Qatar Foundation

Background: Statins are central to primary and secondary prevention of atherosclerotic cardiovascular disease but are often underutilized due to myopathy and intolerance. While individual pharmacogenetic (PGx) variants, particularly in SLCO1B1, are linked to statin-associated muscle symptoms, the real-world impact of both clinical and cumulative PGx burden on regimen modification and adverse outcomes remains unclear. We aimed to evaluate the existing uncertainty regarding whether combined PGx scores can effectively guide statin dose titration and regimen modification, thereby filling a key clinical gap. Methods: A retrospective cohort study of 911 statin-treated patients with coronary artery disease was conducted from the Qatar Cardiovascular Biorepository with available whole-genome sequencing data. Variants in SLCO1B1, ABCG2, and CYP2C9 were combined into a functional PGx burden score, and their associations with statin regimen modification, intolerance, myopathy, liver injury, adherence, and composite adverse events were evaluated. The composite adverse events were defined as the occurrence of any statin-related adverse event, including statin-associated myopathy, liver injury, or poor medication adherence, during the follow-up period. Patients were classified as having experienced the composite outcome if at least one of these events occurred. Results: Over 12 months following statin initiation, 10.2% of patients underwent dose escalation, 11.4% de-escalation, and 78.4% remained on the same regimen. PGx burden is not statistically significantly associated with statin intolerance (OR 1.14; 95% CI: 0.73-1.76), composite adverse outcome (OR 1.08; 95% CI 0.82-1.42), or time to regimen change (HR 1.02; 95% CI 0.77-1.35). However, higher PGx burden showed a directional tendency toward dose de-escalation (RRR 1.18, 95% CI 0.76-1.84) and lower likelihood of escalation (RRR 0.93, 95% CI 0.56-1.54). Conclusions: Clinical factors, particularly statin intensity and myopathy, were the primary determinants of regimen modification. The PGx burden contributes to vulnerability to statin-related adverse effects in a context-dependent manner but does not independently drive statin regimen modification in routine clinical practice. Prospective studies are warranted to assess the clinical utility of PGx-guided workflows in statin therapy.

Abstract The Education and Training Evaluation Commission in Saudi Arabia reports that many students entering university preparatory year programs face difficulties with basic English skills, particularly receptive skills in reading and listening, which are key to academic study. These difficulties are commonly associated with low learner motivation and limited exposure to relevant English practice. Although Mobile-Assisted Language Learning has gained international attention for its gamified and adaptive features, empirical evidence from Saudi preparatory year contexts remains limited. This mixed-methods pilot study considers the use of Duolingo and ReadTheory in relation to learner motivation and receptive skill development. Sixty male students at A1–A2 levels of the Common European Framework of Reference for Languages were purposively selected from a Saudi public university. The intervention was structured using the PF-4M framework, with a focus on authenticity, personalization, autonomy, and contextualization. Quantitative data were collected using a CEFR-aligned receptive skills test administered before and after the intervention and a post-intervention adapted Language Learning Motivation Scale. Qualitative data were drawn from reflective journals and weekly classroom observations documenting learner engagement. Results showed increases in receptive test scores, with mean scores rising from 8.12 to 15.17, alongside higher reported motivation levels. While both applications were well received, some learners reported challenges with comprehension and independent learning. The findings show that Mobile-Assisted Language Learning is associated with receptive skill development among beginner learners.

Background/Objective: Warfarin is the standard anticoagulant for patients with mechanical heart valve replacement (HVR). However, its narrow therapeutic index and interpatient variability complicate early postoperative management. Evidence on how valve position influences warfarin sensitivity is limited. This study evaluated the impact of prosthetic valve position and clinical factors on early warfarin response and developed a prediction model to guide initial warfarin dosing in HVR patients. Methods: A retrospective study was conducted on 310 adults who underwent mechanical aortic, mitral, or double valve replacement at Hamad Medical Corporation (2015-2022). Warfarin was initiated within 24 h postoperatively, and patients were monitored for three days. Outcomes included daily warfarin dose, international normalized ratio (INR) levels, attainment of therapeutic INR, INR overshoot (≥4), and the warfarin dose index on day 3 (WDI3). Predictors of WDI3 were analyzed using multivariable regression, and a LASSO model was applied to a dose prediction algorithm for the day 1 dose. Results: Mitral valve recipients required lower doses than aortic or double valve groups (p = 0.008) but had higher INR overshoot rates (18.75% vs. 16.05% and 4.55%; p = 0.033). Female sex and a higher baseline INR were associated with greater sensitivity (p < 0.01), whereas mitral/double valve position predicted reduced sensitivity (p = 0.010). Only half of the cohort reached therapeutic INR by day 3. The prediction model explained ~28% of dose variance with moderate performance. Conclusions: Valve position, sex, and baseline INR significantly influence early postoperative warfarin response. Incorporating these clinical factors into dosing algorithms may optimize initial warfarin management in HVR patients.

