Search Results (52,048)

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

The rapid proliferation of Internet of Things (IoT) devices in cloud-based e-learning platforms has posed significant security risks, particularly in protecting learner information, authentication of devices, and safe communication in the highly heterogeneous learning settings. Current cryptographic solutions are largely based on classical public-key infrastructure (PKI) protocols such as RSA and ECC, which will become vulnerable with the advent of large-scale quantum computers capable of executing Shor’s algorithm. In addition, traditional perimeter-based security models are inadequate for handling the dynamics, scattered, and resource-limited characteristics of IoT-enabled educational systems. As a solution to these problems, this paper introduces ZeroTrustEdu, a scalable zero-trust cryptographic solution that combines lightweight post-quantum key management with adaptive trust scoring of cloud-connected IoT e-learning infrastructure. The proposed framework makes three fundamental contributions namely: (1) a hierarchical zero-trust security model with no implicit trust, operating across device, edge, and cloud layers; (2) a lightweight key distribution protocol based on the Module-Lattice Key Encapsulation Mechanism (ML-KEM) compliant with NIST FIPS 203 standards and (3) an adaptive behavioral trust scoring engine that dynamically adjusts device and user trust levels based on real-time interaction analytics. The architecture is evaluated using extensive NS-3 network simulations with up to 100,000 concurrent IoT nodes with formal security analysis under Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA) threat models. Comparative evaluation against RSA-2048, ECC-P256, and AES-256 baselines demonstrates that, ZeroTrustEdu delivers a 62% ± 3% (95% CI, 10 independent runs) reduction in ML-KEM encapsulation latency (12.8 ms for key encapsulation/decapsulation, contributing to a complete device authentication latency of 47.3 ms including ML-DSA signature operations), 45% reduced communication overheads, and 38% reduction in energy consumption on ARM Cortex-M4 constrained devices compared to RSA-2048 and achieves provable post-quantum security reducible to the hardness of the Module Learning With Errors (MLWE) problem. These findings demonstrate that the proposed architecture provides a viable, scalable, and quantum-resilient security solution for next-generation IoT-enabled e-learning environments. The cryptographic security of ZeroTrustEdu is guaranteed at the primitive level through NIST-standardized ML-KEM (FIPS 203) and ML-DSA (FIPS 204), with IND-CCA2 and EUF-CMA security formally proven in the respective standards; full protocol-level formal verification using automated theorem provers (ProVerif, Tamarin) is identified as valuable future work to rule out protocol-composition vulnerabilities beyond primitive-level guarantees. Full article

Ascosphaera apis, an obligate lethal fungal pathogen that infects bee larvae, and causes chalkbrood disease, poses a significant threat to the global beekeeping industry. Long non-coding RNAs (lncRNAs) are employed by pathogens to enhance infectivity and evade host immunity. Here, lncRNAs in A. apis spores (AaCK group) and the guts of 4-, 5-, and 6-day-old Apis cerana cerana worker larvae inoculated with A. apis spores (AaT1, AaT2, and AaT3 groups) were identified, characterized, and validated. Additionally, the expression pattern of fungal lncRNAs during infection was analyzed, followed by an investigation of the regulatory manners and roles of differentially expressed lncRNAs (DElncRNAs). A total of 1379 lncRNAs were identified in AaCK, AaT1, AaT2, and AaT3 groups using bioinformatics, involving various types such as sense lncRNAs, antisense lncRNAs, bidirectional lncRNAs, intergenic lncRNAs, and intronic lncRNAs. Additionally, 4, 9, and 75 up-regulated lncRNAs as well as 2, 1, and 15 down-regulated ones were identified in the 4-, 5-, and 6-day-old larval guts following A. apis inoculation. Fifteen DElncRNAs as potential antisense lncRNAs may interact with 15 sense-strand mRNAs in the AaCK vs. AaT3 comparison group. Cis-acting analysis identified 10, 16, and 136 upstream and downstream genes of DElncRNAs in the aforementioned comparison groups, involving a series of GO terms and KEGG pathways like metabolic process and biosynthesis of secondary metabolites. Following the trans-acting investigation, 752, 821, and 1327 co-transcribed genes with DElncRNAs were discovered, spanning an array of functional terms and pathways such as biological processes and glycerophospholipid metabolism. Analysis of a competing endogenous RNA (ceRNA) network indicated that 1 and 5 DElncRNAs in the AaCK vs. AaT1 and AaCK vs. AaT3 comparison groups potentially targeted 1 and 2 miRNAs, further targeting 208 and 286 mRNAs, respectively. Further analysis identified one ceRNA axis relevant to the MAPK signaling pathway and several ceRNA networks associated with the biosynthesis of secondary metabolites. Finally, RT-qPCR results confirmed that the expression trends of six randomly selected DElncRNAs were consistent with those in the transcriptome data. These findings not only offer a foundation for elucidating the mechanisms underlying DElncRNA-mediated A. apis infection but also enrich our understanding of honeybee host–fungal pathogen interactions. Full article

