BackgroundPharmacogenomics (PGx) can optimise cardiovascular therapy, yet routine integration in cardiology remains limited. In the United Arab Emirates, a hybrid public-private health system, the real-world PGx use is still emerging. However, there is limited understanding of how cardiologists perceive and navigate PGx implementation within such complex health system contexts.ObjectiveTo examine cardiologists' perspectives on the feasibility, barriers, and facilitators of implementing PGx using the Consolidated Framework for Implementation Research (CFIR).MethodsA qualitative study using an abductive analytical approach was conducted through semi-structured interviews with 15 cardiologists from public and private institutions. Participants were recruited via purposive, convenience, and snowball sampling. Interviews were transcribed verbatim and thematically analysed in NVivo. The CFIR guided the analysis across intervention characteristics, outer setting, inner setting, individual characteristics, and process.ResultsClinicians expressed strong conceptual support for PGx, especially in higher-risk scenarios, but reported limited hands-on exposure and confidence. Barriers included perceived test complexity, cost, and lack of reimbursement; insufficient laboratory capacity and EHR integration; unclear workflows and role ownership; and turnaround times misaligned with acute care. Outer-setting constraints (ambiguous policy signals and payer criteria) and inner-setting variability (resources, leadership engagement, and communication pathways) further limited uptake. Reported facilitators included multidisciplinary service models (with input from pharmacists and genetics), targeted case-based training, initial deployment in non-acute contexts, and the structured capture of results with EHR-embedded clinical decision support.ConclusionsPGx implementation in cardiology within the UAE is shaped by structural, organisational, and workforce-level gaps. Addressing these through targeted clinical guidance, improved training, stronger reimbursement mechanisms, enhanced laboratory capacity, and integrated digital decision support may enable more equitable and scalable adoption. These findings provide actionable insights for health systems seeking to operationalise PGx within diverse or hybrid healthcare contexts.

BackgroundThe genetic profile may contribute to interindividual differences in clopidogrel response among patients with acute coronary syndrome (ACS). Although CYP2C19 variants have documented established recommendations that they have contributed to reducing the efficacy of medication, the clinical impact of ABCB1 polymorphisms on clopidogrel bioavailability and subsequent clinical outcome remains controversial. This study evaluates the role of ABCB1 and CYP2C19 variants with major adverse cardiovascular events (MACE) in patients with ACS in a sample obtained from the United Arab Emirates (UAE).MethodsThis retrospective cohort study included 174 patients with ACS treated with clopidogrel. Genotyping for ABCB1 and CYP2C19 variants was performed using real-time PCR® TaqMan assays. Associations with 1-year MACE outcome were assessed using genotype, carrier status, and dominant and recessive models. A multivariable logistic regression analysis was also conducted.ResultsThe minor allele frequencies for the ABCB1 gene variants, rs1045642, rs2032582, and rs1128503, were 48.85%, 50%, and 51.44%, respectively. The presence of ABCB1 alleles had no significant correlation outcomes with MACE, regardless of the genetic models used (p-value > 0.05). CYP2C19*2 was associated with a weak trend of increased risk of MACE in the dominant model (RR = 1.41; 95% CI: 0.95–2.10; p-value = 0.08), whereas the rare CYP2C19*3 (1.15%) was not significantly related to any of the outcomes. In multivariable logistic regression, only age showed a non-significant trend toward higher risk.ConclusionThe study did not find any significant link between common ABCB1 variants and MACE in patients with ACS treated with clopidogrel from the UAE sample. None of these variants were found to be predictors of outcomes after a multivariate analysis was performed. These findings suggest limited current utility for ABCB1 variants in clopidogrel response.

