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All Journal Sriwijaya Journal of Neurology Crown: Journal of Dentistry and Health Research
Tiffany Gabriele
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Dentistry Health Professions Medicine & Pharmacology Neuroscience Public Health

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Genetic and Environmental Influences on Autism Spectrum Disorder: A Multi-Center Study Exploring Gene-Environment Interactions and Biomarkers in Indonesia Vita Amanda; Rashidah Unaib Al-Zayid; Winata Putri; Sonya Syarifah; Tiffany Gabriele; Leonardo Simanjuntak; Cinthya Callathea
Sriwijaya Journal of Neurology Vol. 1 No. 2 (2023): Sriwijaya Journal of Neurology
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjn.v1i1.30

Abstract

Introduction: Autism spectrum disorder (ASD) is a neurodevelopmental condition with a complex etiology involving genetic and environmental factors. This multi-center study investigated gene-environment interactions and potential biomarkers associated with ASD in the Indonesian population. Methods: Children diagnosed with ASD (n=500) and age-matched typically developing controls (n=500) were recruited across five major Indonesian cities. Whole-exome sequencing targeted genotyping, and environmental risk factor assessments were conducted. Biomarker analyses included cytokine levels, oxidative stress markers, and neurotransmitters. Results: Genetic analysis revealed both rare and common variants associated with ASD risk, including variants in CHD8, SCN2A, NRXN1, and novel genes. Prenatal exposures (maternal medication use, infections), perinatal complications (preterm birth, low birth weight), and postnatal factors (pesticide exposure, air pollution) were associated with increased ASD risk. Children with ASD exhibited elevated inflammatory markers (TNF-α, IL-6, IL-1β), increased oxidative stress (higher MDA, lower GSH), and altered neurotransmitter levels (lower serotonin and dopamine) compared to controls. Conclusion: This study provides insights into the interplay of genetic and environmental factors contributing to ASD risk in Indonesia. The identified genetic variants, environmental risk factors, and potential biomarkers may contribute to our understanding of ASD etiology and inform the development of targeted interventions and early detection strategies.
Accuracy and Efficiency of Artificial Intelligence-Driven Treatment Planning in Clear Aligner Therapy: A Comparative Study with Conventional Methods in Bandung, Indonesia Dea Albertina; Akmal Hasan; Tiffany Gabriele; Aisyah Andina Rasyid
Crown: Journal of Dentistry and Health Research Vol. 1 No. 1 (2023): Crown: Journal of Dentistry and Health Research
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/crown.v1i1.55

Abstract

Introduction: Clear aligner therapy (CAT) has gained popularity as an esthetic alternative to traditional braces. Artificial intelligence (AI) is increasingly being integrated into CAT treatment planning, promising improved accuracy and efficiency. This study aimed to compare the accuracy and efficiency of AI-driven treatment planning with conventional methods in Bandung, Indonesia. Methods: A retrospective study was conducted involving 100 patients treated with CAT in Bandung. Fifty patients were treated using conventional methods (CM) by experienced orthodontists, while the other 50 were planned with AI-driven software. Accuracy was assessed by comparing the planned tooth movement with the actual outcome using Little's Irregularity Index (LII) and Peer Assessment Rating (PAR) scores at the end of treatment. Efficiency was evaluated by comparing the time required for treatment planning and the number of refinements needed. Results: The AI-driven group demonstrated significantly lower LII scores (p<0.05) and higher PAR scores (p<0.05) compared to the CM group, indicating greater accuracy in achieving the planned tooth movement. Additionally, the AI-driven group showed a significant reduction in treatment planning time (p<0.05) and fewer refinement aligners required (p<0.05) compared to the CM group. Conclusion: AI-driven treatment planning in CAT demonstrated superior accuracy and efficiency compared to conventional methods in Bandung, Indonesia. AI has the potential to optimize treatment outcomes and reduce treatment time, offering a valuable tool for orthodontists.
Co-Authors Aisyah Andina Rasyid Akmal Hasan Cinthya Callathea Dea Albertina Leonardo Simanjuntak Rashidah Unaib Al-Zayid Sonya Syarifah Vita Amanda Winata Putri