Search Results (16,938)

Penetration rate effects and partial drainage can govern piezocone (CPTu) response in intermediate permeability geomaterials, but field testing at a fixed standard rate limits systematic evaluation. This study presents the development and laboratory validation of a miniature piezocone system and testing framework to investigate rate-dependent penetration response in laboratory-prepared silty sand. Baseline dry and flooded specimens were tested using a triaxial-based configuration at penetration velocities of 9.6, 0.28, 0.10, and 0.03 mm/s, including selected holding periods for dissipation. A dedicated servo-controlled penetration system was then implemented for slurry-prepared specimens, enabling continuous constant-velocity penetration over a wider velocity range (0.004–15 mm/s). Cone resistance was interpreted using normalized net resistance (Q) and normalized velocity (V_h), and pore pressure using normalized excess pore pressure (Δu₂/σ′_v0). The results show a monotonic rate dependency, with Q increasing as V_h decreases, while Δu₂/σ′_v0 progressively decreases toward zero at intermediate-to-low V_h; at the lowest rates, pore-pressure readings were affected by instrument signal limitations. A hyperbolic-cosine backbone fitted to the normalized response provided good agreement for resistance (R² = 0.99, RMSE = 3.41) and more limited agreement for pore pressure (R² = 0.30, RMSE = 0.23). The drainage transition for the tested material occurs in an interval of approximately V_h ≈ 0.3~30. The study provides a reproducible laboratory approach—combining miniature instrumentation, controlled specimen preparation, and variable-rate penetration—to generate normalized drainage-transition trends for rate-effect investigations in intermediate geomaterials. Full article

Tumor cell heterogeneity and multicellular interactions critically influence drug resistance, recurrence, and prognosis. Here, CPcellsubpopulation, a computational framework integrating scRNA-seq, bulk RNA-seq, and clinical data was developed to identify cancer progression-associated cell subpopulations. Then, the integrated analyses of scRNA-seq and spatial transcriptomics were performed to predict potential interactions, identify critical transcription factors, and predict candidate anticancer drugs. Across nine cancers, we detected cancer progression-associated cell subpopulations significantly linked to prognosis, with consistent patterns across cancer types. In renal cell carcinoma (RCC), we identified conserved metabolic^high UBE2C+ cancer cells linked to poor outcomes, metabolic reprogramming and low differentiation, and PLK1+ NK cells, plasma cells, and CDC20+ macrophages associated with advanced stages and unfavorable prognosis. Spatial mapping revealed spatial association of RCC progression-associated cancer and immune cell subpopulations, suggesting the potential role of the VEGF, GDF, PTN and IL16 pathways in the remodeling of the tumor microenvironment. Gene regulatory network analysis highlighted RAD21 as a key regulator linking metabolism and therapy resistance. This study provides a systematic pipeline to delineate cancer progression-associated cell subpopulations, uncovers metabolic^high UBE2C+ cancer cells as progression-associated tumor cell population, and nominates critical regulators and compounds as therapeutic targets. Full article

Supercritical carbon dioxide (SC-CO₂) fracturing has emerged as an environmentally friendly alternative to conventional water-based hydraulic fracturing; however, its inherently low viscosity restricts proppant-carrying efficiency and reduces fracture conductivity. To address this limitation, this study systematically investigates the rheological behavior and sand-carrying mechanisms of CO₂ dry fracturing fluid under various thermodynamic and compositional conditions. Rheological measurements were conducted to evaluate the effects of thickener concentration, temperature, and pressure on viscosity, while visualized experiments were performed to examine the influence of injection rate, sand ratio, thickener concentration, and temperature on proppant migration and deposition. A numerical model developed in Fluent was further employed to simulate the temporal evolution of proppant transport within the fracture. The results show that higher thickener concentrations and injection rates significantly enhance proppant transport distance and uniformity, whereas elevated temperature and sand ratio promote localized settling. The simulation results agree well with the experimental observations, validating the model’s reliability. This study elucidates the coupled effects of rheology and operating parameters on CO₂ dry fracturing behavior and provides theoretical and experimental guidance for optimizing CO₂-based fracturing fluids in low-permeability reservoirs. Full article

