Mental ill-health and substance use bear a substantial burden and harm on young people and often arise from co-occurring and compounding risk factors, such as traumatic stress. Trauma-informed prevention of mental ill-health and substance use demonstrates significant promise in reducing this burden. A systematic literature review is required to identify and summarise the effectiveness, feasibility, acceptability, and design principles underpinning existing trauma-informed mental ill-health and/or substance use prevention programmes for young people aged 12–24 years.MEDLINE, Embase, CINAHL, PsychINFO, and Cochrane Library will be searched from 2012 through September 2022. Reference lists of included articles will be citation-chained. Title and abstracts will be screened and two reviewers will review articles full-text. One reviewer will extract data from eligible articles using a piloted data extraction form, and 20% of the data will be verified by a second reviewer. Risk of bias will be assessed using the Cochrane risk-of-bias tool for randomised trials (RoB 2), Risk of Bias in Non-randomised Studies of Interventions (ROBINS-I), and The Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies and The Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research (CASP), depending on the study type. Characteristics of existing trauma-informed mental ill-health and/or substance use prevention programmes for young people will be summarised narratively. Effectiveness, feasibility, and acceptability will be qualitatively described and summarised, with proportions and effect sizes quantitatively synthesised, where possible.Trauma-informed approaches to prevention demonstrate significant promise, yet to date, no study has systematically summarised and synthesised the available literature. To fill this gap, the present review will systematically identify and summarise the effectiveness, feasibility, acceptability, and design principles underpinning existing trauma-informed mental health and/or substance use prevention programmes for young people aged 12–24. This review will inform the development, adaptation, evaluation, and implementation of future trauma-informed mental ill-health and substance use prevention programmes for young people. Findings will inform critical efforts to interrupt and prevent already elevated trajectories of mental ill-health, substance use, and related harms among those young people exposed to adversity.PROSPERO CRD42022353883. Mental ill-health and substance use are among the leading causes of injury, morbidity, disability, and mortality among young people globally , 2. MentAccumulating, robust evidence supports the effectiveness, feasibility, acceptability, and scalability of mental ill-health and substance use prevention programmes for young people , 11. SpeEvidence accumulated over the last few decades has identified one such risk factor, traumatic stress, which is highly prevalent and strongly associated with mental ill-health and substance use in adolescence and early adulthood –24. For Trauma-informed approaches for preventing mental ill-health and substance use demonstrate significant promise . Trauma-Given the clear need for trauma-informed mental ill-health and/or substance use prevention, the present review aims to identify and systematically describe existing trauma-informed mental ill-health and/or substance use prevention programmes for young people through a systematic review of the peer-reviewed and grey literature. This review is concerned with the design and evaluation of existing trauma-informed mental ill-health and/or substance use prevention programmes for young people. Two research questions guide this review: (1) what is the effectiveness, feasibility, or acceptability of existing trauma-informed mental ill-health and/or substance use prevention programmes for young people? and (2) what are the trauma-informed design characteristics of these programmes?Eligible participants include young people aged 12–24 years from setting or geographical location in the world.This review is concerned with the design and evaluation of existing trauma-informed substance use and/or mental ill-health prevention programmes.Prevention programmes at universal , selective (delivered to those at increased risk), and indicated (delivered to those with elevated symptoms) are eligible for inclusion. Tertiary-level prevention programmes  will be excluded from the review. Studies detailing trauma-informed mental health treatment or service provision are outside the scope of the present review and will be excluded. Studies detailing treatment or service provision for substance use among people with exposure to traumatic experiences will also be excluded. Studies that detail trauma prevention programmes (e.g. violence prevention) with secondary mental ill-health and/or substance use outcomes will only be included if they meet the aforementioned definition of being trauma-informed. The main point of delineation between treatment programmes versus the prevention programmes of interest to this review is that prevention programmes aim to prevent the onset of depression, anxiety, and substance use and/or early detection and intervention before symptoms or substance use is present, whereas treatment programmes aim to treat clinically significant levels of depressive/anxiety symptoms and/or harmful substance use .Definitions of ‘trauma-informed’ approaches vary significantly . To idenAppropriate comparators of interest to this review include age-matched (12–24 years) young people who do not participate in eligible trauma-informed prevention programmes or receive control condition alternatives, for example, health education as usual.The present review limits the scope of mental ill-health prevention programmes to symptoms and feelings of depression and anxiety. These symptoms and associated disorders represent the largest burden of mental ill-health among young people .The present review will include prevention programmes targeting licit and illicit drugs as a substantial proportion of the burden of disease related to substance use among young people is attributable to alcohol, tobacco, cannabis, and common illicit drugs such as ecstasy and cocaine.Accordingly, we designed our search terms to capture preventative programmes targeting any anxiety symptoms/disorders, any depressive symptoms/disorders, and any substance use. While depressive symptoms may rise to the level of mood disorders, these three mental ill-health conditions were purposely chosen as these account for a majority of the burden of disease attributable to trauma among young people globally , are theAll measures including qualitative descriptions, non-standardised measures, and standardised measures, of depressive and anxiety symptoms are eligible for the evaluation of the effectiveness, acceptability, and feasibility of programmes within included studies. All measures must be sourced from the young person participating in the prevention programme, i.e. self-reported. Exposure-based measures of substance use, including ever use, recent use, and frequency, are all acceptable for the purposes of this review.Studies must report at least one of the outcomes of interest to be eligible.Randomised controlled trials, non-randomised controlled trials, pre-post designs, and qualitative studies are all eligible study designs for the purposes of determining the effectiveness of included interventions. Qualitative studies will be deemed acceptable for the purposes of examining the acceptability and feasibility of trauma-informed prevention programmes within the included studies.Articles published from 2012 through September 2022 will be searched. This date range has been selected to reflect the relative recency of trauma-informed research . ArticleWe will search for relevant peer-reviewed literature in the Cochrane Library, PsychINFO, CINAHL, Embase, and MEDLINE database reference platforms. There are no language restrictions for the present review. Citation chaining of reference lists of included articles will ensure relevant literature is identified. Through the authors’ professional networks, expert researchers and clinicians within the field of trauma-informed prevention will be contacted to source additional papers not identified within the proposed database search strategy.A medical specialist librarian from Fisher Library at The University of Sydney will be iteratively consulted to develop our search strategy. Table http://www.covidence.org/). Only project staff members will have access to this password-protected Covidence file.Identified articles will be downloaded to Covidence  effectiveness, feasibility, and acceptability levels of existing trauma-informed mental health and/or substance use prevention programmes, and (2) the trauma-informed programme design characteristics.Tables The risk of bias in randomised trials will be assessed using version 2 of the Cochrane risk-of-bias tool for randomised trials (RoB 2), the recommended tool for use in Cochrane reviews. Through systematically identifying features of trial design, process, and results, the RoB 2 tool will generate a judgement about the risk of bias arising from the trial: ‘low’, ‘high’, or ‘some concerns’. The risk of bias in non-randomised studies will be assessed using The Risk of Bias in Non-randomised Studies of Interventions (ROBINS-I) tool as per Cochrane Reviews’ recommendation. The ROBINS-I tool will facilitate domain-targeted judgements specific to non-randomised study design to generate an overall risk of bias judgement: either ‘low’, ‘moderate’, serious’, or ‘critical’. The Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies and The Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research (CASP) will be used to appraise pre-post design  studies and qualitative studies, respectively, generating a recommendation to ‘include’, ‘exclude’, or ‘seek further information’. One reviewer conducted risk of bias assessments and a second reviewer verified a random selection of 20% equivalent of included articles.The characteristics of existing trauma-informed mental ill-health and/or substance use prevention programmes for young people will be summarised in accordance with the standardised data extraction form outlined in Tables A high level of heterogeneity is anticipated; henceforth, meta-analysis is unlikely. Where possible, the authors will conduct a meta-analysis and synthesise studies within each level of prevention, namely primary prevention effects pooled, secondary prevention effects pooled, and tertiary preventions pooled. Notwithstanding this, qualitative synthesis as described above will circumvent any heterogeneity and capture study details relevant to the research questions.All members of the project team will contribute to and supervise all aspects of data analysis and synthesis.This review will advance our understanding of the trauma-informed design principles critical for the development and/or adaptation of effective, acceptable, and feasible trauma-informed mental ill-health and/or substance use prevention programmes. These results will have important equity implications given many young people exposed to early trauma are part of underserved communities that are also at increased risk of mental health and substance use during adolescence, including First Nations communities and sexuality and gender-diverse (LGBTQ +) communities , 50, 51.


