Search Results (3,460)

In this paper, a wireless network architecture is considered that combines double intelligent reflecting surfaces (IRSs), energy harvesting (EH), and non-orthogonal multiple access (NOMA) with cooperative relaying (C-NOMA) to leverage the performance of non-line-of-sight (NLoS) communication mainly and incorporate energy efficiency in next-generation networks. To optimize the phase shifts of both IRSs, we employ a machine learning model that offers a low-complexity alternative to traditional optimization methods. This lightweight learning-based approach is introduced to predict effective IRS phase shift configurations without relying on solver-generated labels or repeated iterations. The model learns from channel behavior and system observations, which allows it to react rapidly under dynamic channel conditions. Numerical analysis demonstrates the validity of the proposed architecture in providing considerable improvements in spectral efficiency and service reliability through the integration of energy harvesting and relay-based communication compared with conventional systems, thereby facilitating green communication systems. Full article

Livestock contributes to economic stability and food security by providing income, employment, and nutrient-dense animal-source foods, particularly in low- and middle-income regions. However, the sector is also a major source of anthropogenic greenhouse gas emissions, primarily methane, nitrous oxide, and carbon dioxide, raising growing environmental and public health concerns. This review synthesizes current evidence on strategies to mitigate greenhouse gas emissions from livestock systems while safeguarding productivity, food security, and human health. Emphasis is placed on the need to balance supply-side mitigation measures with demand-side interventions to avoid unintended nutritional and socio-economic consequences. Key supply-side approaches discussed include genetic improvement, optimized feeding strategies, manure and land resource management, and system-level efficiency gains. Demand-side strategies include food loss and waste reduction, shifts toward sustainable dietary patterns, and the development of alternative protein sources. Central to this review is the integration of these approaches within a planetary health framework, highlighting the interconnectedness of environmental sustainability, human and animal health, and socio-economic resilience. The review underscores that mitigation policies should be context-specific, equity-focused, and health-centered to ensure that climate goals are met without compromising access to affordable, nutritious foods. Collectively, the evidence indicates that coordinated policy action across production, consumption, and health systems is essential for achieving sustainable animal-source food production with reduced climate impact. Full article

Quartz Crystal Microbalance with Dissipation monitoring (QCM-D) systems are widely used for the real-time analysis of mass changes and viscoelastic properties in biological samples, enabling applications such as biomolecular interaction studies, biosensing, and fluid characterization. However, their accessibility has been limited by high acquisition costs. To address this limitation, a low-cost, open-source QCM-D system was developed. Unlike other affordable, open-hardware alternatives, this system is specifically optimized for potential biomedical applications by integrating active thermal control to preserve the physical properties of the samples and dissipation monitoring to characterize their viscoelastic behavior. A 10 MHz quartz crystal with a sensor module and a control and acquisition unit were integrated. The full system was built at a total cost below USD 500. Performance validation showed a temperature stability of ±0.13 °C, a frequency stability of ±2 Hz in air, and a limit of detection (LOD) of 0.46% polyethylene glycol (PEG), thereby enabling stable, reproducible measurements and the sensitive detection of small mass and interfacial changes in low-concentration samples. These results demonstrate that key QCM-D sensing capabilities can be achieved at a fraction of the cost, providing an accessible and reliable platform for potential biomedical research. Full article

News data have become essential resources across various disciplines. Still, access to full-text news corpora remains challenging due to high costs and the limited availability of free alternatives. This paper presents a novel Python package (gdeltnews) that reconstructs full-text newspaper articles at near-zero cost by leveraging the Global Database of Events, Language, and Tone (GDELT) Web News NGrams 3.0 dataset. Our method merges overlapping n-grams extracted from global online news to rebuild complete articles. We validate the approach on a benchmark set of 2211 articles from major U.S. news outlets, achieving up to 95% text similarity against original articles based on Levenshtein and SequenceMatcher metrics. Our tool facilitates economic forecasting, computational social science, information science, and natural language processing applications by enabling free and large-scale access to full-text news data. Full article

