Search Results (21,373)

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

Urban rail transit networks’ rapid expansions have led to increasing intersections between existing and new lines, particularly in dense urban areas where new stations must underpass existing infrastructure at zero distance. Deformation controls during construction are critical for maintaining the operational safety of existing stations, especially in soft soil conditions where construction-induced settlement poses significant risks to structural integrity. This study systematically investigates the influence mechanisms of different construction schemes on base plate deformation when an open-top UBIT (underground bundle composite pipe integrated by transverse pre-stressing) underpasses existing stations. Through precise numerical simulation using PLAXIS 3D, the research comparatively analyzed the effects of 12 pipe jacking sequences, 3 pre-stress levels (1116 MPa, 1395 MPa, 1674 MPa), and 3 soil chamber excavation schemes, revealing the mechanisms between the deformation evolution and soil unloading effects. The continuous jacking strategy of adjacent pipes forms an efficient support structure, limiting maximum settlement to 5.2 mm. Medium pre-stress level (1395 MPa) produces a balanced deformation pattern that optimizes structural performance, while excavating side chambers before the central chamber effectively utilizes soil unloading effects, achieving controlled settlement distribution with maximum values of −7.2 mm. The optimal construction combination demonstrates effective deformation control, ensuring the operational safety of existing station structures. These findings enable safer and more efficient urban underpassing construction. Full article

Road networks are essential for access, resource distribution, and population evacuation during natural events. These challenges are pronounced in rural areas, where network redundancy is limited and communities may have social disparities. While traditional risk management systems often focus on the physical consequences of hazard events alone, specialized literature increasingly suggests the development of a more comprehensive approach for risk assessment, where not only physical aspects associated with infrastructure, such as damage level or disruptions, but also the social and economic attributes of the affected population are considered. Consequently, this paper proposes a Vulnerability Access Index (VAI) to support road network decision-making that integrates the social vulnerability of rural communities exposed to natural events, their accessibility to nearby critical infrastructure, and physical risk. The research methodology considers (i) the Social Vulnerability Index (SVI) calculation based on socioeconomic variables, (ii) Importance Index estimation (I_imp) to evaluate access to critical infrastructure, (iii) VAI calculation combining SVI and I_imp, and (iv) application to a case study in the influence area of the Villarrica volcano in southern Chile. The results show that when incorporating social variables and accessibility, infrastructure criticality varies significantly compared to the infrastructure criticality assessment based solely on physical risk, modifying the decision-making regarding road infrastructure robustness and resilience improvements. Full article

The COVID-19 pandemic precipitated unprecedented social isolation measures, profoundly disrupting daily life, educational routines, and mental health worldwide. University students, already susceptible to psychological distress, encountered intensified challenges under remote learning and prolonged confinement. This longitudinal study examined fluctuations in anxiety and mood among 102 Brazilian university students during the pandemic, distinguishing between those solely engaged in academic pursuits and those simultaneously balancing work and study. Data collected via the Brunel Mood Scale and State-Trait Anxiety Inventory in April and July 2021 revealed that students exclusively focused on studies exhibited significant increases in depressive symptoms, anger, confusion, and anxiety, alongside diminished vigor. Conversely, participants who combined work and study reported reduced tension, fatigue, confusion, and overall mood disturbance, coupled with heightened vigor across the same period. Notably, women demonstrated greater vulnerability to anxiety and mood fluctuations, with socioeconomic disparities particularly pronounced among females managing dual roles, who reported lower family income. These findings suggest that occupational engagement may serve as a protective factor against psychological distress during crises, underscoring the urgent need for tailored mental health interventions and institutional support to mitigate the enduring impacts of pandemic-related adversities on the student population. Full article

