Search Results (39,044)

The growing problem of air pollution and its direct impact on human health have created an urgent need for reliable, intelligent, and machine learning (ML)-enabled gas-sensing technologies. Among various sensing platforms, metal oxide gas sensors (MO-GSs) have emerged as promising candidates owing to their low cost, high sensitivity, and scalability. However, their practical application is limited by poor selectivity, cross-sensitivity, sensor drift, and high operating temperatures. Recent advances in ML have provided effective strategies to overcome these limitations through data-driven optimization of sensing performance. This review summarizes recent progress in ML-assisted MO-GSs, covering sensor array design, feature engineering, and classification algorithms, including support vector machines (SVMs), random forests (RFs), and deep neural networks (DNNs). In addition, key data-processing techniques such as preprocessing, dimensionality reduction, and hybrid learning approaches are critically discussed. The application of ML-enabled MO-GSs in medical diagnostics, environmental monitoring, industrial safety, and food quality assessment is also reviewed. Despite significant progress, challenges including limited dataset availability, sensor drift, and poor model generalization remain. Future research should focus on developing adaptive, energy-efficient, and IoT-enabled smart sensing systems. The integration of machine learning with metal oxide gas sensors represents a significant step toward intelligent, next-generation, high-performance gas-sensing technologies. Full article

Background/Objectives: Digital transformation is a strategic priority for healthcare systems, requiring nurses to develop competencies that enable the safe, effective, and critical use of digital technologies in clinical practice. Understanding nurses’ digital competence profile is essential to inform leadership, education, and workforce development strategies. This study aimed to assess digital competence among Portuguese nurses, examine its relationship with soft skills, and identify priority areas for professional development. Methods: A quantitative, descriptive-correlational, cross-sectional study was conducted with a nationally recruited convenience sample of Portuguese nurses. Data were collected using a Digital Competence Assessment Questionnaire based on the European Digital Competence Framework for Citizens (DigComp) and Soft Skills Inventory. Associations between digital competence and soft skills were analysed using descriptive, inferential, and multivariable statistical methods with IBM SPSS Statistics version 30.0. Results: Based on the exploratory classification of the knowledge/performance score, 52.0% of participants fell within the Intermediate and 27.2% within the Advanced proficiency intervals. Adapting and Coping, Analyzing and Interpreting, and Interacting and Presenting were positively associated with self-reflected digital competence. Multivariable analysis showed that soft skills accounted for a larger proportion of variance in self-reflected digital competence than in the knowledge/performance test score; however, the explanatory value of the latter model was limited. Conclusions: Participants were predominantly classified within the Intermediate and Advanced proficiency intervals, although comparatively lower descriptive performance was observed in Safety. The findings highlight the potential complementary role of technical competencies and soft skills in digital capability and suggest the value of further investigating targeted educational approaches. These findings may inform nursing leadership, education, and workforce-development strategies aimed at supporting digital competence and sustainable digital transformation. 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

Structural analysis using X-rays and electron beams has long provided the average arrangement of atoms and molecules—that is, “structural information”—with high precision. By contrast, static measurements cannot directly yield dynamic information on how a material changes over time; instead, information on motion is convolved into a single numerical value such as the B-factor (atomic displacement parameter). Taking this limitation as its starting point, this review surveys the recent trend of introducing a time axis into measurements to observe material dynamics directly. First, we outline the technological foundations that have made the transition from static to time-resolved measurement possible. It rests on the dramatic shortening of exposure times, enabled by the increased brilliance of X-ray and electron sources and by advances in detection technology such as direct photon-counting detectors. Next, we survey dynamic measurement techniques, including time-resolved X-ray crystallography, coherent X-ray scattering, neutron scattering, and time-resolved electron microscopy. We also point out the essential limitation that most of them still return ensemble or volume averages. Building on this, we systematically describe diffracted X-ray tracking (DXT), diffracted X-ray blinking (DXB), small-angle X-ray blinking (SAXB), transmitted X-ray blinking (TXB), and electron-beam molecular dynamics (EBMD), which use gold nanocrystals and gold nanoparticles as motion probes. We distinguish throughout between methods that follow individual objects—DXT and EBMD, which yield trajectories of single labeled molecules or single particles—and methods that analyze intensity fluctuations arising from many contributors within one pixel or illuminated volume—DXB, SAXB and TXB. The latter are not single-molecule measurements; rather, they replace a global ensemble average by a spatially localized statistical one, retaining local heterogeneity that a bulk measurement would average away. Finally, we discuss the implementation and prospects of the large-volume data analysis—principal component analysis, Bayesian inference, machine learning, and autonomous measurement—needed to handle the explosively increasing amount of information that the time axis introduces. We close with the outlook that time-resolved measurement incorporating AI and big-data analysis will become established as a new measurement platform that complements and extends conventional static structural analysis. Full article