To investigate the association between C-reactive protein (CRP) with sensory symptoms and deficits and measures of small fiber damage. Adults with and without T2D underwent corneal confocal microscopy (CCM) and assessment of vibration perception threshold (VPT), DN4 symptom questionnaire, and fasting blood tests for CRP and metabolic markers. Of the 122 participants, 77 had T2D of whom 23 (29.9%) had DPN, and 45 were controls. CRP levels were significantly higher in those with T2D without and with DPN compared to controls (5.6 ± 2.9 and 5.4 ± 4.0 vs 3.8 ± 2.9 mg/L, P = 0.008 and P = 0.046, respectively), with no difference between those with and without DPN (P = 0.894). Higher CRP was independently associated with lower corneal nerve fiber density (CNFD) (β = -5.5 fibers/mm2 per 10 mg/L increase, P = 0.036), after adjusting for diabetes duration, BMI, HbA1c, and triglycerides. In the DPN group, those with positive symptoms (burning, painful cold, and electric shocks) had higher CRP levels compared to those with negative symptoms (6.5 ± 4.7 vs 3.8 ± 1.9 mg/L, P = 0.039). Elevated CRP is associated with small nerve fiber loss and positive neuropathic symptoms in T2D. These findings suggest that CRP may help identify individuals with inflammation-driven DPN who could benefit from targeted interventions.

Abstract Reservoir simulation requires efficient algorithms for complex, nonlinear systems. We introduce a Residual–Accelerated Jacobian (RAJ) for fully implicit reservoir simulation. RAJ assembles the Jacobian by finite differences with a residual–adaptive step $$h_{\textrm{res}}$$ h res that enlarges far from convergence and shrinks near tolerance; saturation probes are projected to physical bounds. The governing residual is unchanged, so accuracy is preserved while Jacobian columns remain stable across nonlinear episodes. This adaptive mechanism ensures that the RAJ method remains responsive and can effectively adjust to the changing dynamics of the simulation, enabling faster convergence without compromising the accuracy of the solution, a crucial aspect in handling complex, non-linear systems in reservoir simulations. We evaluate RAJ on SPE10 (top five layers) and the Norne field (corner-point with NNCs), reporting CPU time, Newton/linear iterations, and robustness indicators (wasted steps/iterations), alongside accuracy parity. On SPE10 water injection, RAJ runs in 226.73 s vs FD 254.42 s ( –10.9% ); all methods use 43 Newton iterations and $$\approx $$ ≈ 2215 linear iterations, with no wasted steps. On PE10 gas injection, RAJ completes in 1600.55 s vs FD 1772.12 s ( –9.7% ) and lowers wasted work (wasted time-step fraction 31.98% vs 36.38% ; wasted linear iterations 5385 vs 6644 ). On Norne, RAJ takes 276.81 s vs FD 313.85 s ( –11.8% ) with 51 vs 53 Newton iterations and zero wasted steps. As expected, analytical derivatives are fastest where available (water 154.816 s , gas 1177.69 s , Norne 223.935 s ). Overall, RAJ delivers comparable or better CPU times than fixed-step FD while preserving accuracy and reducing wasted work, offering a practical, drop-in alternative when analytical Jacobians are unavailable.