Climate finance has become a major means of fostering sustainability in the construction industry, which encounters higher pressures to mitigate its environmental footprint without sacrificing economic viability. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study employs a hybrid approach, integrating a systematic literature review (SLR) and bibliometric analysis, to provide a comprehensive overview of the role and mechanisms of climate finance for sustainable practices in the construction industry. From 2019 to 2025, 176 papers were identified in the Scopus (73) and Web of Science (103) databases. The SLR enables both systematic collection and qualitative analysis of financial instruments, policy frameworks, and sustainability performance metrics, and bibliometric analysis provides a report of publication behavior, geographic distribution, and thematic network. Findings suggest intense clustering of research in countries, with India, China, and the United States as key focus areas, and that construction firms predominantly accessed climate finance on instruments including green bonds, sustainability-linked loans, public–private partnerships, and multilateral climate funds. Sustainability performance is commonly assessed using indicators such as carbon emissions, energy efficiency, lifecycle costs, and environmental, social, and governance (ESG) metrics. The findings also highlight the critical role of public policies, such as green procurement, carbon pricing, and fiscal incentives, in enabling sustainable construction practices. From a theoretical perspective, this study contributes to the understanding of how financial mechanisms, policy frameworks, and sustainability metrics interact to drive sectoral transformation. Future research should focus on standardizing sustainability metrics, evaluating financing impacts, and expanding studies in emerging economies. Full article

Background/Objectives: Liuwei Dihuang (LWDH) is a classical plant-derived herbal formula widely used for cognitive decline. This study aimed to evaluate its efficacy and safety in cognitive disorders and to explore its potential pharmacological mechanisms using network pharmacology. Methods: We searched 11 databases through November 2024 for randomized controlled trials comparing LWDH plus conventional therapy with conventional therapy alone in cognitive disorders. Meta-analysis was performed for clinical outcomes, and herb–compound–target and disease-target datasets were integrated to identify core molecular modules. Results: Twelve randomized controlled trials involving 1137 participants were included. Adjunctive LWDH was associated with improvements in Mini-Mental State Examination scores (MD = 2.34, 95% CI 0.88–3.79), activities of daily living, and quality of life. However, substantial heterogeneity and methodological limitations, including unclear randomization and blinding, were observed across studies, indicating a potential risk of bias. Fewer adverse events were reported in the LWDH plus conventional treatment group, although reporting quality was limited. The overall risk of bias was judged as “some concerns”. Network pharmacology analysis identified a broad set of overlapping genes between LWDH-associated targets and cognitive disorder-related genes, which were further refined through filtering procedures. Subsequent analyses suggested associations with pathways related to neurodegeneration, apoptosis, and central nervous system function; however, these findings are exploratory and based on in silico predictions. Conclusions: LWDH may be associated with potential adjunctive benefits in cognitive disorders. However, given the methodological limitations and clinical heterogeneity of the included studies, the findings should be interpreted with caution. The proposed pharmacological mechanisms are exploratory and require further validation. Well-designed randomized controlled trials are needed to establish more robust evidence. Full article

