Limited research has assessed associations between schizophrenia and genetic variants of the ankyrin repeat and kinase domain containing 1 (ANKK1) and lymphotoxin-alpha (LTA) genes among individuals of Middle Eastern ancestry. Here we present the first association study investigating theANKK1rs1800497 (T>C) andLTArs909253 (A>G) single-nucleotide polymorphisms in an Egyptian population. Among 120 patients with DSM-IV and PANSS (Positive and Negative Syndrome Scale) assessments of schizophrenia and 100 healthy controls, we determined the genotypes for the polymorphisms using endonuclease digestion of amplified genomic DNA. Results confirmed previous findings from different ethnic populations, in that the rs1800497 and rs909253 polymorphisms were both associated with risk of schizophrenia. Differences between the genotypes of cases and controls were strongly significant (P= 0.0005 for rs1800497 andP= 0.001 for rs909253). The relative risk to schizophrenia was 1.2 (P= 0.01) for the C allele and 0.8 (P= 0.04) for the G allele. The CC, GG, and combined CC/AA genotypes were all more frequent in cases than in controls. These results support an association betweenANKK1andLTAgenetic markers and vulnerability to schizophrenia and show the potential influence of just one copy of the mutant C or G allele in the Egyptian population.

Purpose: Several interacting genes or single nucleotide polymorphisms (SNPs) are vulnerable to the risk of autism spectrum disorder (ASD). Here we explored associations between SNPs in the methylenetetrahydrofolate reductase (MTHFR) gene or combined genotypes and the risk of ASD in a Saudi community. Subjects and methods: ASD severity symptoms were assessed according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) criteria and scores on the childhood autism rating scale (CARS). Genomic DNA from buccal cells was analyzed for 112 cases and 104 healthy controls using TaqMan genotyping assays of 677C>T rs1801133 and 1298A>C rs1801131 SNPs in the MTHFR gene. SNPStats software was utilized to determine the best interactive model of inheritance of genotypic data. Results: Controls were consistent with Hardy-Weinberg equilibrium in the examined SNPs. Our data showed associations between the 677C>T and 1298A>C SNPs and ASD risk (odds ratio [OR]= 5.2; 95% confidence interval [CI], 3.1–9.8 and OR= 22.2; 95% CI, 7.9–62.3, respectively). Genotype associations of 677C>T and 1298A>C were identified in cases compared with controls (P= 0.0012 and P= 0.0008, respectively). The examined SNPs were significantly associated with ASD cases having ≥37 scores (codominant and recessive models; P= 0.001 and P= 0.0005, respectively). Six combined genotypes—C/C-A/A (42.9%), C/T-A/A (17.9%), C/T-C/C (14.5%), C/T-A/C (10.9%), T/T-C/C (10.9%), and T/T-A/A (3.6%)—were found in ASD cases. Global haplotype analysis showed a significant difference in haplotype distribution between cases and controls (P= 0.00057). The two SNPs were found to be in relatively strong linkage disequilibrium (D`= 0.63, r²= 0.260). Conclusion: Our findings suggest that the 677C>T and 1298A>C SNPs add to each other for potential vulnerability to increase the risk of ASD, particularly if they can be confirmed in larger cohorts along with other genetic/environmental factors. Our study could create reference data for future genetic association studies in the Saudi population and for use by government and health experts to develop regional health management programs.

Background: Social media and conversational artificial intelligence (AI) such as ChatGPT are increasingly embedded in daily life, raising questions about their impact on mental health. Prior studies link social media use to both psychological distress and coping resources, but little is known about how ChatGPT relates to validated measures of depression, anxiety, and somatization. This study examined whether patterns of ChatGPT use and exposure to mental health content on social media predict mental health outcomes in university students. Methods: A cross-sectional survey was conducted with 200 students. Participants completed demographic questions, items on social media and ChatGPT use, and three standardized measures: the Beck Depression Inventory-II (BDI-II), the Generalized Anxiety Disorder Scale (GAD-7), and the Patient Health Questionnaire Somatization Subscale (PHQ-15). Social media and ChatGPT variables (e.g., daily hours, exposure to mental health content, use during stress, impact on sleep/social life) were dichotomized for analysis. Independent-samples t -tests and multiple regression analyses were performed in RStudio, controlling for demographic covariates. Results: Time spent on social media was not significantly related to outcomes. However, exposure to mental health content predicted higher depression, anxiety, and somatization. ChatGPT use showed mixed associations: greater comfort discussing mental health with ChatGPT predicted lower symptoms, while frequent use, reliance during stress, preference over human interaction, and reported sleep disruption were linked to poorer outcomes. Conclusion: Both social media and ChatGPT appear to function as double-edged tools. While maladaptive reliance and disruptive effects predict greater distress, reflective engagement and comfort with AI use may buffer symptoms. These results highlight the need for digital literacy education, clinical awareness of AI use as a coping tool, and design safeguards to mitigate risks while promoting healthy engagement with digital technologies.