Search Results (11,155)

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

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

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

Background/Objectives: The Belgian healthcare system is currently organised from a single-disease point of view, which poses a unique challenge for patients suffering from two or more long-term physical and/or mental conditions (multimorbidity) and their healthcare providers. To search for strategies that respond to the constantly changing medical landscape and the complexity of multimorbid care. Patient-centred care programmes for multimorbid patients in primary care were investigated and their intervention elements were assessed as to whether or not they have a positive effect on the Triple Aim. Methods: A scoping review was performed and reported following the PRISMA-ScR guidelines. Online databases (PubMed, Cochrane, Embase) were used to identify papers published between January 2000 and August 2023, supplemented by reference tracking and a manual search in the grey literature. Studies were included if they assessed the efficacy of patient-centred intervention in primary care for multimorbid adult patients. Results: From the 7020 papers identified, 39 were selected and included in the review. Interventions took place at three levels (patient, professional and organisational). The efficacy of the studies included was heterogeneous. The different intervention elements had a more frequent positive effect on healthcare experience than on health status and behaviour. The only intervention that appeared to score partially positive results across all Triple Aim domains was a care coordinator. Conclusions: This scoping review provides an overview of existing patient-centred interventions for multimorbidity. Findings could be used to assist in the development of a framework for multimorbid patient care. Future studies should recognise the importance of patient experience and formulate and evaluate sufficient outcomes within this domain. Full article

Background: TV-44749 is a subcutaneous (sc) long-acting injectable (LAI) formulation of olanzapine that recently demonstrated efficacy and safety as a treatment for schizophrenia in adults without the occurrence of post-injection delirium/sedation syndrome (PDSS) in the phase 3 SOLARIS trial (NCT05693935). TV-44749’s sc route of administration and formulation were designed to provide prolonged olanzapine release over a monthly dosing interval and to avoid the risk of post-injection delirium/sedation syndrome (PDSS). It was designed as a copolymer in situ-forming depot technology to provide a LAI formulation that could withstand physiological and environmental factors that could affect controlled-release kinetics. Methods: To evaluate the robustness of the TV-44749 formulation, an in vitro release (IVR) study in human plasma was conducted, comparing TV-44749 to the commercially available intramuscular (im) long-acting injection formulation of olanzapine pamoate monohydrate. In addition, in vivo studies in rats were conducted to assess the effect of injection site manipulation following TV-44749 sc injection on olanzapine release from the depot. Results: The IVR study showed that upon contact with human plasma, copolymers comprising TV-44749 formulation instantly precipitate and form a solid matrix that entraps olanzapine particles. This prevents an uncontrolled release of olanzapine. Additionally, in vivo rat studies found that manipulation of the injection site after TV-44749 administration, by either heating or rubbing at different time-points, resulted in no meaningful effect on overall olanzapine exposure. Conclusions: The presented findings support the robustness of the TV-44749 formulation in maintaining controlled-release properties, even under conditions that could otherwise compromise release performance. Full article

Background and Objectives: Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are widely performed procedures with high success rates but relevant postoperative complications. Preoperative nutritional status is a key modifiable risk factor influencing surgical outcomes. This study aimed to map and synthesize the available evidence on the association between preoperative nutritional status and postoperative complications in patients undergoing primary THA or TKA. Materials and Methods: A scoping review was conducted following PRISMA-ScR guidelines. A comprehensive search was performed in PubMed, ScienceDirect, and Scopus, with the last update conducted in April 2026. Studies published between 2015 and 2026 in English and Spanish were included. Eligibility criteria followed the PCC framework. Randomized controlled trials and observational studies were included. Risk of bias was assessed using the Newcastle–Ottawa Scale for observational studies and the Cochrane Risk of Bias tool for randomized trials. Results: A total of 1126 records were identified, and 23 studies were included, comprising 447,852 patients. Nutritional status was mainly assessed using serum biomarkers, particularly albumin, followed by anthropometric measures, combined indices, and micronutrients. Poor nutritional status, especially hypoalbuminemia, vitamin D deficiency, and low BMI, was associated with higher rates of infectious complications, prolonged hospital stay, increased readmissions and mortality, and worse functional recovery. Conclusions: Preoperative nutritional assessment is essential for perioperative risk stratification in THA and TKA. Integrating biomarkers, indices, and targeted interventions may improve outcomes and reduce postoperative complications. Full article

Accurate demagnetization fault diagnosis is critical to ensuring the safety and reliability of permanent magnet synchronous motors (PMSMs). However, the number, location, and severity of demagnetized permanent magnets are mutually coupled, leading to a combinatorial explosion of fault patterns. Existing methods are largely limited to idealized assumptions involving single-magnet demagnetization or uniform demagnetization of multiple magnets, making it difficult to characterize the random nature of demagnetization in practical operation. Thus, this paper proposes a precise demagnetization fault diagnosis method based on a novel search coil (SC) configuration, in which only two toroidal-yoke-type search coils are installed in the stator slots. The proposed method partitions the rotor permanent magnets into several modules and categorizes the infinite demagnetization fault patterns into 26 representative patterns, effectively addressing the issue of fault mode explosion. Theoretical analysis and experimental results show that the voltage waveforms of the search coil over a single electrical period exhibit significant and stable differences across the identified patterns. By constructing feature vectors based on these differences, a physically interpretable mapping between the feature vectors and fault patterns is established. Combined with a corresponding pattern recognition algorithm, the proposed method enables fast and accurate differentiation of the 26 patterns without the need for complex machine learning models, thereby achieving precise localization of demagnetized permanent magnets. Simulation and experimental results verify the correctness and effectiveness of the proposed method. Full article