Cannabis Use in Autism: Reasons for Concern about Risk for Psychosis

Teachers and educators' experiences and perceptions of artificial-powered interventions for autism groups

BACKGROUND

Artificial intelligence-powered interventions have emerged as promising tools to support autistic individuals. However, more research must examine how teachers and educators perceive and experience these AI systems when implemented.

OBJECTIVES

The first objective was to investigate informants' perceptions and experiences of AI-empowered interventions for children with autism. Mainly, it explores the informants' perceived benefits and challenges of using AI-empowered interventions and their recommendations for avoiding the perceived challenges.

METHODOLOGY

A qualitative phenomenological approach was used. Twenty educators and parents with experience implementing AI interventions for autism were recruited through purposive sampling. Semi-structured and focus group interviews conducted, transcribed verbatim, and analyzed using thematic analysis.

FINDINGS

The analysis identified four major themes: perceived benefits of AI interventions, implementation challenges, needed support, and recommendations for improvement. Benefits included increased engagement and personalized learning. Challenges included technology issues, training needs, and data privacy concerns.

CONCLUSIONS

AI-powered interventions show potential to improve autism support, but significant challenges must be addressed to ensure effective implementation from an educator's perspective. The benefits of personalized learning and student engagement demonstrate the potential value of these technologies. However, with adequate training, technical support, and measures to ensure data privacy, many educators will likely find integrating AI systems into their daily practices easier.

IMPLICATIONS

To realize the full benefits of AI for autism, developers must work closely with educators to understand their needs, optimize implementation, and build trust through transparent privacy policies and procedures. With proper support, AI interventions can transform how autistic individuals are educated by tailoring instruction to each student's unique profile and needs.

Risk of psychosis in autism spectrum disorder individuals exposed to psychosocial stressors: A 9-year chart review study

Psychosocial stressors have been suggested to precipitate psychotic episodes in patients with pre-existing psychosis and otherwise healthy subjects. However, such a risk has never been formally investigated in individuals with autism spectrum disorder (ASD). Sixty-nine autistic adolescents hospitalized for psychotic/manic symptoms (PSY) and other mental health issues (NPSY) over a 9-year period were compared with reference to their previous exposure to psychosocial stressors. ASD diagnoses satisfied the International Classification of Diseases (ICD)-10 criteria. Psychotic/manic symptom assessment followed the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS). Psychosocial stressor exposure was collected separately at each admission. Preliminarily, univariate between-group comparisons were conducted. Then, a binomial model was adopted to investigate associations with previous exposure to psychosocial stressors. Results were reported with a change in AIC (ΔAIC). PSY patients presented with higher previous exposure to adverse life events (30.43% vs. 6.52%, OR = 6.079 [1.209, 40.926], p = 0.013) and school/work difficulties (30.43% vs. 8.70%, OR = 4.478 [0.984, 23.846], p = 0.034) than NPSY ones. Admissions for psychotic/manic symptoms occurred more likely in the context of family disturbances (OR = 2.275 [1.045, 5.045], p = 0.030) and adverse life events (OR = 3.489 [1.194, 11.161], p = 0.014). The fitted binomial model was found to be significant compared to the random effects model (ΔAIC = -1.962; χ2 10  = 21.96, p = 0.015), with the risk of presenting psychotic/manic symptoms being increased by family disturbances (z = +4.118) and school/work difficulties (z = +2.455). The results suggest a potential psychosis-inducing effect of psychosocial stressors in ASD, which has clinical and policy implications.

Perturbation of 3D nuclear architecture, epigenomic aging and dysregulation, and cannabinoid synaptopathy reconfigures conceptualization of cannabinoid pathophysiology: part 2-Metabolome, immunome, synaptome

The second part of this paper builds upon and expands the epigenomic-aging perspective presented in Part 1 to describe the metabolomic and immunomic bases of the epigenomic-aging changes and then considers in some detail the application of these insights to neurotoxicity, neuronal epigenotoxicity, and synaptopathy. Cannabinoids are well-known to have bidirectional immunomodulatory activities on numerous parts of the immune system. Immune perturbations are well-known to impact the aging process, the epigenome, and intermediate metabolism. Cannabinoids also impact metabolism via many pathways. Metabolism directly impacts immune, genetic, and epigenetic processes. Synaptic activity, synaptic pruning, and, thus, the sculpting of neural circuits are based upon metabolic, immune, and epigenomic networks at the synapse, around the synapse, and in the cell body. Many neuropsychiatric disorders including depression, anxiety, schizophrenia, bipolar affective disorder, and autistic spectrum disorder have been linked with cannabis. Therefore, it is important to consider these features and their complex interrelationships in reaching a comprehensive understanding of cannabinoid dependence. Together these findings indicate that cannabinoid perturbations of the immunome and metabolome are important to consider alongside the well-recognized genomic and epigenomic perturbations and it is important to understand their interdependence and interconnectedness in reaching a comprehensive appreciation of the true nature of cannabinoid pathophysiology. For these reasons, a comprehensive appreciation of cannabinoid pathophysiology necessitates a coordinated multiomics investigation of cannabinoid genome-epigenome-transcriptome-metabolome-immunome, chromatin conformation, and 3D nuclear architecture which therefore form the proper mechanistic underpinning for major new and concerning epidemiological findings relating to cannabis exposure.

Implications of the endocannabinoid system and the therapeutic action of cannabinoids in autism spectrum disorder: A literature review

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, onset in early childhood and associated with cognitive, social, behavioral, and sensory impairments. The pathophysiology is still unclear, and it is believed that genetic and environmental factors are fully capable of influencing ASD, especially cell signaling and microglial functions. Furthermore, the endocannabinoid system (ECS) participates in the modulation of various brain processes and is also involved in the pathophysiological mechanisms of this condition. Due to the health and quality of life impacts of autism for the patient and his/her family and the lack of effective medications, the literature has elucidated the possibility that Cannabis phytocannabinoids act favorably on ASD symptoms, probably through the modulation of neurotransmitters, in addition to endogenous ligands derived from arachidonic acid, metabolizing enzymes and even transporters of the membrane. These findings support the notion that there are links between key features of ASD and ECS due to the favorable actions of cannabidiol (CBD) and other cannabinoids on symptoms related to behavioral and cognitive disorders, as well as deficits in communication and social interaction, hyperactivity, anxiety and sleep disorders. Thus, phytocannabinoids emerge as therapeutic alternatives for ASD.