Quinolines are key heterocyclic motifs with broad utility in pharmaceuticals, agrochemicals, and materials science. The development of efficient and sustainable synthetic routes to access structurally diverse quinolines remains an important goal in organic chemistry. This review focuses on the recent advances in palladium-catalyzed strategies for quinoline synthesis, emphasizing oxidative and tandem annulation methods. Reactions are categorized by substitution patterns on the quinoline scaffold—namely 2-aryl, 4-substituted, 2,3-, 2,4- and 3,4-disubstituted, 2,3,4-trisubstituted, and annulated derivatives—to facilitate mechanistic comparisons and highlight structural scope. Together, the reviewed strategies showcase the range of mechanistic possibilities available for constructing quinoline scaffolds via palladium catalysis. Overall, these Pd-catalyzed approaches offer powerful and versatile tools for the synthesis of complex quinoline frameworks, providing valuable alternatives to classical heterocycle forming reactions. Full article

Background/Objectives: Accurate prognostic assessment remains crucial in metastatic renal cell carcinoma (mRCC), especially as treatment options have expanded beyond vascular endothelial growth factor (VEGF)-targeted therapies to include immune checkpoint inhibitors (ICIs) and ICI–TKI combinations. The widely used IMDC classification shows important limitations in the modern therapeutic era, highlighting the need for complementary prognostic tools. In this context, the Meet-URO and CANLPH scores—incorporating clinical, inflammatory, and nutritional markers—have emerged as promising alternatives. To evaluate and compare the prognostic performance of the Meet-URO and CANLPH scoring systems in a real-world mRCC cohort predominantly treated with first-line tyrosine kinase inhibitor (TKI) monotherapy due to limited access to ICI-based combinations. Methods: This retrospective single-center study included 112 patients with mRCC. The Meet-URO score was calculated for all patients, while the CANLPH score was assessed in 56 patients with complete laboratory data. CAR, NLR, and PHR were computed using baseline pre-treatment measurements. Overall survival (OS) and progression-free survival (PFS), the latter defined exclusively for first-line therapy, were estimated using the Kaplan–Meier method. Correlations between inflammatory markers and survival outcomes were analyzed using Spearman’s rho. Results: Meet-URO demonstrated clear prognostic stratification across all five categories, with the most favorable outcomes in score group 2 and progressively poorer OS and PFS in higher-risk groups. CANLPH also showed meaningful survival discrimination, with the highest inflammatory group (score 3) exhibiting markedly reduced OS and PFS. CAR was the strongest individual predictor of survival, while NLR and PHR showed weaker associations. Conclusions: Both Meet-URO and CANLPH provide strong, complementary prognostic information in mRCC, even in a cohort largely treated with TKI monotherapy. Their integration into routine risk assessment may enhance clinical decision-making, particularly in resource-limited settings. Full article

Microplastic (MP) pollution poses an emerging environmental concern, yet current methods for isolation and quantification are often time-consuming, costly, and poorly adapted to real-world variability. In this study, a workflow for the preparation, filtration, and quantification of MP standards, emphasizing environmental relevance and methodological efficiency, was developed and evaluated. To address the scarcity of irregularly shaped MP standards, low-cost, environmentally representative standards were lab-prepared by grinding and sieving plastic sheets. These MPs were successfully categorized according to sizes up to ~250 μm and dyed for enhanced visibility. The filtration efficiency for two systems, a long-circuit pump (LC-pump) and a short-circuit vacuum (SC-vacuum), was compared. The SC-vacuum method demonstrated a more than 11-fold increase in filtration speed and higher MP recovery rates for both polystyrene and polypropylene standards. Ethanol-based solvents significantly improved MP dispersion and recovery for irregular shapes of the MPs, including polystyrene and polypropylene. Finally, a user-guided machine learning tool (Ilastik) was implemented for automated MP quantification. Ilastik showed a strong correlation with manual counting (r = 0.824) and reduced variability, offering a reproducible and time-efficient alternative. By cutting down cost, time, and technical complexity relative to existing MP analysis techniques, this workflow provides a more accessible path toward consistent and scalable environmental MP assessments. Full article