Cells of higher organisms express numerous cell surface proteins, and their spectrum and level of expression are directly related to cells’ functions. The technology of mass cell selection based on the surface protein expression levels may be highly important both for basic research and cell therapy applications. We have previously developed a method of magnetic selection of cells differing in surface marker expression levels, which we term here MACS-MEL (Magnetic-Assisted Cell Selection by Marker Expression Levels). The method demonstrated its effectiveness in the artificial model system, namely retrovirally transduced NIH 3T3 cells. However, whether it was also applicable to complex natural cell populations remained unclear. In the current study, we validated the MACS-MEL approach by separating mouse bone marrow (BM) cells into fractions according to the expression of pan-hematopoietic marker CD45. In the basic protocol, two-stage fractionation of CD45⁺ cells from BM was performed using selection of cells consecutively with 2 μL and 8 μL of anti-CD45 magnetic beads, resulting in isolation of CD45^high and CD45^int cell populations. To explore in full the potential of the method, the extended protocol was also tested, where a third selection stage with 30 μL of anti-CD45 beads was added. The isolated cell fractions were analyzed by flow cytometry for CD45 expression, as well for CD11b, Gr-1, CD117, CD115 and CD19 markers, while their in vitro progenitor function was assessed by quantitating colony-forming units (CFUs) in methyl cellulose. The results of analysis demonstrate that the isolated cell fractions significantly differed both in their surface phenotypes and CFU potential. In particular, cell fractions with progressively reduced CD45 expression were characterized by decreasing expression of myeloid differentiation markers CD11b and Gr-1, as well as B-lymphoid marker CD19. The expression of stem/progenitor cell marker CD117, on the contrary, significantly increased. The CFU frequency also strongly correlated with decrease in CD45 expression, while the differentiation potential of CFUs differed substantially in various cell fractions. In general, our results demonstrate that less differentiated hematopoietic cells in mouse BM studied using in vitro tests are characterized by lower CD45 expression levels, in full accordance with data obtained in human system. Successful validation of the MACS-MEL in a BM system, characterized by existence of multiple cell types and high phenotypic and functional heterogeneity, demonstrated the effectiveness, simplicity and affordability of this method. The MACS-MEL approach can be applied for mass selection of cells based on differential marker expression and may yield cell subsets suitable for advanced cell therapy applications. Full article

Background/Objectives: The health-protective properties of olive oil phenolics, including their potential chemopreventive and anticancer effects, have attracted considerable scientific interest. However, their in vivo efficacy and mechanisms of action remain insufficiently understood. Recent advances in extraction and purification technologies have enabled large-scale production of highly purified olive oil phenols and phenolic-rich extracts, facilitating translational research. Methods: Herein, the antitumor efficacy of isolated olive oil phenols and phenolic-rich formulations was investigated in an MDA-MB-231 triple-negative breast cancer (TNBC) xenograft model. Results: Intraperitoneal administration of oleocanthal (OLC), oleuropein aglycone (OleA) or their combination reduced endpoint tumor burden, with OLC exhibiting the most pronounced effect. Oral administration of total olive oil phenolics (OOPs) achieved comparable efficacy. Pre-treatment initiated before tumor cell implantation conferred the greatest protection, consistent with a prophylactic mode of action. In contrast, delayed intervention displayed diminished or no antitumor benefit. Phenolic-rich extra virgin olive oil likewise showed an inhibition trend in tumor progression. Mechanistically, OOPs attenuated plasma protein oxidation, modulated proteasome mediated proteolysis, and reduced γH2AX levels in vivo. Furthermore, OOPs negatively affected the MDA-MB-231 cell migration in a concentration-dependent manner in vitro. Conclusions: Collectively, these findings are consistent with antitumor activities of olive oil phenolics via multiple mechanisms and support their further investigation as prophylactic agents in TNBC and as nutraceuticals. 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