Background/Objectives: Diabetic retinopathy is a leading cause of vision impairment worldwide, and the development of reliable automated classification systems is crucial for early diagnosis and clinical decision-making. This study presents a comprehensive comparative evaluation of two state-of-the-art deep learning families for the task of classifying diabetic retinopathy using retinal fundus images. Methods: The models were trained and tested in both binary and multi-class settings. The experimental design involved partitioning the data into training (70%), validation (20%), and testing (10%) sets. Model performance was assessed using standard metrics, including precision, sensitivity, specificity, F1-score, and the area under the receiver operating characteristic curve. Results: In binary classification, the ResNeXt101-64x4d model and RegNetY32GT model demonstrated outstanding performance, each achieving high sensitivity and precision. For multi-class classification, ResNeXt101-32x8d exhibited strong performance in early stages, while RegNetY16GT showed better balance across all stages, particularly in advanced diabetic retinopathy cases. To enhance transparency, SHapley Additive exPlanations were employed to visualize the pixel-level contributions for each model’s predictions. Conclusions: The findings suggest that while ResNeXt models are effective in detecting early signs, RegNet models offer more consistent performance in distinguishing between multiple stages of diabetic retinopathy severity. This dual approach combining quantitative evaluation and model interpretability supports the development of more robust and clinically trustworthy decision support systems for diabetic retinopathy screening. Full article

The detection of seabed boulders is a critical step in mitigating geological hazards during the planning and construction of offshore wind energy infrastructure, as well as in supporting benthic ecological and palaeoglaciological studies. Traditionally, side-scan sonar (SSS) has been favoured for such detection, but the growing availability of high-resolution multibeam echosounder (MBES) data offers a cost-effective alternative. This study presents a semi-automated, hybrid GIS-AI approach that combines bathymetric position index filtering and a Random Forest classifier to detect boulders and delineate boulder fields from MBES data. The method was tested on a 0.24 km² site in Long Island Sound using 0.5 m resolution data, achieving 83% recall, 73% precision, and an F1-score of 77—slightly outperforming the average of expert manual picks while offering a substantial improvement in time-efficiency. The workflow was validated against a consensus-based master dataset and applied across a 79 km² study area, identifying over 75,000 contacts and delineating 89 contact clusters. The method enables objective, reproducible, and scalable boulder detection using only MBES data. Its ability to reduce reliance on SSS surveys while maintaining high accuracy and offering workflow customization makes it valuable for geohazard assessment, benthic habitat mapping, and offshore infrastructure planning. Full article

Soil testing has long been used to optimize fertilization and crop production. More recently, soil health testing has emerged to reflect the growing interest in soil multifunctionality and ecosystem services. Soil health encompasses physical, chemical, and biological properties that support ecosystem functions and sustainable agriculture. Despite its relevance to several United Nations Sustainable Development Goals (SDGs 1, 2, 3, 6, 12, 13, and 15), comprehensive soil health testing is not widely practiced due to complexity and cost. The aim of the study presented here was to contribute to the further development, implementation, and testing of an integrated procedure for soil health assessment in practice. We developed and tested a rapid, standardized soil health assessment tool that combines near-infrared spectroscopy (NIRS) and multi-nutrient 0.01 M CaCl₂ extraction with Inductive Coupled Plasma Mass Spectroscopy analysis. The tool evaluates a wide range of soil characteristics with high accuracy (R² ≥ 0.88 for most parameters) and has been evaluated across more than 15 countries, including those in Europe, China, New Zealand, and Vietnam. The results are compiled into a soil health indicator report with tailored management advice and a five-level ABCDE score. In a Dutch test set, 6% of soils scored A (optimal), while 2% scored E (degraded). This scalable tool supports land users, agrifood industries, and policymakers in advancing sustainable soil management and evidence-based environmental policy. Full article