Tacrolimus, a calcineurin inhibitor with a narrow therapeutic index, requires precise dosing to optimize efficacy and minimize adverse effects in kidney transplant recipients. Although CYP3A5 genetic variants influence tacrolimus pharmacokinetics, they do not fully explain inter-individual differences. This retrospective study evaluated the combined impact of CYP3A4 [*1B (rs2740574), *1 G (rs2242480), *22 (rs35599367)] and CYP3A5 [*3 (rs776746), *6 (rs10264272), *7 (rs41303343)] genetic variants, as CYP3A phenotypes, on tacrolimus dose-adjusted trough concentrations (C0/D), in 94 Greek kidney transplant recipients at five time points during the first-year post-transplantation. Significant differences in tacrolimus C0/D ratios were observed across the groups. Group 4 (CYP3A5 expressers, carriers of CYP3A4*1B or *1 G) had consistently lower C0/D ratios compared to Groups 1 and 2 (CYP3A5 nonexpressers, carriers of CYP3A4*22 or CYP3A4 *1/*1) at multiple timepoints (p ≤ 0.022 and p ≤ 0.004, respectively). These findings suggest that CYP3A phenotypes could improve tacrolimus dosing decisions in kidney transplant recipients.

Diabetes mellitus arises from complex interactions between biological susceptibility and diverse environmental influences that extend beyond traditional “genes versus lifestyle” models. The exposome is defined as the aggregate of non-genetic environmental exposures and their biological consequences across the life course. It provides a systems-level framework to evaluate this interaction, spanning chemical, physical, social, and behavioral domains while integrating them with internal molecular responses to decode how environments shape metabolic health. Emerging assessment strategies, including exposome-wide association studies (ExWAS), high-resolution mass spectrometry–based biomonitoring, geospatial and remote-sensing platforms, and wearable exposure sensors, enable characterization of multi-exposure profiles rather than single agents in isolation. Within exposome domains, air pollution, endocrine-disrupting chemicals, food-borne contaminants, exposome characteristics of the built environment, and chronic psychosocial stress have each been shown to contribute to the development and progression of both type 1 and type 2 diabetes. These diverse exposures often share common pathogenic mechanisms of chronic low-grade inflammation, oxidative and nitrosative stress, alterations in the gut microbiome, and epigenetic changes that ultimately lead to glucose dysregulation. Recent progress in metabolomics, lipidomics, and epigenomics is elucidating the “internal exposome,” providing molecular fingerprints that encode prior exposure and detect early metabolic disruption. This integrated approach argues for a paradigm adjustment in diabetes prevention from behavior-centered strategies to those that also target upstream environmental determinants. Exposomic information can improve risk prediction, inform precision public health and medical practice, and inform policy regarding air pollution, chemical use, urban planning, and food systems. This review summarizes the current state of knowledge of the exposome in diabetes, describing its conceptual underpinnings, major tools of assessment, major epidemiologic findings, and biological mechanisms, and identifying the key challenges and opportunities for exposomics to inform effective approaches to diabetes prevention and planetary health.

Over the past few years, immune checkpoint inhibitors resulted in magnificent and durable successes in treating cancer; however, only a minority of patients respond favorably to the treatment due to a broad-spectrum of tumor-intrinsic and tumor-extrinsic factors. With the recent insights gained into the mechanisms of resistance, combination treatment strategies to overcome the resistance and enhance the therapeutic potential of immune checkpoint inhibitors are emerging and showing promising results in both pre-clinical and clinical settings. This has been derived through multiple interconnected mechanisms such as enhancing tumor immunogenicity, improving neoantigen processing and presentation in addition to augmenting T cell infiltration and cytotoxic potentials. In the clinical settings, several avenues of combination treatments involving immune checkpoint inhibitors were associated with considerable improvement in the therapeutic outcome in terms of patient’s survival and tumor growth control. This, in turn, increased the spectrum of cancer patients benefiting from the unprecedented and durable effects of immune checkpoint inhibitors leading to their adoption as a first-line treatment for certain cancers. Moreover, the significance of precision medicine in cancer immunotherapy and the unmet demand to develop more personalized predictive biomarkers and treatment strategies are also highlighted in this review.

