Search Results (12,872)

Background/Objectives: Glioblastoma (GBM) remains one of the most aggressive and treatment-resistant malignancies, driven in part by heterogeneous, therapy-resistant glioma stem cells (GSCs). Improving clinical outcomes will require innovative therapeutic approaches that target unique molecular vulnerabilities. The mitogen-activated protein kinase (MAPK) pathway drives tumor progression across multiple cancers, including GBM. MAPK-interacting kinases (MNK1/2) represent MAPK downstream effectors that phosphorylate eukaryotic translation initiation factor 4E (eIF4E), a regulator of oncogenic and anti-apoptotic mRNA translation. We previously identified pharmacological MNK inhibition as a promising therapeutic strategy for GBM, but most available MNK inhibitors lack specificity. Methods: Novel MNK inhibitor compounds were developed using medicinal chemistry optimization and evaluated through molecular docking and kinome profiling analyses. Antineoplastic activity was assessed in established GBM cell lines and patient-derived glioma stem cell models cultured as 3-D neurospheres under stem cell-permissive conditions. Effects on MNK signaling, cell viability, neurosphere growth, migration, invasion, and apoptosis were analyzed using immunoblotting, flow cytometry, viability assays, wound healing assays, and 3-D invasion assays. In addition, a compound screen was performed to identify therapeutic agents that enhance MNK-targeted therapy, followed by validation using pharmacological inhibition and siRNA-mediated knockdown approaches. Results: Our next-generation MNK inhibitor NUCC-201893 exhibited high target specificity and greater potency than the lead compound NU808, effectively suppressing eIF4E phosphorylation, GBM cell viability, neurosphere growth, migration, and invasion. Compound screening identified DNA methyltransferase (DNMT) inhibition as a potent enhancer of MNK blockade. Pharmacological DNMT inhibition enhanced the cytotoxic effects of siRNA-mediated MNK1 knockdown, while concurrent pharmacological inhibition of MNKs and DNMT resulted in greater suppression of neurosphere growth and robust induction of apoptotic responses in GSCs. Conclusions: These findings identify dual MNK and DNMT inhibition as a promising combinatorial strategy that effectively triggers antineoplastic effects in GBM cells and GSCs. Full article

Alcohol-associated liver cirrhosis (ALC) lacks aetiology-specific molecular diagnostic biomarkers. This study aims to quantify the association between alcohol and cirrhosis risk, and to identify transcriptomic diagnostic biomarkers and candidate therapeutics. Methods: Survey-weighted logistic regression was applied to 17,007 adults from NHANES (2017–2023) to quantify alcohol-cirrhosis associations. ALC transcriptomic data from four GEO datasets were analysed using weighted gene co-expression network analysis (WGCNA) and three parallel machine learning algorithms (LASSO, Random Forest, SVM-RFE). External validation was performed in an independent cohort of 93 samples. Candidate therapeutics were identified via drug signature database querying and validated by molecular docking. Heavy drinking conferred a 5.14-fold increased cirrhosis risk (95% CI: 2.60–10.20, p < 0.001). Transcriptomic analysis revealed global downregulation of long non-coding RNAs (with 91.7% of dysregulated lncRNAs being suppressed). A five-gene diagnostic signature (IL1B, CCL3, LUM, SPP1, ITGA6), specifically developed to distinguish ALC from histologically normal liver tissue, achieved an area under the receiver operating characteristic curve (AUC) of 0.824 in an external validation cohort. Immune infiltration analysis uncovered global contraction of macrophage-associated transcriptomic signatures across M0, M1, and M2 subtypes, inversely correlated with fibrotic hub gene upregulation. Fluvastatin and honokiol were identified as candidate therapeutic agents, with strong binding affinities to IL1B and CCL3, respectively. This study confirms a dose-dependent alcohol-cirrhosis association and establishes a five-gene diagnostic signature (distinguishing ALC from normal liver tissue) alongside candidate therapeutics, warranting prospective clinical validation. The identified tissue-derived signature and therapeutic candidates provide a foundation for future ALC-specific diagnostic and therapeutic strategies; their translation into a non-invasive (e.g., blood-based) assay will require dedicated validation in circulating samples. Full article