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive interstitial lung disease with limited therapeutic options and increasing global incidence, with a median survival of only 2–5 years. The clinical utility of macrophage polarization to regulate the progression of pulmonary fibrosis remains understudied. This study determined the efficacy of nintedanib and pexidartinib (PLX3397) combination therapy for treating IPF. Combination treatment effectively inhibited the progression of radiation-induced pulmonary fibrosis (RIPF) and prolonged survival in bleomycin-treated mice. Micro-CT analysis revealed a significant tissue repair efficacy. The therapy significantly normalized the abnormal vascular structure observed during RIPF and bleomycin-induced pulmonary fibrosis progression and was accompanied by a decrease in the M2 population. Polarized M1 macrophages enhanced normalized tube formation of irradiated endothelial cells (ECs) in vitro; M2 macrophages increased adhesion in irradiated ECs and abnormal tube formation. Single-cell RNA sequencing data from patients with IPF further supports colony stimulating factor (CSF) 1 upregulation in macrophages and downregulation of capillary EC markers. This study highlights a promising combination strategy to overcome the therapeutic limitations of monotherapy with nintedanib for the treatment of IPF. Full article

Background: Parkinson’s disease (PD) often involves autonomic dysfunction, most notably impaired baroreflex sensitivity (BRS), which disrupts cardiovascular homeostasis and contributes to orthostatic hypotension (OH). Pharmacological and invasive treatments, including deep brain stimulation, have yielded inconsistent benefits and carry procedural risks, highlighting the need for safer, more accessible alternatives. In this systematic review, we evaluated non-invasive interventions—spanning somatosensory stimulation, exercise modalities, thermal therapies, and positional strategies—aimed at improving cardiovascular autonomic function in PD. Methods: We searched PubMed, Embase, MEDLINE (Ovid), Google Scholar, ScienceDirect, and Web of Science for studies published between January 2014 and December 2024. Eight original studies (n = 8) including 205 participants met the inclusion criteria for analyzing cardiac sympathovagal balance. Results: Five studies demonstrated significant post-intervention increases in BRS. Most reported favorable shifts in heart rate variability (HRV) and favorable changes in the low-frequency/high-frequency (LF/HF) ratio. Across modalities, systolic blood pressure (SBP) decreased by an average of 5%, and some interventions produced benefits that persisted up to 24 h. Conclusion: Although sample sizes were small and protocols heterogeneous, the collective findings support the potential of non-invasive neuromodulation to enhance BRS and overall cardiovascular regulation in PD. Future research should focus on standardized, higher-intensity or combined protocols with longer follow-up periods to establish durable, clinically meaningful improvements in autonomic function and quality of life for people living with PD. Full article

Although PD-1/PD-L1 inhibitors have transformed cancer immunotherapy, a substantial proportion of patients derive no clinical benefit due to resistance driven by the tumour microenvironment (TME). Transforming growth factor-β (TGF-β) is a key immunosuppressive cytokine implicated in this resistance. Several bifunctional antibodies that co-target PD-1 and TGF-β signalling have entered clinical trials and shown encouraging efficacy, but the mechanistic basis of their synergy is not fully understood. Here, we engineered 015s, a bifunctional fusion antibody that simultaneously targets murine PD-1 and TGF-β and evaluated its antitumour efficacy and mechanistic impact in pre-clinical models. Antibody 015s exhibited high affinity, dual target binding, and the effective inhibition of PD-1 and TGF-β signalling. In vivo, 015s significantly suppressed tumour growth compared with anti-mPD-1 or TGF-β receptor II (TGF-βRII) monotherapy. When combined with the CD24-targeted ADC, 015s produced even greater antitumour activity and achieved complete tumour regression. Mechanistic studies demonstrated that 015s significantly reduced tumour cell migration and invasion, reversed epithelial–mesenchymal transition (EMT), decreased microvascular density, and attenuated collagen deposition within the TME. Antibody 015s also decreased bioactive TGF-β1 and increased intratumoural IFN-γ, creating a more immunostimulatory milieu. These findings support further development of PD-1/TGF-β bifunctional antibodies for cancers with high TGF-β activity or limited response to immune checkpoint blockade. 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

