Facial and Emotional Recognition Systems are technologies that primarily use AI and machine learning to analyze various inputs like facial expression, speech, and physiological signals, to identify an Show more
Facial and Emotional Recognition Systems are technologies that primarily use AI and machine learning to analyze various inputs like facial expression, speech, and physiological signals, to identify and classify human emotions and link them to a variety of epigenomic traits and states. We conducted a Meta-Meta Analysis via Pharmacogenomics (PGx) and Genome-Wide Association Studies (GWAS) across two separate manifestations, including facial physics and emotional expressions. Applying GWAS datasets, 10 GWAS datasets were included, and following multiple filtrations, a GWAS Meta-Meta analysis led to a Secondary Gene List (SGL) of 586 members. Additionally, various indepth silico analyses, such as Protein-Protein Interactions (PPIs), refined 300 genes into a unified network, then, by adding 10 GARS genes, 309 genes remained. A different analysis of PPIs uncovered 141 connected genes (Final Gene List: FGL); more precisely, we conducted a PGx-based approach on this FGL. Finally, 1,480 annotations were found, among them, 682 annotations were significant; thus, we considered the genes with at least one significant annotation and found 54 Pharmacogenes in FGL (PGx-FGL). Through this in-depth analysis, we identified strong, significant top phenotypic roles for both DRD2 and BDNF linking genes in 48,780,906 subjects. Our PGx-based GWAS meta-meta-analyses, coupled with genetic and epigenetic liability testing, connected Facial and Emotional Recognition Systems to Spectrum Disorders (Attention-Deficit Hyperactivity Disorder: ADHD and Autism), Schizophrenia, Depression, and Anxiety. We propose that these findings could have heuristic therapeutic targeting potential and, as such, require intensive further clinical support. Show less
Pharmacogenomics (PGx) is a scientific field that aims to understand how an individual's genetic code regulates drug metabolism and response. The response to many anesthetic drugs varies widely among Show more
Pharmacogenomics (PGx) is a scientific field that aims to understand how an individual's genetic code regulates drug metabolism and response. The response to many anesthetic drugs varies widely among patients due to many factors including, but not limited to, age, gender, and comorbidities. However, PGx contributes to this variability, particularly regarding adverse drug reactions. This review explores the influence of PGx on five commonly used induction agents in anesthesia: propofol, midazolam, ketamine, etomidate, and thiopental. Propofol metabolism is significantly affected by polymorphisms in CYP2B6, CYP2C9, and UGT1A9, influencing both efficacy and toxicity. Midazolam's PGx is mainly mediated by variations in CYP3A4, CYP3A5, and UDP-glucuronosyltransferase enzymes, with implications for sedation depth and drug clearance. Ketamine response is modulated by polymorphisms in metabolic enzymes (e.g. CYP2B6), as well as neurobiological targets such as brain-derived neurotrophic factor and gamma-aminobutyric acid (GABA) receptors, particularly in psychiatric applications. Etomidate shows less studied but emerging PGx associations, including single-nucleotide polymorphisms in GABA receptor subunits and metabolic enzymes, which may affect both sedative depth and cardiovascular stability. Thiopental is a rapid-acting metabolite whose effect stems from GABA-A receptor potentiation; no studies have yet identified specific genetic polymorphisms influencing its action. Overall, PGx provides a promising avenue for tailoring anesthetic management to improve patient safety and outcomes. However, clinical integration remains limited due to practical and infrastructural barriers. This review highlights the potential and current limitations of pharmacogenomic-guided anesthesia, underscoring its relevance in the era of precision medicine. Show less