Social anxiety disorder (SAD) is a common and debilitating condition marked by intense fear of negative evaluation in social situations. It often co-occurs with other psychiatric disorders, and in particular Autism Spectrum Disorder (ASD). The exact cause of SAD is unclear, though biological, environmental, and genetic factors are believed to contribute. Recent research highlights the involvement of neuropeptides, neurohormones, and immune system components in SAD development, suggesting complex interactions between psychological and biological factors, with a growing focus on inflammation’s role. This study aimed to investigate potential biochemical correlates of SAD in individuals with ASD, their first-degree relatives (BAP group), and healthy controls (HCs), focusing on inflammatory markers, neuropeptides, and growth factors.

Twenty-four adults with ASD, their relatives, and HCs, for a total of 72 participants, were assessed. Biochemical parameters were analyzed using enzyme-linked immunosorbent assays (ELISA), and social anxiety symptoms were evaluated via the Social Phobia Spectrum—Self Report (SHY-SR) questionnaire.

Individuals with ASD exhibited significantly higher SHY-SR scores compared to the BAP and HCs. Subjects in the ASD group tended to make greater use of anxiolytics in order to improve their social interaction skills. Additionally, a specific pattern of correlation and association was found between biochemical variables and specific SAD symptoms, suggesting a potential role of inflammatory pathways in SAD pathophysiology.

Our results confirm the presence of greater social phobic symptoms in subjects with ASD, and support the hypothesis of a role of the inflammatory cascade in the maintenance of the disorder.

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition with substantial functional and societal burden. However, national data on ASD burden remain limited in many middle-income countries, including Jordan.

This study aimed to quantify temporal trends in the prevalence, incidence and disability burden of ASD in Jordan from 1990 to 2023, using estimates from the Global Burden of Disease (GBD) study 2023.

This population-based longitudinal study used GBD 2023 estimates to quantify the burden of ASD in Jordan from 1990 to 2023. As national epidemiological data are limited, estimates were generated with GBD’s Bayesian meta-regression modelling based on regional covariates. Annual prevalence, incidence and years lived with disability (YLDs) were extracted as counts and rates per 100 000 population with 95% uncertainty intervals, and analysed by age, gender and temporal trends.

Between 1990 and 2023, the burden of ASD in Jordan increased substantially, with estimated point-prevalence cases rising from 23 919 to 97 056, and YLDs increasing from 4541 to 18 399. Point estimates of age-standardised prevalence, incidence and YLD rates increased over time, with consistently higher burdens among males and the highest rates observed in early childhood, particularly ages 2–9 years. However, wide and frequently overlapping 95% uncertainty intervals suggest that temporal changes should be interpreted with caution.

This study describes modelled trends in ASD burden in Jordan from 1990 to 2023, showing an overall increase in prevalence, incidence and disability-related measures. These findings should be interpreted cautiously given the reliance on GBD modelled estimates, but they may support Jordanian and similar countries’ future planning of health services and research priorities.

Recent studies indicate that anxiety is one of the most prevalent mental health challenges faced by children and adolescents diagnosed with autism spectrum disorder (ASD).

For young individuals with concurrent Intellectual disability disorder, these rates are projected to be significantly elevated.

Despite the estimated high prevalence of anxiety among young individuals with co-occurring ASD and intellectual developmental disorder (IDD), significant uncertainty remains in the field about the most effective methods for identifying, diagnosing, and treating anxiety in this distinct group.

The main objective of this narrative review is to examine the current literature on effective strategies and adjustments for evaluating and treating anxiety in children and adolescents who have co-occurring ASD and IDD, aiming to support professionals engaged with this significantly neglected group.

To explore existing knowledge on autism in medical education and early clinical training, examine the lived experiences of autistic doctors, review relevant National Health Service policies and legal frameworks, and propose a practical framework for improving inclusion. This study comprised a narrative review of the published literature on autism in medical education, foundation training and healthcare settings, supplemented by a positionality statement informed by the author’s clinical experience.

Common barriers include the hidden curriculum of clinical placements, neurotypical templates in communication assessment, sensory demands of clinical environments, significant risks associated with disclosure and inadequate supervisor awareness. Masking imposes substantial psychological costs, including risk of autistic burnout. Legal frameworks exist but are inconsistently applied in practice. The SUPPORT framework is proposed as a structured model: Structured learning environments, Understanding neurodiversity, Predictability, Practical adjustments, Open culture, Relationships and mentorship, and Trainee well-being.

Autistic doctors bring valuable strengths to clinical practice. The barriers they face during the transition from medical school to clinical practice are documented and preventable. Despite legal protections and growing awareness of neurodiversity, National Health Service training frameworks do not consistently address the needs of autistic clinicians. The SUPPORT framework provides a practical, low-resource approach to addressing these barriers, with implementation requiring greater awareness and institutional commitment. These findings identify the need for medical training to move past recognition of neurodiversity and towards creating clinical environments in which autistic doctors can thrive.

