Search Results (10,727)

With increasing life expectancy, the number of older people living with comorbid diabetes and frailty is increasing. The development of frailty accelerates diabetes-related adverse outcomes. Frailty is a multidimensional syndrome with physical, mental and social aspects which is associated with increased risk of hypoglycaemia, dementia and hospitalisation. Therefore, regular screening for all aspects of frailty should be an integrated part of the care plans of older people with diabetes. In addition, every effort should be made for prevention, which includes adequate nutrition combined with regular resistance exercise training. In already frail older people with diabetes, metabolic targets should be relaxed and hypoglycaemic agents should be of low hypoglycaemic risk potential. Furthermore, the metabolic phenotype of frailty should be considered when choosing hypoglycaemic agents and determining targets. With increasing severity of frailty, proactive chronological plans of de-escalation, palliation and end-of-life care should be considered. These plans should be undertaken in a shared decision-making manner which involves patients and their families. This ensures that patients’ views, wishes and preferences are in the heart of these plans. Full article

The emergence of SARS-CoV-2 variants highlights the urgent need for novel therapeutic strategies, particularly entry inhibitors that could efficiently prevent viral infection. Medicinal herbs and herbal combination formulas have long been recognized for their effects in treating infectious diseases and their antiviral properties, thus providing abundant resources for the discovery of antiviral candidates. While many candidates have been suggested to have antiviral activity against SARS-CoV-2 infection, few have been validated for their mechanisms, including possible effects on viral entry. This study aimed to identify SARS-CoV-2 entry inhibitors from medicinal herbs and herbal formulas that are known for heat-clearing and detoxifying properties and/or antiviral activities. A SARS-CoV-2 pseudoparticle (SARS-CoV-2pp) system was used to assess mechanism-specific entry inhibition. Our results showed that the methanol extract of Anemarrhena asphodeloides rhizome, as well as the water extracts of Cimicifuga foetida rhizome, Xiao Chai Hu Tang (XCHT), and Sheng Ma Ge Gen Tang (SMGGT), have substantial inhibitory effects on the entry of SARS-CoV-2pps into host cells. Given the observation that Cimicifuga foetida exhibited the most potent inhibition and is a constituent of SMGGT, we further investigated the major compounds of the herb and identified caffeic acid as a bioactive component for blocking SARS-CoV-2pp entry. Entry inhibition of Cimicifuga foetida and caffeic acid was validated on both wild-type and the currently dominant JN.1 strain SARS-CoV-2pp systems. Moreover, caffeic acid was able to both inactivate the pseudoparticles and prevent their entry into pretreated host cells. The results support the traditional use of these herbal medicines and underscore their potential as valuable resources for identifying active compounds and developing therapeutic entry inhibitors for the management of COVID-19. Full article

The accurate prediction of significant wave height presents a complex yet vital challenge in the fields of ocean engineering. This capability is essential for disaster prevention, fostering sustainable development and deepening our understanding of various scientific phenomena. We explore the development of a comprehensive predictive methodology for wave height prediction by integrating novel Takagi–Sugeno–Kang fuzzy models within a multiresolution analysis framework. The multiresolution analysis emerges via wavelets, since they are prominent models characterised by their inherent multiresolution nature. The maximal overlap discrete wavelet transform is utilised to generate the detail and resolution components of the time series, resulting from this multiresolution analysis. The novelty of the proposed model lies on its hybrid training approach, which combines least squares with AdaBound, a gradient-based algorithm derived from the deep learning literature. Significant wave height prediction is studied as a time series problem, hence, the appropriate inputs to the model are selected by developing a surrogate-based wrapped algorithm. The developed wrapper-based algorithm, employs Bayesian optimisation to deliver a fast and accurate method for feature selection. In addition, we introduce a projection step, to further refine the approximation capabilities of the resulting predictive system. The proposed methodology is applied to a real-world time series pertaining to spectral wave height and obtained from the Poseidon operational oceanography system at the Institute of Oceanography, part of the Hellenic Center for Marine Research. Numerical studies showcase a high degree of approximation performance. The predictive scheme with the projection step yields a coefficient of determination of $0.9991$, indicating a high level of accuracy. Furthermore, it outperforms the second-best comparative model by approximately $49\%$ in terms of root mean squared error. Comparative evaluations against powerful artificial intelligence models, using regression metrics and hypothesis test, underscore the effectiveness of the proposed methodology. Full article

