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
Autism spectrum disorders are a group of neurodevelopmental disorders involving more than 1100 genes,including Ctnnd2 as a candidate gene.Ctnnd2knockout mice,serving as an animal model of autis m,have been demonstrate...Autism spectrum disorders are a group of neurodevelopmental disorders involving more than 1100 genes,including Ctnnd2 as a candidate gene.Ctnnd2knockout mice,serving as an animal model of autis m,have been demonstrated to exhibit decreased density of dendritic spines.The role of melatonin,as a neuro hormone capable of effectively alleviating social interaction deficits and regulating the development of dendritic spines,in Ctnnd2 deletion-induced nerve injury remains unclea r.In the present study,we discove red that the deletion of exon 2 of the Ctnnd2 gene was linked to social interaction deficits,spine loss,impaired inhibitory neurons,and suppressed phosphatidylinositol-3-kinase(PI3K)/protein kinase B(Akt) signal pathway in the prefrontal cortex.Our findings demonstrated that the long-term oral administration of melatonin for 28 days effectively alleviated the aforementioned abnormalities in Ctnnd2 gene-knockout mice.Furthermore,the administration of melatonin in the prefro ntal cortex was found to improve synaptic function and activate the PI3K/Akt signal pathway in this region.The pharmacological blockade of the PI3K/Akt signal pathway with a PI3K/Akt inhibitor,wo rtmannin,and melatonin receptor antagonists,luzindole and 4-phenyl-2-propionamidotetralin,prevented the melatonin-induced enhancement of GABAergic synaptic function.These findings suggest that melatonin treatment can ameliorate GABAe rgic synaptic function by activating the PI3K/Akt signal pathway,which may contribute to the improvement of dendritic spine abnormalities in autism spectrum disorders.展开更多
BACKGROUND Unhealthy maternal diet leads to heavy metal exposures from the consumption of ultra-processed foods that may impact gene behavior across generations,creating conditions for the neurodevelopmental disorders...BACKGROUND Unhealthy maternal diet leads to heavy metal exposures from the consumption of ultra-processed foods that may impact gene behavior across generations,creating conditions for the neurodevelopmental disorders known as autism and attention deficit/hyperactivity disorder(ADHD).Children with these disorders have difficulty metabolizing and excreting heavy metals from their bloodstream,and the severity of their symptoms correlates with the heavy metal levels measured in their blood.Psychiatrists may play a key role in helping parents reduce their ultra-processed food and dietary heavy metal intake by providing access to effective nutritional epigenetics education.AIM To test the efficacy of nutritional epigenetics instruction in reducing parental ultra-processed food intake.METHODS The study utilized a semi-randomized test and control group pretest-posttest pilot study design with participants recruited from parents having a learning-disabled child with autism or ADHD.Twenty-two parents who met the inclusion criteria were randomly selected to serve in the test(n=11)or control(n=11)group.The test group participated in the six-week online nutritional epigenetics tutorial,while the control group did not.The efficacy of the nutritional epigenetics instruction was determined by measuring changes in parent diet and attitude using data derived from an online diet survey administered to the participants during the pre and post intervention periods.Diet intake scores were derived for both ultra-processed and whole/organic foods.Paired sample t-tests were conducted to determine any differences in mean diet scores within each group.RESULTS There was a significant difference in the diet scores of the test group between the pre-and post-intervention periods.The parents in the test group significantly reduced their intake of ultra-processed foods with a preintervention diet score of 70(mean=5.385,SD=2.534)and a post-intervention diet score of 113(mean=8.692,SD=1.750)and the paired t-test analysis showing a significance of P<0.001.The test group also significantly increased their consumption of whole and/or organic foods with a pre-intervention diet score of 100(mean=5.882,SD=2.472)and post-intervention diet score of 121(mean=7.118,SD=2.390)and the paired t-test analysis showing a significance of P<0.05.CONCLUSION Here we show nutritional epigenetics education can be used to reduce ultra-processed food intake and improve attitude among parents having learning-disabled children with autism or ADHD.展开更多
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
Faced with autism, motherhood and parenthood are turned upside down in many ways. Coping with stress and mental health problems, continuing to assume a rewarding parental role and finding suitable care are the trials ...Faced with autism, motherhood and parenthood are turned upside down in many ways. Coping with stress and mental health problems, continuing to assume a rewarding parental role and finding suitable care are the trials and tribulations that mark out the journey of African parents. Faith and belief have been described as providing meaning and coping mechanisms in the frightening ordeal of disability. An encounter with a young girl and her parents provided an opportunity to analyse the mother’s experience and the impact of beliefs and discourses on her commitment to care. Based on this clinical story, we discuss the place of other-actors (parents and carers) and the Other-God in relation to the psychopathological dynamics of the mother.展开更多
