Search Results (31,653)

Background/Objectives: Laryngeal cancer remains a global health burden, particularly in regions with high tobacco and alcohol consumption. This study aimed to provide a comprehensive epidemiological overview of laryngeal cancer in Western Greece and to assess overall survival (OS), disease-specific survival (DSS), and key prognostic factors over a 21-year period. Methods: A retrospective cohort study was conducted, including patients diagnosed and treated for laryngeal cancer at the Otorhinolaryngology Department of the University General Hospital of Patras between 1997 and 2017. Demographic, clinical, histopathological, and treatment data were collected. Survival outcomes were estimated using Kaplan–Meier analysis and compared using the log-rank test. Multivariable Cox proportional hazards regression was performed to identify independent prognostic factors. Significance was set at p ≤ 0.05. Results: A total of 211 patients were included (mean age 62.7 years; 95.3% male). Active smoking was reported in 97.6% of cases. Most patients (88.6%) were diagnosed at advanced stages (III–IV), with glottic tumors being the most common (61.1%). The 5-year OS and DSS rates were 47.0% and 55.6%, respectively. Larger tumor size, nodal involvement, and advanced stage were significantly associated with reduced DSS in univariable analysis (p < 0.001). Cox regression confirmed tumor size (HR = 1.665, 95% CI: 1.187–2.336) and nodal status (HR = 1.546, 95% CI: 1.176–2.031) as independent predictors of DSS. Conclusions: The findings highlight the impact of advanced disease at diagnosis and the central prognostic role of tumor burden in laryngeal cancer in Western Greece. Early detection and timely management remain essential to improve patient outcomes. Full article

Background/Objectives: Non-alcoholic fatty liver disease (NAFLD), recently redefined as metabolic dysfunction-associated steatotic liver disease (MASLD), represents a growing global health burden closely linked to obesity, insulin resistance, and dietary patterns. Despite intense drug-development efforts, effective and widely approved pharmacological therapies remain limited. Methods: In this work, we systematically evaluated the quality of clinical evidence supporting currently proposed pharmacological treatments for MASLD/MASLD using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework, focusing on phase III and IV clinical trials. Results: Our analysis demonstrates that overall quality of evidence for most pharmacological agents ranges from very low to moderate, primarily due to imprecision and suspected publication bias. Conclusions: Overall, our findings reinforce that, in the current therapeutic landscape, pharmacological therapies should be reserved for carefully selected patients and interpreted in the context of limited evidence certainty. Full article

In line with Saudi Arabia’s Vision 2030, this study reviews the green operations and environmental responsibility revolution in Saudi companies. The study also contributes new cross-sector empirical evidence from Saudi firms, a context that has received limited attention in previous green operations research. This study assesses sustainability advancements in 38 Saudi companies, employing a mixed-research approach. It examines the impact of (i) environmentally friendly technologies, (ii) employee training, (iii) assessing and reporting, (iv) transparent reports, and (v) adherence to recognized standards on sustainability practices through a detailed statistical and correlation analysis. To ensure thoroughness, the selected companies operate across various sectors, differ in sizes, and possess varying levels of experience. The analysis reveals how internal capabilities and governance mechanisms jointly support the operational adoption of sustainability practices in Saudi firms during the Vision 2030 transition. The findings presented have valuable insights for managers and policymakers seeking to promote sustainable practices within their organisations. Full article

During 2022–2023, research groups from 40 nations contributed to the preclinical pharmacology of 173 structurally defined marine-derived compounds, unveiling innovative mechanisms of action. Peer-reviewed publications in the field of marine natural product pharmacology during 2022–2023 included mechanism-of-action studies with 43 compounds showing antibacterial, antifungal, antiprotozoal, antitubercular, and antiviral activity. Additional mechanism-of-action studies were reported for 74 marine compounds that exhibited antidiabetic and anti-inflammatory properties, as well as significant effects on both the immune and nervous systems. Finally, while 65 marine compounds revealed unique and diverse pharmacological mechanisms, further investigation will be required to determine whether they will contribute to a particular therapeutic category. Collectively, the pharmacology of 2022–2023 preclinical marine natural products demonstrated robust activity, offering both novel mechanistic insights and promising chemical scaffolds to enrich the 2026 marine pharmaceutical development pipeline (https://www.marinepharmacology.org/) which currently consists of 17 marine-derived pharmaceuticals approved for clinical use and 29 compounds in either Phase I, II or III of clinical pharmaceutical development. Full article