Earthquakes, as one of the most destructive natural disasters, often cause significant casualties and severe economic losses. Accurate prediction of earthquake fatalities is of great importance for pre-disaster prevention and mitigation planning, as well as post-disaster emergency response deployment. To address the challenges of small sample sizes, high dimensionality, and strong nonlinearity in earthquake fatality prediction, this paper proposes an integrated modeling approach (PCA-IWOA-XGBoost) combining Principal Component Analysis (PCA), the Improved Whale Optimization Algorithm (IWOA), and Extreme Gradient Boosting (XGBoost). The method first employs PCA to reduce the dimensionality of the influencing factor data, eliminating redundant information and improving modeling efficiency. Subsequently, the IWOA is used to intelligently optimize key hyperparameters of the XGBoost model, enhancing the prediction accuracy and stability. Using 42 major earthquake events in China from 1970 to 2025 as a case study, covering regions including the west (e.g., Tonghai in Yunnan, Wenchuan, Jiuzhaigou), central (e.g., Lushan in Sichuan, Ya’an), east (e.g., Tangshan, Yingkou), north (e.g., Baotou in Inner Mongolia, Helinger), northwest (e.g., Jiashi in Xinjiang, Wushi, Yongdeng in Gansu), and southwest (e.g., Lancang in Yunnan, Lijiang, Ludian), the empirical results showed that the PCA-IWOA-XGBoost model achieved an average test set accuracy of 97.0%, a coefficient of determination (R²) of 0.996, a root mean square error (RMSE) and mean absolute error (MAE) reduced to 4.410 and 3.430, respectively, and a residual prediction deviation (RPD) of 21.090. These results significantly outperformed the baseline XGBoost, PCA-XGBoost, and IWOA-XGBoost models, providing improved technical support for earthquake disaster risk assessment and emergency response. Full article

According to the Transportation Safety Board of Canada, between 2013 and 2023, 62% of aviation accidents occurred during the approach, landing, and post-impact phases of flight. Hence, this study targets factors contributing to increased accident rates during the final stages of flight. It will review how pilot experience influences decision-making and identifies mitigation strategies, focusing on go-arounds to prevent accidents during these critical phases. Surveys and roundtable discussions were conducted to identify factors influencing pilot performance during approach, landing, and go-around manoeuvres. By using a mixed-methods approach that combined thematic and statistical analyses, key safety factors were identified, including situational awareness, decision-making, and operational complexity. The study also examined the relationship between experience and decision-making, highlighting areas for targeted interventions to improve safety. The research emphasises the importance of integrating decision-making considerations into training programmes and connecting these to human factors. Through identifying areas for improvement, this study offers a safety-driven framework to address decision-making challenges during approach, landing, and go-around phases, with the objective of reducing accident and incident rates in general aviation. Full article

Cardiovascular disease (CVD) is the leading cause of death worldwide, with pathophysiological mechanisms often involving platelet activation and chronic inflammation. While antiplatelet agents targeting adenosine diphosphate (ADP)-mediated pathways are well established in CVD management, less is known about drug interactions with the platelet-activating factor (PAF) pathway, a key mediator of inflammation. This study aimed to evaluate the effects of several commonly used cardiovascular and anti-inflammatory drug classes—including clopidogrel, non-steroidal anti-inflammatory drugs (NSAIDs), angiotensin II receptor blockers (ARBs), β-blockers, and analgesics—on platelet function via both the ADP and PAF pathways. Using human platelet-rich plasma (hPRP) from healthy donors, we assessed platelet aggregation in response to these two agonists in the absence and presence of graded concentrations of each of these drugs or of their usually prescribed combinations. The study identified differential drug effects on platelet aggregation, with some agents showing pathway-specific activity. Clopidogrel and NSAIDs demonstrated expected antiplatelet effects, while some (not all) antihypertensives exhibited additional anti-inflammatory potential. These findings highlight the relevance of evaluating pharmacological activity beyond traditional targets, particularly in relation to PAF-mediated inflammation and thrombosis. This dual-pathway analysis may contribute to a broader understanding of drug mechanisms and inform the development of more comprehensive therapeutic strategies for the prevention and treatment of cardiovascular, hypertension, and inflammation-driven diseases. Full article