Autism,also known as an autism spectrum disorder,is a complex neurodevelopmental disorder usually diagnosed in the first three years of a child's life.A range of symptoms characterizes it and can be diagnosed at a...Autism,also known as an autism spectrum disorder,is a complex neurodevelopmental disorder usually diagnosed in the first three years of a child's life.A range of symptoms characterizes it and can be diagnosed at any age,including adolescence and adulthood.However,early diagnosis is crucial for effective management,prognosis,and care.Unfortunately,there are no established fetal,prenatal,or newborn screening programs for autism,making early detection difficult.This review aims to shed light on the early detection of autism prenatally,natally,and early in life,during a stage we call as“pre-autism”when typical symptoms are not yet apparent.Some fetal,neonatal,and infant biomarkers may predict an increased risk of autism in the coming baby.By developing a biomarker array,we can create an objective diagnostic tool to diagnose and rank the severity of autism for each patient.These biomarkers could be genetic,immunological,hormonal,metabolic,amino acids,acute phase reactants,neonatal brainstem function biophysical activity,behavioral profile,body measurements,or radiological markers.However,every biomarker has its accuracy and limitations.Several factors can make early detection of autism a real challenge.To improve early detection,we need to overcome various challenges,such as raising community awareness of early signs of autism,improving access to diagnostic tools,reducing the stigma attached to the diagnosis of autism,and addressing various culturally sensitive concepts related to the disorder.展开更多
Play is a pleasurable physical or mental activity that enhances the child’s skills involving negotiation abilities,problem-solving,manual dexterity,sharing,decision-making,and working in a group.Play affects all the ...Play is a pleasurable physical or mental activity that enhances the child’s skills involving negotiation abilities,problem-solving,manual dexterity,sharing,decision-making,and working in a group.Play affects all the brain's areas,structures,and functions.Children with autism have adaptive behavior,adaptive response,and social interaction limitations.This review explores the different applications of play therapy in helping children with autism disorder.Play is usually significantly impaired in children with autism.Play therapy is mainly intended to help children to honor their unique mental abilities and developmental levels.The main aim of play therapy is to prevent or solve psychosocial difficulties and achieve optimal child-healthy growth and development.Play therapy helps children with autism to engage in play activities of their interest and choice to express themselves in the most comfortable ways.It changes their way of self-expression from unwanted behaviors to more non-injurious expressive behavior using toys or activities of their choice as their words.Play therapy also helps those children to experience feeling out various interaction styles.Every child with autism is unique and responds differently.Therefore,different types of intervention,like play therapy,could fit the differences in children with autism.Proper evaluation of the child is mandatory to evaluate which type fits the child more than the others.This narrative review revised the different types of play therapy that could fit children with autism in an evidence-based way.Despite weak evidence,play therapy still has potential benefits for patients and their families.展开更多
Autism Spectrum Disorder(ASD)requires a precise diagnosis in order to be managed and rehabilitated.Non-invasive neuroimaging methods are disease markers that can be used to help diagnose ASD.The majority of available ...Autism Spectrum Disorder(ASD)requires a precise diagnosis in order to be managed and rehabilitated.Non-invasive neuroimaging methods are disease markers that can be used to help diagnose ASD.The majority of available techniques in the literature use functional magnetic resonance imaging(fMRI)to detect ASD with a small dataset,resulting in high accuracy but low generality.Traditional supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text,images,and videos,but their performance and robustness are restricted by the size of the accompanying training data.Deep learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering algorithms.It takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural networks.Generative modelling,also known as Generative Adversarial Networks(GANs),is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original dataset.GANs are an exciting and rapidly changingfield that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains,most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the past.In this paper,we present a novel conditional generative adversarial network,or cGAN for short,which is a form of GAN that uses a generator model to conditionally generate images.In terms of prediction and accuracy,they outperform the standard GAN.The pro-posed model is 74%more accurate than the traditional methods and takes only around 10 min for training even with a huge dataset.展开更多
