Search Results (28,715)

The pathogenesis and progression of liver diseases are characterized by marked zonal heterogeneity, yet conventional research paradigms have long overlooked this intrinsic spatial logic. Ferroptosis and cuproptosis have been widely implicated in liver disease; however, their precise intralobular distribution and zonal susceptibility patterns remain poorly defined. We present a narrative synthesis of the literature on the spatial zonation of hepatic metabolism, redox homeostasis, and metal handling, and assess their potential roles as determinants of region-specific cell death vulnerability. We propose the novel “spatial redox–metalloptosis axis” hypothesis. The pericentral zone (Zone 3), characterized by hypoxia, high cytochrome P450 activity, and a redox environment that may favor lipid peroxidation under specific pathological conditions, is hypothesized to form a ferroptosis-susceptible niche under metabolic stress. Conversely, the periportal zone (Zone 1), characterized by active copper handling and oxidative phosphorylation-dependent metabolism, is hypothesized to be preferentially vulnerable to cuproptosis (proposed hypothesis; direct zone-resolved evidence of cuproptosis execution in Zone 1 is currently absent). Ceruloplasmin is proposed as a candidate molecular link between copper and iron metabolism. We further identify shared molecular hubs and a hypothesized spatial redox–metalloptosis axis linking these two regulated cell death modalities, while direct biological crosstalk remains to be demonstrated. We also highlight critical technological, mechanistic, and translational gaps. This review aims to shift liver disease research from viewing the liver as a homogeneous organ to a functionally compartmentalized zoned ecosystem, providing a testable theoretical framework for deciphering region-specific liver injury and developing spatially informed therapeutic strategies. Full article

As urban underground spaces and infrastructure development accelerate, subsurface elements such as buried pipelines, integrated utility tunnels, subway tunnels, cavity defects, and deep-seated hidden hazards become increasingly intertwined. Consequently, urban target detection is characterized by pronounced scale discrepancies, intense environmental interference, and severely confined operational spaces. The transient electromagnetic method (TEM) is highly valuable for rapid surveys and hazard identification in urban underground spaces owing to its inherent advantages, including non-contact operation, adaptability to hardened pavements, high sensitivity to low-resistivity anomalies, and the ability to probe a broad range of depths. In recent years, research has shifted from improving isolated instrumentation to synergistically optimizing sensing platforms, transmitter–receiver systems, anti-interference methodologies, and imaging interpretation workflows. Specifically, small-loop configurations and high-frequency excitation technologies have improved shallow-sounding capabilities in confined urban spaces; anti-interference techniques have increased data reliability in complex noise environments; and apparent resistivity mapping, virtual wave-field migration, and rapid inversion methodologies have enabled profiling results to transition from qualitative identification to fine-scale interpretation. Concurrently, the evolution of ground-towed, UAV-borne, helicopter-borne, and semi-airborne platforms has progressively endowed urban TEM profiling with continuous, mobile, and scenario-specific operational capabilities. Looking to the future, further technical breakthroughs in urban TEM technology are required to improve shallow-resolution, deep-seated penetration, multi-source interference decoupling, and real-time concurrent imaging. Full article