A healthy vasculature with well-regulated perfusion and pulsatility is essential for the brain. One vascular structure that has received little attention is the carotid siphon. The proximal portion of the siphon is stiff due to the narrow location in the skull base, whilst the distal portion is highly flexible. This flexible part in combination with the specific curves lead to lower pulsatility at the cost of energy deposition in the arterial wall. This deposited energy contributes to damage and calcification. Severe siphon calcification stiffens the distal part of the siphon, leading to less damping of the pulsatility. Increased blood flow pulsatility is a possible cause of stroke and cognitive disorders. In this review, based on comprehensive multimodality imaging, we first describe the anatomy and physiology of the carotid siphon. Subsequently, we review the in vivo imaging data, which indeed suggest that the siphon attenuates pulsatility. Finally, the data as available in the literature are shown to provide convincing evidence that severe siphon calcifications and the calcification pattern are linked to incident stroke and dementia. Interventional studies are required to test whether this association is causal and how an assessment of pulsatility and the siphon calcification pattern can improve personalized medicine, working to prevent and treat brain disease. Full article

As an important ecological barrier and animal husbandry resource base in arid and semi-arid areas, grassland degradation directly affects regional ecological security and sustainable development. Ili Prefecture is located in the western part of Xinjiang, China, and is a typical grassland resource-rich area. However, in recent years, driven by climate change and human activities, grassland degradation has become increasingly serious. In view of the lack of comprehensive evaluation indicators and the inconsistency of grassland evaluation grade standards in remote sensing monitoring of grassland resource degradation, this study takes the current situation of grassland degradation in Ili Prefecture in the past 20 years as the research object and constructs a comprehensive evaluation index system covering three criteria layers of vegetation characteristics, environmental characteristics, and utilization characteristics. Net primary productivity (NPP), vegetation coverage, temperature, precipitation, soil erosion modulus, and grazing intensity were selected as multi-source indicators. Combined with data sources such as remote sensing inversion, sample survey, meteorological data, and farmer survey, the factor weight coefficient was determined by analytic hierarchy process. The Grassland Degeneration Comprehensive Index (GDCI) model was constructed to carry out remote sensing monitoring and evaluation of grassland degradation in Yili Prefecture. With reference to the classification threshold of the national standard for grassland degradation, the GDCI grassland degradation evaluation grade threshold (GDCI reduction rate) was determined by the method of weighted average of coefficients: non-degradation (0–10%), mild degradation (10–20%), moderate degradation (20–37.66%) and severe degradation (more than 37.66%). According to the results, between 2000 and 2022, non-degraded grasslands in Ili Prefecture covered an area of 27,200 km², representing 90.19% of the total grassland area. Slight, moderate, and severe degradation accounted for 4.34%, 3.33%, and 2.15%, respectively. Moderately and severely degraded areas are primarily distributed in agro-pastoral transition zones and economically developed urban regions, respectively. The results revealed the spatial and temporal distribution characteristics of grassland degradation in Yili Prefecture and provided data basis and technical support for regional grassland resource management, degradation prevention and control and ecological restoration. Full article

Molar pregnancy (MP) is a gestational disorder resulting from abnormal fertilization, leading to atypical trophoblastic proliferation and the formation of a complete or partial hydatidiform mole. This condition represents the most common form of gestational trophoblastic disease (GTD) and carries a significant risk of progression to gestational trophoblastic neoplasia (GTN). Although rare in high-income countries, MP remains up to ten times more prevalent in low-income and developing countries, contributing to preventable maternal morbidity and mortality. This narrative review provides an updated, practical overview of the clinical presentation, diagnosis, treatment, and follow-up of MP. A key focus is the challenge of early diagnosis, particularly given the increasing frequency of first-trimester detection, where classical histopathological criteria may be subtle, leading to diagnostic errors. The review innovates by integrating advanced diagnostic methods—combining histopathology, immunohistochemistry using p57Kip2, Ki-67, and p53 markers, along with cytogenetic analysis—to improve diagnostic accuracy in early gestation. The central role of referral centers is also emphasized, not only in facilitating timely treatment and access to chemotherapy, but also in implementing standardized post-molar follow-up protocols that reduce progression to GTN and maternal mortality. By focusing on both advanced diagnostic strategies and the organization of care through referral centers, this review offers a comprehensive, practice-oriented perspective to optimize patient outcomes in GTD and address persistent care gaps in high-burden regions. Full article

