1. Erratum. A Systematic Review of Digital Interventions to Promote Physical Activity in People With Intellectual Disabilities and/or Autism. Adapt Phys Activ Q;2023 (Dec 8):1.

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2. Albekairi TH, Alanazi MM, Ansari MA, Nadeem A, Attia SM, Bakheet SA, Al-Mazroua HA, Aldossari AA, Almanaa TN, Alwetaid MY, Alqinyah M, Alnefaie HO, Ahmad SF. Cadmium exposure exacerbates immunological abnormalities in a BTBR T(+) Itpr3(tf)/J autistic mouse model by upregulating inflammatory mediators in CD45R-expressing cells. J Neuroimmunol;2023 (Dec 3);386:578253.

Autism spectrum disorder (ASD) is a neurodevelopmental illness characterized by behavior, learning, communication, and social interaction abnormalities in various situations. Individuals with impairments usually exhibit restricted and repetitive actions. The actual cause of ASD is yet unknown. It is believed, however, that a mix of genetic and environmental factors may play a role in its development. Certain metals have been linked to the development of neurological diseases, and the prevalence of ASD has shown a positive association with industrialization. Cadmium chloride (Cd) is a neurotoxic chemical linked to cognitive impairment, tremors, and neurodegenerative diseases. The BTBR T(+) Itpr3(tf)/J (BTBR) inbred mice are generally used as a model for ASD and display a range of autistic phenotypes. We looked at how Cd exposure affected the signaling of inflammatory mediators in CD45R-expressing cells in the BTBR mouse model of ASD. In this study, we looked at how Cd affected the expression of numerous markers in the spleen, including IFN-γ, IL-6, NF-κB p65, GM-CSF, iNOS, MCP-1, and Notch1. Furthermore, we investigated the effect of Cd exposure on the expression levels of numerous mRNA molecules in brain tissue, including IFN-γ, IL-6, NF-κB p65, GM-CSF, iNOS, MCP-1, and Notch1. The RT-PCR technique was used for this analysis. Cd exposure increased the number of CD45R(+)IFN-γ(+), CD45R(+)IL-6(+), CD45R(+)NF-κB p65(+), CD45R(+)GM-CSF(+), CD45R(+)GM-CSF(+), CD45R(+)iNOS(+), and CD45R(+)Notch1(+) cells in the spleen of BTBR mice. Cd treatment also enhanced mRNA expression in brain tissue for IFN-γ, IL-6, NF-κB, GM-CSF, iNOS, MCP-1, and Notch1. In general, Cd increases the signaling of inflammatory mediators in BTBR mice. This study is the first to show that Cd exposure causes immune function dysregulation in the BTBR ASD mouse model. As a result, our study supports the role of Cd exposure in the development of ASD.

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3. Garrido D, López B, Carballo G. Bilingualism and language in children with autistic spectrum disorder: a systematic review. Neurologia (Engl Ed);2023 (Dec 6)

INTRODUCTION: Communication and language skills are among the most severely affected domains in individuals with autistic spectrum disorder (ASD). When a child diagnosed with ASD lives in a bilingual environment, the parents often express concerns about whether their child should learn both languages simultaneously, turning to specialists for advice. Despite the lack of evidence of any negative effect, some professionals disagree on this subject. In this systematic review we study whether bilingualism affects language development in children with ASD. METHODS: We reviewed the literature published in 4 different databases. After applying a series of selection criteria, we selected 12 scientific articles, including a total of 328 children diagnosed with ASD (169 bilingual and 159 monolingual), with ages ranging from 3 to 12 years. These patients were evaluated with different receptive and expressive language assessment instruments covering several areas. The assessments were performed directly on the children, although indirect assessment of parents was also performed in some studies. CONCLUSIONS: There seems to be consensus regarding the assertion that bilingualism does not entail any additional difficulty for language development in children with ASD from the age of 3.

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4. Giansanti D. An Umbrella Review of the Fusion of fMRI and AI in Autism. Diagnostics (Basel);2023 (Nov 28);13(23)

The role of functional magnetic resonance imaging (fMRI) is assuming an increasingly central role in autism diagnosis. The integration of Artificial Intelligence (AI) into the realm of applications further contributes to its development. This study’s objective is to analyze emerging themes in this domain through an umbrella review, encompassing systematic reviews. The research methodology was based on a structured process for conducting a literature narrative review, using an umbrella review in PubMed and Scopus. Rigorous criteria, a standard checklist, and a qualification process were meticulously applied. The findings include 20 systematic reviews that underscore key themes in autism research, particularly emphasizing the significance of technological integration, including the pivotal roles of fMRI and AI. This study also highlights the enigmatic role of oxytocin. While acknowledging the immense potential in this field, the outcome does not evade acknowledging the significant challenges and limitations. Intriguingly, there is a growing emphasis on research and innovation in AI, whereas aspects related to the integration of healthcare processes, such as regulation, acceptance, informed consent, and data security, receive comparatively less attention. Additionally, the integration of these findings into Personalized Medicine (PM) represents a promising yet relatively unexplored area within autism research. This study concludes by encouraging scholars to focus on the critical themes of health domain integration, vital for the routine implementation of these applications.