Heparan sulfate-binding proteins (HSBPs) are structurally diverse extracellular and membrane attached proteins that interact with HS under normal physiological conditions. Interactions with HS offer an additional level of control over the localization and function of HSBPs, which enables them to behave in a more refined manner. Because all cell signaling events start at the cell membrane, and cell-cell communication relies on translocation of soluble factors across the extracellular matrix, HS occupies an apical position in cellular signal transduction by interacting with hundreds of growth factors, cytokines, chemokines, enzymes, enzyme inhibitors, receptors and adhesion molecules. These extracellular and membrane proteins can play important roles in physiological and pathological conditions. For most HS-binding proteins, the interaction with HS represents an essential element in regulating their normal physiological functions. Such dependence on HS suggests that manipulating HS-protein interactions could be explored as a therapeutic strategy to selectively antagonize/activate HS-binding proteins. In this review, we will discuss current understanding of the diverse nature of HS-HSBP interactions, and the latest advancements in targeting the HS-binding site of HSBPs using structurally-defined HS oligosaccharides and monoclonal antibodies. Heparan sulfate (HS) is a sulfated linear polysaccharide universally found at the cell surface and in the extracellular matrix of animal cells. Like other complex carbohydrates, the structure of HS is highly diverse because the biosynthesis of HS is not template-driven. Rather, the final structure of HS is differently regulated in different cell types in a temporally and spatially specific manner. Because of the enormous structural diversity, HS is capable of interacting with hundreds of HS-binding proteins (HSBP) and regulating their biological function in a refined manner. In depth understanding of HS-HSBP interactions will not only help delineate the regulatory mechanisms of HSBPs, it is also essential for development of novel therapeutic agents targeting HSBPs, a good portion of which are suitable drug targets. As the structure details of HS and general principles of HS-HSBP interactions have been well-covered in the literature, we will not discuss them in the current review and refer readers to several excellent reviews on these topics .The purpose of this review is twofold. First, to summarize our current understanding of the specificities of HS-HSBP interactions and the benefits and challenges of determining the structural details of HS-HSBP interaction. Second, to analyze the latest advancement in targeting HS-HSBP interactions using two different pharmacological agents: HS-based molecules and monoclonal antibodies (mAbs). We hope this information will promote the need for better understanding of the structural basis of HS-protein interactions, and clarify the main points investigators need to consider in developing novel pharmacological agents that targets HS-HSBP interactions.1. HSBPs that bind rather promiscuously to a wide range of HS structures, displaying little preference to particular classes of sulfation patterns, as long as the minimum level of sulfation is satisfied.O, 6-O or 3-O positions.2. HSBPs that bind in a more stringent manner, usually requires specific sulfation at 2-The key to understanding HS-protein interactions is to keep an open mind with regard to the specificity of the interaction. HS interacts with hundreds of structurally diverse HSBPs, ranging from tiny chemokines to huge extracellular matrix structural proteins . Due to a. HSBPs display clear preferences for sulfation at specific positions, but these specific modifications are not absolutely required, and without them these HSBPs still bind HS, albeit with lower affinities. Examples of these proteins include neuropilin-1 (Nrp-1) , tau Zh, FGFR2b O-sulfation dependent) and antithrombin (3-O-sulfation dependent)  .The second group of HSBPs can be further subdivided into the following two classes based on the degree of stringency with regard to specific modifications.O-sulfation (pgsF)   and N-sun (pgsF) , and endulfation . CHO cel studied . In addi studied , and theO-sulfation is indispensable for activation of antithrombin and for optimum activity of neuropilin-1; and 2-O-sulfation is required for the biological activities of FGF and osteoprotegerin  has been observed to bind the enzyme in both a catalytically competent orientation, and in a reversed polarity across the active site positioning the acceptor hydroxyl outside the catalytic site  . InhibitO-sulfation. What’s interesting is that another more highly sulfated anticoagulant 18-mer (18mer-AXa), despite having 3 times better affinity to HMGB1 than the undersulfated 18-mer, did not provide any protection against APAP overdose. It is suggested the 18mer-AXa did not work because it binds more HSBPs than the undersulfated 18-mer (in particular antithrombin), which negates the beneficial effects afforded by HMGB1 inhibition -induced acute liver injury in a murine model . In APAPhibition .The same 18-mer has also been tested in a murine sepsis model and was found to greatly protect mice from septic shock . In thisFrom these reports, one can clearly see the great potential of structurally defined HS oligosaccharide in treating various inflammatory diseases. They also highlighted the importance of testing and comparing the effectiveness of multiple HS oligosaccharides with different sulfation patterns in a given disease model. Because HS oligosaccharides can interact with a broad array of inflammatory mediators, it is nearly impossible to predict what type of structure will provide better protection. It is also quite possible that an undersulfated oligosaccharide performs better than its more highly sulfated counterparts (as in the case of APAP-induced liver injury), because the undersulfated oligosaccharides tend to be more selective than the latter.2)  and therefore disrupt the normal biological function of HSBPs. An alternative approach to inhibit the interaction would be to block the HS-binding sites of HSBPs. In theory, this can be done with either small molecules inhibitors or with mAbs that specifically target the HS-binding site of an HSBP. But due to the nature of HS-binding sites, which often occupies a large, positively charged surface area (300–600 Å2) , it willTo validate this new strategy of targeting HS-binding site with mAbs, development of mAbs that inhibit the interaction between HS and RAGE is underway. RAGE is a single-transmembrane receptor involved in numerous inflammatory diseases and has been actively pursued as a drug target . HS-RAGEThis approach has also been justified in another study where the researchers aimed to block the interaction between HS and proprotein convertase subtilisin/kexin type 9 (PCSK9), an interaction that is essential for inducing low-density lipoprotein receptor (LDLR) degradation . In thisThe process of purposely developing mAb towards HS-binding sites can be achieved in several ways. If the HS-binding site has already been characterized, mAbs that targets the HS-binding site can be identified straightforwardly by epitope mapping using HS-binding site mutants. This is most conveniently done with a simple, relatively high-throughput, ELISA-based binding assay. Both studies described above used the mutant-based epitope mapping method . In the It is important to note that there are also other reports on mAbs that function through blocking the HS-binding site. These mAbs were originally identified through functional screening, but later were found to target HS-binding sites. Interestingly, many such examples are mAbs that inhibit viral infection. In one study, three neutralizing mAbs towards eastern equine encephalitis virus (EEEV) were found to interact with HS binding residues of its envelope protein . In anotGiven the roles HS plays in so many physiological and pathophysiological processes, the potential is great for new therapies that target HS-HSBP interactions, whether as agonist or antagonist. Advances in synthesis of HS and HS-like oligosaccharides, and a deeper understanding of the structural basis of these interactions should go a long way in accelerating novel treatments for many diseases. In addition, targeting the HS-binding site with mAbs has emerged as a novel strategy of manipulating HS-HSPB interactions. This strategy should be widely applicable because HS-binding sites are always surface-exposed and should in general have good immunogenicity. These two complementary methods will certainly provide researchers with flexibilities in developing novel therapies that targets HS-HSPB interactions.


This study presents wrapper-based metaheuristic deep learning networks (WBM-DLNets) feature optimization algorithms for brain tumor diagnosis using magnetic resonance imaging. Herein, 16 pretrained deep learning networks are used to compute the features. Eight metaheuristic optimization algorithms, namely, the marine predator algorithm, atom search optimization algorithm (ASOA), Harris hawks optimization algorithm, butterfly optimization algorithm, whale optimization algorithm, grey wolf optimization algorithm (GWOA), bat algorithm, and firefly algorithm, are used to evaluate the classification performance using a support vector machine (SVM)-based cost function. A deep-learning network selection approach is applied to determine the best deep-learning network. Finally, all deep features of the best deep learning networks are concatenated to train the SVM model. The proposed WBM-DLNets approach is validated based on an available online dataset. The results reveal that the classification accuracy is significantly improved by utilizing the features selected using WBM-DLNets relative to those obtained using the full set of deep features. DenseNet-201-GWOA and EfficientNet-b0-ASOA yield the best results, with a classification accuracy of 95.7%. Additionally, the results of the WBM-DLNets approach are compared with those reported in the literature. Uncontrolled cell development causes brain abnormalities, such as brain tumors. This syndrome has been associated with a considerable number of deaths worldwide and is commonly recognized as a lethal disease . Brain tNeuro-oncologists can effortlessly and rapidly diagnose brain tumors using computer-aided diagnostic (CAD) technologies. The development of new imaging technologies, such as magnetic resonance imaging (MRI), to visualize the characteristics of brain tumors has increased in recent years. MRI is a useful technique that offers detailed scans of tissues and organs. Owing to its noninvasive nature, MRI is the most reliable method for locating and measuring brain tumors . CorrectIn healthcare imaging, machine learning has been utilized for disease diagnosis in breast ,10, braiThe segmentation challenge was reformulated using supervised learning algorithms as a tumor pixel classification problem. This approach produces the desired segmentation classes as the output of the model after feeding the retrieved features of the image as the input. A previous study  presenteClustering-based segmentation methods divide MRI scans into distinct subcategories and pinpoint the area of interest in each scan. In this method, pixels with a high degree of similarity within each region are categorized as belonging to a particular region. By contrast, pixels that differ from those inside an area are classified as regions that do not belong to the area of interest. K-means clustering is a popular unsupervised machine-learning approach for separating an area