This study investigates the impact of Time-of-Use (TOU) scheduling and battery energy storage systems (BESS) on voltage stability in a typical Malaysian medium-voltage distribution network with high photovoltaic (PV) system penetration. The analyzed network comprises 110 nodes connected via eight feeders to a pair of 132/11 kV, 15 MVA transformers, supplying a total load of 20.006 MVA. Each node is integrated with a 100 kW PV system, enabling up to 100% PV penetration scenarios. A hybrid mitigation strategy combining TOU-based load shifting and BESS was implemented to address voltage violations occurring, particularly during low-load night hours. Dynamic simulations using DIgSILENT PowerFactory were conducted under worst-case (no load and peak load) conditions. The novelty of this research is the use of real rural network data to validate a hybrid BESS–TOU strategy, supported by detailed sensitivity analysis across PV penetration levels. This provides practical voltage stabilization insights not shown in earlier studies. Results show that at 100% PV penetration, TOU or BESS alone are insufficient to fully mitigate voltage drops. However, a hybrid application of 0.4 MWh BESS with 20% TOU load shifting eliminates voltage violations across all nodes, raising the minimum voltage from 0.924 p.u. to 0.951 p.u. while reducing active power losses and grid dependency. A sensitivity analysis further reveals that a 60% PV penetration can be supported reliably using only 0.4 MWh of BESS and 10% TOU. Beyond this, hybrid mitigation becomes essential to maintain stability. The proposed solution demonstrates a scalable approach to enable large-scale PV integration in dense rural grids and addresses the specific operational characteristics of Malaysian networks, which differ from commonly studied IEEE test systems. This work fills a critical research gap by using real local data to propose and validate practical voltage mitigation strategies. Full article

Cell confluence and number are critical indicators for assessing cellular growth status, contributing to disease diagnosis and the development of targeted therapies. Accurate and efficient cell segmentation is essential for quantifying these indicators. However, current segmentation methodologies still encounter significant challenges in addressing multi-scale heterogeneity, poorly delineated boundaries under limited annotation, and the inherent trade-off between computational efficiency and segmentation accuracy. We propose an innovative network architecture. First, a preprocessing pipeline combining contrast-limited adaptive histogram equalization (CLAHE) and Gaussian blur is introduced to balance noise suppression and local contrast enhancement. Second, a bidirectional feature pyramid network (BiFPN) is incorporated, leveraging cross-scale feature calibration to enhance multi-scale cell recognition. Third, adaptive kernel convolution (AKConv) is developed to capture the heterogeneous spatial distribution of glioma stem cells (GSCs) through dynamic kernel deformation, improving boundary segmentation while reducing model complexity. Finally, a probability density-guided non-maximum suppression (Soft-NMS) algorithm is proposed to alleviate cell under-detection. Experimental results demonstrate that the model achieves 95.7% mAP50 (box) and 95% mAP50 (mask) on the GSCs dataset with an inference speed of 38 frames per second. Moreover, it simultaneously supports dual-modality output for cell confluence assessment and precise counting, providing a reliable automated tool for tumor microenvironment research. Full article

Salmonella serovar identification typically requires multiple enrichment steps using selective media, consuming considerable time and resources. This study presents a rapid, culture-independent method leveraging artificial intelligence (AI) to classify Salmonella serovars from rich hyperspectral microscopy data. Five serovars (Enteritidis, Infantis, Kentucky, Johannesburg, 4,[5],12:i:-) were analyzed from samples prepared using only sterilized de-ionized water. Hyperspectral data cubes were collected to generate single-cell spectra and RGB composite images representing the full microscopy field. Data analysis involved two parallel branches followed by multimodal fusion. The spectral branch compared manual feature selection with data-driven feature extraction via principal component analysis (PCA), followed by classification using conventional machine learning models (i.e., k-nearest neighbors, support vector machine, random forest, and multilayer perceptron). The image branch employed a convolutional neural network (CNN) to extract spatial features directly from images without predefined morphological descriptors. Using PCA-derived spectral features, the highest performing machine learning model achieved 81.1% accuracy, outperforming manual feature selection. CNN-based classification using image features alone yielded lower accuracy (57.3%) in this serovar-level discrimination. In contrast, a multimodal fusion model combining spectral and image features improved accuracy to 82.4% on the unseen test set while reducing overfitting on the train set. This study demonstrates that AI-enabled hyperspectral microscopy with multimodal fusion can streamline Salmonella serovar identification workflows. Full article

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article