Importance:Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C-related burden is critical for guiding prevention and treatment strategies. Objectives:To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population:This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure:Population-level LDL-C concentrations. Main Outcomes and Measures:Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C-attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results:In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C-attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance:Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

Statins are widely prescribed lipid-lowering agents, but their use is associated with an increased risk of new-onset type 2 diabetes mellitus (NO-T2DM) ranging from 9

Pharmacogenomics enables precision pharmacotherapy by linking genetic variation to drug response, yet Arab populations are underrepresented in global reference datasets. We systematically synthesized pharmacogenomic allele-frequency evidence across Arab countries, focusing on clinically actionable genes, to describe population variation, identify high-priority variants, and highlight research gaps. We analyzed 295 studies including 94,346 individuals from 19 countries, pooled country-level allele counts for frequently tested variants, and compared pooled estimates with Middle Eastern reference frequencies. Across most loci, allele-frequency profiles were broadly similar between countries, but several variants showed marked, locus-specific differences with direct relevance to anticoagulants, statins, thiopurines, antidepressants, and fluoropyrimidines. Evidence was uneven across countries and often limited by inconsistent genotyping and incomplete reporting of haplotypes and structural variation. These findings support variant-focused implementation, underscore the need for better population coverage and standardized reporting, and motivate development of a regional pharmacogenomics resource to improve the safety and effectiveness of therapy.

Breast cancer's genomic heterogeneity complicates drug discovery, making repurposing an attractive but challenging strategy. Advances in artificial intelligence now enable integration of multi-omics data to reveal drug-gene-disease relationships and generate subtype-specific repurposing hypotheses. In this Review, we examine AI-driven computational approaches from signature-based to multi-modal frameworks and propose an integrated interpretability-driven framework linking mechanistic validation with clinical translation toward more transparent and actionable precision oncology.

Cerebellar ataxia, mental retardation, and disequilibrium syndrome (CAMRQ)-related disorders are rare, nonprogressive, autosomal recessive conditions primarily characterized by cerebellar ataxia, hypotonia, intellectual disability, delayed ambulation, and, in some cases, quadrupedal locomotion. Pathogenic variants in four disease genes, VLDLR, CA8, WRD81, and ATP8A2, have been linked to these disorders, with cases reported across various ethnic groups and geographic regions. However, no reports of CAMRQ1 (OMIM #224050) have been previously made from Africa. In this study, we report the first African family with four affected siblings exhibiting typical CAMRQ1 clinical features with varying levels of phenotypic severity. Genetic analysis revealed a novel missense homozygous variant (c.1694C > A; p.P565Q) in the VLDLR gene in all the affected individuals, with the parents being heterozygous. Biochemical analysis, including immunofluorescence and confocal laser microscopy, western blot, and endoglycosidase H sensitivity and resistance assay, demonstrated the retention of the p.(P565Q) VLDLR protein in the endoplasmic reticulum (ER), impairing its trafficking to the plasma membrane and thus confirming its pathogenic impact. This ER retention is expected to disrupt VLDLR-mediated signaling pathways, including reelin signaling, thereby affecting neuronal migration. Furthermore, due to its ER retention, the p.(P565Q) is expected to induce ER stress and activate the endoplasmic reticulum-associated degradation (ERAD) pathway. Our findings expand the genetic and geographical spectrum of CAMRQ1 and provide further functional insights into its underlying pathogenesis.

The past decade has witnessed an unprecedented convergence of exposomic technologies, population-scale genomics, and AI-enabled data science, creating the conditions for a new integrative discipline. Here we introduce ExposoGenomics, defined as the integrative study of how the genome and exposome, treated as jointly dynamic systems, interact across the life course to shape health and disease. ExposoGenomics moves beyond classical gene-environment interaction models by embedding high-dimensional, temporally resolved exposure data within a multi-omic and AI-enabled analytical architecture oriented toward causal discovery, mechanistic understanding, and translational application. We describe the conceptual foundations of this framework, its mechanistic architecture linking external exposures to genomic responses through physiologically based kinetic models and adverse outcome networks, and the analytical approaches, including causal machine learning, graph-based integration, and foundation models, required to realize its potential. We emphasize that computational prediction must be accompanied by rigorous empirical validation, and that findings must be grounded in biologically plausible, causally supported mechanisms. In conjunction with this Perspective, Human Genomics formally launches ExposoGenomics as a dedicated article category and invites submissions that advance this integrative agenda.