of interest from other components of an image. It has been applied for segmenting brain tumors with fair precision as it uses a modest amount of processing time and power , and is Deep learning segmentation models accurately calculate the features of brain MRI images using the layers of a deep learning network . A deep Therefore, in this study, wrapper-based metaheuristic deep-learning network (WBM-DLNet) feature optimization approaches were investigated to improve the classification accuracy of brain MRI scans. First, brain MRI scans were preprocessed to resize and reduce noise. Various pretrained models, such as DarkNet-19, DarkNet-53, DenseNet-201, EfficientNet-b0, GoogLeNet365, GoogLeNet, Inception-ResNet-v2, Inception-v3, MobileNet-v2, NASNet-Mobile, ResNet-101, ResNet-50, ResNet-18, ShuffleNet, SqueezeNet, and Xception, were used to extract information from brain MRI images. Wrapper approaches with various metaheuristic optimization algorithms, such as the marine predators algorithm (MPA), atom search optimization algorithm (ASOA), Harris hawks optimization algorithm (HHOA), butterfly optimization algorithm (BOA), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWOA), bat algorithm (BA), and firefly algorithm (FA), were used to examine the classification performance using SVM. An empirical approach was used to select the best network. Based on the results of various feature subsets, a new concatenated feature vector was formed to evaluate the performance. The performance of the proposed framework was evaluated using an online brain MRI dataset.In this study, a collection of brain MRI images available online was used. The dataset utilized herein was retrieved from the Kaggle website  and compPreprocessing is required to eliminate unnecessary information and reduce noise from brain MRI scans. Cropping is typically employed for removing nonbrain regions and reducing noise. In this study, a cropping approach was employed to compute the extreme points of the brain area. Dilation and erosion processes, the two fundamental morphological processes, were applied to reduce noise. In an image, dilation involves adding pixels to object borders, whereas erosion involves removing pixels from object boundaries. The size and shape of the structuring element used to process the image determine the number of pixels added to or subtracted from the objects. Additional information on the preprocessing can be found in ,35. In aFeatures are the primary factors when categorizing images. Identifying essential features is vital for improving classification performance. Although brain MRI feature extraction can be performed manually or using a deep learning network, manual extraction is very time-consuming. The considerable diversity in brain MRI scans determines their accuracy. By contrast, deep learning neural networks employ a combination of convolutional, pooling, and fully connected layers to build the model. Pretrained deep learning models can extract deep features using the transfer learning method when the dataset is sufficiently small. Deep neural networks that have been trained on extensive image classification tasks are known as pre-trained deep learning models and are capable of extracting hierarchical features from images. These deep features are acquired by passing an image through the network and analyzing the activation of one or more layers, thus indicating the response of the network to various image features. These activations may be used to categorize or compare images and feed them into other classical machine learning models, such as SVM.DarkNet-19, DarkNet-53, DenseNet-201, EfficientNet-b0, GoogLeNet365, GoogLeNet, Inception-ResNet-v2, Inception-v3, MobileNet-v2, NASNet-Mobile, ResNet-101, ResNet-50, ResNet-18, ShuffleNet, SqueezeNet, and Xception were the prominent pre-trained CNN models used in this study. Each of these models contains distinguishing features that capture multiple aspects of images, such as local and global patterns or fine-grained details. These models can be used in a range of computer vision applications, such as object identification, image segmentation, and image retrieval.Feature selection is essential in numerous machine-learning applications because it affects model accuracy. Therefore, relevant features must be utilized to effectively characterize and classify items. However, the inclusion of irrelevant features may lead to low accuracy. Therefore, the most valuable features must be determined to increase the classification accuracy of the model. During the feature-selection procedure in this study, a subset of a wider set of features was selected to build the machine learning model. Note that a specific criterion is used to assess the quality of the new subset . This caIn this study, wrapper-based algorithms were used to select the most appropriate features for training a machine learning model. Wrapper algorithms are machine learning methods for evaluating the performance of a group of features when used with a particular model (the “wrapper”) . The goaThe goal of metaheuristic techniques is to estimate the solutions to complex problems. They are referred to as “meta” because they integrate multiple low-level heuristics to manage high-level optimization tasks . Exampleectively .MPA is an optimization technique inspired by nature that adheres to principles that naturally control the best foraging tactics and encounter rates between predators and prey in marine environments. The hunting and foraging strategies of marine predators, such as sharks and dolphins, serve as the basis for MPA ,44. The Numerous optimization problems, including feature selection, image segmentation, and data clustering, have been effectively solved using this technique. MPA is very efficient and helpful in addressing optimization issues in various fields because it can obtain global optima in complicated search spaces .The principle of ASOA is the mobility and interaction of atoms in a physical system . It is aBairathi and Gopalani proposed the optimization technique of HHOA, which is based on the cooperative hunting style of Harris hawks . This alThe metaheuristic optimization BOA was developed by Arora and Singh after they studied the feeding habits of butterflies in the environment . The BOAMirjalili and Lewis  proposedMirjalili et al. developed the GWOA in 2014 based on the grey wolf social structure and hunting skills . Grey woYang first presented the BA in 2010  as an opMotivated by the flashing qualities of fireflies, Yang presented the FA in 2010 , based oMRI is the most reliable tool used by radiologists for the detection of brain tumors. This study aimed to build an intelligent model that can detect brain tumors and classify them into subcategories .As mentioned in https://www.mathworks.com/matlabcentral/fileexchange/84139-wrapper-feature-selection-toolbox, accessed on 7 February 2023). Further, the SVM model was used for brain MRI classification [In the network selection phase, the proposed approach selects deep learning networks with a metaheuristic optimizer having a maximum accuracy of more than 94% for the deep features of an individual deep learning network. Herein, MATLAB’s “Jx-WFST,” a Jxwrapper feature selection library, was used to implement all methods  were applied to determine the optimal deep features of all networks using the SVM-based cost function. All metaheuristic optimization algorithms significantly enhanced the classification performance and reduced the feature vector size of each pretrained model. Subsequently, a deep-learning network selection approach was applied to determine the best deep features. The best deep features were concatenated to train the SVM model. The model exhibited the highest classification rate of 95.7% with DenseNet-201-GWOA and EfficientNet-b0-ASOA deep feature-trained SVM models. The model was further validated using a new dataset and exhibited a high classification performance of 96.7%. Therefore, the proposed WBM-DLNet feature optimization algorithm can be considered useful for automatic brain tumor detection.


Exosomes are nanoscale vesicles released by diverse types of cells for complex intercellular communication. Numerous studies have shown that exosomes can regulate the body’s immune response to tumor cells and interfere with the tumor microenvironment (TME). In clinical trials on dendritic cell (DC)-based antitumor vaccines, no satisfactory results have been achieved. However, recent studies suggested that DC-derived exosomes (DEXs) may be superior to DC-based antitumor vaccines in avoiding tumor cell-mediated immunosuppression. DEXs contain multiple DC-derived surface markers that capture tumor-associated antigens (TAAs) and promote immune cell-dependent tumor rejection. These findings indicate the necessity of the further development and improvement of DEX-based cell-free vaccines to complement chemotherapy, radiotherapy, and other immunotherapies. In this review, we highlighted the recent progress of DEXs in cancer immunotherapy, particularly by concentrating on landmark studies and the biological characterization of DEXs, and we summarized their important role in the tumor immune microenvironment (TIME) and clinical application in targeted cancer immunotherapy. This review could enhance comprehension of advances in cancer immunotherapy and contribute to the elucidation of how DEXs regulate the TIME, thereby providing a reference for utilizing DEX-based vaccines in clinical practice. The field of immunotherapy for cancer has noticeably attracted oncologists’ attention since it was discovered in the late 19th-century . Compare+ effector T cells, resulting in a superior antitumor immunity [Exosomes are micro-vesicles with a diameter of 30–150 nm and a density of 1.13–1.19 g/mL, secreted by cells with a phospholipid bilayer structure . In 1983immunity . As scieimmunity ,28. To dimmunity . Hence, immunity .Secretion of exosomes is not a random process, and it necessitates the close coordination of several components. Similar to apoptosis, the production of exosomes entails the flipping of phosphatidylserine from the inner to the outer plasma membrane leaflet ,33. ProdMVBs may also be formed without the use of ESCRT, and the lateral segregation of cargo inside the endosomal membrane appears to be dependent on raft-based microdomains, lipids, the tetramembrane protein family, and the heat-shock protein family (HSP) . TetraspSimilar to DCs, the molecular composition of DEXs includes surface expression of functional MHC-peptide complexes, costimulatory molecules, and other components that interact with immune cells ,47. DEXsDEX-based therapy may be a promising approach to initiate antitumor immune responses . DEXs poBecause of their different compositions, immature and mature DEXs have distinct biological activities . ImmaturCompared with DC-based vaccines, DEXs provide additional benefits . CurrentThe development and progression of cancer depend on its complex and heterogeneous microenvironment, which is composed of both cellular and cell-free components . An esse+ and CD4+ T cells, respectively [DEXs can induce T cell immune responses via direct and indirect pathways . On the ectively ,86. DEXsectively . On the ectively . There aectively .