BACKGROUND Early diagnosis and therapeutic interventions can greatly enhance the developmental trajectory of children with autism spectrum disorder(ASD).However,the etiology of ASD is not completely understood.The pre...BACKGROUND Early diagnosis and therapeutic interventions can greatly enhance the developmental trajectory of children with autism spectrum disorder(ASD).However,the etiology of ASD is not completely understood.The presence of confounding factors from environment and genetics has increased the difficulty of the identification of diagnostic biomarkers for ASD.AIM To estimate and interpret the causal relationship between ASD and metabolite profile,taking into consideration both genetic and environmental influences.METHODS A two-sample Mendelian randomization(MR)analysis was conducted using summarized data from large-scale genome-wide association studies(GWAS)including a metabolite GWAS dataset covering 453 metabolites from 7824 European and an ASD GWAS dataset comprising 18381 ASD cases and 27969 healthy controls.Metabolites in plasma were set as exposures with ASD as the main outcome.The causal relationships were estimated using the inverse variant weight(IVW)algorithm.We also performed leave-one-out sensitivity tests to validate the robustness of the results.Based on the drafted metabolites,enrichment analysis was conducted to interpret the association via constructing a protein-protein interaction network with multi-scale evidence from databases including Infinome,SwissTargetPrediction,STRING,and Metascape.RESULTS Des-Arg(9)-bradykinin was identified as a causal metabolite that increases the risk of ASD(β=0.262,SE=0.064,P_(IVW)=4.64×10^(-5)).The association was robust,with no significant heterogeneity among instrument variables(P_(MR Egger)=0.663,P_(IVW)=0.906)and no evidence of pleiotropy(P=0.949).Neuroinflammation and the response to stimulus were suggested as potential biological processes mediating the association between Des-Arg(9)bradykinin and ASD.CONCLUSION Through the application of MR,this study provides practical insights into the potential causal association between plasma metabolites and ASD.These findings offer perspectives for the discovery of diagnostic or predictive biomarkers to support clinical practice in treating ASD.展开更多
Children with autism spectrum disorders(ASD)or autism are more prone to gastrointestinal(GI)disorders than the general population.These disorders can significantly affect their health,learning,and development due to v...Children with autism spectrum disorders(ASD)or autism are more prone to gastrointestinal(GI)disorders than the general population.These disorders can significantly affect their health,learning,and development due to various factors such as genetics,environment,and behavior.The causes of GI disorders in children with ASD can include gut dysbiosis,immune dysfunction,food sensitivities,digestive enzyme deficiencies,and sensory processing differences.Many studies suggest that numerous children with ASD experience GI problems,and effective management is crucial.Diagnosing autism is typically done through genetic,neurological,functional,and behavioral assessments and observations,while GI tests are not consistently reliable.Some GI tests may increase the risk of developing ASD or exacerbating symptoms.Addressing GI issues in individuals with ASD can improve their overall well-being,leading to better behavior,cognitive function,and educational abilities.Proper management can improve digestion,nutrient absorption,and appetite by relieving physical discomfort and pain.Alleviating GI symptoms can improve sleep patterns,increase energy levels,and contribute to a general sense of well-being,ultimately leading to a better quality of life for the individual and improved family dynamics.The primary goal of GI interventions is to improve nutritional status,reduce symptom severity,promote a balanced mood,and increase patient independence.展开更多
Autism spectrum disorder is classified as a spectrum of neurodevelopmental disorders with an unknown definitive etiology.Individuals with autism spectrum disorder show deficits in a variety of areas including cognitio...Autism spectrum disorder is classified as a spectrum of neurodevelopmental disorders with an unknown definitive etiology.Individuals with autism spectrum disorder show deficits in a variety of areas including cognition,memory,attention,emotion recognition,and social skills.With no definitive treatment or cure,the main interventions for individuals with autism spectrum disorder are based on behavioral modulations.Recently,noninvasive brain modulation techniques including repetitive transcranial magnetic stimulation,intermittent theta burst stimulation,continuous theta burst stimulation,and transcranial direct current stimulation have been studied for their therapeutic properties of modifying neuroplasticity,particularly in individuals with autism spectrum disorder.Preliminary evidence from small cohort studies,pilot studies,and clinical trials suggests that the various noninvasive brain stimulation techniques have therapeutic benefits for treating both behavioral and cognitive manifestations of autism spectrum disorder.However,little data is available for quantifying the clinical significance of these findings as well as the long-term outcomes of individuals with autism spectrum disorder who underwent transcranial stimulation.The objective of this review is to highlight the most recent advancements in the application of noninvasive brain modulation technology in individuals with autism spectrum disorder.展开更多
Background:The Canadian 24-hour movement behavior(24-HMB)guidelines suggest that a limited amount of screen time use,an adequate level of physical activity(PA),and sufficient sleep duration are beneficial for ensuring...Background:The Canadian 24-hour movement behavior(24-HMB)guidelines suggest that a limited amount of screen time use,an adequate level of physical activity(PA),and sufficient sleep duration are beneficial for ensuring and optimizing the health and quality of life(QoL)of children and adolescents.However,this topic has yet to be examined for children and adolescents with autism spectrum disorder(ASD)specifically.The aim of this cross-sectional observational study was to examine the associations between meeting 24-HMB guidelines and several QoLrelated indicators among a national sample of American children and adolescents with ASD.Methods:Data were taken from the 2020 U.S.National Survey of Children’s Health dataset.Participants(n=956)aged 617 years and currently diagnosed with ASD were included.The exposure of interest was adherence to the 24-HMB guidelines.Outcomes were QoL indicators,including learning interest/curiosity,repeating grades,adaptive ability,victimization by bullying,and behavioral problems.Categorical variables were described with unweighted sample counts and weighted percentages.Age,sex,race,preterm birth status,medication,behavioral treatment,household poverty level,and the educational level of the primary caregivers were included as covariates.Odds ratio(OR)and 95%confidence interval(95%CI)were used to present the strength of association between adherence to 24-HMB guidelines and QoL-related indicators.Results:Overall,452 participants(45.34%)met 1 of the 3 recommendations,216(22.65%)met 2 recommendations,whereas only 39 participants(5.04%)met all 3 recommendations.Compared with meeting none of the recommendations,meeting both sleep duration and PA recommendations(OR=3.92,95%CI:1.639.48,p<0.001)or all 3 recommendations(OR=2.11,95%CI:1.034.35,p=0.04)was associated with higher odds of showing learning interest/curiosity.Meeting both screen time and PA recommendations(OR=0.15,95%CI:0.040.61,p<0.05)or both sleep duration and PA recommendations(OR=0.24,95%CI:0.070.87,p<0.05)was associated with lower odds of repeating any grades.With respect to adaptive ability,participants who met only the PA recommendation of the 24-HMB were less likely to have difficulties dressing or bathing(OR=0.11,95%CI:0.020.66,p<0.05)than those who did not.For participants who met all 3 recommendations(OR=0.38,95%CI:0.150.99,p=0.05),the odds of being victimized by bullying was lower.Participants who adhered to both sleep duration and PA recommendations were less likely to present with severe behavioral problems(OR=0.17,95%CI:0.040.71,p<0.05)than those who did not meet those guidelines.Conclusion:Significant associations were found between adhering to 24-HMB guidelines and selected QoL indicators.These findings highlight the importance of maintaining a healthy lifestyle as a key factor in promoting and preserving the QoL of children with ASD.展开更多
Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and communication.Identifying ASD as soon as possible is f...Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and communication.Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD.Recognition of ASD related to objective pathogenicmutation screening is the initial step against prior intervention and efficient treatment of children who were affected.Nowadays,healthcare and machine learning(ML)industries are combined for determining the existence of various diseases.This article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification(JSODL-ASDDC)model.The goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical data.The proposed JSODLASDDC model initially performs min-max data normalization approach to scale the data into uniform range.In addition,the JSODL-ASDDC model involves JSO based feature selection(JFSO-FS)process to choose optimal feature subsets.Moreover,Gated Recurrent Unit(GRU)based classification model is utilized for the recognition and classification of ASD.Furthermore,the Bacterial Foraging Optimization(BFO)assisted parameter tuning process gets executed to enhance the efficacy of the GRU system.The experimental assessment of the JSODL-ASDDC model is investigated against distinct datasets.The experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches.展开更多
Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection ...Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in children.Parents can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five years.This research study aims to develop an automated tool for diagnosing autism in children.The computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition,feature selection,and classification phases.The most deterministic features are selected from the self-acquired dataset by novel feature selection methods before classification.The Imperialistic competitive algorithm(ICA)based on empires conquering colonies performs feature selection in this study.The performance of Logistic Regression(LR),Decision tree,K-Nearest Neighbor(KNN),and Random Forest(RF)classifiers are experimentally studied in this research work.The experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired dataset.The ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator(LASSO)feature selection method and different classifiers.The Exploratory Data Analysis(EDA)phase has uncovered crucial facts about the data,like the correlation of the features in the dataset with the class variable.展开更多
Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction withothers.Advanced information technologywhich employs artificial intelligence(AI...Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction withothers.Advanced information technologywhich employs artificial intelligence(AI) model has assisted in early identify ASD by using pattern detection.Recent advances of AI models assist in the automated identification andclassification of ASD, which helps to reduce the severity of the disease.This study introduces an automated ASD classification using owl searchalgorithm with machine learning (ASDC-OSAML) model. The proposedASDC-OSAML model majorly focuses on the identification and classificationof ASD. To attain this, the presentedASDC-OSAML model follows minmaxnormalization approach as a pre-processing stage. Next, the owl searchalgorithm (OSA)-based feature selection (OSA-FS) model is used to derivefeature subsets. Then, beetle swarm antenna search (BSAS) algorithm withIterative Dichotomiser 3 (ID3) classification method was implied for ASDdetection and classification. The design of BSAS algorithm helps to determinethe parameter values of the ID3 classifier. The performance analysis of theASDC-OSAML model is performed using benchmark dataset. An extensivecomparison study highlighted the supremacy of the ASDC-OSAML modelover recent state of art approaches.展开更多
A neurological abnormality called autism spectrum disorder(ASD)affects how a person perceives and interacts with others,leading to social interaction and communication issues.Limited and recurring behavioural patterns...A neurological abnormality called autism spectrum disorder(ASD)affects how a person perceives and interacts with others,leading to social interaction and communication issues.Limited and recurring behavioural patterns are another feature of the illness.Multiple mutations throughout development are the source of the neurodevelopmental disorder autism.However,a well-established model and perfect treatment for this spectrum disease has not been discovered.The rising era of the clustered regularly interspaced palindromic repeats(CRISPR)-associated protein 9(Cas9)system can streamline the complexity underlying the pathogenesis of ASD.The CRISPR-Cas9 system is a powerful genetic engineering tool used to edit the genome at the targeted site in a precise manner.The major hurdle in studying ASD is the lack of appropriate animal models presenting the complex symptoms of ASD.Therefore,CRISPR-Cas9 is being used worldwide to mimic the ASD-like pathology in various systems like in vitro cell lines,in vitro 3D organoid models and in vivo animal models.Apart from being used in establishing ASD models,CRISPR-Cas9 can also be used to treat the complexities of ASD.The aim of this review was to summarize and critically analyse the CRISPRCas9-mediated discoveries in the field of ASD.展开更多
Language difficulties vary widely among people with autism spectrum disorder(ASD).However,the semantic processing of autistic person and its underlying electrophysiological mechanism are still unclear.This meta-analys...Language difficulties vary widely among people with autism spectrum disorder(ASD).However,the semantic processing of autistic person and its underlying electrophysiological mechanism are still unclear.This meta-analysis aimed to explore the disturbance of semantic processing in patients with ASD.PubMed,Web of Science,and Embase were searched for eventrelated potential(ERP)studies on semantic processing in autistic people published in English before September 01,2022.Pooled estimates were calculated by fixed-effects or random-effects models according to the heterogeneity using Comprehensive Meta-Analysis 2.0.The potential moderators were explored by meta-regression and subgroup analysis.This meta-analysis has been registered at the Prospero International Prospective Register of Systematic Reviews(no.CRD 42021265852).A total of 14 articles and 18 studies,including 254 autistic people and 262 neurodevelopmental people were included in this meta-analysis.Compared to the comparison group,autistic people showed an overall reduced N400 amplitude(Hedges’g=0.350,p<0.001)in response to linguistic stimuli instead of non-linguistic stimuli.The N400 amplitude was affected by verbal intelligence and gender.The reduced overall N400 amplitude in autistic people under linguistic stimuli suggests a linguistic-specific deficit in semantic processing in individuals of autism.The decrease of N400 amplitude might be a promising indication of the pool language capacity of autism.展开更多
Many individuals with autism spectrum disorder(ASD)experience delays in the development of social and communications skills,which can limit their opportunities in higher education and employment resulting in an overal...Many individuals with autism spectrum disorder(ASD)experience delays in the development of social and communications skills,which can limit their opportunities in higher education and employment resulting in an overall negative impact to their quality of life.This systematic review identifies 15 studies that explored the effectiveness of Video-Based Interventions(VBIs)for those with ASD during the critical years of adolescence and young adulthood.The 15 studies described herein found this to be an effective intervention for this population for the improvement of their vocational,daily living,and academic skills.In addition,VBIs allow for the maintenance and generalization of the different target behaviors that were examined.The majority of the studies located by this review also investigated the social validity of the intervention method with participants and caregivers and found these VBIs to have high social validity.Although a few studies that implemented VBIs to improve academic skills were located,the research on their use in this area was found to be lacking,indicating a gap in the research on VBIs.Increased usage of VBIs—including video modeling and video prompting—with the target population of those aged 15–28 with ASD is recommended with specific attention given to the use of VBIs to improve the academic and social skills of adolescents and young adults with ASD.展开更多