Porcine reproductive and respiratory syndrome (PRRS) represents a critical infectious disease caused by the PRRS virus (PRRSV), posing a substantial threat to the global swine industry. In China, there is currently an epidemic trend characterized by the coexistence of multiple evolving genotypes. Effective prevention and control measures are contingent upon the availability of rapid, precise, and sensitive pathogen detection technologies. Addressing the need for swift differentiation of the predominant circulating strains, including the classical strains (PRRSV-C), the highly pathogenic strains (PRRSV-HP), and NADC30-like strains (PRRSV-NA), this study focuses on the NSP2 region of each lineage. It establishes a triple TaqMan-qPCR method capable of simultaneously genotyping these three lineages. The method demonstrated no cross-reactivity with other viruses, including porcine parvovirus (PPV), porcine transmissible gastroenteritis virus (TGEV), porcine pseudorabies virus (PRV), classical swine fever virus (CFSV), African swine fever virus (ASFV), porcine epidemic diarrhea virus (PEDV), porcine rotavirus (RV), and porcine circovirus (PCV2), thereby fully affirming its specificity. The sensitivity analysis demonstrated that the limit of detection (LOD) for the NSP2 gene in each lineage was 1 copy/μL based on the purified plasmids. Both inter-group and intra-group coefficients of variation (CV) were less than 4%, indicating high reproducibility. Comparative studies with commercial kits revealed that the developed TaqMan-qPCR method exhibited 100% relative sensitivity and a relative conformity rate exceeding 98%, suggesting its potential as a viable alternative to commercial kits. Furthermore, the analysis of 1049 clinical samples using the qPCR method indicated that the PRRSV-NADC30-like strains are currently the predominant circulating strain in clinical settings in Hebei Province. In conclusion, this study developed a triple TaqMan-qPCR method capable of simultaneously identifying PRRSV-C, PRRSV-HP and PRRSV-NA, enabling rapid and accurate identification of the PRRSV genotypes prevalent in pig populations. This provides a robust technical tool for the development of targeted immunization and prevention strategies. Full article

Reverse osmosis (RO) desalination is a major technology for freshwater production in arid regions, but its energy demand becomes more challenging when the system is supplied by variable renewable energy. This study presents an offline digital-twin-assisted decision-support framework for dynamic RO under Kuwait solar-availability conditions. Within this framework, the predictive models are driven primarily by the dynamic RO process variables, while NASA Prediction Of Worldwide Energy Resources (POWER) data provide the Kuwait solar-availability context, and the PV power margin serves as a scenario-level energy indicator. The purpose is to predict instantaneous permeate flow rate, estimate specific energy consumption, and identify energy-efficient operating conditions using machine learning. Kuwait City was used as the solar case-study location. Hourly solar and meteorological data were obtained from NASA POWER, while dynamic RO membrane data were obtained from the open experimental wave desalination dataset published by the National Renewable Energy Laboratory (NREL) through Data.gov and the Marine and Hydrokinetic Data Repository. The RO dataset includes steady-state, ramp, sinusoidal, and Wave Energy Converter SIMulator (WEC-Sim) pressure/flow experiments. The process-flow image used in the system description was also taken from the same NREL dataset and is cited in the figure caption. The raw RO files were cleaned, harmonized, and transformed into a process-informed modeling dataset. Derived features included pressure rate, recovery ratio, salt rejection, estimated pump power, specific energy consumption (SEC), PV power margin, and rolling pressure/flow features. Three supervised regression models were tested: Gradient Boosting, Random Forest, and XGBoost. A representative subset of 60,000 records was used to preserve the main experimental conditions while reducing redundancy in the densely sampled sequential data. Results show that permeate flow rate can be predicted with high accuracy using Gradient Boosting (R² = 0.981; RMSE = 0.161 L/min). The moderate energy prediction performance yielded an R² of 0.654 and RMSE of 7.570 kWh/m³ for Random Forest. The accuracy of permeate conductivity predictions was lower (R² = 0.257; RMSE = 245.44 µS/cm) because membrane and feed characterizing parameters should be included for an adequate water quality control. The proposed approach is best suited as an offline decision-support framework for dynamic RO process analysis. Full article

Managing the transition risks of carbon-intensive firms is essential for reconciling climate governance with the stable operation of the real economy; nevertheless, existing scholarship has yet to fully elucidate how climate policy uncertainty contributes to the formation of these risks. In this paper, we develop a firm-specific measure of climate policy uncertainty exposure by integrating China’s aggregate climate policy uncertainty index with climate-risk-related textual data retrieved from listed companies’ annual reports. Drawing on a panel dataset of A-share listed companies in nine carbon-intensive sectors over 2010–2023, we employ a partial-linear double/debiased machine-learning methodology to investigate how climate policy uncertainty exposure influences multidimensional firm transition risk. Our baseline estimations indicate that greater climate policy uncertainty exposure is associated with a statistically significant rise in transition risk among high-carbon firms, with the preferred model producing a coefficient estimate of 0.0243. These findings remain robust to an array of sensitivity checks and endogeneity-correction procedures. Mechanism analysis provides evidence consistent with four potential channels involving weaker intra-industry competition, lower corporate risk-taking, tighter financing constraints, and higher agency costs. Heterogeneity examinations reveal that the detrimental impact is particularly evident among larger enterprises, high-technology companies, and firms characterized by comparatively lower pollution levels. Further analysis based on conditional average treatment effects and best linear predictors reveals that media supervision and the presence of long-term institutional investors substantially reduce the extent to which climate policy uncertainty translates into firm transition risk. This study provides firm-level empirical evidence elucidating how climate policy uncertainty shapes multidimensional transition risk in the low-carbon transformation of high-carbon industries. Full article

