 100 randomized disease projections. Each randomized projection is created by first shuffling node labels on the bipartite miRNA-disease network and then generating the disease projection.Supplementary information.


Objectives: This systematic review with meta-analysis (SRMA) was conducted to compare the effects of SSG-based interventions vs. running-based HIIT interventions on soccer players' sprinting time (ST), vertical height jump (VJH), and change of direction time (CODt).Data Sources: The data sources utilized were Web of Science, Scopus, SPORTDiscus, and PubMed.Results: An electronic search yielded 650 articles, six of which were included in the present study. Between-group analysis found a significant favoring effect of HIIT-based over SSG-based training interventions for the improvement of linear sprinting time . A within-group analysis revealed a significant favoring effect of HIIT-based training interventions for improving linear sprinting time  and CODt  despite a non-significant effect on VJH .Conclusions: The meta-analytical comparison revealed favoring the effect of running-based HIIT over SSG-based interventions in sprinting performance, although no significant differences were observed for jumping and CODt performance. The findings suggest that SSG-based programs should be supplemented by other training methods that benefit determinant capacities in soccer players. SSGs were found in sprinting performance.- None of interventions (running-based HIIT and SSGs) presented significant benefits in vertical jump performance.- The SSG had no meaningful benefits in any of the outcomes .Small-sided games (SSGs)  are conditioned and adjusted forms of a soccer game that are used in a training context to achieve a specific tactical/technical objective while changing physiological, physical, and psychological demands   AND  AND .A comprehensive computerized search of the following electronic databases was performed: (i) Web of Science; (ii) Scopus; (iii) SPORTdiscus; and (iv) PubMed. The searching process for relevant publications had no restriction regarding year of publication and included articles retrieved until 27a priori inclusion criteria for this review were as follows: (i) parallel studies (SSG-based programs vs. running-based HIIT) conducted in soccer players with no restriction of age, sex or competitive level; (ii) isolated intervention programs  with no restrictions for duration; (iii) a pre-post outcome for physical fitness, including ST, VHJ, and CODt; (iv) original per-reviewed articles written in English that provided full-text. Studies were excluded on the basis that they: (i) were observational analytic designs; (ii) included other sports; (iii) used SSG or running-based HIIT combined with other training methods or between them ; (iv) conducted in recreational soccer  or physical education contexts; and (iv) were review articles, letters to the editor, errata, invited commentaries, or conference abstracts.The A data extraction sheet conceived in Microsoft Excel  was made based on Cochrane Consumers and Communication Review Group's data extraction template , age (years), competitive level (if available), and sex; (ii) the SSGs format and pitch size (if available); (iii) period of intervention (number of weeks) and number of sessions per week (n/w); and (iv) regimen of intervention .The outcomes chosen for this SRMA included ST, VHJ, and CODt. The linear ST(s) at different distances was collected, without including values of partial times and extracted only from linear sprinting tests. The VHJ (measured in cm) was usually assessed during a countermovement jump (CMJ) with or without arm swing, squat jump (SJ) or drop jump (DJ). The CODt was regularly measured at COD tests and the time for performing the test was collected. Additionally, the following information was extracted from the included studies: (i) number of participants  was used . The inverse variance random-effects model for meta-analyses was used because it allocates a proportionate weight to trials based on the size of their individual standard errors . Duplicates (370 references) were subsequently removed either automatically or manually. The remaining 280 articles were screened for their relevance based on titles and abstracts, removing 250 studies. The full texts of the remaining 30 articles were examined diligently. After reading full texts, a further 24 studies were excluded due to a number of reasons . The sixn = 76 in SSG-based groups; n = 88 in running-based HIIT groups). The six studies were conducted in youth male soccer players. The shorter intervention lasted 4 weeks . There was a significant favoring effect of HIIT-based over SSG-based training interventions for the improvement of linear sprinting time . There was no significant difference between HIIT-based compared to SSG-based training interventions on VJH changes . There was no significant difference between HIIT-based compared to SSG-based training interventions on CODt changes . The comparisons conducted in the meta-analysis revealed a significant favorable effect of running-based HIIT on linear sprinting, while no significant differences were found for vertical jumping and change-of-direction.While there are similarities between SSGs and real games, several factors differentiate them  mean values of 0, 1.5, and 2.0 m/min, respectively; in official matches, the mean values of the same players was 5 m/min  can be highly efficient in promoting positive adaptations when compared to SSGs, mainly because of the