Available pharmacotherapies for autism spectrum disorders(ASD)are reviewed based on clinical and research experience,highlighting some older drugs with emerging evidence.Several medications show efficacy in ASD,though...Available pharmacotherapies for autism spectrum disorders(ASD)are reviewed based on clinical and research experience,highlighting some older drugs with emerging evidence.Several medications show efficacy in ASD,though controlled studies in ASD are largely lacking.Only risperidone and aripiprazole have Federal Drug Administration approval in the United States.Methylphenidate(MPH)studies showed lower efficacy and tolerability for attention deficit hyperactivity disorder(ADHD)than in the typically developing(TD)population;atomoxetine demonstrated lower efficacy but comparable tolerability to TD outcomes.Guanfacine improved hyperactivity in ASD comparably to TD.Dextroamphetamine promises greater efficacy than MPH in ASD.ADHD medications reduce impulsive aggression in youth,and may also be key for this in adults.Controlled trials of the selective serotonin reuptake inhibitors citalopram and fluoxetine demonstrated poor tolerability and lack of efficacy for repetitive behaviors.Trials of antiseizure medications in ASD remain inconclusive,however clinical trials may be warranted in severely disabled individuals showing bizarre behaviors.No identified drugs treat ASD core symptoms;oxytocin lacked efficacy.Amitriptyline and loxapine however,show promise.Loxapine at 5-10 mg daily resembled an atypical antipsychotic in positron emission tomography studies,but may be weight-sparing.Amitriptyline at approximately 1 mg/kg/day used cautiously,shows efficacy for sleep,anxiety,impulsivity and ADHD,repetitive behaviors,and enuresis.Both drugs have promising neurotrophic properties.展开更多
In the United States,schools offer special education services to children who are diagnosed with a learning or neurodevelopmental disorder and have difficulty meeting their learning goals.Pediatricians may play a key ...In the United States,schools offer special education services to children who are diagnosed with a learning or neurodevelopmental disorder and have difficulty meeting their learning goals.Pediatricians may play a key role in helping children access special education services.The number of children ages 6-21 in the United States receiving special education services increased 10.4%from 2006 to 2021.Children receiving special education services under the autism category increased 242%during the same period.The demand for special education services for children under the developmental delay and other health impaired categories increased by 184%and 83%respectively.Although student enrollment in American schools has remained stable since 2006,the percentage distribution of children receiving special education services nearly tripled for the autism category and quadrupled for the developmental delay category by 2021.Allowable heavy metal residues remain persistent in the American food supply due to food ingredient manufacturing processes.Numerous clinical trial data indicate heavy metal exposures and poor diet are the primary epigenetic factors responsible for the autism and attention deficit hyperactivity disorder epidemics.Dietary heavy metal exposures,especially inorganic mercury and lead may impact gene behavior across generations.In 2021,the United States Congress found heavy metal residues problematic in the American food supply but took no legislative action.Mandatory health warning labels on select foods may be the only way to reduce dietary heavy metal exposures and improve child learning across generations.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),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)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) 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
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
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
Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking ability.The early detection of ASD ...Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking ability.The early detection of ASD is essential for delivering effective,timely interventions.Various facial features such as a lack of eye contact,showing uncommon hand or body movements,bab-bling or talking in an unusual tone,and not using common gestures could be used to detect and classify ASD at an early stage.Our study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial fea-tures.A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet(ASDDTLA)model.Our model achieved a detection accuracy of 87.7%and performed better than other established ASD detection models.Therefore,this model could facilitate the early detection of ASD in clinical practice.展开更多
Autism Spectrum Disorder(ASD)is a complicated neurodevelopmen-tal disorder that is often identified in toddlers.The microarray data is used as a diagnostic tool to identify the genetics of the disorder.However,microarr...Autism Spectrum Disorder(ASD)is a complicated neurodevelopmen-tal disorder that is often identified in toddlers.The microarray data is used as a diagnostic tool to identify the genetics of the disorder.However,microarray data is large and has a high volume.Consequently,it suffers from the problem of dimensionality.In microarray data,the sample size and variance of the gene expression will lead to overfitting and misclassification.Identifying the autism gene(feature)subset from microarray data is an important and challenging research area.It has to be efficiently addressed to improve gene feature selection and classification.To overcome the challenges,a novel Intelligent Hybrid Ensem-ble Gene Selection(IHEGS)model is proposed in this paper.The proposed model integrates the intelligence of different feature selection techniques over the data partitions.In this model,the initial gene selection is carried out by data perturba-tion,and thefinal autism gene subset is obtained by functional perturbation,which reduces the problem of dimensionality in microarray data.The functional perturbation module employs three meta-heuristic swarm intelligence-based tech-niques for gene selection.The obtained gene subset is validated by the Deep Neural Network(DNN)model.The proposed model is implemented using python with six National Center for Biotechnology Information(NCBI)gene expression datasets.From the comparative study with other existing state-of-the-art systems,the proposed model provides stable results in terms of feature selection and clas-sification accuracy.展开更多
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.
