63477 research outputs found
Sort by
The next decade of cardiovascular disease burden in Qatar, a gulf cooperation council country: Projections from 2024 to 2033
No full textBackgroundCardiovascular diseases (CVD) are a great public health challenge in Qatar, with significant impacts on long-term population health and societal costs. ObjectiveWe aimed to forecast the health and economic burden of the CVD in Qatar from 2024 to 2033, from both healthcare and societal perspective. MethodsA validated two-stage dynamic model was structured, spanning a 10-year period and targeting individuals aged 40-79. The CVD incidents (i.e., myocardial infarction [MI], stroke) were estimated using the 2013 Pooled Cohort Equation, while recurrent events were obtained from the global REACH registry. The model outcomes included fatal and non-fatal MI and stroke, years of life lived, quality-adjusted life years (QALYs), total direct costs, and total productivity loss costs. Utility and cost inputs were derived from published sources. Outcomes were discounted at a rate of 3 % per annum. Calibration and validation were performed to ensure model accuracy. A multivariate sensitivity analysis was also conducted. ResultsBy 2033, there will be 271,260 non-fatal MI events (95 % confidence interval [CI] 271,249-271,277), 258,892 non-fatal strokes (95 %CI 258,858-259,094), and 20,413 CVD deaths (95 %CI 20,405-20,429). The cumulative years of life lived and QALYs were 13,806,845 (95 % CI 13,802,149-13,811,541) and 10,655,665 (95 %CI 10,652,720-10,658,611), respectively. The direct costs were QAR71.14 (95 %CI QAR70.62-71.66) billion, and the productivity loss costs were estimated to surpass QAR108.12 (95 %CI QAR106.88-109.36) billion. The exchange rates used were based on 2024 values (1QAR=0.27US$). ConclusionsThis study offers valuable insights into the projected burden of CVD in Qatar, highlighting the need for effective preventive strategies to reduce risk
FOOD COMPOSITION TABLES OF TRADITIONAL QATARI DISHES
No full textThe study assessed the proximate and mineral compositions of 17 traditional Qatari dishes to contribute to the development of an updated and culturally relevant Food Composition Table (FCT). Using standardized recipes, dishes were analyzed for moisture, protein, fat, and mineral content using AOAC (Association of Official Analytical Collaboration)-approved methods and Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) following microwave-assisted digestion. The results showed wide variation in nutrient profiles, with protein ranging from 4.81% in Khanfaroush to 29.30% in Saloona Laham and fat from 0.47% in Rugag to 37.51% in Khanfaroush, largely influenced by ingredient types and cooking methods. Mineral analysis revealed high levels of iron, zinc, and magnesium in meat-rich dishes, while trace amounts of lead and aluminum were detected in some samples, likely due to cookware or environmental contamination. Statistical analysis using Tukey's test confirmed significant differences across dishes, with confidence interval plots supporting these groupings. The study provides updated nutrient data for traditional Qatari foods and offers practical value for dietary assessment, food labeling, and public health planning. Based on these findings, it is recommended that portion size guidance and regular laboratory updating of national food composition data be prioritized to promote healthier consumption and accurate nutrient databases
Legal Liability for Arbitrary Pre-Trial Detention under Qatari and Comparative Law
No full textتطرقت هذه الدراسة الى المسؤولية القانونية عن الحبس الاحتياطي التعسفي في التشريع القطري بصورة مقارنة مع كل من التشريع المصري والتشريع الفرنسي، وفي خضم هذه الدراسة تعرض الباحث الى مفهوم الحبس الاحتياطي في الفقه وما دلت عليه الإجراءات القضائية في تعاملاتها في موضوع الحبس الاحتياطي، وتبين من خلال الدراسة أن الحبس الاحتياطي من المفترض أن يكون إجراء استثنائي ولا يتم الأخذ به الا في حدود ضيقه وذلك لما لهذا الاجراء من مساس بحرية الافراد التي كفلتها الدساتير والمواثيق الدولية. استخدم الباحث في الدراسة المنهج الوصفي والمنهج التحليلي المقارن حيث انتجت الدراسة مجموعة من النتائج من ضمنها نتيجة رئيسية: وهي قصور ملحوظ في القانون القطري من ناحية تنظيم الحبس الاحتياطي وضمانات التعويض عنه، مقارنة بما هو معمول في التشريعات المقارنة، ولم يقتصر