Sustainable poultry nutrition is increasingly challenged by feed variability, environmental constraints, resource competition, and the growing demand for precision feeding strategies. Conventional feed evaluation systems based on proximate analysis, static nutrient tables, and empirical formulation are often insufficient to predict the biological and functional value of modern sustainable feed resources. This critical integrative review examines emerging approaches for evaluating and optimizing sustainable feeds in poultry nutrition through the integration of advanced analytical technologies, biological validation systems, omics sciences, and artificial intelligence (AI). This review was developed as a structured narrative review following a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-inspired workflow and organized around an integrated AI–omics–feed evaluation framework. Recent advances in spectroscopy-based analytical techniques, in vitro digestibility systems, microbiomics, metabolomics, nutrigenomics, machine learning, and predictive modeling are discussed in relation to feed characterization, nutrient utilization, host–microbiota interactions, and precision nutrition, with emphasis on the transition from static compositional assessment toward dynamic, system-oriented feed evaluation. Explainability, biological validation, and generalizability of AI-based models across heterogeneous production systems are highlighted as key challenges for practical implementation, alongside emerging frontiers in AI-driven nutritional decision-support. Integrating analytical, biological, molecular, and computational approaches may support adaptive precision nutrition systems capable of improving nutrient efficiency, reducing environmental emissions, and optimizing sustainable poultry production. Full article

The increasing penetration of renewable energy has significantly intensified the demand for flexible operation of thermal power plants, making dynamic simulation an essential tool for understanding transient behaviors and developing advanced operational strategies for circulating fluidized bed (CFB) power plants. This review critically examines the existing dynamic modeling approaches for industrial-scale CFB power plants, with particular emphasis on their applicability to flexibility studies. Existing CFB flue-gas side models are systematically classified into three categories: 3D physics-based CFD models, behavioral/data-driven models, and semi-empirical mechanistic models. Their characteristics are critically compared in terms of spatial and temporal scales, empirical dependence, model generality, computational and implementation burden, and applicability to CFB flexibility studies. Dynamic modeling of the steam–water cycle is also reviewed, showing that it has reached a relatively mature stage owing to well-established thermo-hydraulic theories and standardized modeling platforms. The current research bottleneck is therefore identified as the dynamic coupling between the flue-gas side and the steam–water cycle for integrated CFB whole-plant simulation. Based on the comparative analysis, semi-empirical mechanistic models are identified as a particularly suitable framework for industrial-scale CFB flexibility studies requiring minute-to-hour transient simulation, physical interpretability, and whole-plant coupling. Finally, future research directions are discussed, highlighting how integrated dynamic models can support CFB flexibility-enhancement technologies and the development of new-generation coal-fired power plants. Full article

Organoid and organ-on-chip technologies are rapidly evolving platforms for viral research that integrate stem cell biology, tissue engineering, and microfluidics to recapitulate key structural, mechanical, biochemical, and cellular features of human and animal physiology. By incorporating multicellular organoids into perfused microfluidic systems, these models can provide complex, dynamic, and physiologically relevant micro-environments for investigating virus–host interactions that are difficult to capture in conventional two-dimensional cultures and static organoids. Controlled flow, shear stress, extracellular matrix organization, tissue–tissue interfaces, and multicellular signaling enable mechanistic investigation of viral infectivity, dissemination, tissue injury and immune activation. Integration of real-time imaging and biosensors further permits longitudinal monitoring of viral replication, host responses, and tissue integrity, expanding the potential of these platforms for antiviral drug discovery. Recent organoid-on-chip studies using brain, skin, vaginal, respiratory, and intestinal models have demonstrated how tissue architecture, mechanical forces, glycocalyx dynamics, and immune–stromal interactions influence viral tropism and pathogenesis. In this review, we provide a mechanistic and translational overview of organoid and organ-on-chip technologies for studying viral infections, with particular emphasis on models of herpes simplex virus (HSV)-mediated disease. We further examine advances in immune integration, multi-organ systems, biosensing, and computational approaches that are expanding the complexity and predictive potential of these models. Importantly, patient-derived organoids and organ-on-chip platforms can capture interindividual differences in viral susceptibility, host responses, and therapeutic efficacy, providing pharmaceutical research with more precise, patient-relevant data to support drug prioritization and precision antiviral medicine. Finally, we discuss key barriers to broader adoption, including organoid maturation, biological and technical variability, reproducibility, scalability, biosafety, cost, standardization, and regulatory validation. Collectively, these advances position organoid and organ-on-chip technologies as powerful human-relevant models that bridge reductionist in vitro systems and human disease, while continued optimization, standardization, and validation will be essential to realize their full potential for mechanistically informed antiviral discovery, therapeutic development, and precision medicine. Full article