Background Breast cancer is a leading cause of mortality among women worldwide. Accurate survival prediction can improve clinical decision-making and support personalized treatment planning. This study aims to develop an interpretable and effective deep learning model for breast cancer survival prediction using multi-omics data. Methods This study proposes a novel deep learning model combining Bi-directional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures, integrated with Minimum Redundancy Maximum Relevance (MRMR) feature selection. The model was evaluated on two large datasets: METABRIC (n=1980) and TCGA-BRCA (n=1080), using clinical, copy number alteration (CNA), and gene expression data. Performance was assessed through metrics such as AUC-ROC and accuracy. Results The proposed model demonstrated superior performance compared to existing algorithms, achieving high AUC-ROC and accuracy values across all data modalities. The integration of BiLSTM and CNN architectures allowed the model to capture temporal and spatial patterns, improving prediction robustness. Notably, the model achieved an accuracy of 98% on the METABRIC dataset and 96% on the TCGA dataset. Conclusions The combination of BiLSTM, CNN, and MRMR offers an interpretable and accurate framework for breast cancer survival prediction using multi-omics data. This approach provides actionable insights for clinicians and highlights its potential for broader applications in oncology.

Background:Risk assessments usually test active ingredients but not full commercial formulations. We compared cytotoxic and genotoxic effects of three glyphosate-based herbicides (Roundup Mega, Glyfos, Fozat-480) and two co-formulants (ROKAmin SR22, EMPIGEN BB) in human HL60 (leukocyte) and HepG2 (hepatocyte) cells. Methods:Cells were exposed for 1 h to increasing concentrations (0.1-10,000 μM depending on formulation). Cytotoxicity was measured by propidium iodide staining; genotoxicity was assessed by the alkaline comet assay (tail DNA %, tail length, tail moment, Olive tail moment). Positive (100 μM H2O2) and negative controls were included. Data are means of three independent experiments. Results:Cytotoxicity occurred at lower concentrations in HL60 than HepG2. Roundup Mega and Glyfos produced the strongest genotoxic responses; Roundup Mega increased tail length in HL60 from 0.1 μM, while Glyfos produced consistent genotoxicity in HepG2 from 100 μM. Co-formulants alone showed limited genotoxicity, though ROKAmin SR22 induced DNA% in tail at higher concentrations. Genotoxic effects often occurred at sub-cytotoxic concentrations. Conclusion:Commercial GBH formulations can be more genotoxic than the active ingredient alone; formulation composition influences potency and target-cell sensitivity. These results support formulation-specific testing to improve human health risk assessment.

Importance Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies. Objectives To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure Population-level LDL-C concentrations. Main Outcomes and Measures Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

Background:Missense variants in breast cancer remain diagnostically challenging due to their functional diversity and complex genomic contexts. Conventional laboratory assays for evaluating pathogenicity are labor-intensive, costly, and often impractical for large-scale screening, creating a pressing need for accurate, scalable, and clinically interpretable computational approaches. Methods:In this study, we present a novel deep learning framework for predicting the pathogenicity of breast cancer missense variants, integrating comprehensive preprocessing, advanced imputation, rigorous model benchmarking, and explainability. Genetic variants were curated from multiple genomic databases, annotated using the Ensembl Variant Effect Predictor (VEP), and processed with Variational Autoencoders (VAE) for missing-value imputation. Seven deep learning models, MLP, CNN, DNN, RNN, LSTM, GRU, and Transformer, were trained and evaluated across 11 performance metrics. To quantify performance stability, each model was trained across five random seeds; mean AUC ± SD across seeds is reported as the primary performance estimate, with the best-seed run used only for LIME and PMI interpretability analyses. Recursive feature elimination, permutation importance (PMI), and Local Interpretable Model-Agnostic Explanations (LIME) were employed to enhance transparency. Statistical analyses, including Z-tests, ANOVA, and calibration assessments, validated performance consistency and inter-model differences. Results:GRU achieved the highest internal AUC (0.9956 [95% CI 0.9936-0.9972]; mean across five seeds 0.9941 ± 0.0011), with precision 0.9967 and calibration ECE 0.0095. Externally, LSTM led with AUC 0.9457, exceeding all eleven standalone predictors benchmarked on the same set. Models showed strong alignment with conservation signals such as phyloP470way and Eigen-PC scores. Notably, the pipeline provides performance metrics with 95% confidence intervals and incorporates case-level LIME visualizations for true positive, true negative, false positive, and false negative predictions, bolstering interpretability and clinical relevance. Conclusion:This work delivers one of the most comprehensive evaluations of deep learning in breast cancer variant classification to date. By combining high-performance sequential models with interpretable AI tools, the proposed framework provides a reproducible, transparent benchmark for variant pathogenicity prediction and a foundation for future research use and translation in cancer genomics.