Osteoporosis in women is strongly influenced by menopause, a major physiological transition that reshapes bone metabolism. Although low bone mineral density (BMD) in premenopausal women and osteoporosis in postmenopausal women share the clinical outcome of skeletal fragility, it remains unclear whether they reflect a shared molecular program or distinct regulatory mechanisms. Here, we compared genetic signals associated with premenopausal and postmenopausal low BMD in Korean women using two independent genotyping platforms with distinct variant coverage. After allele harmonization and heterogeneity testing, variants were classified as reversal signals, showing directionally discordant effects across menopausal status, or stable signals, showing concordant effects. Gene-level association analysis was performed using Multi-marker Analysis of GenoMic Annotation (MAGMA), followed by functional enrichment and network-based analyses. Reversal and stable signals showed distinct biological patterns. Reversal signals consistently converged on cyclic nucleotide-related pathways, including cyclic adenosine monophosphate/cyclic guanosine monophosphate (cAMP/cGMP) signaling and nitric oxide-mediated processes, whereas stable signals were more broadly distributed across pathways related to ion homeostasis, cell–substrate adhesion, and structural maintenance. These pathway-level patterns were reproducible across platforms despite limited SNP-level overlap. These findings suggest that low BMD across the menopausal transition is better resolved at the gene and pathway levels than at the level of individual SNPs. Full article

Ultraviolet radiation (UVR) exposure accelerates skin aging by disrupting cellular homeostasis and inducing epigenetic changes, such as promoter hypermethylation of key regulatory genes. However, the molecular mechanisms underlying UVR-driven epigenetic silencing remain poorly understood. By integrating high-throughput DNA methylation profiling with co-regulatory network analysis, we identified KIT as a hub gene in a photoaging-associated methylation module. Pathway enrichment further revealed coordinated hypermethylation of the canonical Wnt/β-catenin signaling pathway, establishing the Wnt/KIT axis as a critical epigenetic-signaling nexus in UVR-induced skin fibroblast aging. In immortalized human skin fibroblasts (HSFs), UVR suppressed Wnt signaling, leading to KIT promoter hypermethylation, transcriptional silencing, and premature photoaging. Gain-of-function studies revealed that reversing KIT hypermethylation either via Wnt pathway activation or KIT overexpression effectively mitigated photoaging-associated phenotypes. Crucially, we found that puerarin (PUE), a natural isoflavone, reversed UVR-induced epigenetic silencing by directly interacting with β-catenin, reactivating Wnt signaling, and restoring KIT expression. PUE treatment preserved cellular function in UVR-damaged fibroblasts. These findings establish the Wnt/β-catenin-KIT axis as a critical epigenetic driver of skin aging and highlight puerarin as a promising therapeutic candidate for targeted anti-aging intervention. Full article

This study develops a ternary Fe-based geopolymer system composed of metakaolin (MK), red mud (RM), and fly ash (FA) for the preparation of sustainable water-retaining subgrade materials for sponge-city roadbed applications. Unlike conventional formulations primarily designed for structural strength or rapid permeability, the proposed MK–FA–RM system was designed to improve water-storage capacity while maintaining adequate mechanical support and environmental compatibility. In this ternary system, MK provides highly reactive aluminosilicate species for geopolymer network formation, RM introduces Fe-bearing phases and enhances industrial solid-waste utilization, and FA contributes to particle packing, workability, and resource efficiency. A constrained ternary mixture design implemented using Design-Expert software was adopted to optimize precursor proportions. Within the investigated compositional range, the fitted first-order mixture model showed acceptable statistical adequacy for preliminary composition screening (R² = 0.86). The optimal blend (60% MK, 30% RM, and 10% FA) achieved a 7-day compressive strength of 8.37 MPa and a water retention rate of 35.3% under ambient curing conditions, satisfying the strength requirement considered for the target subgrade/base-layer application. Microstructural and phase analyses suggest that the synergistic interaction of the three precursors promoted Fe-modified aluminosilicate gel formation together with conventional geopolymer gel products, while improving matrix continuity and preserving interconnected pore space for water storage. This multiscale structural effect helps explain how the material achieved a balance between water retention capacity and mechanical support. Under the tested conditions, the material maintained acceptable residual strength after short-term exposure to water, acid, and sulfate-containing solutions. Life-cycle assessment indicated a 70% reduction in CO₂ emissions compared with ordinary Portland cement, while pilot-scale cost analysis showed a 39% lower production cost than MetaMax-based geopolymer materials. Pilot-scale application further demonstrated the constructability and water-regulation potential of the material in practical environments. Overall, the proposed ternary Fe-based geopolymer demonstrates that Fe-rich industrial wastes can be engineered into low-carbon and economically viable water-retaining subgrade materials that balance hydraulic regulation, structural adequacy, and sustainability. Nevertheless, long-term durability, cyclic loading performance, and direct nanoscale characterization of Fe-bearing gel evolution still require further investigation. Full article