Peptide therapeutics occupy a unique position between small organic compounds and large protein biomolecules, combining high specificity, strong pharmacological efficacy, and favourable safety profiles. Consequently, they have emerged as important therapeutic agents for a wide range of diseases, including metabolic and oncological disorders. However, oral administration of peptide drugs remains a major challenge due to extensive enzymatic degradation, low intestinal permeability, mucus entrapment, and presystemic metabolism within the gastrointestinal tract. This review provides a comprehensive overview of contemporary strategies for improving oral peptide delivery, with special emphasis on emerging pharmaceutical formulation technologies, bioinspired delivery systems and ingestible device-assisted approaches. A qualitative literature search was conducted using major scientific databases and included relevant publications available up to May 2026. The analysis identified the main barriers responsible for low oral bioavailability of peptide drugs, as well as promising approaches to overcoming these obstacles, including peptide modification, enzyme inhibition, permeation enhancement, mucolytic strategies, and advanced carrier systems. Special attention is given to multifunctional carrier systems, ingestible medical devices and bile acid-inspired technologies as emerging directions in oral peptide delivery. The convergence of pharmaceutical sciences, bioinspired formulation strategies and biomedical engineering is expected to accelerate the clinical translation of oral peptide formulations and enable their therapeutic potential to be fully exploited. Full article

Driven by China’s “Dual Carbon” strategy, medium-deep closed-loop geothermal energy has become a mainstream clean heating technology owing to the advantage of “heat extraction without groundwater production”. However, its large-scale application is restricted by low single-well heat output and an unclear matching mechanism between working fluids and wellbores. Taking sandstone geothermal reservoirs in Dezhou, Northwestern Shandong Depression, as the research object, a 3D coupled heat transfer model of the wellbore–reservoir was established via COMSOL Multiphysics. The heat transfer characteristics of water and CO₂ under variable injection temperature, mass flow rate and wellbore layout were compared. The results show that: (1) injection temperature dominates the heat extraction performance of water, which matches branched wells and delays overall reservoir thermal depletion during long-term exploitation; (2) CO₂ performance is highly sensitive to mass flow rate and suitable for connected wells, and an asymmetric geothermal field with “cooled injection zone and heated production zone” forms under a high flow rate; (3) limited by low specific heat capacity, CO₂ delivers lower heat power at an identical flow rate, while equivalent heat yield can be achieved when its flow rate doubles that of water. This study clarifies matched development schemes for two working fluids and provides a theoretical reference for optimized exploitation of closed-loop geothermal systems in sandstone reservoirs in Northwestern Shandong. Full article

