1. Al-Beltagi M. Enigma of autism regression mechanistic pathways, clinical phenotypes, and early intervention implications. World J Clin Pediatr;2026 (Jun 9);15(2):118495.

Autism regression, defined by the loss of previously acquired social, communicative, and language skills, affects approximately 25%-30% of children on the autism spectrum, most commonly emerging between 12 months and 30 months of age. Once debated as a potential artifact of parental recall or observation bias, contemporary evidence-including home-video analyses and prospective sibling studies-establishes regression as a biologically grounded neurodevelopmental deviation rather than a psychogenic phenomenon. This review presents an integrated model of regression, conceptualizing it as a « neurobiological crisis » in which the convergence of physiological stressors unmasks latent genetic vulnerabilities during a critical period of brain reorganization. Central mechanistic pathways include excessive synaptic pruning, excitatory-inhibitory imbalance, neuroinflammation, mitochondrial dysfunction, and dysbiosis of the gut-brain axis. From a clinical perspective, the period immediately following skill loss represents a window of heightened neuroplasticity. Pediatricians are urged to move beyond a « wait-and-see » approach, adopting a dual-track strategy that combines rapid medical and genetic evaluation to exclude pathological mimics (e.g., Landau-Kleffner syndrome, metabolic disorders) and to initiate immediate intensive behavioral and developmental interventions. Leveraging naturalistic developmental and behavioral interventions, as well as and parent-mediated strategies, many children achieve a meaningful functional reacquisition of lost skills. Ultimately, this review advocates empowered realism, framing regression as a dynamic, modifiable process in which early identification, precision-guided assessment, and evidence-based intervention can optimize long-term developmental and adaptive outcomes.

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2. Allwsh TA, Kucher DS. Role of soluble alpha-klotho as a novel biomarker for characterizing children with autism spectrum disorder in Kurdistan, Iraq. World J Clin Pediatr;2026 (Jun 9);15(2):112164.

BACKGROUND: The identification of bioindicators for the detection and monitoring of autism spectrum disorder (ASD) still remains a major challenge in clinical medicine. The protein klotho has been linked to various neurological disorders. AIM: To investigate the evaluation of soluble alpha-klotho (S-KLα) as a new indicator for the diagnosis and monitoring of patients with ASD. This study was conducted in the absence of any prior studies or research on the link between klotho and ASD, nor on how it affects the characteristics of those impacted. To address this gap, we considered this study. METHODS: The case-control study involved 256 individuals of both sexes, aged between 2 years and 15 years, divided into two groups: 156 children with ASD from autism centers in Dohuk and Zakho cities, Kurdistan Region/Iraq, and 100 healthy individuals serving as the control group. Serum S-KLα level was measured using enzyme-linked immunosorbent assay. Additionally, levels of hemoglobin, iron, glucose, uric acid, creatinine, and vitamin D3 were estimated, with all measurements conducted in duplicate. Afterwards, statistical analyses were performed. An additional component was a questionnaire containing information about the participants. RESULTS: The results showed a significant decrease in S-KLα levels in the sera of patients with ASD (97.5 ± 19.6) compared to the control group (133.7 ± 32.4). Additionally, the area under the curve (0.91) from the receiver operating characteristic analysis confirms the potential of using S-KLα as a marker for ASD. Furthermore, a notable decline in S-KLα was observed with increasing age, and higher levels of S-KLα were found in girls compared to boys, in both the control and patient groups (P < 0.001). The S-KLα levels in patients with ASD were not influenced by family history or birth weight. However, a significant reduction was seen in patients who experienced a difficult birth (dystocia), as well as in preterm births (< 37 weeks) and post-term births (> 42 weeks). It was observed that there was a significantly negative correlation between S-KLα and glucose level. By contrast, positive correlations were found with hemoglobin, iron, and vitamin D3 (P < 0.01); however, no relationship was detected with creatinine and uric acid levels in patients with ASD. CONCLUSION: This is the first case-control study to confirm the strong potential of serum S-KLα as a predictive biomarker in autism risk profiling. The proposals outlined by the study suggest new directions and strategies for future research, linking klotho as a potential diagnostic marker for ASD and its complications, as well as its possible role as a therapeutic target.

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3. Elbeltagi YM, Abd Rab El Rasool AO, Elkashlan AM, Al-Beltagi M. Medical treatment of autism spectrum disorder in children: Current evidence, controversies, and clinical challenges. World J Clin Pediatr;2026 (Jun 9);15(2):117274.

BACKGROUND: Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition associated with debilitating comorbidities [e.g., aggression, irritability, gastrointestinal (GI) issues]. Medical management primarily targets these symptoms, as no drug is Food and Drug Administration-approved for core social-communication deficits. AIM: To synthesize the efficacy and safety of five major pharmacological classes and evaluate the emerging evidence for biomarker-driven (precision medicine) interventions in pediatric ASD. METHODS: Following PRISMA guidelines, we systematically reviewed randomized controlled trials (RCTs) for five classes: Atypical antipsychotics, stimulants, selective serotonin reuptake inhibitors, metabolic/nutritional, and microbiota-gut-brain axis agents. Quantitative meta-analysis for antipsychotics (n = 5 RCTs pooled) used the random-effects model, reporting I (2) to quantify heterogeneity. RESULTS: Atypical antipsychotics are the only drugs with robust, established efficacy for severe irritability: Pooled analysis for risperidone (n = 3 RCTs) showed a significant mean difference of approximately -11.0 on Aberrant Behavior Checklist-Irritability subscale (I (2) approximately 72%). Risperidone carries a greater metabolic burden (e.g., weight gain) than aripiprazole. Stimulants and selective serotonin reuptake inhibitors, respectively. Emerging therapies demonstrate targeted potential: Microbiota transfer therapy significantly improved GI and behavioral symptoms in cohorts with GI disease. Similarly, the efficacy of High-dose folinic acid was concentrated in the subgroup with folate receptor-α autoantibodies. CONCLUSION: The management of ASD demands a shift to a precision medicine model, as the efficacy of interventions is highly variable and concentrated in specific patient subgroups. Future research must prioritize the validation of biological biomarkers (metabolic, genetic, neurophysiological) to reliably predict treatment response, guiding the selection of targeted therapies, and addressing current evidence gaps.

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4. Ran J, Shultz S, Risk BB, Benkeser D. Nonparametric motion control in functional connectivity studies in children with autism spectrum disorder. Biometrics;2026 (Apr 9);82(2)

Autism spectrum disorder (ASD) is a neurodevelopmental condition associated with difficulties with social interactions, communication, and restricted or repetitive behaviors. To characterize ASD, investigators often use functional connectivity derived from resting-state functional magnetic resonance imaging of the brain. However, participants’ head motion during the scanning session can induce motion artifacts. Many studies remove participants with excessive motion and then estimate the effect of diagnosis on functional connectivity using linear regression. However, participant exclusions and linearity assumptions can cause biases. We propose an estimand that quantifies the difference in average functional connectivity in autistic and non-ASD children while standardizing motion relative to the low motion distribution in scans that pass motion quality control. We introduce a nonparametric estimator for motion control, called Motion Controlled (MoCo), that uses all participants and flexibly models the impacts of motion and other relevant features using an ensemble of machine learning methods. We establish large-sample efficiency and multiple robustness of our proposed estimator. The framework is applied to estimate the difference in functional connectivity between 132 autistic and 245 non-ASD children, of which 34 and 126 pass motion quality control, respectively. MoCo appears to dramatically reduce motion artifacts compared to a standard approach with no participant removal, while more efficiently utilizing participant data and accounting for possible selection biases compared to participant removal.

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