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5. Giansanti D. AI-Enabled Fusion of Medical Imaging, Behavioral Analysis and Other Systems for Enhanced Autism Spectrum Disorder. Comment on Jönemo et al. Evaluation of Augmentation Methods in Classifying Autism Spectrum Disorders from fMRI Data with 3D Convolutional Neural Networks. Diagnostics 2023, 13, 2773. Diagnostics (Basel);2023 (Nov 28);13(23)

I am writing to you in regard to the research article « Johan Jönemo, David Abramian, and Anders Eklund-Evaluation of Augmentation Methods in Classifying Autism Spectrum Disorders from fMRI Data with 3D Convolutional Neural Networks » […].

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6. Kereszturi É. Diversity and Classification of Genetic Variations in Autism Spectrum Disorder. Int J Mol Sci;2023 (Nov 26);24(23)

Autism spectrum disorder (ASD) is a neurodevelopmental condition with symptoms that affect the whole personality and all aspects of life. Although there is a high degree of heterogeneity in both its etiology and its characteristic behavioral patterns, the disorder is well-captured along the autistic triad. Currently, ASD status can be confirmed following an assessment of behavioral features, but there is a growing emphasis on conceptualizing autism as a spectrum, which allows for establishing a diagnosis based on the level of support need, free of discrete categories. Since ASD has a high genetic predominance, the number of genetic variations identified in the background of the condition is increasing exponentially as genetic testing methods are rapidly evolving. However, due to the huge amount of data to be analyzed, grouping the different DNA variations is still challenging. Therefore, in the present review, a multidimensional classification scheme was developed to accommodate most of the currently known genetic variants associated with autism. Genetic variations have been grouped according to six criteria (extent, time of onset, information content, frequency, number of genes involved, inheritance pattern), which are themselves not discrete categories, but form a coherent continuum in line with the autism spectrum approach.

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7. Launay JM, Delorme R, Pagan C, Callebert J, Leboyer M, Vodovar N. Impact of IDO activation and alterations in the kynurenine pathway on hyperserotonemia, NAD(+) production, and AhR activation in autism spectrum disorder. Transl Psychiatry;2023 (Dec 9);13(1):380.

Hyperserotonemia is the most replicated biochemical anomaly associated with autism spectrum disorder (ASD) and has been reported in 35-46% of individuals with ASD. Serotonin is synthesised from the essential amino acid tryptophan (TRP). However, the main catabolic route of TRP is the kynurenine pathway (KP), which competes with serotonin synthesis when indoleamine dioxygenase (IDO) is activated. Using the same cohort of individuals with ASD, we used to report extensive studies of the serotonin/melatonin pathway, and found increased kynurenine (KYN), suggesting IDO activation in 58.7% of individuals with ASD (159/271), supported by a strong negative correlation between KYN/TRP ratio and miR-153-3p plasma levels, which negatively regulates IDO. IDO activation was associated with normoserotonemia, suggesting that IDO activation could mask hyperserotonemia which meant that hyperserotonemia, if not masked by IDO activation, could be present in ~94% of individuals with ASD. We also identified several KP alterations, independent of IDO status. We observed a decrease in the activity of 3-hydroxyanthranilate dioxygenase which translated into the accumulation of the aryl hydrocarbon receptor (AhR) selective ligand cinnabarinic acid, itself strongly positively correlated with the AhR target stanniocalcin 2. We also found a deficit in NAD(+) production, the end-product of the KP, which was strongly correlated with plasma levels of oxytocin used as a stereotypical neuropeptide, indicating that regulated neuropeptide secretion could be limiting. These results strongly suggest that individuals with ASD exhibit low-grade chronic inflammation that is mediated in most cases by chronic AhR activation that could be associated with the highly prevalent gastrointestinal disorders observed in ASD, and explained IDO activation in ~58% of the cases. Taken together, these results extend biochemical anomalies of TRP catabolism to KP and posit TRP catabolism as a possible major component of ASD pathophysiology.