الحبس الاحتياطي على الجنايات بل تضمن الجنح ايضاً وهو ما يشكل تهديد صارخ لحرية الافراد. ومن هذا المنطلق خرجت الدراسة بمجموعة من التوصيات كان من ضمنها التمني على المشرع القطري أن ينص صراحة على تعريف للحبس الاحتياطي وحصر الحالات التي يجوز فيها الحبس الاحتياطي مثل الخشية من الهرب أو العبث بالأدلة، وتوصي الدراسة أيضاً بإدراج نص صريحفي قانون الإجراءات الجنائية يقرر حق التعويض المادي والنفسي والأدبي، وتتحمل الدولة ذلك التعويض على أن يكون لها الحق في الرجوع على الموظف أو القاضي في حالة التعسف أو سوء النية.This study addresses the legal liability arising from arbitrary pretrial detention under Qatari legislation, in comparative perspective with both Egyptian and French laws. Throughout the study, the researcher examines the concept of pretrial detention in legal doctrine and explores how judicial procedures have dealt with this measure. The study reveals that pretrial detention is an exceptional measure that should only be applied within narrow limits, given its direct impact on individual liberty – a right safeguarded by constitutions and international conventions . The researcher employed both descriptive and comparative analytical methodologies, leading to several key findings. The most significant of which is the notable deficiency in Qatari law regarding the regulation of pretrial detention and the guarantees for compensation arising from it. Unlike the comparative legislations examined, Qatari law permits pretrial detention not only for felonies but also for misdemeanors, which constitutes a serious threat to personal freedom Consequently, the study puts forth a series of recommendations, foremost among them urging the Qatari legislator to explicitly define pretrial detention and to strictly enumerate the cases in which it may be applied; such as risk of flight or tampering with evidence. The study also recommends the inclusion of an explicit provision in the Code of Criminal Procedure recognizing the right to material, psychological, and moral compensation, to be afford by the State, with the right of recourse against the official or judge in cases of abuse or bad faith
Multiclass classification of oral mucosal lesions by deep learning from clinical images without performing any restrictions
No full textOral cancer is a frequently malignant tumor that can be detected during an oral examination. Unfortunately, it is often diagnosed in advanced stages, which leads to low survival rates of about 50% at five years. Due to the low survival rate, it is crucial to develop automated systems that allow the classification of oral lesions according to their severity, aiding in the early diagnosis of oral cancer.This study aims to investigate the effectiveness of using clinical images and deep learning based models to perform a multiclass classification of oral mucosal lesions in color photographs taken without following any acquisition protocol. The classification differentiated four classes: malignant, potentially malignant, benign and healthy. The dataset included a total of 3246 images from 1013 patients, with 40 different categories of oral lesions, including healthy oral mucosa. The images showed different areas of the oral cavity and were captured from different perspectives by diverse dentists and maxillofacial surgeons in the practice.For the classification, different deep learning architectures were applied and compared, from the best known convolutional neural networks (CNN) and skip connection networks (SCN), to more innovative architectures such as visual transformers and a recent hybrid architecture, ConvNeXt v2. The ConvNeXt v2 Tiny architecture, with 85.53% accuracy, 85.02% precision, 85.50% recall, 84.92% F1-score, and 97.40% ROC AUC for an input image size of 354 × 354 pixels, outperformed the other architectures on the same database. The present model improved on previous proposals by considering a greater number of oral lesions and output classes.This research has been funded by the Instituto de Salud Carlos III (ISCIII) through the project PI22/00905 co-funded by the European Union and by the Spanish Ministry of Science and Innovation through the project PID2019-110686RB-I00
Global, regional, and national burden of chronic respiratory diseases and impact of the COVID-19 pandemic, 1990-2023: a Global Burden of Disease study.