A rock-socketed pile model load test was conducted for the renovation project of the dangerous old bridge at Shaoping Bridge. The experiment focused on the core parameter of the roughness factor (RF) of the pile body, revealing its influence on the bearing characteristics. The study delved into the load–displacement relationship, ultimate bearing capacity evolution, axial force transmission mechanism, average lateral resistance performance characteristics, and pile–soil relative displacement law of test piles in complex rock formations under different RF values. The research results indicated the following: The test pile exhibited typical brittle failure. At the moment of failure, the load at the pile head dropped abruptly, resulting in a steep drop in its load–displacement curve. Under ultimate load conditions, the average attenuation amplitudes of axial force in the four test piles decreased progressively in Rock Layer I, II, and III, measuring 26.96%, 14.86%, and 10.84%, respectively. The average side resistance distribution along the pile shaft showed a single-peak pattern, peaking in Rock Layer I. Increasing RF effectively enhanced the bearing capacity of test piles. However, a higher RF value does not necessarily yield better results, as it exhibits an inverted U-shaped relationship with bearing capacity. Under the specific conditions of this study, the highest bearing capacity among the tested RF values was observed at RF = 0.168; beyond this threshold, performance actually declined. The pile-top load was primarily shared by side resistance and end bearing resistance. Both components initially increased and then decreased with increasing RF, where the end bearing resistance accounted for 43.64~49.47% of the upper load. Full article

Hybrid hydro–solar microgrids offer a practical electrification option for remote and weak-grid communities by combining run-of-river hydropower with photovoltaic generation. However, their performance depends strongly on coordinated decisions across three layers: (i) system sizing and architecture, (ii) turbine selection and rating under variable river flow, and (iii) operational energy dispatch under time-varying solar resource and demand. This paper develops an optimization-driven planning framework for a run-of-river hydro–PV microgrid that co-optimizes component capacities and turbine-related design choices while enforcing time-series operational feasibility. Physics-based component models translate river discharge into hydroelectric output via turbine efficiency characteristics and operating limits, and compute PV generation and storage trajectories under dispatch and state-of-charge constraints. The planning problem is formulated as a multi-objective optimization that quantifies trade-offs among life-cycle cost, supply reliability (e.g., unmet-load metrics), and sustainability indicators (e.g., diesel-free operation or emissions when backup generation is present). A Pareto-optimal set of designs is obtained using a population-based multi-objective algorithm, and representative knee-point (balanced) solutions are selected to illustrate how turbine choice and dispatch strategy interact with seasonal hydrology and solar variability. The proposed approach supports transparent and robust design decisions for hybrid hydro–solar microgrids. Full article

DNA mixture interpretation remains one of the most technically demanding challenges in forensic genetics. While probabilistic genotyping (PG) systems have substantially advanced likelihood ratio (LR) evaluation, comparatively less attention has been devoted to the systematic reconstruction of contributor genotypes, particularly in no-suspect and database-search contexts. This review synthesizes recent developments in DNA mixture deconvolution through a four-strategy framework: (i) physical and biological separation, (ii) high-information genetic markers, (iii) continuous probabilistic algorithms, and (iv) integration with database searching infrastructures. Upstream approaches, including single-cell isolation and sequencing, reduce mixture complexity at the molecular level. Marker innovations such as microhaplotypes, MiniHaps and DIP-STRs increase per-locus information content and enhance resistance to degradation. Downstream probabilistic models—extended from STRs to SNPs and microhaplotypes—leverage quantitative signal data to infer contributor genotypes, with recent advances in Hamiltonian Monte Carlo, variational inference, and deep learning improving inferential stability and reconstruction accuracy. Importantly, genotype deconvolution and LR evaluation represent mathematically distinct objectives, requiring different validation metrics and potentially separate architectural optimization. The convergence of molecular innovation, algorithmic refinement, and LR-based database searching is progressively transforming mixture interpretation from a purely evidential assessment into an integrated investigative framework. Future progress will depend on standardized marker panels, deconvolution-specific performance metrics, and scalable LR-enabled database infrastructures. Full article