Objective This study aimed to explore the clinical value of Children Neuropsychological and Behavioral Scale-Revision 2016(CNBS-R2016)for Autism Spectrum Disorder(ASD)screening in the presence of developmental surveil...Objective This study aimed to explore the clinical value of Children Neuropsychological and Behavioral Scale-Revision 2016(CNBS-R2016)for Autism Spectrum Disorder(ASD)screening in the presence of developmental surveillance.Methods All participants were evaluated by the CNBS-R2016 and Gesell Developmental Schedules(GDS).Spearman’s correlation coefficients and Kappa values were obtained.Taking GDS as a reference assessment,the performance of the CNBS-R2016 for detecting the developmental delays of children with ASD was analyzed with receiver operating characteristic(ROC)curves.The efficacy of the CNBS-R2016 to screen for ASD was explored by comparing Communication Warning Behavior with Autism Diagnostic Observation Schedule,Second Edition(ADOS-2).Results In total,150 children aged 12–42 months with ASD were enrolled.The developmental quotients of the CNBS-R2016 were correlated with those of the GDS(r=0.62–0.94).The CNBS-R2016 and GDS had good diagnostic agreement for developmental delays(Kappa=0.73–0.89),except for Fine Motor.There was a significant difference between the proportions of Fine Motor,delays detected by the CNBS-R2016 and GDS(86.0%vs.77.3%).With GDS as a standard,the areas under the ROC curves of the CNBS-R2016 were above 0.95 for all the domains except Fine Motor,which was 0.70.In addition,the positive rate of ASD was 100.0%and 93.5%when the cut-off points of 7 and 12 in the Communication Warning Behavior subscale were used,respectively.Conclusion The CNBS-R2016 performed well in developmental assessment and screening for children with ASD,especially by Communication Warning Behaviors subscale.Therefore,the CNBS-R2016 is worthy of clinical application in children with ASD in China.展开更多
Autism Spectrum Disorder(ASD)is a multifaceted neurodevelopmental condition characterized by a spectrum of symptoms and behaviors,challenging to fully comprehend due to its variability.This article provides an overvie...Autism Spectrum Disorder(ASD)is a multifaceted neurodevelopmental condition characterized by a spectrum of symptoms and behaviors,challenging to fully comprehend due to its variability.This article provides an overview of ASD,including its characteristics,prevalence,diagnosis,and causes.The prevalence of ASD has been on the rise,with improved awareness and diagnostic tools.While genetics and environmental factors play a role,the exact causes remain elusive.Early intervention and various therapies are crucial for improving outcomes,although there is no cure.Ongoing research aims to uncover the complexities of ASD and develop effective treatments.Embracing diversity and fostering inclusion is essential for supporting individuals with ASD.As we continue to unravel the mysteries of ASD,we move closer to a more understanding and inclusive society.This article explores the role of Transcranial Magnetic Stimulation(TMS)in the treatment of Autism Spectrum Disorder(ASD).TMS,a non-invasive neurostimulation technique,is gaining attention as a potential therapy to address specific aspects of ASD.展开更多
Functional brain networks (FBN) based on resting-state functional magnetic resonance imaging (rs-fMRI) have become an important tool for exploring underlying organization patterns in the brain, which can provide an ob...Functional brain networks (FBN) based on resting-state functional magnetic resonance imaging (rs-fMRI) have become an important tool for exploring underlying organization patterns in the brain, which can provide an objective basis for brain disorders such as autistic spectrum disorder (ASD). Due to its importance, researchers have proposed a number of FBN estimation methods. However, most existing methods only model a type of functional connection relationship between brain regions-of-interest (ROIs), such as partial correlation or full correlation, which is difficult to fully capture the subtle connections among ROIs since these connections are extremely complex. Motivated by the multi-view learning, in this study we propose a novel Consistent and Specific Multi-view FBNs Fusion (CSMF) approach. Concretely, we first construct multi-view FBNs (i.e., multiple types of FBNs modelling various relationships among ROIs), and then these FBNs are decomposed into a consistent representation matrix and their own specific matrices which capture their common and unique information, respectively. Lastly, to obtain a better brain representation, it is fusing the consistent and specific representation matrices in the latent representation spaces of FBNs, but not directly fusing the original FBNs. This potentially makes it more easily to find the comprehensively brain connections. The experimental results of ASD identification on the ABIDE datasets validate the effectiveness of our proposed method compared to several state-of-the-art methods. Our proposed CSMF method achieved 72.8% and 76.67% classification performance on the ABIDE dataset.展开更多
Background: Examining the lives that mothers experience and build will allow us to deepen our understanding of children with ASD and their mothers and facilitate developing support methods. The study aimed to examine ...Background: Examining the lives that mothers experience and build will allow us to deepen our understanding of children with ASD and their mothers and facilitate developing support methods. The study aimed to examine the lives of mothers raising children with autism spectrum disorder (ASD) and investigate their sources of support. Method: We conducted a qualitative inductive study using semi-structured interviews to identify characteristics of the lives that mothers have created. Results: Semi-structured interviews were conducted with 11 mothers having children with ASD. The analysis comprised three stages of coding and yielded eight categories. The lives of these mothers contained three themes: preoccupation with parenting children with ASD and their siblings;evolving mother;and using social resources. Mothers engaged in “assessing the characteristics, growth, and changes in the child with ASD”, had a “preoccupation with parenting children with ASD”, and were “thinking about the future of the child with ASD”, and “having goals and plans for parenting” while having “consideration toward the child’s siblings”. During this process, mothers experienced “changes in perspective or approach” and created lifestyles while “receiving help from people around them” and engaged in the “use of social resources”. Conclusions: To avoid becoming preoccupied with parenting and being burdened by their lifestyle, mothers require social support to monitor their perceptions. Furthermore, the utilization of social resources requires the supporting individuals to understand the characteristics of children with ASD, provide appropriate information, and assist in decision-making.展开更多
Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particula...Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.展开更多