+ T cell immunity [The second mechanism is the direct transfer of DEX-containing MHC/peptide complexes to the surface of bystander APCs, initiating antitumor immune responses in a process called cross-presentation ,84. Afteimmunity ,88.Studies have demonstrated that DEXs have ligands for members of NKG2D-L and IL-15Rα, both of which activate NK cells and stimulate innate immune responses ,78. This+ T cells and CD8+ T cells to respond efficiently to cancer cells and elicit tumor immune responses [+ T cells via exosomes [A growing body of evidence demonstrates that DEXs can induce B cell immunity in vitro and in vivo. Segura et al., showed that DEXs can indirectly activate T cells by delivering antigen-MHC combinations and ICAM-1 molecules to less efficient APCs, such as B cells ,91. Actiesponses . It is nesponses . B cellsexosomes . In an iexosomes . In a moexosomes . In explexosomes . The aboAfter a DEX-based antitumor vaccine was tested in animal models, phase I and phase II clinical trials were completed in patients with advanced cancer. In addition, additional animal experiments on different tumors related to DEXs for immunotherapy were carried out, aiming to produce a new generation of DEX-based antitumor vaccines with higher biological activity.Clinical trials on DEXs are currently in progress. Exosomes isolated from DCs were derived from immature monocytes of melanoma patients and were stimulated by melanoma-associated antigenic peptides (MAGE). Improved immunity to melanoma has been found after patients were inoculated with self-derived DEXs. The study showed that in 15 patients with metastatic melanoma, DEXs had no secondary toxicity at the maximum tolerated dose, demonstrating that auto-DEXs with MAGE would be tolerated well in melanoma patients, and this tolerance was strongly associated with the enhanced activity of NK cells . The res+ T cells and recruit NK cells to tumor sites. This significantly inhibits tumor growth and prolongs the survival of tumor-bearing mice [It has been demonstrated that tumor vaccines for patients require the enhanced biological activity of DEX-stimulated T cells in vivo. In a mouse model, DEXs stimulated by antigens from human B16F10 melanoma cell lysates could consistently induce activation of melanoma-specific CD8ing mice .Recently, Charles et al.  reportedMAGE-specific T cell responses, along with the increased natural killer cell lysis activity, were observed in a phase I trial in patients with NSCLC ,16, demoAn essential innovation in the phase II DEX immunotherapy trial was the use of DEXs from TLR4 ligand (TLR4L) or IFN-γ-stimulated DEXs (IFNγ DCexos). Compared with DEXs from autologous immature monocytes, such DEXs expressed more co-stimulatory molecules and induced a higher immunostimulatory response . The res+ T lymphocyte, as well as decreasing the levels of regulatory T cells (Treg), interleukin-10 (IL-10), and transforming growth factor-β (TGF-β) [AFP was positively correlated with the improvement of the TIME in HCC mice, and T cells contributed to the antitumor function of DEXsAFP. Based on these results, DEXsAFP have a potential clinical value as a novel class of vaccines for the immunotherapy of hepatocellular carcinoma, and the dose and delivery method of DEXs should be optimized in clinical practice. As research progresses, DEXs may be soon used in the treatment of HCC [DEXs have recently shown higher feasibility and efficacy in HCC patients in preclinical trials. Alpha-fetoprotein (AFP) is currently one of the most commonly measured clinical biomarkers for HCC . DEX-exp (TGF-β) . The antt of HCC ,101,102.+ T cells [+ T cells and stimulate a strong CTL response, thereby activating a strong antitumor immune response [Shi et al., showed that in a mouse model of HCC, the use of sorafenib and stimulation of DCs with exosomes from tumor cells reduced the number of regulatory T cells and increased the count of CD8 T cells . Liugangresponse . Meanwhiresponse ,104,105.response . The nexTo date, DEXs have not achieved the expected therapeutic effects in clinical practice. The main limitations of this immunotherapy are the lack of pre-selection criteria for recruiting patients with advanced cancer and the heterogeneity of several patients who have already received immunotherapy. Secondly, patients’ partial or systemic immunoregulatory mechanisms also limit this immunotherapy. The biological activity of DEXs in mediating the tumor immune response requires further improvement. Thirdly, standardized isolation and highly quantitative exosomes are required due to the poor purity of DEXs and the limited amount of DEXs secreted by the cells . The devDespite several challenges in DEXs-based immunotherapy, this approach is still a promising cancer treatment. The biological activity of DEXs can be modulated via various methods, such as modification of exosome-derived cells by genetic engineering and promotion of DEXs production by light sensing based on phototherapy. A previous study showed that the amount of DEXs secreted from vesicles in DCs can be remarkably increased more than 13-fold with phototherapy-based LED lights under more superior light wavelength, intensity, and exposure time conditions . At presDEX-based immunotherapy is a valuable strategy that can be used alone or in combination with other cancer treatments to replace or augment existing immunotherapies. More animal tests with DEX cell-free vaccines targeting various malignancies should be conducted, demonstrating the potency of DEXs. Prospects for this immunotherapy aim at developing high-immunogenicity DEXs with genetic modifications to achieve a larger scale of cell-free immunotherapy. Furthermore, the use of immune adjuvants and their combination with other cancer therapeutic modalities may enable DEXs to stimulate T cells more effectively in the future ,106,109.


The Avoidant Restrictive Food Intake Disorder Genes and Environment (ARFID-GEN) study is a study of genetic and environmental factors that contribute to risk for developing ARFID in children and adults.A total of 3,000 children and adults with ARFID from the United States will be included. Parents/guardians and their children with ARFID (ages 7 to 17) and adults with ARFID (ages 18+) will complete comprehensive online consent, parent verification of child assent (when applicable), and phenotyping. Enrolled participants with ARFID will submit a saliva sample for genotyping. A genome-wide association study of ARFID will be conducted.ARFID-GEN, a large-scale genetic study of ARFID, is designed to rapidly advance the study of the genetics of eating disorders. We will explicate the genetic architecture of ARFID relative to other eating disorders and to other psychiatric, neurodevelopmental, and metabolic disorders and traits. Our goal is for ARFID to deliver “actionable” findings that can be transformed into clinically meaningful insights.clinicaltrials.govNCT05605067ARFID-GEN is a registered clinical trial:  Avoidant Restrictive Food Intake Disorder Genes and Environment (ARFID-GEN) study, which is designed to expand the discovery of genetic and environmental contributions to ARFID risk. ARFID-GEN builds on previous and ongoing genome-wide association studies (GWAS) by the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED) as part of a global effort to fully characterize the genetic architecture of all eating disorders (EDs) and explore their relation to each other and to other psychiatric, neurodevelopmental, and metabolic/anthropometric traits.We describe the ARFID-GEN will ascertain, phenotype, and genotype a large sample of children and adults with ARFID. We will apply advanced analytic strategies to test and refine an etiological model of ARFID, explicate heterogeneity, and simultaneously document environmental risk factors for ARFID.ARFID is associated with high personal and family emotional and financial cost. ARFID, present in 2–5% of the population , is mark2twin] = .79; CI:.71, .86), with the remaining variance attributable to nonshared environmental factors  and is the most common reason for referral . When phLeveraging the existing Eating Disorders Genetics Initiative (EDGI) infrastrWe will interview parents/guardians of 25 children with ARFID and 25 adults with ARFID with the Pica, ARFID and Rumination Disorder Interview (PARDI)  to furthgs), polygenic risk scores (PRS), standard post-GWAS analyses of the non-mutually exclusive ARFID presentations, and rare copy number variants (CNVs) and CNV burden. Hypotheses: We will identify genome-wide significant loci for ARFID, informative rgs, implicated CNVs, and environmental precipitants.We will conduct comprehensive phenotypic and genomic analyses: single nucleotide polymorphism (SNP)-based heritability, GWAS , genetic correlations (rgs and conduct Mendelian Randomization (MR), multi-trait conditional and joint analysis (mtCOJO)  cross-disorder GWAS meta-analysis to identify loci with pleiotropic effects, (b) if indicated, calculate r(mtCOJO) , disorde(mtCOJO) , and, (c(mtCOJO)  to examiDeliverables: (a) dissection of converging and diverging relations among ARFID and other traits informing and refining its etiology; (b) genetic assessment of ARFID’s relation to other phenotypes, informing nosology.To test a conceptualization that ARFID has specific genetic associations with psychiatric, metabolic/anthropometric, and neurodevelopmental phenotypes we will apply: (a) approaches as in Aim 3b, and (b) GSEM to examine genetic, psychiatric, neurodevelopmental, and metabolic/anthropometric factors associated with ARFID. We predict that high sensory sensitivity will be related to autism spectrum disorder (ASD), high levels of phobic avoidance to anxiety and obsessive-compulsive disorder (OCD), and high levels of low interest/appetite to AN. We will engage the infrastructure utilized in EDGI  to ascerSupplement and Inclusion criteria. Individuals ages 7 + who meet DSM-5 criteria for ARFID. An age 7 + cut-off allows adequate phenotyping with a range of valid instruments available for relevant constructs for this age and older. We anticipate most cases will be pediatric as ARFID typically onsets in childhood. Following a brief online eligibility pre-screen, participants/parents/guardians will be screened for eligibility  and for diagnostic purposes using the questionnaires listed in We will use a multi-pronged recruitment approach including: outreach to ED clinicians and programs across the country, traditional media (press releases and newspaper announcements), and social media and other online platforms . This includes use of social media ads on Facebook and Instagram, which have proven successful for other similar studies . We enroSupplement.arfidgen.org) and select “Take Our Survey” with the child available. The first step requires the parent/guardian to answer prescreen questions and to consent to the study which includes answering questionnaires and having their child provide a saliva sample for DNA extraction. Parents also have the option to consent to be recontacted for future research. To determine eligibility of the child, parents/guardians then complete a parent report version of the Nine Item ARFID Screen (NIAS-PR) (Parents/guardians of children with ARFID who are interested in participating in the study will visit the website (NIAS-PR) ; a parenNIAS-PR) ; and VerNIAS-PR) , developNIAS-PR) . If the Children ages 7–13 then complete the ChEDE-Q8 , a childarfidgen.org) and click the “Take Our Survey” link. Participants complete a brief pre-screen, providing online informed consent for the entire study, have the option to consent to be recontacted for future studies, and provide contact information. Next, they complete the NIAS and the adult version of the PARDI-AR-Q to confirm the presence of ARFID, the EDE-Qv6 to rule out other current eating disorder symptoms, and the ED100Kv3. Surveys are presented in REDCap  Biorepository at UNC.Parents and adult participants are sent a gift card ($25) once all required questionnaires are complete and their spit kit is received by the