Background: Technostress has emerged as a major occupational health concern in increasingly digitalized workplaces. Although organizational and technological determinants of technostress have been widely investigated, the potential influence of healthy lifestyle behaviors remains poorly understood. This study aimed to evaluate the association between a combined Healthy Lifestyle Score (HLS) and technostress in a large cross-sectional sample of Spanish workers. Methods: A cross-sectional study was conducted among 104,175 workers who underwent routine occupational health examinations between January 2021 and December 2024. The Healthy Lifestyle Score was constructed by assigning one point each for regular physical activity, high adherence to the Mediterranean diet, and non-smoking status, resulting in scores ranging from 0 to 3. Technostress was assessed using the Technostress Short Questionnaire (TCS-Short) and categorized as low, moderate, high, or very high. Logistic regression models were used to estimate crude and adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for high-to-very high technostress according to HLS categories. Results: Significant differences in technostress levels were observed across HLS categories (p < 0.001). The prevalence of high-to-very high technostress was 50.8% among workers with HLS = 0 and 50.7% among those with HLS = 1, decreasing markedly to 7.5% and 7.2% among workers with HLS = 2 and HLS = 3, respectively. After adjustment for sex, age, educational level, and social class, participants with HLS = 2 and HLS = 3 exhibited substantially lower odds of high technostress (OR = 0.073, 95% CI 0.068–0.078 and OR = 0.074, 95% CI 0.069–0.079, respectively) compared with those with HLS = 0. The categorical analysis suggested a threshold-like rather than progressive association, with substantially lower odds of high-to-very high technostress observed among workers with HLS = 2 and HLS = 3, whereas HLS = 1 did not show lower odds compared with HLS = 0. The final model demonstrated excellent discrimination (AUC = 0.902). Conclusions: Healthy lifestyle profiles were strongly associated with technostress in this large cross-sectional occupational sample. Workers with HLS = 2 and HLS = 3 showed markedly lower odds of high-to-very high technostress than those with HLS = 0, whereas HLS = 1 did not show lower odds. Component-specific analyses indicated that the associations differed substantially across the behaviors comprising the score, supporting interpretation of the HLS as an unweighted behavioral count rather than as a measure of equivalent contributions from each component. Full article

This single-institution case study examined faculty acceptance and instructional use of social media at the United Arab Emirates University (UAEU), with emphasis on individual-level demographic associations and faculty perceptions of technology-enhanced learning. A cross-sectional survey of 246 faculty members was analyzed using descriptive statistics and contingency-table methods. Female faculty reported higher instructional use than male faculty (88.0% vs. 75.3%; χ²(1, N = 246) = 5.07, p = 0.024; Φ = 0.144; OR = 2.41, 95% CI [1.16, 5.00]), indicating a small association. For age, the omnibus 5 × 2 chi-square test was not statistically significant (χ²(4, N = 246) = 8.17, p = 0.085), although an ordered trend test suggested increasing non-use with age (z = 2.34, p = 0.019); descriptively, this pattern was driven mainly by the 61–70 group rather than a steady increase across all age categories. Academic rank differed overall (χ²(3, N = 246) = 8.70, p = 0.034), but the pattern was non-monotonic, and the prespecified junior-versus-senior comparison was not significant (χ²(1, N = 246) = 2.00, p = 0.157). Faculty reported benefits related to communication, collaboration, audiovisual learning, mobile access, and academic networking, alongside concerns about distraction and reduced interpersonal interaction. Because the study was conducted at one university with a 26.25% response rate, the findings are context-specific and should not be generalized to all UAE or Gulf higher-education institutions. Full article

Biochar (BC) and hydrochar (HC) have attracted increasing attention as sustainable soil amendments for improving soil water retention and mitigating agricultural water stress. This review synthesizes and compares the current state of knowledge on the production, physicochemical properties, and hydraulic performance of slow-pyrolysis BC, hydrothermal carbonization hydrochar (HTC HC), and hydrothermal liquefaction hydrochar (HTL HC). The mechanisms governing soil water retention are first examined, followed by a comprehensive review of the effects of amendment properties, feedstock type, thermochemical conversion conditions, particle size, application rate, and soil characteristics on field capacity, permanent wilting point, plant-available water, and water-holding capacity. The available evidence demonstrates that BC generally provides the most consistent improvement in soil hydraulic properties, particularly in coarse-textured soils, whereas the performance of HTC HC is considerably more variable and strongly dependent on hydrothermal conversion conditions and soil characteristics. HTL HC remains largely unexplored but shows promising hydraulic performance and exceptional resistance to biodegradation. Apparently contradictory findings among published studies are shown to arise largely from interactions among feedstock and conversion conditions, resulting amendment properties, soil characteristics, application conditions, and differences in hydraulic evaluation, highlighting the need for integrated mechanistic frameworks rather than interpretation based on individual factors. A comparative assessment of the three materials further considers ecotoxicity, biodegradation, life-cycle assessment, and techno-economic analysis. Overall, BC is currently the most mature soil amendment technology, HTC HC offers important advantages for wet biomass utilization, and HTL HC represents a promising but underdeveloped alternative. Future research should emphasize standardized evaluation methods, long-term field validation, and integrated mechanistic approaches linking production conditions, amendment properties, soil characteristics, and application conditions to enable predictive, application-specific design of carbonaceous soil amendments for sustainable soil water management. Full article