BACKGROUND Autism spectrum disorder(ASD)is a neurodevelopmental condition characterized by deficits in social communication and repetitive behaviors.Metabolomic profiling has emerged as a valuable tool for understandi...BACKGROUND Autism spectrum disorder(ASD)is a neurodevelopmental condition characterized by deficits in social communication and repetitive behaviors.Metabolomic profiling has emerged as a valuable tool for understanding the underlying metabolic dysregulations associated with ASD.AIM To comprehensively explore metabolomic changes in children with ASD,integrating findings from various research articles,reviews,systematic reviews,meta-analyses,case reports,editorials,and a book chapter.METHODS A systematic search was conducted in electronic databases,including PubMed,PubMed Central,Cochrane Library,Embase,Web of Science,CINAHL,Scopus,LISA,and NLM catalog up until January 2024.Inclusion criteria encompassed research articles(83),review articles(145),meta-analyses(6),systematic reviews(6),case reports(2),editorials(2),and a book chapter(1)related to metabolomic changes in children with ASD.Exclusion criteria were applied to ensure the relevance and quality of included studies.RESULTS The systematic review identified specific metabolites and metabolic pathways showing consistent differences in children with ASD compared to typically developing individuals.These metabolic biomarkers may serve as objective measures to support clinical assessments,improve diagnostic accuracy,and inform personalized treatment approaches.Metabolomic profiling also offers insights into the metabolic alterations associated with comorbid conditions commonly observed in individuals with ASD.CONCLUSION Integration of metabolomic changes in children with ASD holds promise for enhancing diagnostic accuracy,guiding personalized treatment approaches,monitoring treatment response,and improving outcomes.Further research is needed to validate findings,establish standardized protocols,and overcome technical challenges in metabolomic analysis.By advancing our understanding of metabolic dysregulations in ASD,clinicians can improve the lives of affected individuals and their families.展开更多
Objective: The demand for pediatric developmental evaluations has far exceeded the workforce available to perform them, which creates long significant wait times for services. A year-long clinician training using the ...Objective: The demand for pediatric developmental evaluations has far exceeded the workforce available to perform them, which creates long significant wait times for services. A year-long clinician training using the Extension for Community Healthcare Outcomes (ECHO<sup>®</sup>) model with monthly meetings was conducted and evaluated for its impact on primary care clinicians’ self-reported self-efficacy, ability to administer autism screening and counsel families, professional fulfillment, and burnout. Methods: Participants represented six community health centers and a hospital-based practice. Data collection was informed by participant feedback and the Normalization Process Theory via online surveys and focus groups/interviews. Twelve virtual monthly trainings were delivered between November 2020 and October 2021. Results: 30 clinicians participated in data collection. Matched analyses (n = 9) indicated statistically significant increase in self-rated ability to counsel families about autism (Pre-test Mean = 3.00, Post-test Mean = 3.89, p = 0.0313), manage autistic patients’ care (Pre-test Mean = 2.56, Post-test Mean = 4.11, p = 0.0078), empathy toward patients (Pre-test Mean = 2.11, Post-test Mean = 1.22, p = 0.0156) and colleagues (Pre-test Mean = 2.33, Post-test Mean = 1.22, respectively, p = 0.0391). Unmatched analysis revealed increases in participants confident about educating patients about autism (70.59%, post-test n = 12 vs. 3.33%, pre-test n = 1, p = 0.0019). Focus groups found increased confidence in using the term “autism”. Conclusion: Participants reported increases in ability and confidence to care for autistic patients, as well as empathy toward patients and colleagues. Future research should explore long-term outcomes in participants’ knowledge retention, confidence in practice, and improvements to autism evaluations and care.展开更多
Chemically engineered agricultural products such as pesticides, insecticides, and herbicides, although used considerably for both industrialized and personal agricultural use, have recently been associated with a numb...Chemically engineered agricultural products such as pesticides, insecticides, and herbicides, although used considerably for both industrialized and personal agricultural use, have recently been associated with a number of serious human health disorders. This rapid literature review aims to accumulate and analyze research from the last ten years, focusing specifically on the effects of exposure to glyphosate-based herbicide products such as Roundup as associated with the formation of various neurological disorders. Specifically, this review focuses on laboratory research using animal models or human cell cultures as well as human population-based epidemiological studies. It associates exposure to glyphosate or glyphosate-based products with the formation or exacerbation of neurological disorders such as Parkinson’s disease, Alzheimer’s disease, seizures, and autism spectrum disorder. In addition, it examines the correlation between the gut-brain axis, exposure to glyphosate, and neurodegeneration.展开更多
Difficulty in communicating and interacting with other people are mainly due to the neurological disorder called autism spectrum disorder(ASD)diseases.These diseases can affect the nerves at any stage of the human bein...Difficulty in communicating and interacting with other people are mainly due to the neurological disorder called autism spectrum disorder(ASD)diseases.These diseases can affect the nerves at any stage of the human being in childhood,adolescence,and adulthood.ASD is known as a behavioral disease due to the appearances of symptoms over thefirst two years that continue until adulthood.Most of the studies prove that the early detection of ASD helps improve the behavioral characteristics of patients with ASD.The detection of ASD is a very challenging task among various researchers.Machine learning(ML)algorithms still act very intelligent by learning the complex data and pre-dicting quality results.In this paper,ensemble ML techniques for the early detec-tion of ASD are proposed.In this detection,the dataset isfirst processed using three ML algorithms such as sequential minimal optimization with support vector machine,Kohonen self-organizing neural network,and random forest algorithm.The prediction results of these ML algorithms(ensemble)further use the bagging concept called max voting to predict thefinal result.The accuracy,sensitivity,and specificity of the proposed system are calculated using confusion matrix.The pro-posed ensemble technique performs better than state-of-the art ML algorithms.展开更多
Background: Active video games(AVGs) encourage whole body movements to interact or control the gaming system, allowing the opportunity for skill development. Children with autism spectrum disorder(ASD) show decreased ...Background: Active video games(AVGs) encourage whole body movements to interact or control the gaming system, allowing the opportunity for skill development. Children with autism spectrum disorder(ASD) show decreased fundamental movement skills in comparison with their typically developing(TD) peers and might benefit from this approach. This pilot study investigates whether playing sports AVGs can increase the actual and perceived object control(OC) skills of 11 children with ASD aged 6–10 years in comparison to 19 TD children of a similar age.Feasibility was a secondary aim.Methods: Actual(Test of Gross Motor Development) and perceived OC skills(Pictorial Scale of Perceived Movement Skill Competence for Young Children) were assessed before and after the intervention(6 × 45 min).Results: Actual skill scores were not improved in either group. The ASD group improved in perceived skill. All children completed the required dose and parents reported the intervention was feasible.Conclusion: The use of AVGs as a play-based intervention may not provide enough opportunity for children to perform the correct movement patterns to influence skill. However, play of such games may influence perceptions of skill ability in children with ASD, which could improve motivation to participate in physical activities.展开更多
基金Supported by The Guangdong Basic and Applied Basic Research Foundation,No.2023A1515011432The Guangzhou Science and Technology Planning Project,No.2023A04J0627and National Natural Science Foundation of China,No.82004256.