study team.DNA extraction and GWAS genotyping are standard. We will use the most contemporary chip appropriate for diverse ancestry populations when genotyping occurs.We will conduct descriptive analyses to characterize the sample stratified by pediatric and adult cases. We will report demographics, symptom patterns, onset, course of illness, comorbid psychiatric conditions, neurodevelopmental characteristics, environmental exposures, and health-related quality of life in child (parent/guardian and child report) and adult ARFID cases.Diagnoses obtained with the PARDI interview will be compared with those obtained with the online ARFID-GEN battery. We will calculate positive predictive value (PPV) to confirm diagnostic properties of our ARFID-GEN battery. Data on weight, height, body mass index (BMI), and BMI percentile (for those < 18) will be verified during remote interviews.gs, and PRS, and standard post-GWAS analyses of ARFID and the non-mutually exclusive ARFID presentations, and rare CNVs and CNV burden. The following sections briefly summarize the Aim 2 analyses; a more detailed description can be found in the Supplement.We will conduct a comprehensive set of genomic analyses including SNP-based heritability, GWAS, ra)Supplement, we will use “Ricopili” software Supplement.The following outlines standard analytic strategy for post-GWAS analysis to maximize information yield and interpretability. The field and methodology evolve rapidly. Below represents what we would do today; however, novel proven methods may emerge before analyses are conducted. Greater detail can be found in the X chromosome (chrX) variants in the pseudo-autosomal regions will be handled separately. SNPs with MAF > 0.01 and INFO > 0.70 will be retained.We will conduct secondary GWAS analysis separately on females and males to determine similarity of the results to the primary combined GWAS.P< 5×10−8 will be identified and “clumping” will be used to convert significant SNPs to regions.GWAS results implicate genomic regions (“loci”). To define a locus, SNPs with Conditional and joint analysis will be conducted using genome-wide complex trait analysis-conditional and joint analysis (GCTA-COJO) . GCTA-COWe now routinely use functional genomic results from CommonMind, PsychENCODE, and other efforts to understand GWAS results . Much ofLinkage disequilibrium score regression (LDSC) will be used to estimate SNP-based heritabilities for ARFID and related presentations , 37.PRS aggregate risk alleles across the genome weighted by effect sizes. We will use both the classical p-value thresholding method and a summary statistics version of Bayesian multiple regression (SBayesR) .ARFID presentations are overlapping. We will examine the association between ARFID PRS and: a) PARDI-AR-Q subscale scores  as continuous scores; and b) a variable categorizing individuals as having a predominant ARFID presentation  defined as highest subscale score.Multi-marker analysis of genomic annotation (MAGMA)  will be h2) across functional annotation categories.Partitioned heritability will be investigated using stratified LDSC , which eWe will investigate whether ARFID heritability is enriched in tissue/cell type specifically expressed genes using publicly available gene expression data .We will predict differential gene expression using S-PrediXcan v1.0  and genoWe will evaluate whether genes associated with ARFID are enriched in specific pathways, tissues, or cell types. To do this, we will use FUMA to annotate SNPs, identify independent loci, perform pathway analysis, and integrate with a wide array of functional genomic data including gene expression, single cell gene expression, and all available brain epigenetic information.c)The PGC CNV working group is actively optimizing CNV calling using global screening array (GSA) data and we will follow these developments closely. We will maximize comparability between cases and controls by applying rigorous quality control (QC) –44. FollSupplement.Greater detail on Aim 3 analyses can be found in the We will conduct disorder-specific GWAS for ARFID  combining ARFID-GEN with EDGI and existing PGC-ED data using imputed variant dosages and an additive model. We anticipate aggregate sample sizes for the PGC-ED by ~ 2025 (when our collection would be complete) of: . GWAS meta-analysis will be conducted with ARFID-GEN and any other ARFID samples that may be introduced to the PGC-ED by time of analysis.Combining ARFID-GEN with PGC-ED GWAS of AN, BN, and BED , conduct GWAS meta-analysis of all EDs and of component behaviors that cross-cut disorders , increasing power to identify genetic risk factors that are common across the four disorders.b)rg) measure the extent to which two traits or disorders share common genetic variation. We will calculate SNP-rg for ARFID, and selected traits using GWAS summary statistics via an analytical extension of LDSC  and test the correlation in effect sizes in a second trait (the outcome). Under pleiotropy, there is an expectation that the mean effect size in the outcome trait is different from zero, but under causality a directional relationship in effect sizes is expected. Different versions of MR analysis are highly related; at the time of analysis, we will implement best practices for MR.Guided by the a) and b) will inform these analyses. For example, we expect to detect genetic correlations of BMI with EDs, and MR analyses will aid interpretation of correlation by causality. We will conduct conditional GWAS analyses to determine if the detected SNP associations for EDs can be explained through their relationship with correlated traits. We will perform a multi-trait-based conditional and joint analysis (GCTA-mtCOJO) (a above) provides insights into the forces shaping the shared genetic relationships between disorders.Results from -mtCOJO)  using an-mtCOJO) . This meA key question in ED research is to understand the differences as well as the similarities among the EDs. This question is very similar to the one posed by PGC colleagues interested in understanding the similarity/differences between SCZ and bipolar disorder (BIP) . Guided ARFID-GEN with EDGI and existing AN, BN, and BED cohorts in the PGC-ED samples should yield a sample size of ~ 95,000 ED cases and > 500,000 controls. We will increase power by using MPS to combine the predictive power of several PRS in one regression model. Training data will combine the best available GWAS summary statistics on psychiatric, metabolic, BMI, anthropometric, personality, physical activity, and educational phenotypes. These PRS will be used as genetic predictors in models of ARFID, AN, BN, BED, and more granular phenotypes. This approach is ideal for our overarching intentions of predicting outcomes rather than discovering their etiology . MPS is valuable when trait prediction is a priority.To gain insight into factors underlying ED heterogeneity, we will use the largest available GWAS summary statistics for psychiatric and somatic disorders/traits and combine derived PRS into MPS to predict target outcomes , and then more granular phenotypes . Combining c)We will use lifetime ARFID, AN, BN, and BED GWAS summary statistics. We will employ GSEM  to identgs, we will follow methods applied in the Anorexia Nervosa Genetics Initiative-PGC-ED (ANGI-PGC-ED) Freeze 2 analysis  How do genetic and environmental exposures act and co-act to influence risk for ARFID? 2) If detected, how will carriers of CNVs differ on PRS and environmental exposures? 3) Can we identify genetic factors that influence course of illness ? 4) Can we answer precision-medicine questions regarding identification of optimal interventions informed by genotype and environmental exposures? Given the paucity of effective interventions for ARFID (and other EDs) , ultimat


Rural community resilience (RCR) is crucial to rural sustainable development in the context of rural decline globally. Previous studies seem to underestimate the role of the built environment (BE) in the proactive aspect of RCR (P-RCR), that is, a rural community’s ability to cope with change proactively. This study explores BE’s effects on P-RCR with a holistic framework involving objective BE (OBE), perceived BE (PBE), place attachment (PA) and P-RCR, using structural equation modeling (SEM) based on a sample of 7528 rural respondents from eastern, central and western China. The results are as follows: (1) Both OBE (population density and accessibility) and PBE  can significantly affect P-RCR in terms of social, economic and environmental dimensions. (2) In all regions, PBE’s impacts were consistent and positive on social and economic dimensions at both the individual and community levels (except the community-level economic dimension in western regions), but negative on the individual-level environmental dimension; OBE’s impacts were varied among regions. (3) In certain regions, PA and PBE were mediators in the BE-P-RCR relationship. This study can help researchers to construct a more detailed picture of the BE-P-RCR relationship and identify BE-related factors that contribute to P-RCR enhancement. Fostering rural community resilience (RCR) is gaining increasing attention along with a series of rural issues confronted by many rural communities around the world, such as depopulation, economic depression, employment reduction and increasing disaster vulnerability ,2,3,4. RAs a result, researchers have shown great interest in factors that enhance or undermine RCR , includiNevertheless, studies in rural health, psychology and environmental psychology indicate that BE, either objectively measured (OBE) or perceived (PBE), is not dispensable to P-RCR and has multiple approaches to influence P-RCR not merely through the facility-economy way. Researchers focusing on rural health and the resilience of individuals find that “facilities” also are of non-monetary value to rural communities and their resilience by offering locations for people’s social interactions and influencing social networks through perceived availability (PBE attribute) ,27,28. THowever, although a few researchers test the links between rural people’s perceptions of facilities and P-RCR in case studies , the impDo OBE and PBE significantly affect P-RCR in the social, economic and environmental dimensions?How do OBE and PBE affect these dimensions, respectively?Does PA or PBE play a mediating role in the BE-P-RCR relationship?Specifically, in this study, we focused on the following questions: We propose a holistic framework  to depicWe used both PBE and OBE to explore BE’s impacts on P-RCR, and we recognized that OBE has effects on PBE based on the framework proposed by Marans . Except Social, economic and environmental capital are fundamental to P-RCR. P-RCR relies on the personal and collective agency of members ,11,46, aThe existing work suggests that OBE/PBE may influence P-RCR in terms of all three dimensions. Regarding Soc, BE components such as schools, stores, and recreational and healthcare facilities provide physical spaces for rural residents’ social interactions ,28. ConsIt should be noted that the abovementioned influences of OBE/PBE on P-RCR might be direct, indirect or both. We assume that OBE/PBE affects P-RCR in both ways. Direct paths from OBE/PBE to P-RCR are included in the framework.PA is described as “an emotional connection to a place”  (p. 560)In rural or agriculture studies relevant to BE, researchers have found the importance of OBE/PBE to PA. For example, Bunkus et al.  emphasizThe data used in this study were derived from China Family Panel Studies (CFPS). CFPS, conducted by the Institute of Social Science Survey (ISSS) of