Low-density lipoprotein (LDL) receptor-related protein 6 (LRP6) is crucial for the canonical wingless signaling pathway and the clearance of LDL from the bloodstream. Genetic variants in the LRP6 gene have been conclusively associated with cardiovascular diseases (CVDs) and metabolic syndrome. However, the structural, cellular, and functional implications of these variations have not been fully elucidated. In this study, we examined the subcellular localization, stability, and degradation of 10 LRP6 missense variants (K82N, R360H, Y418H, N433S, R473Q, S488Y, R611C, P1066T, P1206H, and I1264V) previously reported to be associated with various CVD conditions. We assessed the effect of these missense variants on LRP6 subcellular localization by overexpressing them in HeLa and human embryonic kidney (HEK293T) mammalian cell lines. Molecular dynamic (MD) simulation was performed on two variants to evaluate their stability. In addition, the stability of all the variants was evaluated experimentally by measuring their half-lives and comparing them to the wild-type (WT) protein, using cycloheximide chase assays and inhibitor treatments. Our findings suggest that approximately 45% of the wild-type LRP6 protein achieves its mature form within 24-48 h of overexpression, indicating its modest trafficking through the endoplasmic reticulum (ER), maturation, and transport to the plasma membrane. On the other hand, CVD-associated LRP6 variants Y418H, N433S, R473Q, and P1206H exhibited significantly lower maturation levels and, in some cases, were semi-quantitatively present in the immature form, suggesting retention within the ER and failure to pass the highly stringent ER quality control systems. The in silico stability assessment revealed that all 10 LRP6 missense variants are predicted to have a negative impact on protein stability. Interestingly, MD simulation elaborated that one fully ER-retained variant, P1066T, has altered structural interactions of the protein, affecting its folding. ER retention of some CVD-associated LRP6 variants could contribute to diseases via the reduction in LRP6 plasma membrane localization and consequently loss or reduction of LRP6 function, potentially leading to dysregulated signaling efficiency. This study contributes to improving our understanding of the cellular behavior of several LRP6 missense variants causing CVD conditions and has potential applications in diagnosis and the development of new therapies for their associated conditions.

The last decade has witnessed unprecedented succusses with the use of immune checkpoint inhibitors in treating cancer. Nevertheless, the proportion of patients who respond favorably to the treatment remained rather modest, partially due to treatment resistance. This has fueled a wave of research into potential mechanisms of resistance to immune checkpoint inhibitors which can be classified into primary resistance or acquired resistance after an initial response. In the current review, we summarize what is known so far about the mechanisms of resistance in terms of being tumor-intrinsic or tumor-extrinsic taking into account the multimodal crosstalk between the tumor, immune system compartment and other host-related factors.