bioenergetic and mechanical conditions  had significant benefits on jumping performance after interventions. Additionally, no significant differences were found between groups. These findings are reasonable considering that both groups were focused on short- and long-interval running , which could have influenced the final results. Additionally, the formats and pitch configurations of the SSGs were very small. As a result, the players did not have longitudinal distances to cover, and this could have affected outcomes related to distance-related qualities . Thus, it is highly recommended that future research include different formats and larger pitches, thus allowing different physical demands to be fairly assessed.Despite the limitations presented in the included parallel studies, it was observed that SSG-based interventions had no significant effect in any of the outcomes included. Also, running-based HIIT was significantly better than SSG in terms of linear sprinting. This suggests that, despite the apparent positive aspects of SSGs in some respects  . All of them were conducted in youth male soccer players. This represents a glaring gap in the literature that should be progressively corrected. Additionally, as an experimental approach, it is also important to start reporting data for responders and non-responders while also noting the influence of specific factors as baseline levels, maturation status, or load accumulated in a field-based training context.This SRMA aimed to compare the effects of SSG and running-based HIIT interventions on linear sprint performance, vertical jump, and change of direction performance. In short, running-based HIIT yielded significantly greater improvements than SSG-based interventions in terms of sprinting performance, while no significant differences were found for jumping performance and CODt. The within-group analysis revealed no significant benefits of SSG-based interventions on any of the outcomes, while running-based HIIT presented significant positive effects for linear sprinting and COD performance.Despite similarities of SSGs and running-based HIIT in improving aerobic performance, it seems that effects are not the same in powerful actions as sprinting, vertical jump, or COD. Therefore, it may be not prudent to totally replace running-based methods with SSGs. Combining SSG and running-based HIIT may be a possible alternative. Another alternative training method involves implementing strength and power interventions as part of the standard supplementation of SSG-based periodization.The original contributions presented in the study are included in the article/FC lead the project, run the data search and methodological assessment, and wrote and revised the original manuscript. RR-C analyzed and interpreted the data, wrote the statistical report, and revised the original manuscript. JA wrote and revised the original manuscript. HS run the data search and methodological assessment and wrote and revised the original 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.


Ankylosing spondylitis (AS), as a common inflammatory rheumatic disease, often causes depression and impaired health-related quality of life (QoL). Although positive effects of patient education have been demonstrated, limited studies explored the benefits of education via mobile applications for AS patients. This study aimed to evaluate the effects of the WeChat-based educational intervention on depression, health-related QoL, and other clinical outcomes in AS patients.We conducted a single-blind randomized controlled trial from March to December 2017. Patients were recruited and randomized into the intervention group which received a 12-week WeChat-based educational intervention  or the control group receiving standard care. Data was collected at baseline and 12 weeks. Outcomes were measured by Beck Depression Inventory-II, the Medical Outcomes Study Short Form 36-item Health Survey (SF-36), Bath Ankylosing Spondylitis Functional Index (BASFI), Bath Ankylosing Spondylitis Patient Global Score (BAS-G), and visual analog scales.A total of 118 patients with AS were included and analyzed. Measures at baseline were comparable between groups. After the intervention, the intervention group reported significant higher scores of all domains of SF-36 except for physical functioning and validity, compared with the control group. Additionally, patients in the intervention group had lower depressive symptoms than the control group. No significant difference in other outcomes was observed at 12 weeks.This study found that the 12-week educational intervention via WeChat had positive effects on reducing depressive symptoms and improving health-related QoL in Chinese patients with AS. We suggest that this intervention can be integrated into current routine care of AS patients.ChiCTR-IPR-16009293).This study has been approved by the hospital’s ethics committee (ID: 20160364) in 2016 and registered at the Chinese Clinical Trail Registry (registry number:  Ankylosing spondylitis (AS) is a common inflammatory rheumatic disease primarily affecting axial skeleton , 2. AS sThe best management for AS patients requires pharmacological and non-pharmacological treatment . PatientEducational interventions for AS patients are mainly provided through face-to-face mode, such as group education, peer-led education , 11, 14.The purpose of this study was to develop and explore the benefits of a 12-week educational intervention delivered by WeChat on health-related quality of life measured by