Peking University, is a national and comprehensive social survey aiming to collect longitudinal data at the individual, family and community levels in contemporary China for research on Chinese social phenomena . It coveSpecifically, we combined the variables of OBE and P-RCR  from CFPS 2014 (datasets on communities and adults), the variables of PBE, P-RCR  and PA from 2016 (adult dataset) by linking “community ID” after keeping all samples of rural communities . However, we excluded the respondents who had moved to a new residential address or had a primary job and income change during 2014–2016, for these respondents may make less reliable evaluations of OBE and individual economic well-being in the context of our study. At last, we obtained a sample of 7528 rural community respondents in China. We divided this sample into three groups based on the geographic locations of 25 provinces . These were the groups of eastern regions (n = 2719), central regions n = 2130) and western regions (n = 2679) . This wa0 and wesFor our research purpose, OBE in this study was quantified with accessibility and population density ,62,63. WFor PBE assessment, we used the data on residents’ perceptions of their neighborhood BE in CFPS 2016. It contains overall perceptions of public facilities, safety and surrounding environment of neighborhood , which have been seen as critical PBE attributes in studies pertaining to a rural context, social capital or PA ,66. We tIn this study, P-RCR was quantified based on the framework explored by Markantoni et al. , which iAt the individual level, Soc was assessed by happiness , and basThe covariate was the socioeconomic status of residents. Lewicka  asserts Specific questions used to derive indicators of PBE, PA, different dimensions of P-RCR and the covariate are displayed in Structural equation modeling (SEM) has been an important tool for analyzing the interactions between the physical environment and rural society ,57. SEM 1.Step one: Measurement model testing and descriptive statistical analysisThe reasons we employed SEM as an analytical tool in this study were manifold. First, SEM allows researchers to investigate complex relationships between multiple constructs in a single model and provides an easier way to discuss the model ,69. TherIn this step, confirmatory factor analysis (CFA) was performed using different groups of data and AMOS Version 26. Meanwhile, descriptive statistical analysis was conducted using STATA Version15.2.Step two: Structural equation model buildingIn this step, two basic structural equation models were established based on the framework shown in 3.Step three: Application of structural equation modelIndividual-level model with population density (Model 1);Individual-level model with accessibility (Model 2);Community-level model with population density (Model 3);Community-level model with accessibility (Model 4).Since population density and accessibility are highly correlated, these two indicators were examined separately in the basic models for multicollinearity reduction. Consequently, there were four models we needed to explore: Each model was tested using three groups of data separately; therefore, 12 models were applied using AMOS Version 26, and the paths to all endogenous variables were controlled for the covariate.All composite reliability (CR) values for latent variables with multiple indicators derived from three groups of data were above 0.6 (0.776 ≥ CR ≥ 0.631), indicating a high degree of internal consistency  Table A. For accAll 12 models had acceptable goodness of fit. Because in each model, the Chi-square/degrees of freedom < 5, the comparative fit index > 0.95, the root mean square error of approximation < 0.05, and the standardized root mean square residual was below 0.05. The specific fits of each model are shown in To examine whether and how BE influenced three dimensions of P-RCR, we first focused on whether there were positive or negative significant total effects of OBE/PBE on three dimensions of P-RCR, since it was more rational to infer that BE can affect P-RCR when significant total effects of BE are identified. Then, we paid attention to the indirect effects that PA or PBE can mediate. We highlighted the mediating role that PA and PBE play in the BE-P-RCR relationship when the total effects of OBE/PBE on P-RCR are significant. This study did not elaborate on the significant mediation effects related to insignificant total effects.In eastern regions, the mediation effects of PA were found in the relationships between PBE and Soc/Eco at the individual and community levels. In central regions, PA significantly mediated the effects of PBE on Soc  and individual-level Env. No mediation effect of PA was observed in the western regions.The values in Concerning indirect effects, the values in Our findings show that both PBE and OBE significantly affected three dimensions of P-RCR. PBE had significant total effects on three dimensions of P-RCR in all regions at both the individual and community levels (except community-level Env in western regions). OBE significantly affected individual-level Soc (eastern regions), Eco  and community-level Env . These findings statistically support that the way BE influences P-RCR is varied rather than constrained in a facility-economy approach.PBE’s impacts on P-RCR were consistent among regions regarding Soc, Eco and individual-level Env. PBE was positively associated with Soc and Eco in all regions at both levels, with the single exception of community-level Eco in the western regions. This indicates that better PBE leads to a greater P-RCR in Soc and Eco in most cases. This is in line with the findings based on a Western context that perceived adequacy of facilities, attractive BE and feelings of safety contribute to richer social capital and greater economic resilience ,49,50. RCompared to PBE, OBE’s impacts on the three dimensions of P-RCR were varied in different regions and levels. This implies that regional disparities may be critical to OBE’s impacts on P-RCR, and OBE plays different roles in fostering individual- and community-level P-RCR. For instance, our results show that in the eastern coastal regions where rural industrial development levels were higher and rural income inequality levels were lower ,61, highOur findings indicate that there are two significant mediation roles that PA plays in the BE-P-RCR relationship. One is the positive role in P-RCR enhancement. PA provides a critical indirect path through which better PBE bring an increment in Soc  and individual-level Eco (eastern regions). It counteracts the negative effects of OBE (accessibility/density) on happiness and OBE (accessibility) on personal financial stability in the eastern regions. It offsets the adverse effects of PBE on residents’ attitudes toward environmental protection in the central regions. The other is the negative role in P-RCR enhancement. In the eastern regions, it reduces the positive impacts PBE has on community economic well-being. This suggests that the mediation effects of PA are not always beneficial to P-RCR, which is in line with the findings that different types of PA play different roles in RCR . For exaIn the western regions, OBE significantly influences Eco through PBE, and in eastern regions, accessibility has significant impacts on individual-level Eco through PBE. This supports the OBE-PBE relationship proposed by Marans  and the This study focused on the questions of whether and how BE affects P-RCR, subjects that attracted less attention in previous studies but are crucial to RCR enhancement. We investigated the effects of OBE/PBE on three dimensions of P-RCR using SEM based on the empirical data obtained from CFPS and a holistic framework combining OBE/PBE, PA and P-RCR. Our findings provide a more-detailed picture of the BE-P-RCR relationship and new empirical evidence of BE’s multi-effects on P-RCR in terms of Soc, Eco and Env. This study has several limitations. First, it was difficult to determine whether BE positively or negatively influences the overall level of P-RCR based on our study, since OBE and PBE had inverse effects on the same dimension at different levels in specific regions, and their effects on different dimensions were also inverse sometimes. Nevertheless, our findings are useful for researchers in understanding BE’s impacts on specific dimensions and levels of P-RCR. Second, as stated earlier, the mechanism leading to PBE’s impacts on community-level Env is still unclear. This may be a result of our measurement strategies  and data limitation. Therefore, more data and sophisticated research designs are required to understand the relationships between PBE and Env in future study. Third, the data we used in this study was collected in different waves, which might not have been beneficial for our research in terms of reliability. However, many researchers recognize that it is acceptable to apply data obtained from different waves of CFPS in the same analysis or SEM model considering the reality of China ,72. MoreThe results of our study imply that BE’s impacts on P-RCR are multifaceted and should be fully considered in RCR study and practice, especially with regard to Soc and Eco. When assessing or analyzing P-RCR in terms of Soc and Eco, rather than simplifying BE as facilities that purely increase rural communities’ economic resilience, specific PBE and OBE attributes should be taken into account. To enhance P-RCR, besides the number of facilities and population density, PBE may be a key factor. Furthermore, one-size-fits-all criteria for accessibility might not be appropriate in different rural communities, for the effects of accessibility on P-RCR may be uneven among regions. Additionally, greater consideration needs to be given to the influences of PA on Soc and Eco at both the individual and community levels  when any BE changes occur within rural reconstruction.Improvements to the rural built environment, such as new rural reconstruction and rural settlement remediation, should not focus only on infrastructure development while ignoring people’s perceptions and evaluations of their surrounding environment.Top-down planning activities initiated by the government should develop more detailed and targeted planning schemes for rural service accessibility and village mergers, which will be helpful for increasing P-RCR in different regions.The development and implementation of built environment policies should consider rural people’s emotional ties with their communities, including both the pros and cons of these emotional ties for P-RCR.Based on our findings, the following recommendations are offered for rural community development and P-RCR enhancement in China or other countries or regions facing rural issues similar to those in China:To explore BE’s effects on P-RCR, this study proposes a framework holistically depicting the BE-P-RCR relationship and tested this framework using SEM with a sample of 7528 rural respondents from eastern, central and western China. Our findings include the following: (1) Both OBE and PBE can significantly affect three fundamental dimensions of P-RCR; (2) apart from community-level Eco in western regions, PBE consistently and positively influenced Soc and Eco but negatively influenced individual-level Env despite regional disparities; OBE’s impacts on three dimensions of P-RCR were varied among regions; (3) PA and PBE were mediators in the BE-P-RCR relationship in certain regions. Based on these findings, we argue that the multi-effects of BE on P-RCR should be taken into account in RCR research and practice, especially regarding Soc and Eco.