文摘BACKGROUND Early diagnosis and therapeutic interventions can greatly enhance the developmental trajectory of children with autism spectrum disorder(ASD).However,the etiology of ASD is not completely understood.The presence of confounding factors from environment and genetics has increased the difficulty of the identification of diagnostic biomarkers for ASD.AIM To estimate and interpret the causal relationship between ASD and metabolite profile,taking into consideration both genetic and environmental influences.METHODS A two-sample Mendelian randomization(MR)analysis was conducted using summarized data from large-scale genome-wide association studies(GWAS)including a metabolite GWAS dataset covering 453 metabolites from 7824 European and an ASD GWAS dataset comprising 18381 ASD cases and 27969 healthy controls.Metabolites in plasma were set as exposures with ASD as the main outcome.The causal relationships were estimated using the inverse variant weight(IVW)algorithm.We also performed leave-one-out sensitivity tests to validate the robustness of the results.Based on the drafted metabolites,enrichment analysis was conducted to interpret the association via constructing a protein-protein interaction network with multi-scale evidence from databases including Infinome,SwissTargetPrediction,STRING,and Metascape.RESULTS Des-Arg(9)-bradykinin was identified as a causal metabolite that increases the risk of ASD(β=0.262,SE=0.064,P_(IVW)=4.64×10^(-5)).The association was robust,with no significant heterogeneity among instrument variables(P_(MR Egger)=0.663,P_(IVW)=0.906)and no evidence of pleiotropy(P=0.949).Neuroinflammation and the response to stimulus were suggested as potential biological processes mediating the association between Des-Arg(9)bradykinin and ASD.CONCLUSION Through the application of MR,this study provides practical insights into the potential causal association between plasma metabolites and ASD.These findings offer perspectives for the discovery of diagnostic or predictive biomarkers to support clinical practice in treating ASD.
文摘Children with autism spectrum disorders(ASD)or autism are more prone to gastrointestinal(GI)disorders than the general population.These disorders can significantly affect their health,learning,and development due to various factors such as genetics,environment,and behavior.The causes of GI disorders in children with ASD can include gut dysbiosis,immune dysfunction,food sensitivities,digestive enzyme deficiencies,and sensory processing differences.Many studies suggest that numerous children with ASD experience GI problems,and effective management is crucial.Diagnosing autism is typically done through genetic,neurological,functional,and behavioral assessments and observations,while GI tests are not consistently reliable.Some GI tests may increase the risk of developing ASD or exacerbating symptoms.Addressing GI issues in individuals with ASD can improve their overall well-being,leading to better behavior,cognitive function,and educational abilities.Proper management can improve digestion,nutrient absorption,and appetite by relieving physical discomfort and pain.Alleviating GI symptoms can improve sleep patterns,increase energy levels,and contribute to a general sense of well-being,ultimately leading to a better quality of life for the individual and improved family dynamics.The primary goal of GI interventions is to improve nutritional status,reduce symptom severity,promote a balanced mood,and increase patient independence.
基金supported by translational grant from the HERA Foundation(to AAE).
文摘Autism spectrum disorder is classified as a spectrum of neurodevelopmental disorders with an unknown definitive etiology.Individuals with autism spectrum disorder show deficits in a variety of areas including cognition,memory,attention,emotion recognition,and social skills.With no definitive treatment or cure,the main interventions for individuals with autism spectrum disorder are based on behavioral modulations.Recently,noninvasive brain modulation techniques including repetitive transcranial magnetic stimulation,intermittent theta burst stimulation,continuous theta burst stimulation,and transcranial direct current stimulation have been studied for their therapeutic properties of modifying neuroplasticity,particularly in individuals with autism spectrum disorder.Preliminary evidence from small cohort studies,pilot studies,and clinical trials suggests that the various noninvasive brain stimulation techniques have therapeutic benefits for treating both behavioral and cognitive manifestations of autism spectrum disorder.However,little data is available for quantifying the clinical significance of these findings as well as the long-term outcomes of individuals with autism spectrum disorder who underwent transcranial stimulation.The objective of this review is to highlight the most recent advancements in the application of noninvasive brain modulation technology in individuals with autism spectrum disorder.
基金supported by Start-up Research Grant of Shenzhen University(20200807163056003)Start-Up Research Grant(PeacockPlan:20191105534C).