Heavy and extra-heavy oils represent a large and growing share of recoverable hydrocarbon resources, yet their extreme viscosity, high heteroatom content, and non-Newtonian behavior routinely defeat empirical correlations developed for conventional crude. Machine learning has emerged as a candidate response to this modeling gap, but existing reviews largely catalog applications without asking whether the technology is actually ready for industrial deployment. This critical review synthesizes machine learning applications across five thematic domains of the heavy-oil value chain: physicochemical property prediction, enhanced oil recovery, flow assurance, reactive recovery, and downstream upgrading. Studies are read through a three-phase historical lens, tracing the field’s progression from empirical-correlation replacement to methodological diversification to physics-informed and closed-loop integration, and evaluated against a Technology Readiness Level (TRL) scale adapted specifically for heavy-oil machine learning. The multilayer perceptron anchors more of the primary corpus than any other architecture, a pattern that, in our interpretation, reflects small-sample, low-dimensional regression needs rather than any demonstrated advantage over other architectures. Enhanced oil recovery is the only cluster to reach organizational-scale deployment, anchored by a single multi-decade operator program, Chevron’s San Joaquin Valley i-field; the remaining clusters are constrained less by modeling sophistication than by single-basin datasets and undisclosed uncertainty. Measured against three falsifiable deployability criteria, fidelity preservation below 10° API, operator-grade interpretability, and demonstrated laboratory-to-field transferability, no study in the reviewed corpus is documented to satisfy all three simultaneously; because industrial implementations are frequently proprietary, this is a statement about the published record identified by this search, not a claim that the capability does not exist. Federated learning, physics-informed architectures, and sequence-aware models emerge as the directions most likely to close this gap. Full article

Alternative pre-mRNA splicing is a central layer of gene regulation that enables a limited number of genes to generate a far larger and more context-dependent transcriptome and proteome. In cancer, splicing is disrupted by mutations in cis-regulatory sequences, recurrent lesions in spliceosome components, altered abundance or activity of RNA-binding proteins, and changes in transcription, chromatin, RNA modification, metabolism and stress signalling. These alterations are not merely by-products of malignant transformation. They can create oncogenic protein isoforms, eliminate tumour-suppressive products, remodel cellular identity, promote metastasis and drug resistance, and generate tumour-restricted peptides that are visible to the immune system. Large pan-cancer datasets, long-read sequencing, single-cell isoform profiling, proteogenomics and functional perturbation screens are now resolving this complexity at unprecedented scale. In parallel, multiple therapeutic strategies are advancing, including modulators of the SF3B complex, molecular glues that degrade RBM39, inhibitors of protein arginine methyltransferases and splicing kinases, splice-switching oligonucleotides, programmable RNA-targeting systems, and vaccines or T-cell receptors directed against splicing-derived neoantigens. This review integrates the molecular logic of splice-site selection with the cancer-specific mechanisms that perturb it, summarizes representative isoform switches across the hallmarks of cancer, evaluates emerging technologies and clinical biomarkers, and discusses the opportunities and constraints of translating splicing biology into precision oncology. Particular emphasis is placed on tumour specificity, intratumoural heterogeneity, proteomic validation, therapeutic windows and rational combination strategies. Full article