In clinical and preclinical magnetic resonance imaging (MRI), image quality is often limited by intrinsic tissue contrast, so paramagnetic agents are used to amplify relaxation differences and improve lesion detectability. Widely used gadolinium-based contrast agents present recognized drawbacks, stimulating interest in nanoscale platforms with tuneable magnetic and biological properties. This review provides a critical analysis on the use of metal-doped carbon nanodots (C-dots) as MRI contrast candidates. We briefly revisit MRI signal formation, spin–lattice ($T_1$) and spin–spin ($T_2$) relaxation, and relaxometric parameters $r_1$ and $r_2$ and outline how pulse-sequence choice favours $T_1$- or $T_2$-dominant agents. We compare approved small-molecule agents with nanostructured systems, highlighting unmet needs in safety, field-strength dependence, multimodality, and organ-specific imaging. A central focus is how nano- and molecular architectures of metal-doped carbon dots govern $r_1$ and $r_2$: the metal species and oxidation state, its location within the carbon matrix, surface chemistry and hydration, and the accessibility for proton and water exchange can shift performance toward $T_1$ or $T_2$. Engineered C-dots with controlled composition and metal dopants have proven to pair improved relaxivity with fluorescence, targeting ligands, or therapeutic payloads. Overall, metal-doped C-dots represent a flexible and potentially safer alternative to classical contrast agents; however, successful clinical translation and market uptake will still require standardized relaxometry at clinical field strengths, scalable and reproducible synthesis, and comprehensive in vivo safety and efficacy validation. Full article

Antimicrobial resistance (AMR) remains a major clinical challenge, with Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species (ESKAPE) accounting for a substantial share of multidrug-resistant (MDR) infections worldwide. These organisms undermine antibiotic efficacy through reduced permeability, surface shielding, biofilm formation, and rapid genetic adaptation, mechanisms that primarily restrict effective exposure at infection sites. Bacteriophages, phage-derived enzymes, and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based antimicrobials provide selective and mechanistically distinct alternatives to conventional antibiotics, but their performance in vivo is often limited by instability in physiological environments, immune neutralization, uneven tissue distribution, and insufficient access to bacteria protected by biofilms or surface-associated barriers. This narrative review examines how nanotechnology-based delivery systems can address these constraints. We first outline the delivery-relevant biological barrier characteristic of ESKAPE pathogens, then summarize the therapeutic potential and inherent limitations of whole phages, phage-derived enzymes, and CRISPR-based antimicrobials when used without formulation. Major nanotechnology platforms for antibacterial delivery are reviewed, followed by analysis of how nano-enabled systems can improve stability, localization, and persistence of these biological agents. A pathogen-aware integration framework is presented that links dominant barriers in each ESKAPE pathogen to the biological modality and nano-enabled delivery strategy most likely to enhance exposure at infection sites. Translational challenges, regulatory considerations, and emerging directions, including responsive delivery systems and personalized approaches, are also discussed. Overall, nano-enabled phage-based therapeutics represent a realistic and adaptable strategy for managing MDR ESKAPE infections. Therapeutic success depends on both continued discovery and engineering of antibacterial agents and effective delivery design. Full article

Endothelial ion signaling is crucial for the proper function of the arterial microcirculation, regulating local blood flow to meet metabolic demands and contributing to the regulation of systemic arterial pressure. The role of endothelial ion channels in the precise control of vascular resistance has been primarily investigated in animal models, where the microvasculature is more readily accessible. This review aims to discuss current knowledge on the role of endothelial ion signaling in vasomotor regulation in the human microcirculation, focusing on potassium (K⁺) channels (K_IR2.1, K_ATP, SK_Ca/IK_Ca), Transient Receptor Potential (TRP) channels, particularly TRP Vanilloid 1 (TRPV1) and TRPV4, and Piezo1 channels. The analysis examines the organization of the endothelial ionic signaling machinery in the most extensively studied human microvascular beds, such as the skin, skeletal muscle, and brain, while also discussing vascular reactivity in vessels isolated ex vivo. Accumulating evidence indicates that a distinct repertoire of endothelial ion channels engages diverse endothelium-dependent vasorelaxant pathways across different vascular beds. Understanding how endothelial channels regulate the microvascular unit is predicted to foster the search for alternative therapeutic strategies for treating cardiovascular and neurodegenerative disorders associated with endothelial dysfunction. Full article