文摘Background:The Canadian 24-hour movement behavior(24-HMB)guidelines suggest that a limited amount of screen time use,an adequate level of physical activity(PA),and sufficient sleep duration are beneficial for ensuring and optimizing the health and quality of life(QoL)of children and adolescents.However,this topic has yet to be examined for children and adolescents with autism spectrum disorder(ASD)specifically.The aim of this cross-sectional observational study was to examine the associations between meeting 24-HMB guidelines and several QoLrelated indicators among a national sample of American children and adolescents with ASD.Methods:Data were taken from the 2020 U.S.National Survey of Children’s Health dataset.Participants(n=956)aged 617 years and currently diagnosed with ASD were included.The exposure of interest was adherence to the 24-HMB guidelines.Outcomes were QoL indicators,including learning interest/curiosity,repeating grades,adaptive ability,victimization by bullying,and behavioral problems.Categorical variables were described with unweighted sample counts and weighted percentages.Age,sex,race,preterm birth status,medication,behavioral treatment,household poverty level,and the educational level of the primary caregivers were included as covariates.Odds ratio(OR)and 95%confidence interval(95%CI)were used to present the strength of association between adherence to 24-HMB guidelines and QoL-related indicators.Results:Overall,452 participants(45.34%)met 1 of the 3 recommendations,216(22.65%)met 2 recommendations,whereas only 39 participants(5.04%)met all 3 recommendations.Compared with meeting none of the recommendations,meeting both sleep duration and PA recommendations(OR=3.92,95%CI:1.639.48,p<0.001)or all 3 recommendations(OR=2.11,95%CI:1.034.35,p=0.04)was associated with higher odds of showing learning interest/curiosity.Meeting both screen time and PA recommendations(OR=0.15,95%CI:0.040.61,p<0.05)or both sleep duration and PA recommendations(OR=0.24,95%CI:0.070.87,p<0.05)was associated with lower odds of repeating any grades.With respect to adaptive ability,participants who met only the PA recommendation of the 24-HMB were less likely to have difficulties dressing or bathing(OR=0.11,95%CI:0.020.66,p<0.05)than those who did not.For participants who met all 3 recommendations(OR=0.38,95%CI:0.150.99,p=0.05),the odds of being victimized by bullying was lower.Participants who adhered to both sleep duration and PA recommendations were less likely to present with severe behavioral problems(OR=0.17,95%CI:0.040.71,p<0.05)than those who did not meet those guidelines.Conclusion:Significant associations were found between adhering to 24-HMB guidelines and selected QoL indicators.These findings highlight the importance of maintaining a healthy lifestyle as a key factor in promoting and preserving the QoL of children with ASD.
文摘Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and communication.Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD.Recognition of ASD related to objective pathogenicmutation screening is the initial step against prior intervention and efficient treatment of children who were affected.Nowadays,healthcare and machine learning(ML)industries are combined for determining the existence of various diseases.This article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification(JSODL-ASDDC)model.The goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical data.The proposed JSODLASDDC model initially performs min-max data normalization approach to scale the data into uniform range.In addition,the JSODL-ASDDC model involves JSO based feature selection(JFSO-FS)process to choose optimal feature subsets.Moreover,Gated Recurrent Unit(GRU)based classification model is utilized for the recognition and classification of ASD.Furthermore,the Bacterial Foraging Optimization(BFO)assisted parameter tuning process gets executed to enhance the efficacy of the GRU system.The experimental assessment of the JSODL-ASDDC model is investigated against distinct datasets.The experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number(IF2-PSAU-2022/01/22043)。
文摘Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in children.Parents can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five years.This research study aims to develop an automated tool for diagnosing autism in children.The computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition,feature selection,and classification phases.The most deterministic features are selected from the self-acquired dataset by novel feature selection methods before classification.The Imperialistic competitive algorithm(ICA)based on empires conquering colonies performs feature selection in this study.The performance of Logistic Regression(LR),Decision tree,K-Nearest Neighbor(KNN),and Random Forest(RF)classifiers are experimentally studied in this research work.The experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired dataset.The ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator(LASSO)feature selection method and different classifiers.The Exploratory Data Analysis(EDA)phase has uncovered crucial facts about the data,like the correlation of the features in the dataset with the class variable.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project Under Grant Number(61/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R114)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR26).
文摘Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction withothers.Advanced information technologywhich employs artificial intelligence(AI) model has assisted in early identify ASD by using pattern detection.Recent advances of AI models assist in the automated identification andclassification of ASD, which helps to reduce the severity of the disease.This study introduces an automated ASD classification using owl searchalgorithm with machine learning (ASDC-OSAML) model. The proposedASDC-OSAML model majorly focuses on the identification and classificationof ASD. To attain this, the presentedASDC-OSAML model follows minmaxnormalization approach as a pre-processing stage. Next, the owl searchalgorithm (OSA)-based feature selection (OSA-FS) model is used to derivefeature subsets. Then, beetle swarm antenna search (BSAS) algorithm withIterative Dichotomiser 3 (ID3) classification method was implied for ASDdetection and classification. The design of BSAS algorithm helps to determinethe parameter values of the ID3 classifier. The performance analysis of theASDC-OSAML model is performed using benchmark dataset. An extensivecomparison study highlighted the supremacy of the ASDC-OSAML modelover recent state of art approaches.
文摘A neurological abnormality called autism spectrum disorder(ASD)affects how a person perceives and interacts with others,leading to social interaction and communication issues.Limited and recurring behavioural patterns are another feature of the illness.Multiple mutations throughout development are the source of the neurodevelopmental disorder autism.However,a well-established model and perfect treatment for this spectrum disease has not been discovered.The rising era of the clustered regularly interspaced palindromic repeats(CRISPR)-associated protein 9(Cas9)system can streamline the complexity underlying the pathogenesis of ASD.The CRISPR-Cas9 system is a powerful genetic engineering tool used to edit the genome at the targeted site in a precise manner.The major hurdle in studying ASD is the lack of appropriate animal models presenting the complex symptoms of ASD.Therefore,CRISPR-Cas9 is being used worldwide to mimic the ASD-like pathology in various systems like in vitro cell lines,in vitro 3D organoid models and in vivo animal models.Apart from being used in establishing ASD models,CRISPR-Cas9 can also be used to treat the complexities of ASD.The aim of this review was to summarize and critically analyse the CRISPRCas9-mediated discoveries in the field of ASD.
基金The National Key Research and Development Program of China(Grant Number 2021ZD0202004).
文摘Language difficulties vary widely among people with autism spectrum disorder(ASD).However,the semantic processing of autistic person and its underlying electrophysiological mechanism are still unclear.This meta-analysis aimed to explore the disturbance of semantic processing in patients with ASD.PubMed,Web of Science,and Embase were searched for eventrelated potential(ERP)studies on semantic processing in autistic people published in English before September 01,2022.Pooled estimates were calculated by fixed-effects or random-effects models according to the heterogeneity using Comprehensive Meta-Analysis 2.0.The potential moderators were explored by meta-regression and subgroup analysis.This meta-analysis has been registered at the Prospero International Prospective Register of Systematic Reviews(no.CRD 42021265852).A total of 14 articles and 18 studies,including 254 autistic people and 262 neurodevelopmental people were included in this meta-analysis.Compared to the comparison group,autistic people showed an overall reduced N400 amplitude(Hedges’g=0.350,p<0.001)in response to linguistic stimuli instead of non-linguistic stimuli.The N400 amplitude was affected by verbal intelligence and gender.The reduced overall N400 amplitude in autistic people under linguistic stimuli suggests a linguistic-specific deficit in semantic processing in individuals of autism.The decrease of N400 amplitude might be a promising indication of the pool language capacity of autism.