SF-36, depression measured by Beck Depression Inventory-II (BDI-II), and clinical outcomes among patients with AS in China.The aim of this study was to evaluate the effects of a 12-week patient education program delivered by WeChat on depression, quality of life, and clinical outcomes in Chinese patients with AS. This study was an assessor-blind, parallel-group, randomized controlled trial conducted between March and December 2017.This study was conducted in accordance with the Helsinki declaration. Ethical approval was obtained from West China Hospital Medical Ethics Committee in 2016(ID: 20160364). All participants were informed the content and procedure of this study and provided informed consent.Eligible patients were recruited from the Department of Rheumatology and Immunology at West China Hospital of Sichuan University, Chengdu, China. The inclusion criteria were as follows: fulfilling the modified New York classification criteria for AS , aged ≥ Patients were enrolled and randomly allocated into the intervention group or the control group using a randomization code generated by the Excel software. The assessors who collected data were blind to the allocation of the participants.Participants in the intervention group received the 12-week educational intervention delivered by WeChat and standard care. The educational intervention was mainly carried out by experienced research nurses who were well-trained the study protocol. The content of the educational intervention was developed on literature review, expert consultation, and a pilot study. The core content included: basic knowledge, exercise, medication, daily life management, psychological support, and self-assessment.During the 12-week intervention, participants in the intervention group received four individual educational sessions and online educational information on WeChat platform. The four individual educational sessions were conducted through WeChat voice/video calls based on participants’ preference, and each session lasted 20–30 min depending on their willingness to communicate. At the first educational session, the research nurses assessed participants’ needs, problems, and barriers of managing their life. Then, participants were taught the knowledge and skills of managing their condition, including exercise, medication taking, daily life instruction, and psychological management strategies. The participants were also taught how to use validated instruments to assess their disease condition. During subsequent sessions, the research nurses taught participants strategies to manage their disease based on participants’ problems and health behaviors over the previous 2 or 4 weeks. In addition, the research nurses also sent online educational information including articles and videos to participants once a week. During the whole intervention phase, we encouraged participants to ask questions and share their experience at any time.The control group received standard care at hospital. The standard care consisted of basic health advice and brief guidance on medication and exercise. Participants in the control group could ask the researchers questions about disease via WeChat or mobile phone, but did not access to the educational program.All participants completed baseline assessments at the Department of Rheumatology and Immunology. Outcome measures, including quality of life, depression, overall well-being, physical function, morning stiffness, and pain, were collected at the baseline and after the 12-week educational intervention. Two well-trained research assistants who were blind to group allocation collected study data at baseline and after the 12-week educational intervention. If participants had difficulties in completing the questionnaires, the research assistants would help them read each item and record their responses.The baseline demographic data included age, gender, educational level, marital status, household income, medical insurance, and smoking status. Disease-specific data included disease duration  and current medication.Health-related QoL was measured using the Medical Outcomes Study Short Form 36-item Health Survey (SF-36) , 26. TheDepression was evaluated using the Chinese version of Beck Depression Inventory-II (BDI-II) . The BDIThe Bath Ankylosing Spondylitis Patient Global Score (BAS-G) was used to assess the effect of AS on overall patients’ well-being over the last week and during the last 6 months . The test test or Mann-Whitney U test from continuous variables and chi-squared test for categorical variables between the intervention and control group. P values < 0.05 were considered statistically significant.All data were analyzed using the Statistical Package for Social Sciences 22.0. The intention-to-treat analysis (ITT) method was used to analyze data. Participants’ characteristics and outcome scores were summarized using means ± standard deviation (SD) or medians [interquartile ranges (IQR)] for continuous variables or frequencies (percentages) for categorical variables. Baseline characteristics and outcomes were compared using independent samples N = 59) or control group (N = 59). Of 118 participants, 106 completed the 12-week educational intervention and post-test. We included 118 patients in statistical analysis since an ITT analysis was used.Figure The baseline characteristics of the participants are described in Table P < 0.05). BDI-II score was lower in the intervention than those in the control group (P < 0.05). However, there were no statistically significant differences