Metabolic reprogramming is closely linked to tumor proliferation, invasion, and immune escape. Despite its central role in amino acid metabolism, the regulatory mechanisms of asparagine metabolism in hepatocellular carcinoma (HCC) progression remain poorly characterized. Rather than focusing on canonical metabolic genes, prognostic markers were identified from co-expression modules associated with asparagine metabolism signatures. Using the TCGA database and asparagine metabolism-related gene sets, a prognostic risk-scoring model was developed through differential expression analysis, univariate Cox regression, and the LASSO algorithm and externally validated with the GEO dataset (GSE14620). Survival analysis, ROC curve evaluation, nomogram construction, scRNA-seq, GSEA, and drug sensitivity analysis were performed to systematically delineate the molecular mechanisms by which asparagine metabolism drives HCC progression. A three-gene signature comprising BOP1, SAC3D1, and PDE2A effectively stratified patients into high- and low-risk groups. High-risk patients exhibited markedly poorer overall survival, enrichment in tumor proliferation-associated pathways, increased tumor purity, reduced immune cell infiltration, and a substantially higher TP53 mutation rate (38% vs. 13%). In contrast, the low-risk group showed enrichment in pathways linked to hepatoblastoma suppression and liver function, alongside improved predicted response to immunotherapy. Single-cell analysis identified NK cells and endothelial cells as central mediators of asparagine metabolism-driven HCC progression, with BOP1, SAC3D1, and PDE2A displaying dynamic expression patterns during differentiation. Furthermore, the high-risk group was predicted to be more sensitive to chemotherapeutics such as cyclophosphamide and 5-fluorouracil. These findings highlight a potential interplay between nitrogen metabolism and asparagine metabolism in HCC and suggest mechanisms by which these pathways may influence NK cell and endothelial cell function to promote disease progression. This study establishes a novel prognostic model and identifies potential chemotherapeutic vulnerabilities in high-risk patients, warranting further experimental and clinical validation. Full article

Immersive technologies—such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR)—are increasingly adopted in cultural heritage settings to support education, public engagement, and digital preservation. This scoping review systematically maps existing research on immersive learning within cultural heritage contexts, identifying major trends, pedagogical approaches, and reported outcomes. Following the PRISMA-ScR framework, nineteen studies were selected from 235 publications published between 2016 and 2025 across four databases: ACM Digital Library, Web of Science, ProQuest, and Scopus. Findings reveal a predominant focus on enhancing learner motivation, engagement, and the perceived authenticity of immersive experiences. However, empirical validation of learning outcomes—particularly regarding sustained knowledge retention, critical reflection, and inclusive participation—remains scarce. Persistent gaps are also evident in accessibility and scalability, alongside ethical concerns related to cultural sensitivity, power asymmetries, and the representation of diverse heritage voices. By foregrounding participatory and co-creation approaches, this review highlights how collaborative design processes can enhance learner engagement and support the sustainable digital preservation of cultural heritage. Full article

Acute kidney injury (AKI) lacks targeted pharmacological interventions. While the DanShen–DaHuang (DS-DH) herb pair shows clinical potential for AKI treatment, and our prior study has validated its nephroprotective efficacy in a cisplatin-induced murine model, its specific molecular targets within the renal microenvironment remain undefined. In this study, we integrated network pharmacology and weighted gene co-expression network analysis (WGCNA) to screen AKI-related targets of the DS-DH pair. A multi-algorithmic machine learning pipeline (including LASSO, Boruta, Random Forest, GBM, XGBoost, and Decision Trees) was utilized to calculate feature importance scores and rank core genes. Subsequently, single-cell RNA sequencing (scRNA-seq) data (GSE197266) were analyzed for transcriptomic mapping, pseudotime trajectory, and cell–cell communication. Finally, molecular docking evaluated theoretical binding affinities. After database screening, a total of 603 drug–disease intersecting targets were obtained. Subsequently, 917 module genes significantly associated with AKI were identified by WGCNA, and 62 core candidate genes were determined after intersecting with the above targets. Multi-algorithm machine learning ranked the importance of the 62 targets, with transketolase (TKT) ranking the highest. To elucidate the mechanism of TKT in AKI, scRNA-seq analysis was performed on 77,593 high-quality cells. The results showed that Tkt was specifically enriched in renal macrophages, with the highest expression in the M2-polarized subset. Pseudotime analysis further revealed that Tkt expression dynamics were highly synchronized with the differentiation trajectory of M2 macrophages and positively correlated with the repair markers Arg1 and Mrc1. Cell–cell communication analysis predicted that Tkt⁺ M2 macrophages act as active communication hubs via the Spp1 and Mif signaling axes. Molecular docking validated the favorable binding affinity between core DS-DH compounds and the TKT active pocket. This computational framework predicts that the DS-DH herb pair might mitigate AKI by potentially targeting TKT, a metabolic enzyme closely associated with macrophage M2 polarization. By prioritizing targets via multi-algorithmic scoring, we provide a data-driven rationale and candidate targets for future experimental validation. Full article