Early diagnosis of autism spectrum disorder (ASD) remains a significant challenge due to the time-consuming and subjective nature of traditional diagnostic methods. This study proposes a reliability-oriented hybrid deep learning framework that provides a low-cost, scalable, non-invasive, and AI-assisted pre-screening tool for early ASD risk indication and referral support.

The proposed system integrates two independent deep learning architectures: (1) a ResNet18 model optimized with 10-fold cross-validation using static periocular image data, and (2) a multi-CNN facial image classification ensemble combining ResNet50, EfficientNet-B0, and DenseNet121 architectures. The periocular pathway uses the fine-tuned ResNet18 fully connected softmax layer as its decision boundary. The facial pathway uses weighted probabilistic averaging, and the two subsystem outputs are subsequently combined through an OR-based reliability fusion rule. Additionally, explainable artificial intelligence (Grad-CAM) was employed to visualize decision-relevant regions.

The static periocular ResNet18 model achieved a sensitivity of 90%, while the multi-CNN facial classification ensemble reached a sensitivity of 87.1% and an AUC of 0.948. Under the conditional-independence assumption, OR-based reliability fusion yielded an analytically estimated system-level sensitivity of 0.9871, corresponding to a joint false-negative probability of approximately 1.29%.

The developed hybrid model is positioned as an AI-assisted pre-screening and early-referral support tool, not as a replacement for clinical evaluation. By combining reliability-oriented parallel fusion with explainable AI, the system supports clinical transparency and offers a scalable pathway for early ASD risk indication.

Problematic teacher–pupil relationships (TPR) affect children’s educational outcomes and mental health, particularly for children with psychiatric disorders and neurodevelopmental conditions, yet few population-based studies have explored this association.

To estimate the association between problematic TPRs and psychiatric disorder classifications including neurodevelopmental conditions and poor mental health, and whether this varied by informant.

A cross-sectional analysis of the Mental Health in Children and Young People 2017 survey (n = 6634) was performed. Mental health was assessed using the Strengths and Difficulties Questionnaire, whereas classification of psychiatric disorders, including neurodevelopmental conditions, was assessed using the Development and Well-Being Assessment. TPR quality was evaluated through reports from parents and children (aged >11 years). Bivariable and multivariable regression analyses were stratified by age (5–10 years, n = 3509; 11–16 years, n = 3010), controlling for covariates. Multiple imputation was used to address missing data.

Data-set information: NatCen Social Research (2024), Mental Health in Children and Young People 2017, UK Data Service, SN 8467.

Problematic TPRs were associated with increased odds of any psychiatric disorder in children aged 5–10 years (adjusted odds ratio = 4.80, 95% CI: 2.74–8.40) and 11–16 years (adjusted odds ratio = 4.07, 95% CI: 2.76–6.01). Strengths and Difficulties Questionnaire total difficulties scores were higher by 4.26 points (95% CI: 3.09–5.42) and 3.93 points (95% CI: 3.04–4.83) for children aged 5–10 and 11–16 years, respectively. Problematic TPRs were also associated with attention-deficit hyperactivity disorder, with no significant differences between informant types. Teacher recognition of poor mental health did not moderate these associations.

These findings support the importance of TPRs in school policy and of supporting teachers to foster positive relationships in schools.

Despite growing recognition as a public health concern, the burden of autism spectrum disorder (ASD) in Ethiopia remains poorly characterised owing to limited diagnostic capacity and epidemiological data, hampering evidence-based policy and intervention planning.

This study aimed to quantify the longitudinal burden of ASD in the Ethiopian paediatric population from 1990 to 2023 and project future trends to 2050 to inform national health strategies.

Using Global Burden of Disease (GBD) 2023 data, we estimated ASD prevalence, incidence and disability-adjusted life-years (DALYs) among Ethiopian children and adolescents. Trends were analysed using joinpoint regression and future burden was forecast to 2050 using autoregressive integrated moving average (ARIMA) modelling.

Between 1990 and 2023, ASD prevalence increased by 155%, from 271 805 to 693 644 cases. Correspondingly, the age-standardised prevalence rate increased from 950 to 1185 cases per 100 000 population. Although annual incident cases nearly doubled (from 24 576 to 47 274), the age-standardised incidence rate declined slightly from 86 to 81 cases per 100 000. DALYs increased by 162% to 134 386 in 2023, while the age-standardised DALY rate increased from 179 to 230 per 100 000. Projections to 2050 suggest that the overall ASD burden will persist, with a stable incidence rate but an increasing DALY rate.

Nearly 700 000 Ethiopian children and adolescents were living with ASD in 2023. The significant increases in prevalence and DALYs over three decades underscore the urgent need for a comprehensive national strategy, including enhanced diagnostic infrastructure and early intervention programmes.

Neurodevelopmental disorder traits are associated with a range of psychosocial difficulties, yet the longitudinal processes linking these traits to mental health remain insufficiently explored. This study examined developmental associations among autism spectrum disorder (ASD) traits, attention-deficit/hyperactivity disorder (ADHD) traits, social maladjustment, and mental health from childhood through adolescence.