in other outcomes between the two groups (P > 0.05).As shown in Table We developed and evaluated the effects of the 12-week educational intervention delivered by WeChat among AS patients in China. The results demonstrated that the 12-week educational intervention was able to improve health-related QoL and reduce depressive symptoms, but no significant effects on physical function, overall well-being, pain, and morning stiffness. Our finds suggested that WeChat was a feasible and effective method to deliver health service for Chinese patients with AS in clinical setting.Patient education via WeChat is more convenient, timely, and cost-effective, because WeChat has various interactive functions and numerous users . During We found that the WeChat-based educational intervention had a positive effect on reducing depressive symptoms in patients with AS, which was in line with several studies among AS patients , 23. Hu Some studies revealed that educational programs are effective for AS patients to improve health-related QoL , 11, 13,This intervention did not have significant effects on physical functioning and validity domains of QoL. Physical functioning of QoL reflects limitations in physical activities because of health problems and is influenced by functional status , 27. ValWe did not detect the short-term effects of the intervention on physical function, overall well-being, pain, and morning stiffness in AS patients. Similarly, Kaya et al.  and O’DwThe strength of this study was that we used an assessor-bind randomized controlled trial study design to explore effects of the intervention. However, this study also has several limitations. First, this study was limited by a small sample size and all participants from a tertiary hospital due to limited resources, which may affect the generalizability of our findings. Second, an important limitation is the short follow-up period of 12 weeks. Thus, we did not ensure if the effect of the intervention sustains after the end of the intervention. Third, our study only compared the effects of educational intervention delivered by WeChat with standard care, but it is also important to compare the benefits of different educational methods, such as group education, education via telephone call, and web-based education. Although the socioeconomic benefits of the education program are important, we did not conduct a full cost-benefit analysis because of the limited resource. Finally, we did not collect laboratory data, including erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), Platelet, and HLA-B27, because this study did not have adequate financial support.Our findings supported that WeChat was a feasible and effective approach to deliver patient education in China , 43. HeaThis study found that the 12-week educational intervention via WeChat had positive effects on reducing depressive symptoms and improving health-related QoL in Chinese patients with AS. This intervention can be integrated into current routine care of AS patients. Further multicenter studies should explore the long-term effects of education delivered by WeChat.


Novel treatment options for eating disorders (EDs) are critically needed to enhance treatment outcomes and reduce the rates of treatment dropouts. On average, only 50% of individuals receiving evidence-based care remit, whereas 24% drop out before treatment completion. One particularly promising direction involves integrating virtual reality (VR) with existing evidence-based treatments (EBTs) such as cue exposure therapy (CET). Across psychiatric disorders, VR-based interventions are demonstrating at least preliminary efficacy and noninferiority to traditional treatments. Furthermore, VR technology has become increasingly portable, resulting in improved acceptance, increased access, and reductions in cost. However, more efficient research processes may be needed to uncover the potential benefits of these rapid technological advances. This viewpoint paper reviews existing empirical support for integrating VR with EBTs (with a focus on its use with EDs) and proposes key next steps to more rapidly bring this innovative technology-based intervention into real-world clinic settings, as warranted. VR-CET for EDs is used to illustrate a suggested process for developing such treatment enhancements. We recommend following a deployment-focused model of intervention development and testing to enable rapid implementation of robust, practice-ready treatments. In addition, our review highlights the need for a comprehensive clinical protocol that supports clinicians and researchers in the implementation and testing of VR-CET and identifies key missing protocol components with rationale for their inclusion. Ultimately, this work may lead to a more complete understanding of the full potential of the applications and integrations of VR into mental health care globally. Eating disorders (EDs) pose serious risks to psychological and physical health ,2, resulVirtual reality (VR) is a technology that can augment existing EBTs with the possibility of enhancing treatment outcomes. Through the creation of immersive computer-generated experiences, users interact naturally with stimuli representing the real world while simultaneously benefiting from a clinical, supervised setting . MoreoveThese features offer different potential advantages for using VR to augment existing EBTs for EDs -16. AdvaNevertheless, traditional models for testing treatments, which include case series and randomized controlled trials to