Lunar quadruped robots face landing challenges including weak gravity, large mass variations, uncertain sloped terrain, and strict payload acceleration limits, requiring effective impact mitigation and rapid post-landing stabilization. This paper presents a novel end-to-end reinforcement learning-based landing controller with three key novelties: (i) a phase-structured yet implicitly encoded formulation that distinguishes contact preparation, energy dissipation, and stabilization without explicit phase switching; (ii) a terrain-agnostic state and control representation using equivalent support direction construction and contact-gated modulation to decouple normal–tangential dynamics; and (iii) an extremum oriented learning strategy that directly captures peak impact suppression and buffering sufficiency, addressing limitations of cumulative rewards in hybrid, peak-dominated tasks. A hybrid control model for lunar quadruped landing dynamics is established, incorporating variable mass, low impact, and full stroke as key constraints during training. Simulation and full-scale experimental prototypes are built to validate the controller. Simulation results demonstrate robust landing buffering and stability control under varying mass, landing velocity, and slope conditions, with favorable robustness against parameter variations. Experimental verification is conducted under diverse conditions including different masses (200 kg, 250 kg), vertical/horizontal landing velocities (0.8 m/s, 0.2 m/s), and slopes (0, 8). The deviation between simulation and experimental results does not exceed 30%, confirming the effectiveness and transferability of the proposed approach. Full article

The main purpose of the research is to rank EU member states by the intensity of their efforts to implement the CE model. Understanding EU member states’ differences is crucial to formulating effective policy measures that foster sustainable development and enhance economic resilience across the EU. The degree of CE development was examined through three sub-indicators: (i) private investment related to CE sectors; (ii) persons employed in CE sectors; and (iii) gross value added as a percentage of GDP. Data from the Eurostat database for the last five available years were used. Hierarchical agglomerative cluster analysis is used to identify groups of structurally similar countries. Countries are ranked using the PROMETHEE II multi-criteria decision-making method with objectively derived CRITIC weights, complemented by GAIA visualisation. The analysis identifies five distinct clusters with a highly heterogeneous CE landscape across the EU. The PROMETHEE-GAIA research results reveal two different paths on which European countries are moving towards CE. The first, characterized by high structural maturity but limited dynamic flexibility, is evident in Sweden and Belgium. And the second path, illustrated by Estonia and Croatia, is distinguished by a rapid pace of transformation and lower historical structural capacities. Full article

As an emerging oilseed crop in China, peony seed oils account for 0.41% of the annual production of Chinese edible vegetable oils, and the oil-type peony seed is rich in alpha-linolenic acid (ALA). Moisture content and temperature are key factors in the storage of oilseeds. In this study, the adsorption and desorption isotherms of ten species of peony seeds and one species of cake were determined in the range of 20–30 °C and 10–90% equilibrium relative humidity (ERH). The adsorption and desorption isotherms of peony seeds and cake were type II (sigmoidal) or type III curves. Nine equilibrium moisture content (EMC) equations were used to fit the isotherms of peony samples, with the optimal equations being our developed 7-parameter polynomial (Poly), modified Halsey equation (MHAE), and modified Oswin equation (MOE). For Poly, the fitting parameter determination coefficient (R²) was 0.9816–0.9986, and the mean relative error (MRE) was 0.83–6.52%; for MHAE, R² was 0.7815–0.9973, and MRE was 4.18–17.84%. Poly contains the terms of temperature and ERH interaction; therefore, Poly could analyze the safe moisture content of peony seeds and cake during storage and transportation, and the three-parameter reversible MHAE could be used for calculating the sorption isosteric heats. The adsorption monolayer moisture content (M₀) in peony seeds and cake estimated by MGAB were 3.64 ± 0.42% and 4.28%, respectively, while their desorption M₀ values, respectively, were 6.21 ± 0.47% and 4.83%. At ERH ≤ 65%, for preventing the growth of storage pests and fungi, the absolutely safe storage moisture content (MC) predicted by Poly at 25 °C and 65% ERH was 12.48% wet basis (w.b.) for seeds and 11.92% for cake. The heat of sorption of peony seeds and cake approached that of pure water at about 11% and 15% w.b. MC estimated by the MHAE model, respectively. Microstructure analysis showed that the rich liposomes in peony seeds were attached to the inner surface of the cell wall and the outer surface of the protein storage vacuole, and the rich protein bodies and hydrophilic polysaccharides explained why the safe storage moisture for yellow peony seeds was higher than for Ziyan Feishuang seeds. This study provides the basic data for drying simulation, and the safe storage and transportation of peony seed and cake products. Full article