Autonomous navigation in dynamic, multi-vessel maritime environments presents a formidable challenge, demanding strict adherence to the International Regulations for Preventing Collisions at Sea (COLREGs). Conventional approaches often struggle with the dual imperatives of global path optimality and local reactive safety, and they frequently rely on simplistic state representations that fail to capture complex spatio-temporal interactions among vessels. We introduce a novel hybrid reinforcement learning framework, D* Lite + Transformer-Enhanced Soft Actor-Critic (TE-SAC), to overcome these limitations. This hierarchical framework synergizes the strengths of global and local planning. An enhanced D* Lite algorithm generates efficient, long-horizon reference paths at the global level. At the local level, the TE-SAC agent performs COLREGs-compliant tactical maneuvering. The core innovation resides in TE-SAC’s synergistic state encoder, which uniquely combines a Graph Neural Network (GNN) to model the instantaneous spatial topology of vessel encounters with a Transformer encoder to capture long-range temporal dependencies and infer vessel intent. Comprehensive simulations demonstrate the framework’s superior performance, validating the strengths of both planning layers. At the local level, our TE-SAC agent exhibits remarkable tactical intelligence, achieving an exceptional 98.7% COLREGs compliance rate and reducing energy consumption by 15–20% through smoother, more decisive maneuvers. This high-quality local control, guided by the efficient global paths from the enhanced D* Lite algorithm, culminates in a 10–32 percentage point improvement in overall task success rates compared to state-of-the-art baselines. This work presents a robust, verifiable, and efficient framework. By demonstrating superior performance and compliance with rules in high-fidelity simulations, it lays a crucial foundation for advancing the practical application of intelligent autonomous navigation systems. Full article

Background/Objectives: Antibiotic prophylaxis is a key component of infection prevention strategies. This study aimed to evaluate whether the duration of antibiotic prophylaxis influences the incidence of HAIs in patients undergoing elective colorectal surgery. Methods: This prospective cohort study included 278 adult patients who underwent elective colorectal surgery at a single tertiary care center. Patients were categorized into two groups based on the duration of antibiotic prophylaxis: one day or more than one day. Data on demographics, clinical characteristics, perioperative variables, and infection outcomes were collected. Results: The overall incidence of HAIs was 16.9%, with no significant difference between patients receiving one-day versus extended antibiotic prophylaxis. However, traditional multivariate analysis showed that prophylaxis lasting more than one day was independently associated with a significantly lower risk of HAI (RR = 0.30, 95% CI: 0.12–0.75, p = 0.010) and surgical site infections (RR = 0.24, 95% CI: 0.08–0.72, p = 0.011). After adjusting for confounders using propensity score matching, this association was no longer statistically significant. No significant association was found between prophylaxis duration and urinary tract infections. Regarding antibiotic selection, first-generation cephalosporins were the most commonly used agents, accounting for 78.8% of prophylactic prescriptions. This was followed by fluoroquinolones (14.4%) and third-generation cephalosporins (5.0%). All patients received metronidazole, a nitroimidazole-class antimicrobial, in combination with the above agents. Conclusions: One day of prophylactic antibiotics may be sufficient in SSI prevention in patients undergoing elective colorectal surgery. The use of extended antibiotic prophylaxis beyond one day should be considered for high-risk patients at high risk of infection, particularly those requiring ICU care. Full article

This article describes a new software architecture for the non-invasive detection of canine leishmaniasis disease. The proposed platform combines gas-sensing technologies, artificial intelligence (AI), and modular cloud-based software components to identify disease-specific volatile organic compounds (VOCs) found in dog breath and hair samples. The system, which has a multi-tier architecture that includes data collection, pre-processing, machine learning-based analysis, diagnosis-request processing, and user interfaces for veterinarians, faculty researchers, and dog owners, has been integrated into a Li-ion Power website plug-in. The primary goal of implementing the proposed platform is to detect parasites at any point they are infectious to a host. This includes detecting parasites at all stages of their life cycle, where they can infect a new host. In addition, this is crucial for accurate diagnosis, effective treatment, and preventing further transmission. Full article