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8. Lewandowska-Pietruszka Z, Figlerowicz M, Mazur-Melewska K. Microbiota in Autism Spectrum Disorder: A Systematic Review. Int J Mol Sci;2023 (Nov 23);24(23)

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by several core symptoms: restricted interests, communication difficulties, and impaired social interactions. Many ASD children experience gastrointestinal functional disorders, impacting their well-being. Emerging evidence suggests that a gut microbiota imbalance may exacerbate core and gastrointestinal symptoms. Our review assesses the gut microbiota in children with ASD and interventions targeting microbiota modulation. The analysis of forty-four studies (meta-analyses, reviews, original research) reveals insights into the gut microbiota-ASD relationship. While specific microbiota alterations are mixed, some trends emerge. ASD children exhibit increased Firmicutes (36-81%) and Pseudomonadota (78%) and decreased Bacteroidetes (56%). The Bacteroidetes to Firmicutes ratio tends to be lower (56%) compared to children without ASD, which correlates with behavioral and gastrointestinal abnormalities. Probiotics, particularly Lactobacillus, Bifidobacterium, and Streptococcus strains, show promise in alleviating behavioral and gastrointestinal symptoms (66%). Microbiota transfer therapy (MTT) seems to have lasting benefits for the microbiota and symptoms in one longitudinal study. Prebiotics can potentially help with gastrointestinal and behavioral issues, needing further research for conclusive efficacy due to different interventions being used. This review highlights the gut microbiota-ASD interplay, offering potential therapeutic avenues for the gut-brain axis. However, study heterogeneity, small sample sizes, and methodological variations emphasize the need for comprehensive, standardized research. Future investigations may unveil complex mechanisms linking the gut microbiota to ASD, ultimately enhancing the quality of life for affected individuals.

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9. Molani-Gol R, Alizadeh M, Kheirouri S, Hamedi-Kalajahi F. The early life growth of head circumference, weight, and height in infants with autism spectrum disorders: a systematic review. BMC Pediatr;2023 (Dec 8);23(1):619.

BACKGROUNDS: The Autism spectrum disorder (ASD) prevalence has increased significantly over the past two decades. This review summarizes the current knowledge of the association between the early life growth of head circumference (HC), weight, and height with ASD in infants. METHODS: PubMed, Scopus, Science Direct, and Google Scholar databases were searched up to November 2021 using relevant keywords. All original articles are written in English evaluating the early life growth of HC, weight, and height in infants with ASD were eligible for the present review. RESULTS: Totally, 23 articles involving 4959 infants were included in this review. Of 13 studies that evaluated HC of infants at birth, 10 studies (83.33%) showed that the HC at the birth of autistic children was similar to that of the average found in the control group. Among 21 studies that evaluated the HC and weight status in infants, 19 studies (90.47%) showed that autistic children had larger HC and weight than the control group or abnormal acceleration of head growth during infancy. Height growth of infants was investigated in 13 studies, of which 10 cases (76.92%) reported that infants with ASD were significantly longer than control groups. Most of he included studies had a good quality. CONCLUSIONS: The findings suggest that in infants with ASD, without the contribution of birth growth factors and sex of the child, the growth of HC, weight, and height probably was faster than in infants with normal development, in early life. Therefore, these measurements might be useful as initial predictive biomarkers for the risk of developing ASD.

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10. Önal S, Sachadyn-Król M, Kostecka M. A Review of the Nutritional Approach and the Role of Dietary Components in Children with Autism Spectrum Disorders in Light of the Latest Scientific Research. Nutrients;2023 (Nov 21);15(23)

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects several areas of mental development. The onset of ASD occurs in the first few years of life, usually before the age of 3 years. Proper nutrition is important to ensure that an individual’s nutrient and energy requirements are met, and it can also have a moderating effect on the progression of the disorder. A systematic database search was conducted as a narrative review to determine whether nutrition and specific diets can potentially alter gastrointestinal symptoms and neurobehavioral disorders. Databases such as Science Direct, PubMed, Scopus, Web of Science (WoS), and Google Scholar were searched to find studies published between 2000 and September 2023 on the relationship between ASD, dietary approaches, and the role of dietary components. The review may indicate that despite extensive research into dietary interventions, there is a general lack of conclusive scientific data about the effect of therapeutic diets on ASD; therefore, no definitive recommendation can be made for any specific nutritional therapy as a standard treatment for ASD. An individualized dietary approach and the dietician’s role in the therapeutic team are very important elements of every therapy. Parents and caregivers should work with nutrition specialists, such as registered dietitians or healthcare providers, to design meal plans for autistic individuals, especially those who would like to implement an elimination diet.