Gender bias in generative artificial intelligence (GenAI) is both a technical and a social phenomenon: it emerges from historically patterned data, model design, and interactions in institutional use, and it cannot be understood by engineering or by social critique alone. This critical integrative review develops a more differentiated account. It connects feminist epistemology, Science and Technology Studies, critical AI scholarship, natural language processing, and governance research to examine five levels: historical knowledge production, technical representation and generation, benchmark evaluation, institutional deployment, and accountability. The review explains tokenization, next-token prediction, transformers, and the transition from static embeddings to contemporary language models before assessing evidence from standard fairness tests—coreference tests (WinoBias), sentence-pair tests (CrowS-Pairs), and stereotype tests (StereoSet)—as well as open-ended generation, multilingual testing, and text-to-image systems. It shows that measured bias varies with task, prompt, language, model version, and metric. What a test records and what that record means are therefore distinct questions: measurements are situated and depend on the instrument, and their interpretation draws on theory rather than following from the numbers alone. Evidence from employment, education, healthcare, and translation further indicates that the relevant unit of analysis is the model-in-context—the model together with the institution and workflow in which its outputs are used. Technical mitigation can reduce specific harms but does not repair unequal criteria, incomplete evidence bases, or weak institutional accountability. The review proposes a multilevel governance approach combining technical evaluation, documentation, professional and community oversight, appeals, remedies, and public-interest knowledge infrastructure. Its distinctive contribution is to connect three observations usually kept apart—how bias is measured, how generative systems concentrate epistemic authority, and how statistical learning is oriented toward past data—and to show why democratic and feminist governance can keep alternative technological futures open. Full article

We investigate the potential of technology-based attention training (AT), particularly virtual reality (VR), as a stress-management tool. Mental stress is rising globally, and researchers increasingly use immersive technologies, wearable sensors, and machine learning (ML) for its detection and control. This feasibility study examines the impact of fully immersive VR-based AT on mental stress using electroencephalogram (EEG) signals and automated classification. We designed virtual exercises targeting different attention types and analyzed EEG responses with an ML framework; the resulting dataset, collected at the Arab Academy for Science and Technology (Alexandria, Egypt), is publicly available. For an unbiased estimate, we adopt a leakage-free evaluation in which the train/test split is performed by time, before segmentation into overlapping windows, so neighboring windows cannot appear in both sets. Subject-specific tree-based classifiers detected stress with a mean accuracy of about 97%, whereas leave-one-subject-out (LOSO) validation yielded about 67%, indicating strongly individual stress signatures and motivating a subject-specific strategy. Using these models, we compared the number of classifier-predicted stress segments before and after AT and visualized the feature space with T-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). Under subject-specific models the number of stress-predicted segments decreased after AT (Wilcoxon signed-rank $p<0.05$); however, because this reduction was corroborated neither by a non-circular (LOSO) detector nor by a centroid-separation measure, we interpret it as an exploratory, classifier-predicted effect rather than independently validated stress reduction. The results highlight the promise—and the current limits—of integrating immersive VR with EEG-guided analytics for mental-health support. Full article

Therapeutic glycoproteins, a pivotal class of biopharmaceuticals, have transformed modern medicine through their broad applications in oncology, immunotherapy, and infectious disease management. Their structural complexity and biological specificity make them highly effective in targeting disease pathways; however, challenges related to stability, bioavailability, and delivery efficacy limit their full potential. Recent advancements in delivery technologies have sought to address these challenges through innovative approaches such as nanotechnology-based carriers, controlled-release systems, and molecular engineering. These strategies have demonstrated the ability to enhance glycoprotein stability, optimize pharmacokinetics, and achieve targeted delivery with minimal off-target effects. This review provides a comprehensive overview of state-of-the-art lipid-based delivery systems specifically designed to overcome the unique pharmaceutical challenges associated with therapeutic glycoproteins, highlighting their design principles, formulation strategies, mechanisms of encapsulation and release, and therapeutic advantages in improving glycoprotein stability, bioavailability, targeted delivery, and treatment efficacy. In addition to surveying the current landscape, this review delves into the key challenges impeding the widespread adoption of advanced delivery systems, including immunogenicity, manufacturing scalability, and clinical translation. The review concludes with insights into emerging trends in the development of lipid-based delivery systems, positioning glycoprotein therapeutics at the forefront of innovation in biopharmaceuticals. This overview of advancements and challenges aims to provide a roadmap for future progress in the field of glycoprotein delivery and therapeutic applications. Full article