Hydrogen is considered one of the most promising energy carriers for sustainable and carbon-neutral energy systems. However, the large-scale deployment of hydrogen technologies is limited by the lack of efficient, safe, and cost-effective hydrogen storage methods. This review examines current hydrogen storage technologies and the physical and chemical mechanisms underlying hydrogen adsorption. Traditional storage approaches, including compressed gas and liquid hydrogen, are briefly analyzed with respect to their advantages, limitations, safety concerns, and energy requirements. Special focus is given to solid-state hydrogen storage systems based on metal hydrides, which offer high storage capacities and enhanced operational safety. Recent advances in intermetallic hydrides, magnesium-based materials and complex hydrides are discussed, along with challenges related to thermodynamic stability, sorption kinetics, thermal management, and cycling durability. This review also highlights recent developments in nanoporous carbon materials and the role of the hydrogen spillover mechanism in improving adsorption performance. Experimental studies reporting hydrogen adsorption capacities above 7 wt.% and up to 11.2 wt.% are analyzed. Based on the reviewed literature, key research directions are identified for optimizing the adsorption properties of advanced materials and accelerating the development of efficient and sustainable hydrogen storage technologies for future energy applications. 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

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

Data centers are large and rapidly growing electricity consumers, and cooling systems account for a substantial share of their energy demand. A key contribution of this study is a climate-sensitive, hourly techno-economic comparison of three data-center cooling configurations under consistent operating assumptions: a conventional water-cooled centrifugal chiller baseline, a magnetic bearing chiller (MBC) system, and an MBC system integrated with aquifer thermal energy storage (ATES). The comparison is performed for Phoenix, Arizona, and Fairbanks, Alaska, which represent substantially different cooling climates in the U.S. Hourly simulations use identical information technology (IT) load profiles, identical aggregate installed chiller capacity represented by two 4058 kW chiller units, common water-side economizer controls, and site-specific weather and electricity tariffs. Results show that the MBC system reduces annual cooling-system electricity consumption from 1169.4 to 957.4 MWh in Phoenix (18.1%) and from 361.6 to 319.4 MWh in Fairbanks (11.7%). Peak cooling-system electrical demand decreases by 119.4 kW in Phoenix and 71.6 kW in Fairbanks. Relative to the centrifugal baseline, the MBC case gives a 5.8-year simple payback in Phoenix but is not economically attractive in Fairbanks under the assumed tariff. The MBC-only case gives the lowest annual cooling electricity use in both climates. The MBC+ATES case is treated only as a screening-level, discharge-assisted cold-storage scenario rather than a full techno-economic assessment of seasonal ATES, and no site-specific hydrogeological feasibility assessment is performed. Under the assumed O&M cost structure, MBC+ATES gives a higher discounted value of savings than MBC-only, but this economic result is not caused by additional cooling-electricity savings relative to MBC-only. The MBC+ATES case also has a longer payback period because of its higher capital cost. These results show that the value of advanced cooling configurations depends on climate, free-cooling availability, electricity pricing, storage assumptions, and economic assumptions within the modeling framework considered in this study. Full article

Diabetes mellitus continues to impose a substantial global health burden, underscoring the need for therapeutic systems capable of achieving precise, adaptive, and patient-friendly glycemic control. Conventional diabetes treatments, including repeated insulin injections and oral hypoglycemic agents, are often constrained by non-physiological drug release, poor adherence, systemic side effects, and the persistent risk of hypoglycemia. In this context, glucose-responsive nanomedicine has emerged as a promising platform for next-generation diabetes therapy by enabling self-regulated and glucose-triggered delivery of insulin and other antidiabetic agents. This review highlights recent advances in glucose-responsive nanomedicine, focusing on the principal sensing mechanisms, including glucose oxidase-based, phenylboronic acid-based, and lectin-mediated systems, as well as the nanoscale carriers engineered to support them, such as polymeric nanoparticles, nanogels, micelles, liposomes, and hybrid nanostructures. These smart platforms offer significant potential to improve drug stability, enhance targeting efficiency, reduce dosing frequency, and more closely mimic endogenous insulin secretion. The review further examines their emerging role in precision diabetes care, particularly in combination with continuous glucose monitoring technologies, wearable devices, and closed-loop therapeutic systems. Despite notable progress at the preclinical level, important barriers to clinical translation remain, including challenges related to biocompatibility, long-term safety, reproducibility, scalable manufacturing, and regulatory approval. Collectively, glucose-responsive nanomedicine represents a rapidly advancing and clinically relevant field with the potential to redefine diabetes management through intelligent and personalized therapeutic strategies. This review provides a focused overview of current developments, key translational challenges, and future directions toward clinical implementation. Full article