No full textChronic respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease (ILD) and pulmonary sarcoidosis, are major global causes of mortality and morbidity. Although the COVID-19 pandemic has influenced acute respiratory health, its impact on chronic respiratory conditions remains unclear. We estimated the global, regional and national burden of chronic respiratory diseases from 1990 to 2023, including risk factors, and evaluated how these burdens have shifted during the COVID-19 pandemic using the Global Burden of Disease Study 2023. In 2023, chronic respiratory diseases accounted for 569.2 million (95% uncertainty interval (UI), 508.8-639.8) cases and 4.2 million (3.6-5.1) deaths. The age-standardized death rate declined by 25.7% globally from 1990 to 2023 despite an increase in ILD and pulmonary sarcoidosis. Mortality declined in younger males, especially for asthma, whereas older adults experienced a rise in ILD and pulmonary sarcoidosis. Smoking was the primary risk factor for COPD, whereas high body mass index and silica exposure were key risk factors for asthma and pneumoconiosis. During the pandemic, the incidence of chronic respiratory diseases increased modestly, but the decline in mortality rates became more pronounced, highlighting the need for sustained global attention and action to address their long-term burden
Low-Intensity Ultrasound and Neural Repair: Unlocking Brain Plasticity and Functional Recovery
No full textEmerging evidence highlights the potential of transcranial focused ultrasound in enhancing brain function and repair. Low-intensity focused ultrasound (LIFUS) enables precise, non-invasive modulation of neuronal activity, promoting cellular repair and brain plasticity. By targeting specific brain regions, LIFUS facilitates the release of neurotrophic factors, strengthens synaptic connectivity, and modulates molecular and cellular pathways essential for neural recovery. These mechanisms collectively support neuronal proliferation, differentiation, and functional integration, leading to cognitive improvements and neuroprotection without causing thermal damage. This review provides a comprehensive overview of LIFUS's effects on brain function, emphasizing its role in neuromodulation, cellular adaptation, and long-term viability for treating brain injuries and neurodegenerative diseases. The discussion covers optimized ultrasound parameters, efficacy in cellular and behavioral models, and its therapeutic potential for brain repair and functional recovery. By extending its scope beyond isolated mechanisms, LIFUS emerges as a versatile neuromodulation tool with significant clinical promise for enhancing brain resilience and cognitive outcomes
Exploring the hub gene CERS6 as a therapeutic target in type 1 diabetes through a bioinformatics and network analyst approach
No full textInsulin-producing β-cells are destroyed in type 1 diabetes mellitus (T1DM), a chronic autoimmune disease that results in complete insulin insufficiency and metabolic dysfunction. According to a survival study that used p values, some hub genes are important for predicting and diagnosing illness. Scientists have inferred medicines to identify possible therapies that interact with the identified hub genes. The GSE10586 gene expression dataset from the Gene Expression Omnibus (GEO) was used for this investigation, which included 27 samples from 15 healthy controls and 12 diabetic patients. Normalization methods such as variance stabilization normalization (VSN) were used as part of the data pretreatment. A protein‒protein interaction (PPI) network was constructed, principal component analysis (PCA) was performed, heatmaps were created, and the Limma algorithm was used to analyze differential gene expression. Using DAVID v6.8 and KEGG pathway annotations, the functional enrichment of differentially expressed genes (DEGs) was evaluated. Furthermore, a computational study revealed CERS6 to be one of the potential hub genes. Four drugs, methotrexate, eliglustat, myriocin and statin, were the focus of further studies on the basis of predictions made via ChemSpider and PubChem database analysis. To determine the optimal binding positions of these drugs with CERS6, we used molecular docking techniques. The binding affinity of methotrexate was 8.48 kcal/mol, that of myriocin was 7.85 kcal/mol, that of eliglustat was − 6.62 kcal/mol, and that of serine was − 4.90 kcal/mol against the binding pocket’s active residues. To determine how consistently each drug interacted with the CERS6 protein over time, molecular dynamics (MD) simulations were run. Throughout the simulation intervals, both medications were confirmed to be stable, with minor alterations in the CERS6 protein loop region. Therefore, the investigation of structure-based drug design has potential for identifying specific therapeutic targets. Ten hub genes were identified via network analysis of differentially expressed genes. These hub genes could serve as novel targets for T1DM detection, prognosis, and targeting. CERS6 exhibited the highest degree of interaction. Methotrexate, eliglustat, myriocin and statins were identified as potential drugs for CERS6. Overall, these findings provide valuable insights that could pave the way for new experimental strategies in T1DM therapy
The role of AI-driven communication in delirium prevention, detection, and care for critically ill ICU patients: A systematic review with inductive thematic synthesis