test efficacy under highly controlled research conditions in academic centers, followed by effectiveness and implementation studies, may render these rapidly changing technologies obsolete by the time they are clinic-ready. For example, since the early 2000s, immersive VR has become increasingly sophisticated with improved 3-dimensional constructed environments; compatibility with mobile phones; and a wide variety of head-mounted display devices equipped with increased field of view, higher-resolution images, and lightweight and comfortable designs . TestingIn this paper, we discuss the great potential of VR-based interventions in the treatment of psychiatric disorders and research efforts needed to support the development of effective clinic-ready treatments in a timely manner. Of the many applications of VR in the treatment of EDs ,22, virtVR-CET provides traditional cue exposure therapy (CET) in a virtual environment. CET is a subcategory of exposure therapy that is specific to eating and substance-related disorders. Both techniques involve repeated, controlled exposures to relevant stimuli. However, CET was initially specifically designed to target cravings (vs fear) . CET is In VR-CET for EDs, patients are repeatedly exposed to emotionally provoking eating-related situations that typically result in maladaptive behaviors . Exposure is planned gradually and designed to eliminate the ED behavior. New associations develop with eating-related anxiety and cravings in response to stimuli decreasing over time ,33. EmotVR offers several promising advantages to in vivo exposure that may result in reduced therapist burden, more rapid symptom improvement, improved acceptance of treatment, reduced treatment dropouts, and more accurate measurement-based care. VR environments may more closely approximate the settings in which problematic eating behaviors take place compared with the clinician’s office or imaginal exercises. A therapist can manipulate a larger number of stimuli within the VR environment than in the real world  and personalize both contextual cues  and sensorial cues . VR environments can be tailored precisely to a patient’s fear hierarchy . Extensive evidence exists showing VR environments produce emotional, behavioral, and physiological responses in patients similar to those observed in real-life situations . Thus, VUnfortunately, studies indicate that many therapists hold negative beliefs about in vivo exposure therapy for anxiety disorders , which may pose a barrier to treatment dissemination . In contd=0.95, ranging from 0.87 in PTSD to 1.79 in panic disorder with agoraphobia) [The intention of this paper is not to systematically review the literature on VR-based exposure therapy because that would be outside the scope of this viewpoint paper. Instead, we provide an overview of the empirical support for VR-based exposure with a focus on EDs to highlight the foundational work and provide context for our arguments in the subsequent sections. A substantial body of research supports the efficacy of VR-CET and VR-based exposures for numerous psychiatric disorders, notably those that frequently co-occur with EDs such as anxiety, substance use disorders, and posttraumatic stress disorder (PTSD) -44. VR eaphobia) . Meta-anaphobia) ,49 and saphobia) . These raphobia) ,45. Addiaphobia)  and the aphobia) .Compared with research on the use of VR-based exposures for anxiety and related disorders, the number of studies that have specifically investigated the use of VR-CET for EDs is small. With so few individual studies conducted to date, publication of meta-analyses or reviews of VR-CET for EDs has not yet been warranted. However, the limited research conducted thus far is favorable. For example, patients with bulimia nervosa (BN) and BED who remained symptomatic after CBT showed a significantly greater reduction in binge eating and higher percentages of abstinence from binge eating and purging after randomization to six sessions of VR-CET vs six sessions of additional CBT . Results2 to 16.8 kg/m2 [eating a virtual food, enabling productive therapeutic discussions to take place in an “‘ecological’ environment, one that was clinically significant but also safe” [A total of three case reports using a similar nonimmersive VR-CET program as a complementary tool to CBT demonstrated positive effects with patients diagnosed with restrictive anorexia nervosa (AN-R), binge/purge anorexia nervosa (AN-B/P), and BN -54. All .8 kg/m2 , the pat.8 kg/m2 , and the.8 kg/m2 -54. Of nso safe” .Given the aforementioned research, VR-CET for EDs may have value for advancing the field of EDs. The patients’ responses to VR-CET outlined in the research above are rapid. Faster acting treatments may reduce treatment length and, as a result, reduce clinic wait times—thereby positively addressing access to care issues. Given these potential advantages, we discuss the importance of pilot testing in real-world clinical settings at this stage in the developmental process.revolutionary in that the results of VR treatment may surpass those of a standard course of EBT [A recent systematic review of VR in the assessment, understanding, and treatment of mental health disorders broadly argues that the progress in implementing and disseminating VR applications has been slow despite growing interest . The aute of EBT . Reilly e of EBT  specificHistorically, across disorders, implementation of VR in treatment was limited by the cost of technology and concerns about acceptability  ,58. HoweGiven the limited