Gestational diabetes mellitus (GDM) is a common metabolic disorder characterized by hyperglycemia that is first detected during pregnancy, which is not overt diabetes. GDM poses a substantial risk for prenatal and postnatal adverse outcomes affecting both the mother and the offspring. These complications include, but are not limited to, fetal macrosomia, shoulder dystocia, respiratory distress, neonatal hypoglycemia, type 2 diabetes (T2D), and cardiovascular diseases. Screening for GDM typically occurs between 24 and 28 weeks of gestation, a timing that is considered late and may increase the risk of all the adverse outcomes associated with GDM. Treatment and prevention strategies are not standardized globally, may be suboptimal, and are often initiated after a diagnosis has been made. Therefore, our primary goal was to identify DNA methylation signatures specific to GDM to understand its underlying mechanisms. We conducted genome-wide DNA methylation profiling for normal and GDM pregnant women across the three trimesters of pregnancy in the discovery cohort. DNA methylation levels were measured using the Infinium MethylationEPIC v2.0 BeadChip. Subsequently, our differentially methylated sites were validated in a second cohort. Furthermore, we performed downstream analyses, including KEGG pathway and Gene Ontology enrichment analysis, trait enrichment analysis, and gene expression regulation analysis for the validated differentially methylated sites identified in the second and third trimesters. In this study, we uncovered and validated new DNA methylation signatures that may significantly influence the expression of genes associated with GDM. Furthermore, we discovered new genes (RSL1D1, HOXD4, and MROH6) that may play a role in GDM and might be related to the risk of developing T2D and cardiovascular disease later in life. Trait analysis of the differentially methylated probes revealed that lifestyle and environmental factors are associated with the observed DNA methylation signatures in GDM. We conclude that DNA methylation changes during pregnancy might not fully explain GDM pathogenesis but can reflect population-specific environmental and behavioral factors before and during pregnancy. Some of these discovered CpG sites might regulate previously reported genes linked to GDM and diabetes, highlighting shared and distinct epigenetic mechanisms across populations.

• Novel Hybrid Model : Developed a novel hybrid machine learning model (MLR-LSTM-FFNN) for short-term load forecasting, integrating statistical regression with deep learning for enhanced predictive performance. • Comprehensive Performance Evaluation : Conducted extensive experiments on real-world datasets (Qatar and Panama City) across multiple time resolutions (5 min, 15 min, 30 min, 1 hour), demonstrating superior accuracy and efficiency. • Empirical Hyperparameter Optimization : Utilized Bayesian optimization for hyperparameter tuning, ensuring optimal performance while balancing computational efficiency. • Model Explainability and Interpretability : Applied LIME to interpret the contributions of key features in the forecasting model, enhancing transparency in AI-driven decision-making. • Statistical Validation of Model Superiority : Employed the Diebold-Mariano test to statistically validate the significant improvements of the hybrid model over traditional approaches. • Efficiency-Accuracy Trade-off : Analyzed computational complexity and training duration, demonstrating that MLR-LSTM-FFNN achieves high accuracy with reduced computational cost compared to alternative hybrid models. Efficient energy management and grid stability strongly rely on accurate Short-Term Load Forecasting (STLF). Existing forecasting models, unfortunately, are often inaccurate and computationally demanding. To overcome these challenges, a novel hybrid model, combining both linear regression and machine learning techniques, is proposed in this study. The hybrid model, MLR-LSTM-FFNN, captures both temporal and non-linear dependencies in load data by integrating multi-linear regression (MLR) with long short-term memory (LSTM) networks and feed-forward neural networks (FFNN). Using datasets from Qatar, with 5-minutes, 15-minutes, 30-minutes, and 1-hour time intervals and from Panama City with a 1-hour interval, experiments were conducted to thoroughly test the robustness of the model. The results showed that the MLR-LSTM-FFNN hybrid model outperformed the baseline and state-of-the-art hybrid models for each of the datasets, in terms of lower RMSE, MAE, and MAPE values along with a faster training time. This superior performance across different datasets underscores the model’s scalability and reliability as an STLF approach, providing a practical solution to energy demand prediction tasks. The improvement in short-term forecasting accuracy provides utilities with a practical tool to optimize demand-side management, reduce operational costs, and enhance grid reliability. Proposed hybrid framework for STLF, integrating MLR, LSTM, and FFNN.