Background: Neuroinflammation is increasingly recognized as a central mechanism in the pathogenesis of epilepsy, particularly drug-resistant epilepsy (DRE), where conventional anti-seizure medications fail to achieve adequate control. Microglia, the resident immune cells of the central nervous system, play a critical role in mediating inflammatory responses that contribute to seizure initiation, propagation, and pharmacoresistance. Persistent microglial activation promotes the release of pro-inflammatory mediators, exacerbating neuronal hyperexcitability and epileptogenesis. Objectives: This scoping review aimed to systematically map the existing evidence on microglial activation in DRE and to identify emerging therapeutic strategies targeting microglia-mediated neuroinflammation. Methods: The review was conducted in accordance with Joanna Briggs Institute (JBI) methodology and reported following PRISMA-ScR guidelines. A comprehensive search of PubMed, PubMed Central, Scopus, Google Scholar, Embase, and Web of Science was performed without date restrictions. Eligible studies included preclinical, clinical, and review articles investigating microglial activation, neuroinflammatory pathways, or microglia-targeted therapies in epilepsy. Data were charted and synthesized using a narrative approach. Results: A total of 521 records were identified, of which 53 studies met the inclusion criteria after screening and full-text review. The included studies, published between 1998 and 2021, demonstrated a growing research interest in microglia-related mechanisms in epilepsy. Evidence consistently highlighted the role of microglial activation in promoting neuroinflammation and seizure persistence. Emerging therapeutic strategies included anti-inflammatory pharmacotherapies, microglial modulators, cannabinoid-based interventions, gene therapy, and stem cell-based approaches. Conclusions: Targeting microglial activation represents a promising and evolving therapeutic strategy for DRE. While preclinical and early clinical evidence is encouraging, challenges related to specificity, timing, and translational applicability remain. Future research should focus on precision-based interventions to optimize clinical outcomes and enable disease modification beyond seizure control. Full article

Emerging technologies offer significant opportunities for sustainable rural development; however, their applications have not been systematically mapped across all dimensions of sustainability. This scoping review aims to identify, classify, and synthesize the literature on emerging technologies in rural development, structured around four pillars: economic, social, environmental, and governance. Eligible studies included English-language scientific articles published between 2015 and 2025 that propose solutions based on emerging technologies in rural contexts, identified in the Web of Science Core Collection database, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Data extracted from the 129 eligible articles were synthesized in thematic tables and comparatively analyzed for each pillar. Results indicate an accelerated growth in publications after 2020, with machine learning, deep learning, and the Internet of Things dominating applications such as precision agriculture, telemedicine, and water management. Critical gaps persist in biodiversity monitoring, climate adaptation, elderly care services, and rural circular economy, with the governance pillar remaining the least represented. This study proposes an integrated framework and a knowledge map to guide future research and public policies toward balanced and sustainable rural transformation. Full article

The high-rate recycling of scrap steel introduces persistent residual copper (Cu), which accumulates at prior austenite grain boundaries at the surface, during high-temperature reheating, leading to Cu-induced sensitization and deleterious “hot shortness”. To address this, a predictive analytical framework was derived using Fick’s Second Law and the Sekerka, Jeanfils, and Heckel (SJH) approach to model the dissolution of Cu-rich films as a 1D planar moving boundary problem. The validity of this analytical framework was first established through experimentation on controlled Cu-coated steel wire rods, where theoretical concentration profiles showed strong agreement with empirical depth profiles. When applied to a 0.21 wt.% Cu steel at 1000 °C, the model predicted a critical extinction time (t*) of approximately 8.57 min for the complete dissolution of a 20 nm sensitized film. Experimental trials on sensitized wire rods confirmed this prediction, demonstrating an 89% reduction in the frequency of detectable sensitized zones and a significant decrease in zone width following a 10 min thermal dwell. The approach provides a standardized, scalable, and composition-adaptable methodology, grounded in a 1D planar approximation, for optimizing desensitization heat treatments across a range of Cu contents, offering a practical strategy to increase scrap steel utilization while mitigating hot shortness. Full article