Given the urgent need for corporate green transformation in the context of global climate governance, the sustainable development goals, and China’s dual carbon goals, this study examines the spillover effects of venture capital networks formed through common shareholder ties on green technology innovation from a complex network perspective. Based on regression analysis of panel data from Chinese A-share STAR and ChiNext Market listed companies between 2015 and 2023, we find the following: (1) Within venture capital networks, enterprises with higher centrality and structural hole positions exhibit more significant green technology innovation performance. (2) This facilitation effect varies across firm types. Private enterprises, foreign-invested enterprises and enterprises with weaker ESG performance rely more heavily on network advantage for innovation. (3) The mechanism analysis shows that occupying advantageous positions in venture capital networks enables firms to increase R&D personnel and R&D expenditure, thereby strengthening their ability to absorb external knowledge and transform innovation resources, which further enhances green technology innovation output. Full article

Background/Objectives: Dietary cholesterol intake significantly influences liver health, yet the specific molecular mechanisms by which it accelerates fibrogenesis remain incompletely defined. This study aimed to characterize the dose-dependent effects of dietary cholesterol on hepatic injury and fibrogenesis, identify cholesterol-responsive gene networks through transcriptomic analysis, and investigate Annexin A2 (ANXA2) as a candidate molecular mediator linking dietary cholesterol to hepatic fibrosis progression. Methods: A CCl₄-induced liver fibrosis mouse model was established and supplemented with dietary cholesterol (1–2%). Liver injury and fibrosis were assessed by liver-to-body weight ratios, serum biochemical markers, histological analysis, and fibrogenic gene expression. RNA sequencing combined with multiple hepatic fibrosis database analyses was performed to identify potential molecular mediators. Results: Dietary cholesterol supplementation aggravated CCl₄-induced hepatic fibrosis in mice, with dose-dependent increases in liver-to-body weight ratios and serum AST and ALT levels. Histological analysis showed enhanced collagen deposition and upregulation of fibrogenic genes. By integrating RNA-sequencing with multiple hepatic fibrosis database analysis and correlation analysis, we identified Annexin A2 (ANXA2) as a cholesterol-responsive gene associated with fibrosis. Conclusions: Dietary cholesterol promotes liver fibrosis progression, and ANXA2 may act as a potential mediator linking cholesterol metabolism to hepatic fibrogenesis. Full article

The persistence of an immunosuppressive microenvironment remains a formidable challenge for cancer immunotherapy, particularly in tumors with immune-excluded or immune-desert phenotypes. Increasing evidence indicates that chronic inflammation and tumor progression are intrinsically linked through shared signaling hubs, including NF-κB and PI3K/Akt. Osthole, a natural coumarin compound, has been reported to exhibit both potent anti-inflammatory and antitumor activities; however, whether these effects reflect a coordinated regulation of the inflammation–cancer axis remains unclear. In this study, we deployed an integrative framework founded on network pharmacology, molecular docking, and rigorous molecular dynamics simulations, complemented by literature-based evidence synthesis, to computationally explore the potential mechanisms underlying Osthole’s dual activities. Our analysis revealed that Osthole’s predicted targets are significantly enriched in signaling pathways bridging inflammatory and oncogenic processes, most notably the PI3K/Akt, NF-κB, and TGF-β/Smad pathways. Crucially, MD simulations provided supportive computational evidence, suggesting that Osthole forms stable, energetically favorable complexes with core protein hubs (AKT1, RELA, and TGFB1) under the simulated conditions. Evidence from representative inflammatory and tumor models supports the biological plausibility of these predictions, including suppression of pro-inflammatory signaling, mitigation of maladaptive tissue remodeling, and induction of apoptosis. Furthermore, in hepatocellular carcinoma models, Osthole-mediated apoptosis appeared linked to HMGB1-related inflammatory signaling, highlighting its potential to modulate the local immune niche. Collectively, this convergence of systems-level predictions and dynamic structural evidence identifies Osthole as a promising multi-target candidate for the coordinated regulation of inflammation-associated tumor progression, providing a robust rationale for further experimental validation. 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

