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This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic prioritization solution is challenging for resolving conflicts or trade-offs among ASD criteria. This research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In the first phase, the study identifies a triaged ASD dataset, considering 19 critical medical and sociodemographic criteria for the three ASD levels. The second phase introduces a new Decision Matrix (DM) designed to manage the prioritization process effectively. The third phase focuses on the new extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) to construct the criteria weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). The fourth phase formulates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to rank patients within each urgency level. Results from the SVN2TL-FWZIC weights offer significant insights, including the higher criteria values \"C12 = Laughing for no reason\" and \"C16 = Notice the sound of the bell\" with 0.097358 and 0.083832, indicating their significance in identifying potential ASD symptoms. The SVN2TL-FWZIC weights offer the base for prioritizing the three triage levels using MABAC, encompassing medical and behavioral dimensions. The methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming the consistency of the prioritization results with critical analysis points. The methodology compares with three benchmark studies, using four distinct points, and achieves a remarkable 100% congruence with these prior investigations. The implications of this study are far-reaching, offering a valuable guide for clinical psychologists in prioritizing complex cases of ASD patients.

Autism spectrum disorder (ASD) presents challenges for accurate diagnosis, prompting researchers to search for an optimal diagnostic process. Feature selection (FS) approaches and classification methods considering medical tests and socio-demographic characteristics are crucial for diagnosing autism. However, evaluating and benchmarking hybrid diagnosis machine learning (ML) models in the presence of multiple evaluation performance metrics, criteria trade-offs, and varying criteria importance present complex multi-criteria decision-making (MCDM) problems. This study proposes a three-phase methodology integrating FS, ML, and fuzzy MCDM to develop and evaluate diagnosis models. Firstly, an ASD dataset combining medical tests and socio-demographic characteristics is identified and preprocessed. Secondly, 72 hybrid diagnosis models are developed by combining eight FS techniques and nine ML algorithms using an intersection process. Thirdly, the following steps are performed: (i) A decision matrix is formulated based on nine evaluation metrics, including classification accuracy (CA), specificity, precision, F1 score, recall, test time, train time, log loss, and area under the curve (AUC); (ii) a new extension of fuzzy-weighted zero inconsistency is developed using 2-tuple linguistic neutrosophic fuzzy sets (2TLNFSs) to assign weights to the evaluation metrics criteria and address related issues; (iii) a new extension of the fuzzy decision-by-opinion score method is developed using 2TLNFSs as well to benchmark the 72 models. Results indicate that the selected FS techniques vary in the number of features chosen, with the sets ranging from 19 to 46 out of the 48 available features. Socio-demographic features were predominantly selected over medical tests. Regarding the evaluation and benchmarking results, the weights constructed by three experts suggest that CA holds high importance, precision and recall are assigned equal weights, and AUC and test time carry moderate weights. At the same time, F1 and log loss are considered less crucial in the decision-making process. Specificity and train time are assigned relatively lower weights, indicating their lower importance. The best-performing hybrid model identified was sequential feature selection/logistic regression (SFS/LR)-decision tree, with a score value of 4.3964. Decision trees and gradient boosting consistently achieved high rankings, demonstrating their effectiveness in diagnosing ASD, while SVM, random forest, and logistic regression showed mixed results across different hybrid models. The sensitivity analysis assessments were conducted to verify the efficiency of the proposed evaluation and benchmarking methodology. We benchmarked the proposed framework against three other benchmark studies and achieved a score of 100% across five key areas. The developed methodology can potentially advance and accelerate the selection of diagnostic tools for ASD therapy, benefiting individuals with ASD.

Autism has attracted a great deal of attention in recent years, thanks to dramatically increasing rates of diagnosis, extensive organizational mobilization, journalistic coverage, biomedical research, and clinical innovation. Understanding Autism, a social history of the expanding diagnostic category of this contested illness, takes a close look at the role of emotion--specifically, of parental love--in the intense and passionate work of biomedical communities investigating autism.

Accessing an autism diagnosis is a key milestone, both for an individual and their family. Using a qualitative methodology, the current study examined the views and experiences of ten autistic adults, ten parents of children on the autism spectrum, and ten professionals involved in autism diagnosis, all based in the United Kingdom (UK). Interviewing these 30 respondents about the diagnostic process and subsequent support options, the goal was to identify aspects of the diagnostic process that are working well, and areas in which improvements are needed. Using thematic analysis, three key themes were identified: the process of understanding and accepting autism; multiple barriers to satisfaction with the diagnostic process; and inadequate post-diagnostic support provision.