基金supported by the Chongqing Science and Technology CommitteeNatural Science Foundation of Chongqing,No.cstc2021jcyj-msxmX0065 (to YL)。
文摘Autism spectrum disorders are a group of neurodevelopmental disorders involving more than 1100 genes,including Ctnnd2 as a candidate gene.Ctnnd2knockout mice,serving as an animal model of autis m,have been demonstrated to exhibit decreased density of dendritic spines.The role of melatonin,as a neuro hormone capable of effectively alleviating social interaction deficits and regulating the development of dendritic spines,in Ctnnd2 deletion-induced nerve injury remains unclea r.In the present study,we discove red that the deletion of exon 2 of the Ctnnd2 gene was linked to social interaction deficits,spine loss,impaired inhibitory neurons,and suppressed phosphatidylinositol-3-kinase(PI3K)/protein kinase B(Akt) signal pathway in the prefrontal cortex.Our findings demonstrated that the long-term oral administration of melatonin for 28 days effectively alleviated the aforementioned abnormalities in Ctnnd2 gene-knockout mice.Furthermore,the administration of melatonin in the prefro ntal cortex was found to improve synaptic function and activate the PI3K/Akt signal pathway in this region.The pharmacological blockade of the PI3K/Akt signal pathway with a PI3K/Akt inhibitor,wo rtmannin,and melatonin receptor antagonists,luzindole and 4-phenyl-2-propionamidotetralin,prevented the melatonin-induced enhancement of GABAergic synaptic function.These findings suggest that melatonin treatment can ameliorate GABAe rgic synaptic function by activating the PI3K/Akt signal pathway,which may contribute to the improvement of dendritic spine abnormalities in autism spectrum disorders.
文摘BACKGROUND Unhealthy maternal diet leads to heavy metal exposures from the consumption of ultra-processed foods that may impact gene behavior across generations,creating conditions for the neurodevelopmental disorders known as autism and attention deficit/hyperactivity disorder(ADHD).Children with these disorders have difficulty metabolizing and excreting heavy metals from their bloodstream,and the severity of their symptoms correlates with the heavy metal levels measured in their blood.Psychiatrists may play a key role in helping parents reduce their ultra-processed food and dietary heavy metal intake by providing access to effective nutritional epigenetics education.AIM To test the efficacy of nutritional epigenetics instruction in reducing parental ultra-processed food intake.METHODS The study utilized a semi-randomized test and control group pretest-posttest pilot study design with participants recruited from parents having a learning-disabled child with autism or ADHD.Twenty-two parents who met the inclusion criteria were randomly selected to serve in the test(n=11)or control(n=11)group.The test group participated in the six-week online nutritional epigenetics tutorial,while the control group did not.The efficacy of the nutritional epigenetics instruction was determined by measuring changes in parent diet and attitude using data derived from an online diet survey administered to the participants during the pre and post intervention periods.Diet intake scores were derived for both ultra-processed and whole/organic foods.Paired sample t-tests were conducted to determine any differences in mean diet scores within each group.RESULTS There was a significant difference in the diet scores of the test group between the pre-and post-intervention periods.The parents in the test group significantly reduced their intake of ultra-processed foods with a preintervention diet score of 70(mean=5.385,SD=2.534)and a post-intervention diet score of 113(mean=8.692,SD=1.750)and the paired t-test analysis showing a significance of P<0.001.The test group also significantly increased their consumption of whole and/or organic foods with a pre-intervention diet score of 100(mean=5.882,SD=2.472)and post-intervention diet score of 121(mean=7.118,SD=2.390)and the paired t-test analysis showing a significance of P<0.05.CONCLUSION Here we show nutritional epigenetics education can be used to reduce ultra-processed food intake and improve attitude among parents having learning-disabled children with autism or ADHD.