Background: Risk stratification in patients with bicuspid aortic valve (BAV) and severe aortic stenosis (AS) remains challenging. Valvulo-arterial impedance (Zva), an integrated marker of global left ventricular (LV) afterload, has shown prognostic value in tricuspid AS; however, data in BAV are limited. This study aimed to evaluate the association of Zva with LV remodeling, symptoms, and all-cause death in patients with BAV and severe AS. Methods: In this retrospective, two-center cohort study, 147 patients with severe AS and BAV were included. Zva was calculated at the time of the first echocardiographic diagnosis of severe AS. The study endpoint was all-cause mortality. Results: Over a median follow-up of 9.8 years, 24 patients (16%) died. A Zva threshold of 5 mmHg/mL/m² was identified as optimal by ROC analysis. Patients with Zva ≥ 5 mmHg/mL/m² showed higher mortality rates (29% vs. 10%; p = 0.003), more advanced symptoms (NYHA III-IV: 41% vs. 9%; p < 0.001), adverse LV remodeling, lower LVEF (60% (IQR 36–66) vs. 66% (IQR 61–71); p = 0.001), and worse LV global longitudinal strain (14.8% ± 2.7 vs. 16.5% ± 3.0; p = 0.016). Zva ≥ 5 mmHg/mL/m² was independently associated with worse long-term survival after adjustment (HR 2.885; 95% CI 1.119–7.438; p = 0.028). Conclusions: Among patients with BAV and severe AS, an increased Zva was associated with more advanced symptoms, adverse LV remodeling, impaired LV systolic function, and worse long-term survival, and might therefore help in risk stratification of these patients. Full article

Photoplethysmography (PPG) has emerged as an attractive modality for non-invasive cardiovascular monitoring due to its low cost, unobtrusive nature, and ubiquity in consumer wearable devices. Despite its potential, existing PPG-based arrhythmia detection systems remain limited in scope: (i) most target only atrial fibrillation, (ii) temporal localization of abnormal segments is rarely provided, and (iii) deep learning models lack explainability, hindering adoption in clinical workflows. We present a comprehensive and fully integrated framework for multi-class arrhythmia detection, segmentation, and explainability based on PPG waveforms, Heart Rate Variability (HRV), and structured clinical metadata. The proposed system introduces a CLIP-style contrastive learning module aligning PPG waveforms with clinical variables and rhythm-state textual descriptions using BioBERT; a multitask U-Net architecture performing 4-class classification and 1D segmentation; a Retrieval-Augmented Generation (RAG) pipeline leveraging Gemini Flash large language models to produce guideline-grounded diagnostic reports; and a real-time Streamlit-based web platform supporting inference, visualization, and database storage. The system significantly improves classification accuracy (from 86.27% to 91.19%) and segmentation Dice (from 0.5815 to 0.7167). These results demonstrate the feasibility of a robust, multimodal, and explainable PPG-based arrhythmia monitoring system for real-world applications. Full article

Background: We previously pioneered a multigene mRNA test, qMIDS^V2, validated through an international multicohort study with geographically and ethnically diverse oral squamous cell carcinoma (OSCC) patients from Europe and Asia. This study aimed to repurpose the qMIDS^V2 test for nasopharyngeal carcinoma (NPC). A molecular test independent of Epstein–Barr virus (EBV) status would be clinically useful for risk stratification in NPC patients with undetectable or low levels of EBV. Methods: This study investigated a Chinese cohort of 62 participants (18 donated normal nasopharyngeal mucosa (NPM) and 44 donated NPC tissue samples). Messenger RNA levels of 16 genes in each sample were quantified using the qPCR method, and an algorithm computed a malignancy index for cancer risk stratification. Results: We identified a unique 10-gene panel (containing eight target genes, namely NEK2, INHBA, FOXM1, TOP2A, BIRC5, CXCL8, NR3C1, and IVL, relative to two reference genes, YAP1 and POLR2A, collectively named qMIDS^NPC) that demonstrated the best overall diagnostic performance in segregating NPM from NPC, with AUC = 0.909 and positive/negative predictive values of 91% PPV and 78% NPV, respectively. Furthermore, we demonstrated prognostic value of qMIDS^NPC in segregating NPM from NPC stage III + IV, with AUC = 0.936, 92% PPV, and 84% NPV. Conclusions: Here, we present a simple qPCR-based 10-gene mRNA test, qMIDS^NPC, with potential clinical utilities for rapid (1 h) prognostic stratification of NPC. Further studies involving geographically and ethnically independent NPC cohorts would be needed to validate the clinical use of qMIDS^NPC in non-endemic NPC populations. Full article