Early and reliable detection of cardiac disease is crucial for preventing complications and enhancing patient outcomes. Phonocardiogram (PCG) signals, which encode rich information about cardiac function, offer a non-invasive and cost-effective way to identify abnormalities such as valvular disorders, arrhythmias, and other heart pathologies. This study investigates advanced diagnostic methods for heart sound analysis to improve the detection and classification of cardiac abnormalities. In the proposed framework, recurrence plots (RPs) are used for feature extraction, while machine learning algorithms are applied for classification, creating a diagnostic model that can recognize cardiac conditions from composite acoustic signals. This method serves as an efficient alternative to more computationally intensive deep learning methods and other high-dimensional ML-based solutions. Experimental results demonstrate that the multiclass classification task achieves up to 98.4% accuracy, and the binary classification reaches 99.5% accuracy using 2 s signal segments. The techniques assessed in this research demonstrate the potential of automated heart sound analysis as a screening tool in both clinical and remote healthcare settings. Overall, the findings highlight the significance of machine learning in heart sound classification and its potential to facilitate timely, accessible, and cost-effective cardiovascular care. Full article

Background: Obstructive Sleep Apnea (OSA) is one of the most prevalent sleep disorders associated with cardiovascular complications, cognitive impairments, and reduced quality of life. Early and accurate diagnosis is essential. The present gold standard, polysomnography, is expensive and resource-intensive. This work develops a non-invasive machine-learning-based framework to classify four OSA severity groups (non, mild, moderate, and severe) using tracheal breathing sounds (TBSs) and anthropometric variables. Methods: A total of 199 participants were recruited, and TBS were recorded whilst awake (wakefulness) using a suprasternal microphone. The workflow included the following steps: signal preprocessing (segmentation, filtering, and normalization), multi-domain feature extraction representing spectral, temporal, nonlinear, and morphological features, adaptive feature normalization, and a three-stage feature selection that combined univariate filtering, Shapley Additive Explanations (SHAP)-based ranking, and recursive feature elimination (RFE). The classification included training ensemble learning models via bootstrap aggregation and validating them using stratified k-fold cross-validation (CV), while preserving the OSA severity and anthropometric distributions. Results: The proposed framework performed well in discriminating among OSA severity groups. TBS features, combined with anthropometric ones, increased classification performance and reliability across all severity classes, providing proof for the efficacy of non-invasive audio biomarkers for OSA screening. Conclusions: TBS-based model’s features, coupled with anthropometric information, offer a promising alternative or supplement to PSG for OSA severity detection. The approach provides scalability and accessibility to extend screening and potentially enables earlier detection of OSA, compared to cases that might remain undiagnosed without screening. Full article

Background: Sickle cell disease (SCD) is a prevalent hereditary hemoglobinopathy associated with substantial morbidity, particularly in regions with limited access to advanced laboratory diagnostics. Conventional diagnostic workflows, including manual peripheral blood smear examination and biochemical or molecular assays, are resource-intensive, time-consuming, and subject to observer variability. Recent advances in artificial intelligence (AI) enable automated analysis of blood smear images and offer a scalable alternative for SCD screening. Methods: This study presents a controlled benchmark of CNNs, Vision Transformers, hierarchical Transformers, and hybrid CNN–Transformer architectures for image-level SCD classification using a publicly available peripheral blood smear dataset. Eleven ImageNet-pretrained models were fine-tuned under identical conditions using an explicit leakage-safe evaluation protocol, incorporating duplicate-aware, group-based data splitting and repeated splits to assess robustness. Performance was evaluated using accuracy and macro-averaged precision, recall, and F1-score, complemented by bootstrap confidence intervals, paired statistical testing, error-type analysis, and explainable AI (XAI). Results: Across repeated group-aware splits, CNN-based and hybrid architectures demonstrated more stable and consistently higher performance than transformer-only models. MaxViT-Tiny and DenseNet121 ranked highest overall, while pure ViTs showed reduced effectiveness under data-constrained conditions. Error analysis revealed a dominance of false-positive predictions, reflecting intrinsic morphological ambiguity in challenging samples. XAI visualizations suggest that CNNs focus on localized red blood cell morphology, whereas hybrid models integrate both local and contextual cues. Conclusions: Under limited-data conditions, convolutional inductive bias remains critical for robust blood-smear-based SCD classification. CNN and hybrid CNN–Transformer models offer interpretable and reliable performance, supporting their potential role as decision-support tools in screening-oriented research settings. Full article