number of efficacy trials of VR-CET for EDs to date, some may argue that translating this treatment into clinical practice at this stage of development is premature. In traditional models of EBT development, efficacy trials that provide substantial evidence for the treatment’s success under controlled conditions are conducted before studying the treatment in real-world settings. However, as other researchers have observed , the gapAs an alternative to traditional models of treatment development, Weisz et al  describepresence  for treatment effects to occur [Weisz et al  propose to occur ,65; (4) to occur -68; and to occur . AddressIn addition, future research should take advantage of the unique methods of data collection and assessment available within VR, including the collection of real-time self-reported data in virtuo and biological measurements that can track eye movements, facial gestures, and the movement of body parts. Given the large number of stimuli that can be manipulated and tightly controlled within VR environments , basic science research can utilize VR applications to advance the understanding of ED mechanisms of change..Given that we are suggesting a deployment-focused model of treatment development for a novel iteration (VR-based) of an already validated therapy (CET), it is important to consider the risks compared with potential benefits of treatment implementation. One important risk associated with the utilization of any VR platform includes cybersickness, a side effect that 20% to 80% of VR users may experience ,70. The Overall, the evidence thus far suggests minimal known risk associated with VR exposure therapy, which may reduce concern for deployment-focused treatment development. In addition, the standard of care in clinical practice involves the use of an informed consent process by which patients are provided with clear information regarding their treatment options, including the relative risks and potential benefits of each approach. Given the minimal known risks associated with VR-based treatments and the ability of VR-based exposures to provide an acceptable and effective treatment option for those who refuse, drop out of, or do not progress via in vivo exposures, we believe the potential benefits outweigh the costs of offering VR-based exposures as an alternative treatment option. To further minimize any associated risks of offering a novel treatment alternative to an already validated treatment approach, we suggest that the real-world trials of this technology should assess the patients’ progress and suggest in vivo exposures or a different active treatment if a patient is not making progress.Given evidence that integrating VR technology with EBTs for EDs such as CBT leads to significantly improved outcomes ,15, withOverall, comprehensive study of VR-CET in real-world clinic settings is a fruitful direction that may advance intervention protocols for EDs. In particular, a clear protocol for VR-CET for EDs will help translate the exciting research supporting the applications of VR into a clinic-ready intervention, providing a model use and, ultimately, a more comprehensive understanding of the full potential of the applications of VR on mental health care globally. We suggest that continued research efforts focus on advancing VR-CET following the clinic-based treatment development approach to more rapidly move technology-based interventions from research settings into the real world, as warranted.


Cerebrospinal fluid (CSF) plays an essential role in early postnatal brain development. Extra-axial CSF (EA-CSF) volume, which is characterized by CSF in the subarachnoid space surrounding the brain, is a promising marker in the early detection of young children at risk for neurodevelopmental disorders. Previous studies have focused on global EA-CSF volume across the entire dorsal extent of the brain, and not regionally-specific EA-CSF measurements, because no tools were previously available for extracting local EA-CSF measures suitable for localized cortical surface analysis. In this paper, we propose a novel framework for the localized, cortical surface-based analysis of EA-CSF. The proposed processing framework combines probabilistic brain tissue segmentation, cortical surface reconstruction, and streamline-based local EA-CSF quantification. The quantitative analysis of local EA-CSF was applied to a dataset of typically developing infants with longitudinal MRI scans from 6 to 24 months of age. There was a high degree of consistency in the spatial patterns of local EA-CSF across age using the proposed methods. Statistical analysis of local EA-CSF revealed several novel findings: several regions of the cerebral cortex showed reductions in EA-CSF from 6 to 24 months of age, and specific regions showed higher local EA-CSF in males compared to females. These age-, sex-, and anatomically-specific patterns of local EA-CSF would not have been observed if only a global EA-CSF measure were utilized. The proposed methods are integrated into a freely available, open-source, cross-platform, user-friendly software tool, allowing neuroimaging labs to quantify local extra-axial CSF in their neuroimaging studies to investigate its role in typical and atypical brain development. Cerebrospinal fluid (CSF) is a clear, colorless fluid that circulates in the brain, providing necessary mechanical and immunological protection to the brain. In addition to its protective purpose, recent findings have shown that CSF circulation plays a crucial role in brain