Background/Objectives: The association between chronic kidney disease (CKD) and body size phenotypes in metabolically diverse but apparently healthy adult populations remains inadequately understood. This study investigated the association between CKD and body size phenotypes in a nationally representative sample of healthy Korean adults. Methods: Data from 8227 participants in the 2019–2021 Korean National Health and Nutrition Examination Survey were analyzed. Participants were categorized into four body size phenotypes by combining BMI status (normal weight or obese) with metabolic health status (healthy or abnormal)—MHNW (Metabolically Healthy Normal Weight), MANW (Metabolically Abnormal Normal Weight), MHO (Metabolically Healthy Obese), or MAO (Metabolically Abnormal Obese). CKD was defined based on the urine albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR). To assess the association between CKD and body size phenotypes, multivariable logistic regression analyses were performed. Results: CKD prevalence was 4.4%. MANW and MAO made up 12.6% and 26.4% of the CKD group, compared to 5.0% and 13.2% of the non-CKD group. CKD prevalence by phenotype was observed as follows: MHNW, 3.2%; MANW, 10.5%; MHO, 4.0%; and MAO, 8.5%. CKD odds were highest in the MAO group (OR: 3.770, 95% CI: 2.648–5.367), followed by the MANW (OR: 2.492, 95% CI: 1.547–4.016) and MHO (OR: 1.974, 95% CI: 1.358–2.870) groups. MAO individuals carried a higher CKD risk than MHO individuals (OR: 1.897, 95% CI: 1.221–2.945). Conclusions: Among apparently healthy adults, body size phenotypes—particularly those with metabolic abnormalities—were significantly associated with the presence of CKD. These findings highlight the need to assess both metabolic health and body composition for effective CKD prevention and management. Full article

Electrohydrodynamic (EHD) printing offers mask-free, high-resolution deposition across a broad range of ink viscosities, yet combining void-free filling of high-aspect-ratio through-glass vias (TGVs) with ultrafine drop-on-demand (DOD) line printing on the same platform requires balancing conflicting requirements: for example, high field strengths to drive ink into deep and narrow vias; sufficiently high ink viscosity to prevent gravity-induced leakage; and stable meniscus dynamics to avoid satellite droplets and charge accumulation on the glass surface. By coupling electrostatic field analysis with transient level-set simulations, we establish a dimensionless regime map that delineates stable cone-jetting regime; these predictions are validated by high-speed imaging and surface profilometry. Operating within this window, the platform achieves complete, void-free filling of 200 µm × 1.52 mm TGVs and continuous 10 µm-wide traces in a single print pass. Demonstrating its capabilities, we fabricate transparent Ku-band substrate-integrated waveguide antennas on borosilicate glass: the printed vias and arc feed elements exhibit a reflection coefficient minimum of −18 dB at 14.2 GHz, a −10 dB bandwidth of 12.8–16.2 GHz, and an 8 dBi peak gain with 37° beam tilt, closely matching full-wave predictions. This physics-driven, all-in-one EHD approach provides a scalable route to high-performance, glass-integrated RF devices and transparent electronics. Full article

Landslides are destructive geological hazards, making accurate landslide susceptibility assessment essential for disaster prevention and mitigation. However, existing studies often lack scientific rigor in negative sample construction and have unclear model applicability. This study focuses on Ya’an City, Sichuan Province, China, and proposes an innovative approach to negative sample construction using Geographically Weighted Regression (GWR), which is then integrated with Tabular Network (TabNet), a deep learning architecture tailored to structured tabular data, to assess landslide susceptibility. The performance of TabNet is compared against Random Forest, Light Gradient Boosting Machine, deep neural networks, and Residual Networks. The experimental results indicate that (1) the GWR-based sampling strategy substantially improves model performance across all tested models; (2) TabNet trained using the GWR-based negative samples achieves superior performance over all other evaluated models, with an average AUC of 0.9828, exhibiting both high accuracy and interpretability; and (3) elevation, land cover, and annual Normalized Difference Vegetation Index are identified as dominant predictors through TabNet’s feature importance analysis. The results demonstrate that combining GWR and TabNet substantially enhances landslide susceptibility modeling by improving both accuracy and interpretability, establishing a more scientifically grounded approach to negative sample construction, and providing an interpretable, high-performing modeling framework for geological hazard risk assessment. Full article