文摘Many individuals with autism spectrum disorder(ASD)experience delays in the development of social and communications skills,which can limit their opportunities in higher education and employment resulting in an overall negative impact to their quality of life.This systematic review identifies 15 studies that explored the effectiveness of Video-Based Interventions(VBIs)for those with ASD during the critical years of adolescence and young adulthood.The 15 studies described herein found this to be an effective intervention for this population for the improvement of their vocational,daily living,and academic skills.In addition,VBIs allow for the maintenance and generalization of the different target behaviors that were examined.The majority of the studies located by this review also investigated the social validity of the intervention method with participants and caregivers and found these VBIs to have high social validity.Although a few studies that implemented VBIs to improve academic skills were located,the research on their use in this area was found to be lacking,indicating a gap in the research on VBIs.Increased usage of VBIs—including video modeling and video prompting—with the target population of those aged 15–28 with ASD is recommended with specific attention given to the use of VBIs to improve the academic and social skills of adolescents and young adults with ASD.
基金This study was supported by Emergency Technology Research Project of Huazhong University of Science and Technology(No.2020kfyXGYJ020).
文摘Objective This study aimed to explore the clinical value of Children Neuropsychological and Behavioral Scale-Revision 2016(CNBS-R2016)for Autism Spectrum Disorder(ASD)screening in the presence of developmental surveillance.Methods All participants were evaluated by the CNBS-R2016 and Gesell Developmental Schedules(GDS).Spearman’s correlation coefficients and Kappa values were obtained.Taking GDS as a reference assessment,the performance of the CNBS-R2016 for detecting the developmental delays of children with ASD was analyzed with receiver operating characteristic(ROC)curves.The efficacy of the CNBS-R2016 to screen for ASD was explored by comparing Communication Warning Behavior with Autism Diagnostic Observation Schedule,Second Edition(ADOS-2).Results In total,150 children aged 12–42 months with ASD were enrolled.The developmental quotients of the CNBS-R2016 were correlated with those of the GDS(r=0.62–0.94).The CNBS-R2016 and GDS had good diagnostic agreement for developmental delays(Kappa=0.73–0.89),except for Fine Motor.There was a significant difference between the proportions of Fine Motor,delays detected by the CNBS-R2016 and GDS(86.0%vs.77.3%).With GDS as a standard,the areas under the ROC curves of the CNBS-R2016 were above 0.95 for all the domains except Fine Motor,which was 0.70.In addition,the positive rate of ASD was 100.0%and 93.5%when the cut-off points of 7 and 12 in the Communication Warning Behavior subscale were used,respectively.Conclusion The CNBS-R2016 performed well in developmental assessment and screening for children with ASD,especially by Communication Warning Behaviors subscale.Therefore,the CNBS-R2016 is worthy of clinical application in children with ASD in China.
文摘Autism Spectrum Disorder(ASD)is a multifaceted neurodevelopmental condition characterized by a spectrum of symptoms and behaviors,challenging to fully comprehend due to its variability.This article provides an overview of ASD,including its characteristics,prevalence,diagnosis,and causes.The prevalence of ASD has been on the rise,with improved awareness and diagnostic tools.While genetics and environmental factors play a role,the exact causes remain elusive.Early intervention and various therapies are crucial for improving outcomes,although there is no cure.Ongoing research aims to uncover the complexities of ASD and develop effective treatments.Embracing diversity and fostering inclusion is essential for supporting individuals with ASD.As we continue to unravel the mysteries of ASD,we move closer to a more understanding and inclusive society.This article explores the role of Transcranial Magnetic Stimulation(TMS)in the treatment of Autism Spectrum Disorder(ASD).TMS,a non-invasive neurostimulation technique,is gaining attention as a potential therapy to address specific aspects of ASD.
文摘Functional brain networks (FBN) based on resting-state functional magnetic resonance imaging (rs-fMRI) have become an important tool for exploring underlying organization patterns in the brain, which can provide an objective basis for brain disorders such as autistic spectrum disorder (ASD). Due to its importance, researchers have proposed a number of FBN estimation methods. However, most existing methods only model a type of functional connection relationship between brain regions-of-interest (ROIs), such as partial correlation or full correlation, which is difficult to fully capture the subtle connections among ROIs since these connections are extremely complex. Motivated by the multi-view learning, in this study we propose a novel Consistent and Specific Multi-view FBNs Fusion (CSMF) approach. Concretely, we first construct multi-view FBNs (i.e., multiple types of FBNs modelling various relationships among ROIs), and then these FBNs are decomposed into a consistent representation matrix and their own specific matrices which capture their common and unique information, respectively. Lastly, to obtain a better brain representation, it is fusing the consistent and specific representation matrices in the latent representation spaces of FBNs, but not directly fusing the original FBNs. This potentially makes it more easily to find the comprehensively brain connections. The experimental results of ASD identification on the ABIDE datasets validate the effectiveness of our proposed method compared to several state-of-the-art methods. Our proposed CSMF method achieved 72.8% and 76.67% classification performance on the ABIDE dataset.
文摘Background: Examining the lives that mothers experience and build will allow us to deepen our understanding of children with ASD and their mothers and facilitate developing support methods. The study aimed to examine the lives of mothers raising children with autism spectrum disorder (ASD) and investigate their sources of support. Method: We conducted a qualitative inductive study using semi-structured interviews to identify characteristics of the lives that mothers have created. Results: Semi-structured interviews were conducted with 11 mothers having children with ASD. The analysis comprised three stages of coding and yielded eight categories. The lives of these mothers contained three themes: preoccupation with parenting children with ASD and their siblings;evolving mother;and using social resources. Mothers engaged in “assessing the characteristics, growth, and changes in the child with ASD”, had a “preoccupation with parenting children with ASD”, and were “thinking about the future of the child with ASD”, and “having goals and plans for parenting” while having “consideration toward the child’s siblings”. During this process, mothers experienced “changes in perspective or approach” and created lifestyles while “receiving help from people around them” and engaged in the “use of social resources”. Conclusions: To avoid becoming preoccupied with parenting and being burdened by their lifestyle, mothers require social support to monitor their perceptions. Furthermore, the utilization of social resources requires the supporting individuals to understand the characteristics of children with ASD, provide appropriate information, and assist in decision-making.