development and function prenatally and during the lifespan , which can serve as indirect markers of abnormal CSF production and absorption. Current findings in lateral ventricles (LV) volume related to ASD have indicated no consistent, significant group differences in children , which utilizes a statistical approach of parametric mapping  included N = 32 males (96 scans) and 19 females (57 scans); see We analyzed 153 structural MR brain images, which were obtained at 3 time points  from 51 typically-developing infants, who were at low risk for ASD . These infants were scanned longitudinally at 6, 12, and 24 months of age as part of a National Institutes of Health-funded, multi-site, Autism Centers of Excellence (ACE) Network study: the Infant Brain Imaging Study (IBIS). Data collection sites had study protocols approval from their Institutional Review Boards (IRB), and all enrolled subjects had informed consent provided by parent/guardian. The MRI scans were acquired at 4 different sites , each equipped with 3T Siemens Tim Trio scanners  brick-based phantom . Five brain masks were utilized, namely FSL-BET Smith, , two in-Different brain tissues were then segmented using a framework of atlas-moderated expectation-maximization (EM) implemented in the AutoSeg toolkit  and a corresponding outer surface (Souter). While the CSF hull surface  that is enclosed between boundaries Sinner and Souter. The Laplace PDE takes the following formFollowing the reconstruction of the cortical surfaces, the next step is to solve a Laplace's equation between the inner surface ( surface  is defin surface  as Sinneu(x) = uL for x ∈ Sinner and u(x) = uH for x ∈ Souter. To correctly measure local EA-CSF, a boundary condition map that defines the Laplace PDE boundary condition must be defined in an anatomically consistent manner. In the solution of the Laplace PDE in the proposed framework, the solution domain is bounded by the Dirichlet condition and the Neumann condition. The Dirichlet condition specifies values of the solution itself on the boundary, while the Neumann boundary condition defines values for the first-order derivative of the solution. The interface with the Dirichlet condition defines Sinner and Souter where streamlines start and arrive, and the Neumann condition defines an open boundary that is parallel to the streamlines  integration method . Choosing the gray matter surface as inner surface would likely exclude minor EA-CSF sections due to segmentations errors of the gray matter boundary.Y, the following linear mixed model was fitted for each subject i:The extracted raw local EA-CSF maps were first mapped to the common MNI surface template space  , were scanned with the same pulse sequence at four different sites using Siemens 3T Tim Trio scanners, at irregular intervals over 2.5 years. This resulted in 35 MRI scans for subject I and 31 MRI scans for subject II. The tissue segmentation, brain surface reconstruction, and computation of local EA-CSF maps were performed independently. Local EA-CSF maps were then analyzed using the local coefficient of variation (CV) as a measure of stability. The CV for a vertex It is noteworthy that the EA-CSF spaces in these two adult subjects were visually smaller than those we observed in our infant dataset. In general, in our experience infants have visually larger EA-CSF spaces than adults. It is thus likely our results yield a conservative estimate of the expected reproducibility in the infant settings.The CV analysis showed excellent stability with mean across-site CV of 1.15 and 1.56% for all cortical regions in Case I and Case II respectively . Higher Q < 0.01) for the age effect resulting from the longitudinal mixed-effects model. Overall 16.9% of the brain showed a significant decrease in local EA-CSF in the first 2 years of infant's life. Local EA-CSF showed a highly negative correlation with age in most of the frontal lobe areas, including bilateral middle frontal gyrus and bilateral superior frontal gyrus. Negative correlation with age was also observed in some temporal regions, such as the bilateral superior temporal gyrus. No cortical areas showed a significantly positive association of age and the local EA-CSF.Consistent with our previous global EA-CSF report , with higher local EA-CSF in males compared to females in regions such as: right middle frontal gyrus, inferior and medial orbital gyri, bilateral inferior frontal gyri, right insula, and right superior temporal gyrus, right supplementary motor cortex, and right superior frontal gyrus. 6.2% of the brain showed more local EA-CSF in males compared to females. There were no regions where females showed larger EA-CSF . All tools developed at the UNC Neuro Image Research and Analysis Laboratory are open source. All raw MRI datasets and associated demographic information employed in this study is available at: NIH/NDA: The studies involving human participants were reviewed and approved by the Institutional Review Boards (IRB) of all participating institutions. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.Method development and computational analysis was mainly performed by MM under guidance by MAS and MDS, with additional guidance and methodological input by GG and SP. Parts of processing was performed by SK and JG. Study design, data acquisition was performed by AE, SD, AE. RM, KB, RS, HH, and JP. Interpretation of results was performed by MM, MAS, and MDS. Document preparation was performed by all co-authors.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.