Modeling thermal environments across scales is crucial for climate-adaptive design and energy management. Traditional numerical methods (e.g., CFD) offer high accuracy and physical consistency, but they are computationally expensive. In contrast, purely data-driven models, though efficient, lack physical consistency and generalization capability. This review systematically examines Physics-Informed Neural Networks (PINNs), a hybrid paradigm in which physical prior knowledge is embedded directly into the neural network training process. A structured keyword search of the Web of Science Core Collection was performed, and 94 peer-reviewed journal articles were analyzed. The evolution from numerical simulations and data-driven surrogate models to PINNs is outlined. PINN methods are classified according to the stage at which physical prior information is integrated (i.e., dataset development, model construction, or loss function formulation). Current research remains heavily focused on loss function constraints, whereas systematic integration into data augmentation and model construction remains limited. Application domains span indoor environments, outdoor environments, and building systems, with each domain exhibiting unique prior integration strategies tailored to specific problems. Future PINN modeling should evolve toward multi-physics coupling, adaptive loss balancing, cross-scenario transfer learning, and unified evaluation benchmarks. PINNs in this field are promising but remain at an early stage, especially for complex urban-scale deployment. This review synthesizes existing research around the three stages of dataset development, model construction, and loss function formulation, summarizes the prior integration strategies adopted in the domain of building thermal environments, and provides a practical workflow for embedding physical prior knowledge at different stages of model development. Full article

Laser powder bed fusion (LPBF) is a widely utilized metal additive manufacturing (AM) process for fabricating intricate geometries with high mechanical strength. However, achieving defect-free parts remains challenging due to complex thermodynamics and process variability. Component quality is primarily determined by mel-pool morphology, which depends on key process parameters such as laser power, scan speed, and layer thickness. Improper parameter selection causes defects like porosity (keyhole and lack of fusion), balling, and residual stresses, compromising structural integrity. Optimizing these parameters is crucial but difficult due to the multi-scale, multi-physics nature of the process, which traditionally relies on costly, time-intensive experimental trials. We present results from a data-driven approach using machine learning (ML) models to predict and optimize LPBF melt-pool characteristics, reducing reliance on trial-and-error experimentation. We find that laser power and scan speed predominantly influence the melt-pool formation. Higher scan speeds produce more favorable melt pools, whereas excessive laser power at low scan speeds leads to deep keyhole defects. To predict and classify melt pools efficiently, several ML models are deployed, including logistic regression, decision trees, ensemble learning, and fully connected neural networks. The standard neural network achieved the highest cross-validated macro-F1 score of 0.978 ± 0.014, while the weighted neural network achieved the highest recall for the rare optimal melt-pool class, 0.967 ± 0.050. These findings show that class-weighted learning provides a recall-oriented strategy for identifying suitable LPBF process windows, while avoiding overreliance on single train-test split performance. The findings underscore the effectiveness of ML in accurately classifying LPBF melt pools to rapidly identify optimal process parameters. Full article

Endoplasmic reticulum (ER) stress is a common state of cellular adversity experienced by tumor cells under unfavorable conditions such as hypoxia, nutrient deprivation, and oncogene activation. As the most conserved signaling branch of the unfolded protein response (UPR), the inositol-requiring enzyme 1α (IRE1α)- X-box-binding protein 1 (XBP1) pathway plays a central role in sustaining tumor cell survival, driving malignant progression, and remodeling the tumor microenvironment (TME). XBP1, the terminal transcription factor of this pathway, finely orchestrates tumor cell fate through both its canonical and non-canonical functions. This review systematically summarizes the dual mechanisms of XBP1 in cancer: within cancer cells, XBP1 promotes proliferation, metastasis, and chemoresistance via metabolic reprogramming, anti-apoptotic proteins, and DNA repair; within immune cells (macrophages, dendritic cells, T cells), XBP1 fosters an immunosuppressive microenvironment, while also modulating cancer-associated fibroblasts, endothelial cells, and osteoclasts. Despite its therapeutic promise, several major unresolved questions remain, including the precise molecular switch governing XBP1’s pro-tumorigenic versus anti-tumorigenic functions, the functional divergence between XBP1u and XBP1s isoforms in different cellular contexts, and the lack of reliable predictive biomarkers for patient stratification. Key translational challenges involve the on-target toxicity of systemic XBP1/IRE1α inhibition due to its essential roles in normal tissues, the cell-type-specific and context-dependent effects that complicate therapeutic outcomes, and the limited selectivity and off-target effects of current inhibitors, as well as compensatory activation of other UPR branches that may drive adaptive resistance. Finally, this review discusses XBP1-targeted therapeutic strategies, including small-molecule inhibitors, nucleic acid-based drugs, immunotherapeutic combination approaches, and XBP1-based tumor vaccines, and provides perspectives on future research directions, aiming to establish a theoretical foundation for the development of more effective and precise XBP1-targeted therapies for tumorigenesis and cancer progression. Full article