Real-Time In-Line Monitoring (RTIM) of rheological properties such as slurry yield stress is important in different industries for its various benefits such as significant time savings and increased safety/efficiency of processes while reducing secondary waste due to sampling or inaccurate procedures. This paper discusses two methods for characterizing yield stress in real time: the Pressure Loss method and the Liquid Rise method. The Liquid Rise method uses the height of the slurry in a vertical column and the pressure difference to quantify the yield stress. The Pressure Loss method uses the drop of pressure in a laminar flow of slurry to determine the yield stress. Kaolin–water slurry is used as a simulant of the non-Newtonian fluid. An experimental setup is built to demonstrate the methods, and data obtained from the experimental setup is compared with the yield stress obtained from a conventional table-top rheometer (baseline rheology). The results show a good agreement between the experimental yield stress and baseline rheology. Full article

CO₂ adsorption on subnanometric metal clusters is highly sensitive to the computational protocol used to describe the potential energy surface, particularly when several low-lying geometries and spin states are accessible. In this work, CO₂ adsorption on Cu₄ and Sc₄ clusters was investigated using density functional theory (DFT) to evaluate how the choice of functional/basis-set protocol, spin multiplicity, initial geometry, and vibrational stability affects the predicted adsorption behavior. Four representative computational protocols (TPSSh, r²SCAN-3c, PBE-D4/def2-TZVP, and PBE0-SDD) were assessed for isolated clusters and cluster–CO₂ complexes. The lowest harmonic vibrational frequency, ω_min, was used as a diagnostic criterion to distinguish true minima from unstable or weakly defined stationary points. Selected cases were also cross-checked using the ORCA and Gaussian quantum-chemistry packages to assess whether comparable computational settings yielded consistent stationary-point character. The results show that Cu₄ generally exhibits weak CO₂ binding, whereas Sc₄ displays stronger but more protocol-dependent adsorption, consistent with its higher structural flexibility and more pronounced Lewis-acid character. Low-frequency and imaginary modes were found in several optimized structures, indicating that adsorption energies should not be interpreted without prior vibrational validation. The comparison also shows that variations in functional/basis-set treatment and spin multiplicity can alter both the optimized geometry and the predicted adsorption strength. Therefore, CO₂ adsorption on small metal clusters should be discussed using combined structural, vibrational, and energetic criteria rather than electronic adsorption energies alone. Overall, this study provides a protocol-oriented framework for evaluating the reliability of DFT predictions in CO₂ adsorption on Cu₄ and Sc₄ clusters. Full article

Meningiomas are the most common intracranial tumors. DNA methylation analysis in benign and aggressive meningiomas showed decreased MIR21 methylation and overexpression of hsa-miR-21-5p in atypical and anaplastic tumors. Transcriptomic analysis of distinct WHO grade meningiomas showed multiple predicted hsa-miR-21-5p target genes as differentially expressed. They were mainly related to processes of intercellular and intracellular signaling. Intercellular communication in meningioma was investigated using the deposited scRNA-seq dataset and deconvolution of our RNA-seq data. We found WHO grade-related differences in the microenvironment including inverse correlation between the count of border-associated macrophages (BAM) and the level of hsa-miR-21-5p. Single-cell transcriptomics suggests the role of interleukin 6 in direct communication between tumor cells and BAMs. IL6R and IL6ST are predicted targets of hsa-miR-21-5p downregulated in atypical/anaplastic meningiomas. IL6R downregulation was also confirmed by immunohistochemistry. Hsa-miR-21-5p enhanced proliferation and viability of KT21-MG1 meningioma cells and showed a regulatory effect on IL6R, IL6ST and other predicted target genes TIMP3, PIK3R, RHOB, and SASH1 by interacting with 3′UTRs. DNA hypomethylation-related overexpression of hsa-miR-21-5p contributes to aggressive meningioma growth by interaction with multiple target genes, and probably affects microenvironment communication between meningioma cells and BAMs by lowering the IL6R level in tumor tissue. Full article