Background and Objectives: Shortages of dermatologists create significant barriers to care, particularly for inflammatory and history-dependent conditions where image-only artificial intelligence (AI) classifiers have limited applicability. Current teledermatology solutions largely focus on single-task, morphology-based neoplasm classifiers, leaving the vast majority of dermatologic presentations underserved. This study evaluated the diagnostic accuracy and management plan quality of Dermflow (Prava Medical, Delaware, USA), a proprietary dermatology-focused Multimodal Image-and-Data Assistant (MIDA) that autonomously gathers dermatology-specific history, integrates data with patient-submitted images, and outputs structured differential diagnoses and management summaries. Materials and Methods: Two AI systems, Dermflow and Claude Sonnet 4 (Claude, a leading vision–language model), analyzed 87 clinical images from the Skin Condition Image Network and Diverse Dermatology Images databases, representing 10 inflammatory dermatoses and 9 neoplastic conditions stratified across Fitzpatrick Skin Tone (FST) categories (I–II, III–IV, V–VI). For the diagnostic comparison, Dermflow received images and autonomously gathered clinical history, while Claude received identical images without history. For the management plan comparison, both systems received the correct diagnosis and the clinical histories gathered by Dermflow. The primary outcome was diagnostic accuracy. The secondary outcome was management plan quality, assessed by two blinded dermatologists across eight clinical dimensions using 5-point Likert scales. Chi-square tests compared diagnostic accuracy between models; t-tests and ANOVA compared management quality scores. Results: Dermflow achieved markedly superior diagnostic accuracy compared to Claude (86.2% vs. 24.1%, p < 0.001). Both models maintained consistent diagnostic performance across FST categories without significant within-model differences (Dermflow p = 0.924; Claude p = 0.828). Management plan quality showed no significant overall differences between models. However, composite management quality scores declined significantly for darker skin tones across both systems: Dermflow scored 4.20 (FST I–II), 3.99 (FST III–IV), and 3.47 (FST V–VI); Claude scored 4.35, 3.97, and 3.44, respectively (p < 0.001 for most pairwise FST comparisons within each model). Conclusions: Multimodal AI integrating targeted history with image analysis achieves substantially higher diagnostic accuracy than image-only approaches across both inflammatory and neoplastic dermatologic conditions. Autonomous history gathering addresses fundamental limitations of morphology-only classifiers and enables scalable, patient-facing triage across the full spectrum of dermatologic disease. However, both models demonstrated reduced management plan quality for darker skin tones despite receiving the correct diagnosis, suggesting persistent training data limitations that require targeted bias-mitigation strategies beyond domain-specific instruction. Full article

Nucleoside diphosphate kinase (NDPK) is a ubiquitous enzyme that maintains cellular nucleotide balance by catalyzing the transfer of phosphate groups between nucleoside diphosphates and triphosphates. Although the evolutionary conservation of NDPK is well established, several aspects of its diversification and functional adaptation remain unclear. The central question of this work is how NDPK evolved across plant species, focusing on the Solanaceae family and how its evolutionary history relates to the diversification of its cellular functions. Phylogenetic and molecular dating analyses showed that the division between NDPK groups 1 and 2 predates the divergence of plants and animals, whereas plant-specific NDPK types (I–IV) originated early in streptophyte evolution. Solanaceae species retain a conserved set of NDPK genes, including a type III isoform with features consistent with mitochondrial targeting. Functional assays in isolated potato tuber mitochondria revealed high NDPK activity in the intermembrane space, sustaining ADP supply to oxidative phosphorylation. Activation of mitochondrial NDPK induced a phosphorylative respiratory state, which partially dissipated the mitochondrial membrane potential and significantly reduced reactive oxygen species (ROS) production. GDP and UDP were preferentially phosphorylated, conferring a stronger antioxidant effect than other nucleotides. Consistently, the mitochondrial isoform StNDPK3 was upregulated during tuber development. Together, our results demonstrate that NDPKs are evolutionarily conserved yet functionally diversified enzymes in plants and identify mitochondrial NDPK as a key modulator of mitochondrial redox homeostasis. By linking nucleotide metabolism to Δψ_m control and ROS suppression, this study highlights a previously underappreciated antioxidant mechanism that integrates mitochondrial energy metabolism with developmental and stress-related processes in plants. Full article