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11. Pandya S, Jain S, Verma J. A comprehensive analysis towards exploring the promises of AI-related approaches in autism research. Comput Biol Med;2023 (Dec 7);168:107801.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that presents challenges in communication, social interaction, repetitive behaviour, and limited interests. Detecting ASD at an early stage is crucial for timely interventions and an improved quality of life. In recent times, Artificial Intelligence (AI) has been increasingly used in ASD research. The rise in ASD diagnoses is due to the growing number of ASD cases and the recognition of the importance of early detection, which leads to better symptom management. This study explores the potential of AI in identifying early indicators of autism, aligning with the United Nations Sustainable Development Goals (SDGs) of Good Health and Well-being (Goal 3) and Peace, Justice, and Strong Institutions (Goal 16). The paper aims to provide a comprehensive overview of the current state-of-the-art AI-based autism classification by reviewing recent publications from the last decade. It covers various modalities such as Eye gaze, Facial Expression, Motor skill, MRI/fMRI, and EEG, and multi-modal approaches primarily grouped into behavioural and biological markers. The paper presents a timeline spanning from the history of ASD to recent developments in the field of AI. Additionally, the paper provides a category-wise detailed analysis of the AI-based application in ASD with a diagrammatic summarization to convey a holistic summary of different modalities. It also reports on the successes and challenges of applying AI for ASD detection while providing publicly available datasets. The paper paves the way for future scope and directions, providing a complete and systematic overview for researchers in the field of ASD.

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12. Rogala J, Żygierewicz J, Malinowska U, Cygan H, Stawicka E, Kobus A, Vanrumste B. Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis. Sci Rep;2023 (Dec 8);13(1):21748.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder hallmarked by challenges in social communication, limited interests, and repetitive, stereotyped movements and behaviors. Numerous research efforts have indicated that individuals with ASD exhibit distinct brain connectivity patterns compared to control groups. However, these investigations, often constrained by small sample sizes, have led to inconsistent results, suggesting both heightened and diminished long-range connectivity within ASD populations. To bolster our analysis and enhance their reliability, we conducted a retrospective study using two different connectivity metrics and employed both traditional statistical methods and machine learning techniques. The concurrent use of statistical analysis and classical machine learning techniques advanced our understanding of model predictions derived from the spectral or connectivity attributes of a subject’s EEG signal, while also verifying these predictions. Significantly, the utilization of machine learning methodologies empowered us to identify a unique subgroup of correctly classified children with ASD, defined by the analyzed EEG features. This improved approach is expected to contribute significantly to the existing body of knowledge on ASD and potentially guide personalized treatment strategies.

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13. Sari NP, Tsompanidis A, Wahab RJ, Gaillard R, Aydin E, Holt R, Allison C, Baron-Cohen S, van IMH, Jansen PW. Is the association between mothers’ autistic traits and childhood autistic traits moderated by maternal pre-pregnancy body mass index?. Mol Autism;2023 (Dec 8);14(1):46.

BACKGROUND: Previous studies showed that there is a positive association between mothers’ and children’s autistic traits. We also tested if this association is more pronounced in mothers with a higher pre-pregnancy body mass index (BMI). METHOD: The study was embedded in two cohorts with information available for 4,659 participants from the Generation R and for 179 participants from the Cambridge Ultrasound Siblings and Parents Project (CUSP) cohort. In both cohorts, maternal autistic traits were assessed using the short form of the Autism Spectrum Quotient, and information about maternal height and weight before pregnancy was obtained by questionnaire. Child autistic traits were assessed with the short form of Social Responsiveness Scale in Generation R (M = 13.5 years) and with the Quantitative Checklist for Autism in Toddlers (Q-CHAT) in the CUSP cohort (M = 1.6 years). RESULT: Higher maternal autistic traits were associated with higher autistic traits in toddlerhood (CUSP cohort; β(adjusted) = 0.20, p < 0.01), in early childhood (Generation R; β(adjusted) = 0.19, p < 0.01), and in early adolescence (Generation R; β(adjusted) = 0.16, p < 0.01). Furthermore, a higher maternal pre-pregnancy BMI was associated with higher child autistic traits, but only in Generation R (β(adjusted) = 0.03, p < 0.01). There was no significant moderating effect of maternal pre-pregnancy BMI on the association between autistic traits of mothers and children, neither in Generation R nor in CUSP. In addition, child autistic traits scores were significantly higher in mothers who were underweight and in mothers who were overweight compared to mothers with a healthy weight. CONCLUSION: We confirm the association between maternal and child autistic traits in toddlerhood, early childhood, and early adolescence. Potential interacting neurobiological processes remain to be confirmed.

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