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Faced with autism, motherhood and parenthood are turned upside down in many ways. Coping with stress and mental health problems, continuing to assume a rewarding parental role and finding suitable care are the trials and tribulations that mark out the journey of African parents. Faith and belief have been described as providing meaning and coping mechanisms in the frightening ordeal of disability. An encounter with a young girl and her parents provided an opportunity to analyse the mother’s experience and the impact of beliefs and discourses on her commitment to care. Based on this clinical story, we discuss the place of other-actors (parents and carers) and the Other-God in relation to the psychopathological dynamics of the mother.
文摘Autism,also known as an autism spectrum disorder,is a complex neurodevelopmental disorder usually diagnosed in the first three years of a child's life.A range of symptoms characterizes it and can be diagnosed at any age,including adolescence and adulthood.However,early diagnosis is crucial for effective management,prognosis,and care.Unfortunately,there are no established fetal,prenatal,or newborn screening programs for autism,making early detection difficult.This review aims to shed light on the early detection of autism prenatally,natally,and early in life,during a stage we call as“pre-autism”when typical symptoms are not yet apparent.Some fetal,neonatal,and infant biomarkers may predict an increased risk of autism in the coming baby.By developing a biomarker array,we can create an objective diagnostic tool to diagnose and rank the severity of autism for each patient.These biomarkers could be genetic,immunological,hormonal,metabolic,amino acids,acute phase reactants,neonatal brainstem function biophysical activity,behavioral profile,body measurements,or radiological markers.However,every biomarker has its accuracy and limitations.Several factors can make early detection of autism a real challenge.To improve early detection,we need to overcome various challenges,such as raising community awareness of early signs of autism,improving access to diagnostic tools,reducing the stigma attached to the diagnosis of autism,and addressing various culturally sensitive concepts related to the disorder.
文摘Play is a pleasurable physical or mental activity that enhances the child’s skills involving negotiation abilities,problem-solving,manual dexterity,sharing,decision-making,and working in a group.Play affects all the brain's areas,structures,and functions.Children with autism have adaptive behavior,adaptive response,and social interaction limitations.This review explores the different applications of play therapy in helping children with autism disorder.Play is usually significantly impaired in children with autism.Play therapy is mainly intended to help children to honor their unique mental abilities and developmental levels.The main aim of play therapy is to prevent or solve psychosocial difficulties and achieve optimal child-healthy growth and development.Play therapy helps children with autism to engage in play activities of their interest and choice to express themselves in the most comfortable ways.It changes their way of self-expression from unwanted behaviors to more non-injurious expressive behavior using toys or activities of their choice as their words.Play therapy also helps those children to experience feeling out various interaction styles.Every child with autism is unique and responds differently.Therefore,different types of intervention,like play therapy,could fit the differences in children with autism.Proper evaluation of the child is mandatory to evaluate which type fits the child more than the others.This narrative review revised the different types of play therapy that could fit children with autism in an evidence-based way.Despite weak evidence,play therapy still has potential benefits for patients and their families.
文摘Autism Spectrum Disorder(ASD)requires a precise diagnosis in order to be managed and rehabilitated.Non-invasive neuroimaging methods are disease markers that can be used to help diagnose ASD.The majority of available techniques in the literature use functional magnetic resonance imaging(fMRI)to detect ASD with a small dataset,resulting in high accuracy but low generality.Traditional supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text,images,and videos,but their performance and robustness are restricted by the size of the accompanying training data.Deep learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering algorithms.It takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural networks.Generative modelling,also known as Generative Adversarial Networks(GANs),is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original dataset.GANs are an exciting and rapidly changingfield that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains,most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the past.In this paper,we present a novel conditional generative adversarial network,or cGAN for short,which is a form of GAN that uses a generator model to conditionally generate images.In terms of prediction and accuracy,they outperform the standard GAN.The pro-posed model is 74%more accurate than the traditional methods and takes only around 10 min for training even with a huge dataset.