Next-generation human papillomavirus (HPV) vaccines encompass newly licensed and emerging formulations that employ alternative production platforms, expanded valency, or novel antigenic targets beyond conventional L1-based vaccines. These vaccines aim to address affordability challenges, supply limitations, and suboptimal vaccination coverage, particularly in low- and middle-income countries. This review aggregates current clinical, immunological, and programme-related evidence on newly licensed vaccines, including the World Health Organization (WHO)-prequalified bivalent formulations (Cecolin^® and Walrinvax^®), the quadrivalent Cervavac^®, and the Escherichia coli-derived nonavalent Cecolin 9^®, which received national licensure in 2025. Additionally, emerging high-valency candidates in Phase I–III trials—9-valent, 11-valent, and 14-valent formulations—are critically assessed. Clinical trials demonstrate that next-generation HPV vaccines provide robust protection; for example, Cecolin^® showed 100% efficacy against HPV-16/18-associated high-grade squamous intraepithelial lesions (HSIL) and up to 97.8% efficacy against persistent HPV infection, while Walrinvax^® demonstrated 78.6% protection against CIN2+ lesions. Cervavac^® showed non-inferior immunogenicity compared with established vaccines. While comparative analyses of efficacy, immunogenicity, and safety indicate that these vaccines are strong alternatives to established products, robust long-term effectiveness and real-world impact data remain essential before full clinical equivalence can be definitively established. Advances in L2-based platforms further aim to broaden cross-type protection, simplify manufacturing, and enable thermostable formulations, thereby enhancing applicability in resource-limited settings. Economic evaluations demonstrating favorable cost-effectiveness emphasize the essential role of next-generation vaccines in improving access and reducing inequity. Overall, innovations in valency, technology, and delivery strategies have the potential to significantly expand global HPV prevention coverage and accelerate progress toward cervical cancer elimination. Full article

Small island developing states (SIDS) face persistent energy security challenges due to heavy reliance on imported fossil fuels, with Jamaica experiencing residential electricity costs often exceeding 0.30 USD/kWh. This study presents the first national-scale, spatially explicit assessment of ocean thermal energy conversion (OTEC) potential around Jamaica, integrating oceanographic conditions, bathymetry, and infrastructure constraints with an archival-calibrated economic framework. Vertical thermal gradients between surface (20 m) and deep (1000 m) waters consistently exceed the 20 °C threshold required for closed-cycle operation across the entire Exclusive Economic Zone. Principal component analysis (PCA) identified five priority offshore zones where steep bathymetry enables deep-water access within 5–15 km of the coastline. To ensure technical realism, economic screening was calibrated against archival benchmarks adjusted via the U.S. Manufacturing Price Index (MPI). Results indicate that 10 MW offshore configurations yield a mean levelized cost of electricity (LCOE) of 0.81 USD/kWh, exceeding current retail benchmarks. However, a strategic “economic window” was identified for near-shore onshore configurations; specifically, site ON-4 achieves an LCOE of 0.26 USD/kWh, effectively undercutting Jamaica’s all-in residential electricity price (≈0.33 USD/kWh). While offshore OTEC remains capital-intensive at the 10 MW scale, this study demonstrates that Jamaica’s exceptional nearshore bathymetry provides a credible pathway for first-of-a-kind onshore deployment, offering a stable, baseload alternative to volatile imported fuels. Full article