Longitudinal data were drawn from 9,619 students across 11 grade cohorts in mainstream classrooms. Cross-lagged structural equation modeling was used to estimate independent longitudinal associations among ASD traits, ADHD traits, social maladjustment, and mental health, while controlling for confounding factors. ASD and ADHD traits were modeled simultaneously to clarify their unique developmental patterns.

ASD traits were consistently associated with later internalizing problems and peer difficulties. ADHD traits were associated with later internalizing and externalizing problems, as well as academic challenges and strained teacher–parent relationships. These associations differed by trait type, suggesting distinct developmental pathways. Several bidirectional associations also emerged: poorer mental health was associated with subsequent social maladjustment, which was subsequently linked to persistent developmental difficulties.

The findings indicate that social maladjustment is involved in the longitudinal associations between developmental disorder traits and mental health and may shape developmental trajectories. These results highlight the need for comprehensive support approaches that consider peer relationships, learning environments, and family–school relationships.

Neuroimaging research in autism often excludes individuals with co-occurring intellectual impairment or minimally verbal status, limiting the generalisability of findings. Understanding magnetic resonance imaging (MRI) scan success predictors in a representative autistic sample is crucial for equitable research.

This study identified factors predicting successful brain MRI acquisition and data quality in diverse autistic individuals, focusing on including those with intellectual impairment or minimally verbal status.

A total of 122 participants (83 autistic individuals (27 with intellectual impairment, 19 with minimally verbal status) and 39 typically developing controls) received multi-modal brain MRI scans (including structural, resting-state functional and diffusion MRI). Scan success, assessed using both binary criteria and quantitative data-quality metrics, was related to participant characteristics.

Although overall scan success was high, specific factors differentiated success within subgroups. Key factors contributing to scan success included age, non-verbal intelligence and attention-deficit hyperactivity disorder (ADHD) symptoms. Older participants, those with fewer ADHD symptoms and those with higher non-verbal intelligence were more likely to achieve successful scans, regardless of autism diagnosis. Higher data quality, particularly in structural and functional MRI, was associated with higher intelligence, better adaptive functioning, fewer autistic and ADHD symptoms, and fewer behavioural problems.

Identifying these factors is key to designing more inclusive and effective neuroimaging protocols. This work paves the way for more comprehensive research into the neurobiology of the full autism spectrum, and offers insights for improving the clinical MRI experience for autistic individuals with diverse support needs. Individualised strategies may also be useful in clinical settings, helping to improve the experience of MRI scanning for autistic individuals.

This study investigated mental health diagnoses in autistic adults to determine whether there were any sex differences in presentation. Autistic adults attending the neurodevelopmental service at Surrey and Borders Partnership NHS Foundation Trust were included.

As part of a service audit, 150 random adults (75 males and females) were selected and their case notes were reviewed. Mental health diagnoses were common: 36% had current suicidal ideation, 20% had attempted suicide, 40% had a past or current diagnosis of anxiety and 62% had a past or current diagnosis of depression. There were more women diagnosed with an eating disorder (9% female, 0% male) and with a historical suicide attempt (21% female, 9% male). However, using a Holm–Bonferroni correction, there were no statistically significant sex differences between mental health diagnoses. Among both sexes, a significant number had been exposed to prenatal and early childhood trauma, nearly 10% had experienced physical trauma, abuse, neglect or assault, and nearly a third had been through parental separation.

This demonstrates that autistic people presenting to a National Health Service diagnostic clinic are more at risk of experiencing trauma, which subsequently increases their risk of mental illness, alongside any neurological predisposition.

The increasing recognition of neurodivergent conditions in healthcare settings in general highlights the necessity for better understanding and management in neuropsychiatric settings. This study aims to investigate the prevalence and co-occurrence of neurodivergent conditions and traits among patients with neuropsychiatric conditions. A descriptive quantitative cross-sectional study was conducted at a tertiary neuropsychiatric clinic. Participants included 166 consecutive patients, assessed using the Comprehensive Autistic Trait Inventory (CATI) and the Adult ADHD Self-Report Scale (ASRS, version 1.1), with demographic characteristics considered.

In our final analysed cohort (n = 79), among patients with functional neurological disorder (FND) 11% had a confirmed diagnosis of adult spectrum disorder (ASD), 17% were believed to have undiagnosed ASD and 55% exhibited clinically significant ASD traits on the CATI tool. Additionally, 13% had a confirmed diagnosis of attention-deficit hyperactivity disorder (ADHD), 25% were believed to have undiagnosed ADHD and 67% exhibited clinically significant ADHD traits on the ASRS tool.

The findings indicate a higher than expected prevalence of neurodivergent conditions in people with FND. Enhanced early identification and tailored treatment approaches are likely to be crucial for improving clinical outcomes and patient experiences in neuropsychiatric settings.