Escherichia coli (ExPEC) are designated by their isolation site and grouped based on the type of host and the disease they cause, most diarrheagenic E. coli (DEC) are subdivided into several pathotypes based on the presence of specific virulence traits directly related to disease development. This scenario of a well-categorized E. coli collapsed after the German outbreak of 2011, caused by one strain bearing the virulence factors of two different DEC pathotypes (enteroaggregative E. coli and Shiga toxin-producing E. coli). Since the outbreak, many studies have shown that this phenomenon is more frequent than previously realized. Therefore, the terms hybrid- and hetero-pathogenic E. coli have been coined to describe new combinations of virulence factors among the classic E. coli pathotypes. In this review, we provide an overview of these classifications and highlight the E. coli genomic plasticity that results in some mixed E. coli pathotypes displaying novel pathogenic strategies, which lead to a new symptomatology related to E. coli diseases. In addition, as the capacity for genome interrogation has grown in the last few years, it is clear that genes encoding some virulence factors, such as Shiga toxin, are found among different E. coli pathotypes to which they have not traditionally been associated, perhaps foreshowing their emergence in new and severe outbreaks caused by such hybrid strains. Therefore, further studies regarding hetero-pathogenic and hybrid-pathogenic E. coli isolates are necessary to better understand and control the spread of these pathogens.Although extraintestinal pathogenic  Escherichia coli is a gram-negative, facultative anaerobic rod, which produces catalase but not oxidase. Taxonomically, it belongs to class Gammaproteobacteria, order Enterobacteriales, and family Enterobacteriaceae , which is subclassified into seven different pathotypes: enterotoxigenic E. coli (ETEC), enteroinvasive E. coli (EIEC), enteroaggregative E. coli (EAEC), and enteropathogenic E. coli (EPEC), both sub-grouped in typical and atypical Shiga toxin-producing E. coli (STEC), diffusely adherent E. coli (DAEC), and adherent-invasive E. coli (AIEC)  are defined primarily by their site of isolation. The most clinically important ExPEC groups are uropathogenic E. coli (UPEC), neonatal meningitis-associated E. coli (NMEC), avian pathogenic E. coli (APEC), and septicemic E. coli (SEPEC)  pathotype is involved worldwide in many outbreaks, severe symptomatology, and lethal outcomes to the human host , a type 3 secretion system, and other effectors involved in A/E lesion formation, which are used to clinically classify pathogenic E. coli strains as EPEC  and sequence type (ST2678).The EPEC pathotype is sub-grouped based on the presence of the bundle-forming pilus (BFP) in the strains classified as typical and, on its absence, in the strains classified as atypical. In general, EPEC/STEC hetero-pathogens comprise atypical EPEC carrying Stx   are rare but have been noted in different countries since 1979  hetero-pathogenic It is interesting to note the diversity of sequence types (STs), phylogroups, and serotypes (more than 40) that was observed among the STEC/ETEC hetero-pathogenic strains . Such diIn 1991, a community-acquired UTI outbreak occurred in Copenhagen  (Clermont et al., E. coli are distributed among all, some pathotypes are frequently assigned to specific phylogroups; for example, ExPEC strains are often referred to as belonging to phylogroups B2 and D, while many DEC strains are of phylogroup B1. In this context, phylogroup A strains are classically related to those that make up the gut microbiota and are avirulent, such as the E. coli K-12 prototype strain MG1655 (Leimbach et al., The Interestingly, some clonal groups inside phylogroup A are frequently reported to be involved in a myriad of infections and multidrug-resistance, such as ST10 (Olesen et al., E. coli pathotypes and could not be seen as restricted to any ST or phylogroup. The characteristics of the hybrid/hetero-pathogenic strains are detailed in Gati et al.  have shoE. coli strains began. Some of these pathogens were reported long ago, like EPEC/STEC strains, while others, like ExPEC/STEC strains, emerged in recent years and are being pointed only now as the cause of diseases. Although the hybrid- and hetero-pathogenic strains might have appeared long ago, the interest in their significance as more virulent pathogens is a recent phenomenon.Some hybrid- and hetero-pathogenic strains have been isolated from human and animal infections since the extensive study of pathogenic Citrobacter freundii and Enterobacter cloacae, which were associated with HUS and one outbreak (Tschäpe et al., Shigella species besides Shigella dysenteriae type 1, mainly in Shigella flexneri. Moreover, viable Stx-converting phages were shown to be present in water and sewage (Muniesa and Jofre, Sequencing technologies have helped to understand the events involved in hybrid/hetero-pathogen evolution, the most common being the transference of virulence genes by mobile plasmids and the acquisition of converting phages. Most of the hybrid strains described until now are related to the STEC pathotype, probably because of the broad host range of Shiga-toxin-converting bacteriophages, since their occurrence has been reported in different species including Currently, there is not enough published data to confirm if hybrid/hetero-pathogens are always more virulent than their parental pathotypes. Although some studies pointed out that the disease prognoses were worst (Bratoeva et al., E. coli genome is a dynamic entity; thus, hybrid- and hetero-pathogens will probably continue to emerge and expand the current set of recognized E. coli pathotypes. The great challenge for both human and veterinary medicine will be to promptly identify and hinder these pathogens from spreading and causing massive outbreaks, such as the German outbreak of 2011. Considering that these hybrid/hetero-pathogens can carry virulence-associated makers as well as multidrug resistance genes, there is an urgency to identify them and address appropriate measures of containment.It is well-known that the AS and FS designed and conceptualized this review, wrote the first draft, and edited the manuscript. RS and TG wrote and revised it critically. All authors contributed to manuscript revision, read, and approved the submitted version.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.