Mechanized harvesting in the industrial tomato sector is currently bottlenecked by excessive mechanical injuries and elevated levels of foreign materials generated during electro-mechanical combine harvesting operations. To combat these limitations, this comprehensive review explores recent breakthroughs in harvester-mounted smart grading systems engineered specifically for complex, open-field conditions. Rather than relying solely on conventional optical inspection, the study examines the transition toward advanced, heterogeneous edge-computing frameworks—incorporating FPGAs and embedded GPUs—deployed within electro-mechanical harvesting platforms. This architectural evolution plays a crucial role in mitigating unpredictable processing delays caused by intense operational vibrations, although achieving absolute real-time stability under extreme field conditions remains an ongoing challenge. To minimize bruising and physical deterioration, our analysis synthesizes findings from multi-scale explicit dynamic finite element simulations, unpacking the underlying microstructural failure modes of the crop. We illustrate how regulating applied forces via soft robotic effectors can help approach a ‘damage-free’ handling threshold, though empirical results vary depending on fruit maturity and dynamic operational speeds. Furthermore, coupling multi-modal sensor fusion with Convolutional Neural Networks (CNNs) shows promising potential for non-destructive internal property evaluation under the vibration, dust, and throughput constraints of electro-mechanical harvesters, pending broader validation across diverse field datasets. Ultimately, by projecting future trends in onboard electro-mechanical harvester separation and advocating for a closer synergy between agronomic practices and machine engineering, this paper delivers a comprehensive blueprint for building next-generation, highly resilient, and gentle sorting machinery. Full article

Low-cost IoT-based air quality sensors enable dense monitoring networks but suffer from significant measurement noise and instability particularly in dynamic environments. Conventional fixed-window smoothing reduces noise but introduces a trade-off between signal stability and temporal responsiveness, often attenuating short-term pollution events. This paper proposes an adaptive filtering algorithm that dynamically adjusts the averaging window size based on short-term signal variability. The method relies on real-time variance estimation to balance noise suppression and sensitivity to rapid changes without increasing computational complexity. The approach is implemented within an IoT-based monitoring framework and evaluated using parallel measurements with a certified reference device. Comparative analysis against a certified reference device demonstrates strong agreement, with Pearson correlation coefficients reaching r = 0.88 for PM_2.5 and r = 0.86 for PM₁₀, and low error levels (RMSE ≈ 2.1–2.2 µg/m³). The proposed adaptive filtering approach preserves temporal dynamics while improving signal stability and robustness compared to raw and fixed-window filtering. In addition, this method improves event detection stability, achieving low false alarm rates and near real-time response (latency < 1 sampling interval), supporting RPA-based workflow triggering. The results show that the proposed adaptive filtering provides an efficient and lightweight solution for real-time signal processing on resource-constrained devices, making it suitable for large-scale deployment in environmental monitoring systems. Full article

In the context of the increasing demands of precision manufacturing and nanotechnology, especially for emerging fields such as Oxide oxide films in Nuclear nuclear fuel assemblies, the measurement of multi-layer inhomogeneous thin films faces significant challenges. Traditional spectroscopic interference thickness measurement techniques have limitations in handling dispersion interference, parameter coupling, and the efficient solution of nonlinear inverse problems. This study proposes a new model that integrates deep learning and physical model fitting. It constructs a theoretical model of multi-layer thin-film interference spectroscopy based on the Lorentz–Drude formula, uses a generative adversarial network (GAN) for initial structure analysis, and builds a two-layer optimization framework of “deep learning rough positioning—physical model fine fitting”. The research aims to break through the limitations of traditional methods, improve measurement accuracy and anti-noise ability, and provide a key technical support for emerging fields. Full article