文摘Available pharmacotherapies for autism spectrum disorders(ASD)are reviewed based on clinical and research experience,highlighting some older drugs with emerging evidence.Several medications show efficacy in ASD,though controlled studies in ASD are largely lacking.Only risperidone and aripiprazole have Federal Drug Administration approval in the United States.Methylphenidate(MPH)studies showed lower efficacy and tolerability for attention deficit hyperactivity disorder(ADHD)than in the typically developing(TD)population;atomoxetine demonstrated lower efficacy but comparable tolerability to TD outcomes.Guanfacine improved hyperactivity in ASD comparably to TD.Dextroamphetamine promises greater efficacy than MPH in ASD.ADHD medications reduce impulsive aggression in youth,and may also be key for this in adults.Controlled trials of the selective serotonin reuptake inhibitors citalopram and fluoxetine demonstrated poor tolerability and lack of efficacy for repetitive behaviors.Trials of antiseizure medications in ASD remain inconclusive,however clinical trials may be warranted in severely disabled individuals showing bizarre behaviors.No identified drugs treat ASD core symptoms;oxytocin lacked efficacy.Amitriptyline and loxapine however,show promise.Loxapine at 5-10 mg daily resembled an atypical antipsychotic in positron emission tomography studies,but may be weight-sparing.Amitriptyline at approximately 1 mg/kg/day used cautiously,shows efficacy for sleep,anxiety,impulsivity and ADHD,repetitive behaviors,and enuresis.Both drugs have promising neurotrophic properties.
文摘In the United States,schools offer special education services to children who are diagnosed with a learning or neurodevelopmental disorder and have difficulty meeting their learning goals.Pediatricians may play a key role in helping children access special education services.The number of children ages 6-21 in the United States receiving special education services increased 10.4%from 2006 to 2021.Children receiving special education services under the autism category increased 242%during the same period.The demand for special education services for children under the developmental delay and other health impaired categories increased by 184%and 83%respectively.Although student enrollment in American schools has remained stable since 2006,the percentage distribution of children receiving special education services nearly tripled for the autism category and quadrupled for the developmental delay category by 2021.Allowable heavy metal residues remain persistent in the American food supply due to food ingredient manufacturing processes.Numerous clinical trial data indicate heavy metal exposures and poor diet are the primary epigenetic factors responsible for the autism and attention deficit hyperactivity disorder epidemics.Dietary heavy metal exposures,especially inorganic mercury and lead may impact gene behavior across generations.In 2021,the United States Congress found heavy metal residues problematic in the American food supply but took no legislative action.Mandatory health warning labels on select foods may be the only way to reduce dietary heavy metal exposures and improve child learning across generations.
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.
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.
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 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.
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.
文摘Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking ability.The early detection of ASD is essential for delivering effective,timely interventions.Various facial features such as a lack of eye contact,showing uncommon hand or body movements,bab-bling or talking in an unusual tone,and not using common gestures could be used to detect and classify ASD at an early stage.Our study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial fea-tures.A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet(ASDDTLA)model.Our model achieved a detection accuracy of 87.7%and performed better than other established ASD detection models.Therefore,this model could facilitate the early detection of ASD in clinical practice.
文摘Autism Spectrum Disorder(ASD)is a complicated neurodevelopmen-tal disorder that is often identified in toddlers.The microarray data is used as a diagnostic tool to identify the genetics of the disorder.However,microarray data is large and has a high volume.Consequently,it suffers from the problem of dimensionality.In microarray data,the sample size and variance of the gene expression will lead to overfitting and misclassification.Identifying the autism gene(feature)subset from microarray data is an important and challenging research area.It has to be efficiently addressed to improve gene feature selection and classification.To overcome the challenges,a novel Intelligent Hybrid Ensem-ble Gene Selection(IHEGS)model is proposed in this paper.The proposed model integrates the intelligence of different feature selection techniques over the data partitions.In this model,the initial gene selection is carried out by data perturba-tion,and thefinal autism gene subset is obtained by functional perturbation,which reduces the problem of dimensionality in microarray data.The functional perturbation module employs three meta-heuristic swarm intelligence-based tech-niques for gene selection.The obtained gene subset is validated by the Deep Neural Network(DNN)model.The proposed model is implemented using python with six National Center for Biotechnology Information(NCBI)gene expression datasets.From the comparative study with other existing state-of-the-art systems,the proposed model provides stable results in terms of feature selection and clas-sification accuracy.