文摘Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.
文摘BACKGROUND Autism spectrum disorder(ASD)is a neurodevelopmental condition characterized by deficits in social communication and repetitive behaviors.Metabolomic profiling has emerged as a valuable tool for understanding the underlying metabolic dysregulations associated with ASD.AIM To comprehensively explore metabolomic changes in children with ASD,integrating findings from various research articles,reviews,systematic reviews,meta-analyses,case reports,editorials,and a book chapter.METHODS A systematic search was conducted in electronic databases,including PubMed,PubMed Central,Cochrane Library,Embase,Web of Science,CINAHL,Scopus,LISA,and NLM catalog up until January 2024.Inclusion criteria encompassed research articles(83),review articles(145),meta-analyses(6),systematic reviews(6),case reports(2),editorials(2),and a book chapter(1)related to metabolomic changes in children with ASD.Exclusion criteria were applied to ensure the relevance and quality of included studies.RESULTS The systematic review identified specific metabolites and metabolic pathways showing consistent differences in children with ASD compared to typically developing individuals.These metabolic biomarkers may serve as objective measures to support clinical assessments,improve diagnostic accuracy,and inform personalized treatment approaches.Metabolomic profiling also offers insights into the metabolic alterations associated with comorbid conditions commonly observed in individuals with ASD.CONCLUSION Integration of metabolomic changes in children with ASD holds promise for enhancing diagnostic accuracy,guiding personalized treatment approaches,monitoring treatment response,and improving outcomes.Further research is needed to validate findings,establish standardized protocols,and overcome technical challenges in metabolomic analysis.By advancing our understanding of metabolic dysregulations in ASD,clinicians can improve the lives of affected individuals and their families.
文摘Objective: The demand for pediatric developmental evaluations has far exceeded the workforce available to perform them, which creates long significant wait times for services. A year-long clinician training using the Extension for Community Healthcare Outcomes (ECHO<sup>®</sup>) model with monthly meetings was conducted and evaluated for its impact on primary care clinicians’ self-reported self-efficacy, ability to administer autism screening and counsel families, professional fulfillment, and burnout. Methods: Participants represented six community health centers and a hospital-based practice. Data collection was informed by participant feedback and the Normalization Process Theory via online surveys and focus groups/interviews. Twelve virtual monthly trainings were delivered between November 2020 and October 2021. Results: 30 clinicians participated in data collection. Matched analyses (n = 9) indicated statistically significant increase in self-rated ability to counsel families about autism (Pre-test Mean = 3.00, Post-test Mean = 3.89, p = 0.0313), manage autistic patients’ care (Pre-test Mean = 2.56, Post-test Mean = 4.11, p = 0.0078), empathy toward patients (Pre-test Mean = 2.11, Post-test Mean = 1.22, p = 0.0156) and colleagues (Pre-test Mean = 2.33, Post-test Mean = 1.22, respectively, p = 0.0391). Unmatched analysis revealed increases in participants confident about educating patients about autism (70.59%, post-test n = 12 vs. 3.33%, pre-test n = 1, p = 0.0019). Focus groups found increased confidence in using the term “autism”. Conclusion: Participants reported increases in ability and confidence to care for autistic patients, as well as empathy toward patients and colleagues. Future research should explore long-term outcomes in participants’ knowledge retention, confidence in practice, and improvements to autism evaluations and care.
文摘Chemically engineered agricultural products such as pesticides, insecticides, and herbicides, although used considerably for both industrialized and personal agricultural use, have recently been associated with a number of serious human health disorders. This rapid literature review aims to accumulate and analyze research from the last ten years, focusing specifically on the effects of exposure to glyphosate-based herbicide products such as Roundup as associated with the formation of various neurological disorders. Specifically, this review focuses on laboratory research using animal models or human cell cultures as well as human population-based epidemiological studies. It associates exposure to glyphosate or glyphosate-based products with the formation or exacerbation of neurological disorders such as Parkinson’s disease, Alzheimer’s disease, seizures, and autism spectrum disorder. In addition, it examines the correlation between the gut-brain axis, exposure to glyphosate, and neurodegeneration.
文摘Difficulty in communicating and interacting with other people are mainly due to the neurological disorder called autism spectrum disorder(ASD)diseases.These diseases can affect the nerves at any stage of the human being in childhood,adolescence,and adulthood.ASD is known as a behavioral disease due to the appearances of symptoms over thefirst two years that continue until adulthood.Most of the studies prove that the early detection of ASD helps improve the behavioral characteristics of patients with ASD.The detection of ASD is a very challenging task among various researchers.Machine learning(ML)algorithms still act very intelligent by learning the complex data and pre-dicting quality results.In this paper,ensemble ML techniques for the early detec-tion of ASD are proposed.In this detection,the dataset isfirst processed using three ML algorithms such as sequential minimal optimization with support vector machine,Kohonen self-organizing neural network,and random forest algorithm.The prediction results of these ML algorithms(ensemble)further use the bagging concept called max voting to predict thefinal result.The accuracy,sensitivity,and specificity of the proposed system are calculated using confusion matrix.The pro-posed ensemble technique performs better than state-of-the art ML algorithms.
基金supported by an Alfred Deakin Fellowshipsupported by internal university funding
文摘Background: Active video games(AVGs) encourage whole body movements to interact or control the gaming system, allowing the opportunity for skill development. Children with autism spectrum disorder(ASD) show decreased fundamental movement skills in comparison with their typically developing(TD) peers and might benefit from this approach. This pilot study investigates whether playing sports AVGs can increase the actual and perceived object control(OC) skills of 11 children with ASD aged 6–10 years in comparison to 19 TD children of a similar age.Feasibility was a secondary aim.Methods: Actual(Test of Gross Motor Development) and perceived OC skills(Pictorial Scale of Perceived Movement Skill Competence for Young Children) were assessed before and after the intervention(6 × 45 min).Results: Actual skill scores were not improved in either group. The ASD group improved in perceived skill. All children completed the required dose and parents reported the intervention was feasible.Conclusion: The use of AVGs as a play-based intervention may not provide enough opportunity for children to perform the correct movement patterns to influence skill. However, play of such games may influence perceptions of skill ability in children with ASD, which could improve motivation to participate in physical activities.