Jacareubin (2), nujiangexanthone A, and α-mangostin display the highest anti-allergic effects among the active xantones through still not well-known mechanisms. This study investigates the SAR of jacareubin, its precursor xanthone V (1) and their peracetylated (1a and 2a), permethylated (1b and 2b) derivatives and their anti-allergic and anti-inflammatory effects. To characterize the inhibitory effect of jacareubin, 2a and 2b on the anaphylactic reaction, we first utilized in vitro models of bone marrow derived mast cells (BMMCs), determining their capacity of inhibiting the IgE/Antigen-induced degranulation, myeloperoxidase (MPO), and xanthine oxidase (XO) activation. Also, we utilized in vivo models of passive cutaneous anaphylaxis (PCA) and TPA-induced ear edema. In vitro tests showed that the compound 2b was more effective than jacareubin in the inhibition of BMMCs degranulation. Besides, in vivo models of PCA revealed that the fourth cyclized ring of jacareubin is the critical structural element for anti-allergic efficacy, as compound 1 was less effective. Additionally, hydroxyl groups were found to be essential for inhibiting MPO. Jacareubin was the only tested xanthone that directly inhibited XO, a result supported by molecular docking. Overall, jacareubin represents a promising multi-target scaffold that could be used for developing new treatments for inflammatory and allergic diseases. Full article

Innovative models of cancer care have emerged in response to advances in cancer treatment, expanding technologies that bring care closer to home and address COVID-19-related challenges and concerns about a shrinking healthcare workforce. Despite the advancements made, the psychosocial impact on people affected by cancer persists. The psychosocial burden of cancer underlines the need for patient access to evidence-based psychosocial oncology (PSO) and supportive care (SC) interventions. As models of care evolve, hiring practices of PSO professionals must also evolve for cancer patients to access properly staffed PSO programs that deliver high-quality and efficient services. In 2019, the Canadian Association of Psychosocial Oncology (CAPO)–Clinical Advisory Committee consulted administrators and clinicians across Canada to understand caseload volumes of PSO professionals with a goal to set staffing ratios. The engagement process revealed that there is no consistency in staffing PSO programs across Canada, let alone staffing ratios for PSO disciplines. In 2022, CAPO introduced a 10-point staffing framework and formula to calculate staffing ratios for hiring PSO professionals, beginning with the social work discipline. The goal of this paper is to provide updates to the existing framework and demonstrate how the formula can be adapted to other PSO disciplines. To our knowledge, this is the first published paper in Canada outlining the calculations for a PSO staff framework and formula. The authors advocate for greater transparency when reporting PSO staffing ratios across Canada, using this framework as a reference point. Organizations reporting on the cancer system performance are encouraged to develop PSO indicators, starting with tracking patient access to PSO services. Full article

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor with extremely poor prognosis. Conventional diagnostic and prognostic approaches remain inadequate, highlighting the need for integrative strategies to improve patient outcomes. Methods: We analyzed ligand–receptor (L–R) interactions in TCGA-GBM transcriptomes using BulkSignaL-R, and validated their spatial expression patterns with single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics datasets. Prognostic histopathological features were extracted from hematoxylin and eosin (H&E)-stained sections through omics-guided feature identification, followed by classification using machine learning algorithms. Results: We identified four pivotal L–R pairs (LTB–CD40, VEGFA–ITGB1, FN1–COL13A1, and TGM2–ITGB1) to construct a risk model, which served as an independent prognostic factor for overall survival. The multivariate Cox regression analyses revealed that the risk score was significantly associated with Overall Survival (OS) (HR = 1.67, 95% CI: 1.25–2.25, p < 0.001). High-risk patients exhibited distinct molecular signatures, including CALN1 mutations, specific CNV patterns, and enriched Notch/interferon-γ signalings. scRNA-seq and spatial transcriptomics revealed that these L–R pairs were predominantly expressed in gMES-like glioma cells, OPC-like cells, and pericytes. Finally, our deep learning model successfully stratified risk groups based on histological features, identifying specific tumor regions (Clusters 0, 2, 4, and 5) as critical determinants of prognosis (AUC = 0.750 by Logistic Regression). Conclusions: We developed a novel multi-modal framework integrating L–R interactomics and deep learning-based pathomics. This approach not only elucidates the molecular and spatial landscape of glioma intercellular communication but also provides a methodological framework for risk stratification. Full article