Colonoscopies are effective in the prevention of colorectal cancer (CRC) but considered burdensome. An alternative for or addition to this procedure might be found in circulating tumor DNA (ctDNA) analysis. However, to date, it is not clear which analysis is most suitable for such a ctDNA-based blood test for CRC. Therefore, we assessed this in ten patients with colonoscopies for Lynch syndrome or in the context of the Dutch national screening program who were diagnosed with CRC or its precursor lesion (advanced adenoma). The results of this proof-of-principle study could form the foundation for subsequent studies on ctDNA-based blood test development for CRC screening and management, specifically in carriers of Lynch syndrome.Colorectal cancer (CRC) colonoscopic surveillance is effective but burdensome. Circulating tumor DNA (ctDNA) analysis has emerged as a promising, minimally invasive tool for disease detection and management. Here, we assessed which ctDNA assay might be most suitable for a ctDNA-based CRC screening/surveillance blood test. In this prospective, proof-of-concept study, patients with colonoscopies for Lynch surveillance or the National Colorectal Cancer screening program were included between 7 July 2019 and 3 June 2022. Blood was drawn, and if advanced neoplasia  was detected, it was analyzed for chromosomal copy number variations, single nucleotide variants, and genome-wide methylation (MeD-seq). Outcomes were compared with corresponding patients’ tissues and the MeD-seq results of healthy blood donors. Two Lynch carriers and eight screening program patients were included: five with CRC and five with advanced adenomas. cfDNA showed copy number variations and single nucleotide variants in one patient with CRC and liver metastases. Eight patients analyzed with MeD-seq showed clustering of Lynch-associated and sporadic microsatellite instable lesions separate from microsatellite stable lesions, as did healthy blood donors. In conclusion, whereas copy number changes and single nucleotide variants were only detected in one patient, cfDNA methylation profiles could discriminate all microsatellite instable advanced neoplasia, rendering this tool particularly promising for LS surveillance. Larger studies are warranted to validate these findings. Colorectal cancer (CRC) is the third-leading cause of cancer-related death worldwide . For thiBesides this nationwide program, additional dedicated screening programs exist for individuals who are at high risk of developing CRC, such as carriers of a pathogenic variant in one of the DNA mismatch repair (MMR) genes, resulting in Lynch Syndrome (LS) ,4,5,6,7.Despite its high efficiency, the iFOBT has a low sensitivity. Nearly half of the people who are advised to have a colonoscopy do not have colorectal anomalies after all. Similarly, the majority of LS carriers do not have CRC or AA during biennial colonoscopic surveillance. Additionally, colonoscopies are burdensome. Therefore, new, less invasive strategies for early CRC detection are warranted.Cell-free DNA (cfDNA)-based blood tests seem to be particularly promising for this purpose . cfDNA iIdeally, cfDNA could even be used for the detection of precursor lesions to prevent cancer outgrowth and for locating the origin of this lesion. This is particularly of importance to LS carriers and other individuals with a hereditary predisposition to develop cancer. Specifically, cfDNA methylation profiles seem to be promising for this purpose. Previous research showed clustering of AA separate from healthy blood donors based on 11 methylation markers . More inRecently, we presented a cheaper alternative to genome-wide methylation analysis (MeD-seq)  and showFor this proof-of-concept study, we approached participants of the National CRC screening program with a positive iFOBT, as well as molecularly proven LS carriers, with a planned colonoscopy in the Erasmus MC, Rotterdam (The Netherlands) between 7 July 2019 and 3 June 2022. The Erasmus MC Institutional Review Board approved this study (NL68955.078.19). Additionally, this study was reported in the International Clinical Trials Registry Platform (NL8695). The study procedures were performed as described in the Declaration of Helsinki. Written informed consent was obtained from every participant. Due to the explorative nature of this study, no sample size calculation was performed. The results of this study are intended as the foundation for a larger follow-up study.Potential participants were informed about the study and the theoretical chance of finding unexpected findings. Upon informed consent, blood was drawn just before the colonoscopy was carried out via peripheral venous catheter, which is required for colonoscopy sedation; first, blood was collected in one mock EDTA tube, and subsequently, it was collected in two Streck Cell-Free BCT tubes  . The tubPlasma isolation was performed as described previously . Plasma To assess the presence of CNVs in the cfDNA, an automated NGS workflow was performed using the VeriSeq  and the Microlab Star Robot  according to the manufacturer’s instructions, as described before . In brieTo assess whether SNVs were present in the cfDNA, 10 ng cfDNA was analyzed with the Next Generation Sequencing (NGS) OncomineTM Colon cfDNA Assay, as described previously, using variantCaller version v5.10.0 in cases of at least three or more unique molecules . SequencFormalin-fixed paraffin-embedded (FFPE) tissue, obtained as the standard of care, was retrieved for patients included in this study. The tissue was analyzed by means of NGS analysis to assess the presence of tumor-specific SNVs using the Ion Torrent S5 sequencing system . For this, the Ion Ampliseq Library Plus Kit 2.0 was used, with a custom-made primer panel including the same 14 genes as the cfDNA Assay panel . TemplatMethylation patterns in cfDNA were analyzed by MeD-seq, as described previously . In shorSubsequent data processing was carried out as previously described and included a filtering step based on LpnPI restriction site occurrence between 13 and 17 bp from the 5’ or 3’ end of the read, after which only reads originating from methylated DNA fragments remained ,29. UsinDifferentially methylated regions (DMRs) between patients and HBDs were identified using LIMMA, resulting in 2798 regions .Principal component analysis (PCA) was performed using the singular value decomposition function (svd) in the R base package  on mean-centered MeD-seq values from either the most variable regions  or the DMRs. The resulting principal components were uncorrelated summary statistics, each reflecting the methylation data for a mixture of correlated genomic regions. To generate an overall score for aberrant cfDNA methylation per patient, Z-scores were calculated per region for every patient relative to our normal control panel of 12 HBDs. These Z-scores per region were squared and summed into a methylation score, as described before ,29.MLH1 promoter hypermethylation; the other four CRCs were found to be microsatellite-stable. The remaining five patients presented with AA; one of them turned out to be MSI-H (LS carrier).Of the 211 patients willing to participate, ten patients were diagnosed with AA or CRC and were thus included in this study . Of thesTumor tissue from all included patients harbored at least one mutation covered by the cfDNA Oncomine panel . Two patUnbalanced chromosome aberrations were convincingly detected in CATCA016 but not in any of the other patients . For an For eight patients, plasma was subjected to MeD-seq; for the remaining two patients, insufficient cfDNA was left to perform this analysis. Genome-wide cfDNA methylation profiles were successfully generated for these patients as well as for 12 healthy blood donors (HBDs). Exploratory unsupervised analyses showed a clear distinction between the two LS-associated AA and the sporadic MSI-H CRC on the one hand and the MSS lesions and HBDs on the other hand .p-value of <0.05 in LIMMA analysis, Using a supervised approach in which we only included differentially methylated regions between the eight patients included in our study on the one hand and HBDs on the other hand  or the 2798 DMRs between patients and HBDs . The resulting scores are shown in In this proof-of-principle study, we assessed to what extent different cfDNA analysis methods could be used in screening and surveillance for AA and CRC. SNVs and CNVs were only detected in the plasma of one patient presenting with liver metastases at diagnosis. By contrast, genome-wide cfDNA methylation profiling (MeD-seq) followed by supervised data analysis could distinguish advanced neoplasia samples from HBDs. This suggests that genome-wide cfDNA methylation profiling could sensitively reflect tumor-specific anomalies, and as such, it may represent a promising and minimally invasive tool for CRC screening and surveillance. Interestingly, both LS-associated AA and the sporadic MSI-H CRC showed very distinct cfDNA methylation profiles, particularly highlighting the potential of cfDNA methylation profiling for LS surveillance.We did not find tumor-specific SNVs in cfDNA that were present in matched tumor tissue, except for one patient with metastasized CRC. This is in line with previous studies, which have also found that ctDNA levels increase with the CRC stage ,40. EvenResults similar to SNVs were observed for CNVs: in only one patient, an abnormal CNV pattern was identified. The literature indicates reliable CNV analysis in plasma requires at least 5% ctDNA within the total pool of cfDNA, which may explain our results . AccordiIn contrast to the other assays performed, genome-wide methylation profiling by MeD-seq on blood plasma-derived cfDNA discriminated MSS advanced neoplasia samples from both MSI-H and LS-driven neoplasia and HBDs. This is in line with the known differences in phenotype between MSS and MSI-driven CRC lesions  ,45,46. LInterestingly, we found that MSI-H - and LS-associated advanced neoplasia clustered together based on their methylation profiles. Of note, even the advanced adenoma in the LS carrier clustered more toward the sporadic and LS-associated MSI-H lesions, despite still being microsatellite-stable. Therefore, this tool might be particularly promising in LS CRC surveillance, although larger studies are needed to validate the performance of this assay in larger groups of LS patients, and sporadic microsatellite stable and MSI-H lesions. MeD-seq cfDNA profiling might contribute to the decision of who needs further colonoscopic assessment in the context of LS colorectal cancer surveillance. Additionally, we need to explore whether extra-colonic LS-associated tumors can also be detected by MeD-seq to establish whether cfDNA methylation profiling represents a promising tool to use in surveillance for all LS-associated tumors. Previous research pointed out that methylation profiles contain information about the tissue of origin, enabling further work-up and treatment . In thisOur study has several limitations. First, due to the proof-of-principle nature of this study, we had a small number of patients with CRC or AA with a mixed etiology (LS versus sporadic). Due to the small and heterogeneous patient group, we do not know to what extent the DMRs identified here are representative of the different populations of interest.Future studies focusing on the added value of cfDNA surveillance in LS patients should consist of a multicenter approach to increase the number of patients. Second, because our study explored multiple techniques, for two patients, we unfortunately had too little material to perform all envisioned assays. On the other hand, this underlines the need for assays requiring a limited amount of input while generating a rich data output (for example combining methylation with SNVs and/or CNVs) ,49. ThirIn conclusion, this proof-of-principle study highlights the promise of cfDNA methylation profiling, specifically in the context of LS surveillance, and provides a starting point for future research in this area.