IntroductionThe extensive size and multi-exon structure and the tissue-restricted expression of the associated gene FBN1 challenge the genetic diagnosis of Marfan Syndrome (MFS). Current genetic diagnostic methods adopted clinically to confirm or rule out the disease diagnosis rely on high throughput DNA sequencing approaches, including whole exome or genome sequencing. While these approaches are powerful, they are costly, time-consuming, and labor-intensive, and they generate vast data sets that require computational and bioinformatic infrastructure to interpret. This study introduces an alternative sensitive, comprehensive, rapid, and cost-effective assay for genetic screening for MFS using whole blood RNA.MethodsWhole blood samples were collected in EDTA tubes, followed by immediate RNA and DNA extraction. A targeted RNA sequencing assay was designed to amplify and sequence the full coding region of FBN1 from whole blood, where Large overlapping cDNA fragments amplified from FBN1- RNA and directly sequenced, effectively addressing the challenge of low transcript expression utilizing nested PCR technique. The assay Applied to five unrelated families with suspected MFS enabled reliable detection of pathogenic variants identified by exome sequencing, and functional characterization of their transcriptional effects.ResultsThe assay identified four pathogenic FBN1 variants including one nonsense, two frameshift, and one missense, establishing the diagnosis in four cases. This corresponds to a diagnostic yield of 80%, exceeding that of whole exome sequencing, which identified variants in only three of the five families (60%).ConclusionBeyond variant detection, the assay elucidates how these variants influence RNA transcription and contribute to pathogenic mechanisms. An insight that is often overlooked by DNA sequencing approaches. This allowed us to identify distinct effects of each identified variant and recognize RNA slippage as a novel disease mechanism that has not been reported before in MFS. The developed assay introduces an improved approach to the clinical genetic testing of MFS, with potential applicability for diagnosing other conditions involving large, multi-exon genes.

Cytochrome P450 enzymes, particularly CYP2D6 and CYP2C19, play a crucial role in metabolizing various prescribed medications. While common CYP2C19 variants, such as *2 and *3 alleles, are well-studied, rare and novel variants remain less understood, especially in understudied populations. This study investigated the functional impact of seven rare or novel CYP2C19 missense variants (p.T55S, p.E92D, p.V113I, p.D262N, p.F267L, p.P337S, and p.I387V) identified in the Emirati population, some of which have also been reported in other populations. In silico prediction programs and molecular modeling have been used to evaluate and predict the expected impacts of these variants. In addition, we employed site-directed mutagenesis to generate these variants in CYP2C19 cDNA, which was cloned into a mammalian expression vector, and evaluated their functional consequences using in vitro enzymatic assays. Our findings revealed that five of the seven variants (p.T55S, p.V113I, p.D262N, p.F267L, and p.P337S) significantly reduced CYP2C19 4'-hydroxylation catalytic activity towards (S)-mephenytoin, suggesting detrimental effects on drug metabolism. These results underscore the clinical importance of considering the impact of rare variants and, consequently, the need for their detailed functional analysis to integrate them into the implementation of pharmacogenomics and personalized medicine. This research contributes to the growing understanding of population-specific genetic variations in CYP2C19 and their potential implications for the response and safety of a significant number of medications metabolized by this enzyme.

Pharmacogenomic (PGx) testing improves treatment outcomes by tailoring therapy to a patient’s genetic profile. However, PGx implementation faces global challenges, including costs, reimbursement, and regulations. Initial PGx guidelines exist in the United Arab Emirates (UAE), but insurers’ perspectives remain understudied. This study explores insurers’ views on policies and strategies to expand PGx adoption and overcome implementation barriers. This qualitative study used a semi-structured interview design to explore the perspectives of twelve executive and middle management insurance representatives selected through purposive convenience and snowball sampling. Thematic analysis was conducted inductively, supported by comparative analysis, the Institutional Theory, the TAM, and SWOT analysis to interpret the findings. Analysis revealed variable awareness of PGx, highlighting both perceived benefits and significant barriers. Key findings included economic constraints, limited physician and public awareness, and policy challenges related to cost-effectiveness and infrastructure. Ethical and privacy concerns were minimal but were noted, with potential implications for insurance premiums. Participants stressed the need for collaborative efforts to align PGx with UAE healthcare goals and highlighted the role of advanced health information systems in facilitating integration. Differences emerged between executive and middle-level management: the former emphasised strategic policies and long-term returns on investment, while the latter focused on practical operational barriers. Advancing PGx in the UAE requires local cost-effectiveness studies, clear government-led coverage guidelines, and collaborative action among insurers, providers, regulators, and academia. These findings may inform health systems with similar public–private insurance arrangements, where phased adoption strategies and education initiatives are key to sustainable implementation.