The social validity of autism behavioral intervention has been questioned. Naturalistic Developmental Behavioral Interventions (NDBIs) attempt to address some concerns, but it is unclear whether autistic people consider NDBIs socially valid. Social validity of an NDBI, Pivotal Response Treatment (PRT), was investigated through autistic adults commenting on videos of autistic children receiving PRT. Qualitative coding of responses generated three themes: respect for individuals; assessment of intervention implementation; and socioemotional considerations. Although video brevity limits the scope of the present study’s conclusions, participants highlighted PRT components that appeared socially valid (e.g., reinforcing attempts, following the child’s lead) and aspects appearing invalid (e.g., overemphasis on spoken language). Therefore, adjustments appear necessary for PRT to be fully acceptable to the autistic community.

Despite the clinical and genetic heterogeneity of autism, bulk gene expression studies show that changes in the neocortex of autism patients converge on common genes and pathways. However, direct assessment of specific cell types in the brain affected by autism has not been feasible until recently. We used single-nucleus RNA sequencing of cortical tissue from patients with autism to identify autism-associated transcriptomic changes in specific cell types. We found that synaptic signaling of upper-layer excitatory neurons and the molecular state of microglia are preferentially affected in autism. Moreover, our results show that dysregulation of specific groups of genes in cortico-cortical projection neurons correlates with clinical severity of autism. These findings suggest that molecular changes in upper-layer cortical circuits are linked to behavioral manifestations of autism.

The COVID-19 infectious disease pandemic has caused significant fear and uncertainty around the world and had significant adverse psychological impact. Children, adolescents and adults with autism spectrum disorder (ASD) are a particularly vulnerable population, impacted by stay-at-home orders, closures at nonessential services, and social distancing standards. This commentary describes various challenges faced by individuals with ASD in the United States including disruptions caused by educational and vocational changes, challenges to home and leisure routines, limited access to behavioral health services and changes in health services delivery due to the pandemic. We highlight the need for ongoing skills development for individuals and development within systems to better respond to needs of the ASD population in future emergencies.

Since Hans Asperger’s first description (Arch Psych Nervenkrankh 117:76–136, 1944), through Lorna Wing’s translation and definition (Psychol Med 11:115–129, 1981), to its introduction in the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM, 1994), Asperger Syndrome has always aroused huge interest and debate, until vanishing in the DSM fifth edition (2013). The debate regarded its diagnostic validity and its differentiation from high functioning autism (HFA). The present study aimed to examine whether AS differed from HFA in clinical profiles and to analyze the impact of DSM-5’s innovation. Differences in cognitive, language, school functioning and comorbidities, were revealed when 80 AS and 70 HFA patients (3–18 years) were compared. Results suggested that an AS empirical distinction within autism spectrum disorder should be clinically useful.

Autism spectrum disorders are a genetically heterogeneous constellation of syndromes characterized by impairments in reciprocal social interaction. Available somatic treatments have limited efficacy. We have identified inactivating mutations in the gene BCKDK (Branched Chain Ketoadd Dehydrogenase Kinase) in consanguineous families with autism, epilepsy, and intellectual disability. The encoded protein is responsible for phosphorylation-mediated inactivation of the E1α subunit of branched-chain ketoacid dehydrogenase (BCKDH). Patients with homozygous BCKDK mutations display reductions in BCKDK messenger RNA and protein, E1α phosphorylation, and plasma branched-chain amino acids. Bckdk knockout mice show abnormal brain amino acid profiles and neurobehavioral deficits that respond to dietary supplementation. Thus, autism presenting with intellectual disability and epilepsy caused by BCKDK mutations represents a potentially treatable syndrome.

Social adaptation requires specific cognitive and emotional competences. Individuals with high-functioning autism or with Asperger syndrome cannot understand or engage in social situations despite preserved intellectual abilities. Recently, it has been suggested that oxytocin, a hormone known to promote mother-infant bonds, may be implicated in the social deficit of autism. We investigated the behavioral effects of oxytocin in 13 subjects with autism. In a simulated ball game where participants interacted with fictitious partners, we found that after oxytocin inhalation, patients exhibited stronger interactions with the most socially cooperative partner and reported enhanced feelings of trust and preference. Also, during free viewing of pictures of faces, oxytocin selectively increased patients' gazing time on the socially informative region of the face, namely the eyes. Thus, under oxytocin, patients respond more strongly to others and exhibit more appropriate social behavior and affect, suggesting a therapeutic potential of oxytocin through its action on a core dimension of autism.