No full textBackgroundDelirium remains one of the most consequential complications among critically ill patients in ICUs, exerting profound effects on morbidity, mortality, and annual healthcare costs exceeding $81 billion. Communication barriers between sedated or mechanically ventilated patients, their families, and multidisciplinary teams frequently delay recognition and impair management of delirium. This systematic review examines how AI-driven communication technologies can address these barriers, enhance early detection, and promote more integrated, patient- and family-centered delirium care. MethodsA systematic review of literature published between 2015 and 2025 was conducted across five electronic databases: Scopus, PubMed, Web of Science, Embase, and IEEE Xplore. The search strategy employed keywords as “delirium,” “intensive care,” “artificial intelligence,” “AI-driven communication technologies”, “natural language processing”, “computer vision”, “multidisciplinary clinical collaboration”, and “family engagement”. Studies were eligible for inclusion if they focused on AI-enhanced communication in ICU delirium care. The included studies were analyzed using an inductive thematic synthesis approach. ResultsFrom 87 screened records, 16 studies demonstrated AI’s significant benefits across three clinical domains: 1) Prevention using AI-driven tools; 2) Early Detection via multimodal AI systems; and 3) Patient Care through Natural Language Processing (NLP)-powered support. An inductive thematic synthesis of these findings further delineated six core thematic domains: (1) inherent communication barriers; (2) AI as a multidirectional interface; (3) passive AI listening for early detection; (4) AI-enhanced family engagement; (5) AI-structured handovers for teamwork; and (6) ethical-regulatory-practical challenges. ConclusionAI-driven communication tools effectively bridge critical gaps in ICU delirium care, facilitating early detection, prevention, and patient-centered management. By enabling proactive interventions and fostering a collaborative care environment, these technologies demonstrate direct potential to reduce delirium duration, decrease antipsychotic use, improve long-term cognitive outcomes, and alleviate the substantial economic burden on healthcare systems. These findings validate AI’s role in transforming delirium care through enhanced multidirectional communication. Implications for Clinical PracticeICU nurses are pivotal in utilizing AI tools through interpreting NLP-generated alerts, calibrating computer vision outputs, and facilitating family engagement to translate AI insights into empathetic, tailored bedside interventions, thereby reinforcing human-AI collaboration
IMPACT OF MEDICATION BURDEN AND MEDICATION REGIMEN COMPLEXITY ON GLYCEMIC CONTROL AMONG PATIENTS WITH DIABETES
No full textDiabetes mellitus represents a major global health challenge, with poor glycemic control being highly prevalent despite therapeutic advancements. The increasing complexity of medication regimens, often driven by polypharmacy and multimorbidity, imposes a significant burden on patients and may undermine adherence and treatment effectiveness. This study examined the impact of medication burden and regimen complexity on glycemic control among adults with diabetes. A two-phase approach was employed: a systematic review and meta-analysis of international studies, and a cross-sectional analysis using data from 710 patients attending Qatar's Primary Health Care Corporation clinics. The systematic review of 12 studies demonstrated that higher medication regimen complexity was consistently associated with poor glycemic control, lower adherence, and increased treatment burden (pooled aOR = 0.18; 95% CI 0.07-0.47). In the Qatar cohort, medication burden showed no significant effect on glycemic control, whereas higher diabetes-specific Medication Regimen Complexity Index (MRCI) scores were linked to uncontrolled HbA1c. The finding suggest that simplifying medication regimens may improve adherence and ultimately glycemic outcomes
Small cell lung cancer (SCLC): At the door of targeted therapies
No full textSmall-cell lung cancer (SCLC) is a tobacco-associated neuroendocrine tumor comprising ~15% of lung cancers (~150,000 cases/year). For decades, outcomes stagnated: most patients present with extensive-stage disease, screening rarely detects early tumors, surgery is seldom feasible, and platinum–etoposide remained the first-line standard with median overall survival (OS) 80% of SCLC, enables T-cell–redirecting therapy: the bispecific T-cell engager (BiTE®) tarlatamab improved OS to 13.6 vs 8.3 months over standard second-line chemotherapy, with manageable cytokine release syndrome and occasional ICANS. B7 homolog 3 (B7-H3, CD276), uniformly expressed across SCLC subtypes and linked to poor prognosis, is another compelling target: the antibody–drug conjugate ifinatamab deruxtecan achieved a 54.8% response rate and meaningful survival in heavily pretreated patients, earning FDA Breakthrough designation. Together, DLL3- and B7-H3–directed therapies (with additional ADCs against Trop-2 and SEZ6 in development) are redefining second-line and later care. Key next steps include optimizing sequencing/combination strategies, managing BiTE® specific toxicities, and developing predictive biomarkers. After decades of futility, SCLC is transitioning from uniform chemotherapy to a precision-medicine paradigm with cautious optimism