PURPOSE:Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by persistent deficits in social communication and the presence of restrictive, repetitive patterns of behaviour. Although its exact aetiology remains multifaceted and partially understood, recent clinical interest has shifted towards neurobiological substrates, specifically neuroaxonal and astroglial integrity. This study aims to compare serum levels of Neurofilament Light Chain (NfL), Glial Fibrillary Acidic Protein (GFAP), Tau and S100B between children with ASD and healthy controls, while investigating the influence of these biochemical variables on autism severity and behavioural manifestations. METHODS:The study cohort consisted of 44 children (aged 24-72 months) diagnosed with ASD according to DSM-5-TR criteria and 40 age-matched healthy controls. Clinical assessments were conducted using the Childhood Autism Rating Scale (CARS), the Aberrant Behaviour Checklist (ABC) and the Autism Behaviour Checklist. Serum concentrations of the targeted biomarkers were measured using the ELISA method from venous blood samples. RESULTS:Serum NfL, Tau, GFAP and S100B concentrations did not differ significantly between children with ASD and healthy controls. Exploratory analyses suggested possible associations between selected biomarkers and clinical characteristics; however, these associations did not remain statistically significant after age adjustment and correction for multiple comparisons. Further studies using larger cohorts and ultrasensitive analytical platforms are needed to validate these preliminary findings. CONCLUSION:These findings indicate that serum NfL, Tau, GFAP and S100B did not differentiate children with ASD from healthy controls. Exploratory biomarker-clinical associations did not remain statistically significant after age adjustment and correction for multiple comparisons. Larger longitudinal studies using ultrasensitive analytical platforms are warranted.
Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition characterized by inattention, hyperactivity and impulsive behaviour, which impair patient functioning. There is a growing trend among parents to use complementary and alternative medicines alongside conventional treatments to manage their children's ADHD symptoms. The aim of this review is to elucidate the therapeutic methods of Traditional Chinese Medicine (TCM) by discussing their scientifically established mechanisms of action, effectiveness and side effects, thereby creating a comparative understanding relative to current pharmaceutical treatments for ADHD. Psychostimulants are the first-choice pharmacological treatment; they are effective in alleviating symptoms but are often associated with side effects. These issues have prompted interest in complementary and alternative medicine (CAM) approaches. TCM offers various methods for managing ADHD. This review synthesizes current evidence on the TCM perspective regarding ADHD aetiology and its main therapeutic options, including herbal treatments, acupuncture, Tai Chi and paediatric tuina, along with their proposed neurobiological mechanisms. The clinical safety and efficacy of these treatments were evaluated based on randomized controlled trials (RCTs) and meta-analyses. These findings demonstrate that TCM interventions significantly reduce core ADHD symptoms, often with acceptable safety profiles. This review concludes that TCM represents a valuable complementary approach; however, more high-quality research is needed to standardize treatments and facilitate their evidence-based integration into conventional psychiatric management and therapy.
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a broad spectrum of symptoms, which makes timely and accurate diagnosis challenging. The development of machine learning (ML) and deep learning (DL) has created opportunities for automated ASD screening and detection. This systematic review focuses on the analyses of 59 peer-reviewed studies on unimodal and multimodal approaches to ASD detection that were published between 2019 and 2025. The results demonstrated that classical ML algorithms (such as logistic regression [LR], support vector machines [SVM] and random forests [RF]) and DL models (convolutional neural networks [CNN], recurrent neural networks (RNN) and transformers) were used to assess the accuracy of the diagnosis for a variety of data modalities ranging from behavioural measures to neuroimaging, electroencephalography (EEG), eye tracking and speech, with accuracy from 68% to 99%. A careful examination of these studies, however, shows that they share certain common flaws, including small sample size, demographic bias, overfitting and absence of external validation. Hybrid multimodal frameworks have been shown to yield consistent performance improvements over unimodal frameworks, with accuracies of 95%-99% achieved through attention, graph-based learning and hybrid fusion approaches. This review highlights four major points: (1) a critical review of dataset ethics and validity, even for non-clinical facial image datasets; (2) an architectural comparison of multimodal fusion strategies (early fusion, late fusion and hybrid fusion) focusing on computational complexity and clinical applicability; (3) a quantitative summarization of the performance trends by modalities and sample size; and (4) a structured review of indicators of reproducibility and regulatory hurdles for clinical translation. This review suggests the need to develop large, well-balanced datasets, the application of explainable AI (XAI) techniques, standardization (e.g., brain imaging data structure [BIDS]) and regulatory guidelines for facilitating the clinical translation of ASD detection systems.
SLC13A3 pathogenic variants are associated with acute reversible leukoencephalopathy and α-ketoglutarate accumulation (ARLIAK), a rare neurological disorder characterized by recurrent episodes of encephalopathy and transient white matter abnormalities. Pathogenic variants reported so far include missense, nonsense and small deletion variants, highlighting substantial allelic heterogeneity. We describe a patient presenting with multiple episodes of acute encephalopathy, elevated urinary α-ketoglutarate and reversible white matter lesions on MRI, consistent with SLC13A3-related ARLIAK. Genetic analysis identified novel compound heterozygous variants: a missense variant (p.Leu46Pro) in a highly conserved region and a larger deletion encompassing exons 2-3. Our findings indicate that conventional sequencing alone may miss larger deletions, suggesting the need for copy number analysis as part of the diagnostic protocol for suspected ARLIAK cases. The elevated urinary α-ketoglutarate in our patient supports its potential as a noninvasive biomarker. MRI findings demonstrated typical transient and reversible white matter abnormalities, aligning with previously reported cases. This study expands the molecular and phenotypic spectrum of SLC13A3-related ARLIAK and underscores the importance of combining sequencing with copy number analysis for accurate diagnosis. The identification of novel variants contributes to a better understanding of the disease mechanism and suggests a broader allelic heterogeneity than previously recognized.
BACKGROUND:This study compared peripheral haematological inflammation indices obtained from complete blood count (CBC) parameters between preschoolers with developmental language disorder (DLD) and typically developing peers (TD) and examined the role of peripheral inflammatory processes in preschoolers with DLD. METHODS:One hundred twenty-seven children (61 with DLD and 66 TD) aged 36-60 months were included in this hospital-based, retrospective, case-controlled study. Complete blood count parameters and the resulting indices were compared: Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI). Statistical analyses included group comparisons, multivariable logistic regression, and Receiver Operating Characteristic (ROC) analysis. RESULTS:The DLD registered significantly lower lymphocyte, higher monocyte, and NLR, SII and SIRI values compared to the TD (p = 0.015, p < 0.001, p < 0.001, p = 0.007, p < 0.001, respectively). Analysis of covariance revealed that the groups had a significant main effect on monocytes, NLR, SII and SIRI levels and that this effect was independent of age and gender. At multivariable logistic regression, SIRI emerged as a significant independent predictor of DLD, alongside male gender and a lower paternal education level ((p = 0.002, p = 0.007, p = 0.009, respectively). ROC analysis showed that SIRI had discriminative ability for DLD (AUC = 0.765, p < 0.001). CONCLUSION:The findings suggest that peripheral inflammatory processes, reflected by NLR, SII and particularly SIRI, may be associated with DLD in preschoolers. Although SIRI showed high specificity but low sensitivity, the present results provide preliminary evidence that it may represent a potential peripheral inflammation index associated with DLD.