The objective of the present systematic review it to summarize the evidence supporting the use of 7-keto-DHEA in overweight and obese population. The systematic search was conducted in Medline, Embase, Cochrane Library, CINAHL, Web of Science, Scopus, ICTRP, and ClinicalTrials.gov. Additionally, reference lists of eligible studies were considered, and authors of relevant studies were contacted. Two authors independently screened the studies against the inclusion criteria and assessed their risk of bias. In total, 4 out of 686 studies were included in the review. They all held a low risk of bias. Half of them showed a significant reduction in body weight. One study found a decrease in body fat percentage and another one reported a decrease in BMI. Two studies stated an increase in resting metabolic rate. No serious adverse effects were reported. Various possible mechanisms in favor of weight loss are discussed; however, with the evidence currently available, no clear answer can be given regarding 7-keto-DHEA and weight loss. Further studies need to be conducted to clarify the efficacy and safety of this drug before it can be recommended for therapeutic use.The online version contains supplementary material available at 10.1007/s00404-022-06884-8. In the last past decades, obesity has turned into a major global epidemic that continues to worsen and challenge the health-care system throughout the world. Economically, the cost of treating obesity and its complications account for a big proportion of health-care spending . Overwei7-Ketodehydroepiandrosterone (7-keto-DHEA), chemically known under 3-acetyl-7-oxo-dehydroepiandrosterone and shown in Fig. rmediary .Fig. 1BiAn increase in thermogenesis was proposed as a possible mechanism to explain the weight loss effect 7-keto-DHEA is associated with. The elevation in energy expenditure manifests itself through an increase in metabolic rate, consequently leading to fat and weight loss . Many st-OH-DHEA . Their t-OH-DHEA . They usmentclasspt{minima-OH-DHEA . The ene-OH-DHEA . This su-OH-DHEA .Another mechanism could be mediated through 11 cortisol . Elevatecortisol . 7-Keto-rticoids . In addiFor all these reasons, 7-keto-DHEA has been described as a thermogenic agent with weight loss potential. Bobyleva et al.  had repoThis systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist  for the Medline (Ovid)  Embase (Ovid) Cochrane Library (Wiley) CINAHL (Ebsco) Web of Science (Clarivate) Scopus (Elsevier) ICTRP Trials Register (WHO)ClinicalTrials.gov (NLM)To identify all potentially relevant documents on the topic, literature searches were designed and executed for the following information sources:An initial search strategy in Medline was drafted by a medical information specialist and tested against a list of core references to see if they were all included in the search results. After refinement and consultation, complex search strategies were set up for each information source based on database-specific controlled vocabulary (thesaurus terms/subject headings) and text words. No limits have been applied in any database considering study types, languages, publication years or any other formal criteria. Besides the standard medical bibliographic databases Medline, Embase, Cochrane Library and CINAHL, two international trials registers  and interdisciplinary search portals (Web of Science and Scopus) were searched. All searches were run on October 28th, 2020. In addition to electronic database searching, reference lists and bibliographies from relevant publications were checked to identify any further reports missed in the search strategy. Authors of eligible studies were contacted to identify additional studies.The search strategy followed the PICO  concept and focused on the two concepts “7-keto-DHEA” and “weight loss”. The search terms included abbreviations, synonyms, derivatives, and product names of 7-keto-DHEA. There exists no thesaurus term for 7-keto-DHEA yet. “DHEA” was added to the search concept to extend the sensitivity of the search results. The final detailed search strategy for the Medline Ovid database is shown in Table All identified citations were imported into EndNote and duplicates were removed. Two authors  independently screened the title, abstract and key words of the search results based on the inclusion criteria outlined in Table The methodological quality of the selected studies was independently evaluated by two reviewers  using the Jadad scale . It is cFrom each eligible study, one author (N.J.) extracted information and the relevant data were entered into Table 1250 records were retrieved from the various information sources. After removal of duplicates, 686 items were left to be screened from which ten papers were chosen for full-text examination. The full text of one paper could not be found. Another five papers were excluded because they were not clinical trials, leaving a final total of four studies , 21. No The results of the quality assessment are shown in Table 2  (PDF 72 KB)Below is the link to the electronic supplementary material.


Brain-machine interfaces (BMIs), also known as brain-computer interfaces (BCIs), allow their users to control external devices directly using brain signals without relying on the peripheral nervous system and muscles  when the number of recorded EEG signals is limited. To deal-with the over-complete issue while applying independent component analysis (ICA) to remove EOG artifacts from single-channel EEG recordings, s et al.  that canWang et al. proposed an EEG joint feature classification algorithm based on instance transfer and ensemble learning. In their method, spatial and frequency domain features are extracted through common spatial pattern -based BCIs, Shibu et al. proposed an explainable artificial intelligence (xAI) system that attempts to decompose the CNN model's output onto the input variables  of fNIRS signals recorded during motor execution or motor imagery. Specially, a method called DeepShap is applied to compute the shapley values, which are further used to explain the model's output , using both simulated and real LFP data recorded from ET patients. The synthetic data consisted of 7 different oscillatory patterns including power changes in the beta and gamma bands, beta waveform sharpness, non-linear phase, theta-gamma phase amplitude coupling (PAC), cross-channel phase shifts, and beta burst length. The LFP data were recorded from the bilateral ventra intermediate (VIM) nucleus of the thalamus of ET patients while they perform self-paced upper limb movement tasks -based methods. However, there tends to be a trade-off between the accuracy and the interpretability of the trained models, and in many BCI applications, the former is more important. Focusing on this trade-off, Martineau et al. introduced a Bayesian optimization (BO)-based pipeline for automatic hyper-parameter tuning that is applicable through the entire decoding process, including feature extraction, channel selection, classification, and state-transition stages. The authors compared the proposed method with five other real-time feature extraction methods paired with four classifiers to decode voluntary movement asynchronously based on LFPs recorded with DBS electrodes implanted in the sub-thalamic nucleus (STN) of PD patients, and demonstrated the effectiveness of the proposed pipeline. This study provides a promising solution to deal with the challenges surrounding hyper-parameter tuning while developing BCI systems.The performance and interpretability of a BCI system is both somehow determined and represented by a set of hyper-parameters optimized in the trained models. In the last paper collected in this Research Topic, We believe explainable and advanced intelligent processing is an important perspective while developing BCI systems, which can help to improve the performance and interpretability of the developed systems, and will further facilitate the understanding of disease mechanisms . In collXX: Conceptualization, Project administration, Writing—review and editing. DZ: Writing—review and editing. TY: Writing—review and editing. YD: Writing—review and editing. ID: Writing—review and editing. SH: Conceptualization, Visualization, Writing—original draft, Writing—review and editing.