Background/Objectives: Iatrogenic nerve injury is a significant challenge in urologic surgery, with radical prostatectomy posing a high risk due to complex pelvic neural anatomy. Despite advances in robotic-assisted and nerve-sparing techniques, postoperative urinary incontinence and erectile dysfunction remain prevalent, adversely affecting patients’ quality of life and imposing substantial healthcare costs. Methods: A narrative review was conducted using PubMed, MEDLINE, and the Cochrane Library (searches through February 2026) for studies on radical prostatectomy epidemiology, mechanisms of nerve injury, functional outcomes, and economic burden. Emerging intraoperative fluorescence imaging technologies, surgical strategies to mitigate iatrogenic nerve injuries, and the financial costs of post-prostatectomy complications were assessed. Results: Robotic-assisted radical prostatectomy now accounts for >80% of procedures in the United States, and has been associated in observational studies with improved early recovery of erectile function compared with open and laparoscopic approaches. However, the lack of real-time nerve visualization remains a limiting factor. Recent milestones (January 2026) include the Food and Drug Administration Investigational New Drug clearance for the nerve-specific fluorophore LGW16-03 (NerveTrace), which enables real-time identification of sub-millimeter nerve branches, and the 510(k) premarket clearance of Dendrite imaging (November 2025). Conclusions: Enhanced intraoperative nerve discrimination via molecularly targeted imaging has the potential to reduce iatrogenic complications and improve long-term functional and economic outcomes in prostate cancer surgery, although these benefits have yet to be demonstrated in prospective clinical and health-economic studies. Full article

DNA vaccines have emerged as a promising immunization platform, offering key advantages over conventional vaccine approaches, including superior stability, a favorable safety profile, rapid and flexible antigen design, and scalable manufacturing. However, their clinical efficacy has remained limited, primarily due to inefficient cellular uptake, poor endosomal escape, and degradation of the plasmid DNA within host cells. Recent advances have highlighted minicircle DNA (mcDNA) as a next-generation alternative to conventional plasmid vectors. mcDNA constructs are compact, backbone-free episomal vectors containing only the expression cassette, including the promoter, transgene, and polyadenylation signal, while lacking bacterial sequences such as antibiotic resistance genes and origins of replication. This reduced vector size reduced vector-driven innate immune activation and susceptibility to epigenetic silencing, thereby improving transfection efficiency and supporting more sustained transgene expression in both dividing and non-dividing cells. This review provides a comprehensive overview of mcDNA technology in the context of vaccine development, discussing its structural design and production principles, mechanistic advantages over conventional plasmid DNA, and current applications across infectious disease and cancer vaccine platforms. In addition, we explore recent delivery strategies to enhance mcDNA transfection and immunogenicity, summarize existing limitations that hinder translation into applications, and outline future directions to optimize mcDNA-based vaccine technologies. Full article

The primary objective of this article is to examine the organizational, economic, and sustainability-related implications of implementing artificial intelligence (AI) systems in small and medium-sized enterprises (SMEs) in Poland. The study combines a survey of 112 SMEs in the Kuyavian–Pomeranian region, including 70 AI-using firms, with 13 in-depth interviews with managers. The quantitative analysis applies logit models to identify determinants of perceived AI effects on internal processes: working time and workload reduction, automation, cost effects, and creativity. The qualitative component explains how AI is adopted and embedded in business practice. The results show that AI adoption in SMEs is increasingly common but remains uneven and mostly operational. The strongest effects concern workload reduction and time efficiency, particularly in service firms and where AI is used intensively. Advanced AI adoption increases the probability of perceiving workload and cost-related effects. However, these effects should not be interpreted simply as direct cost reduction. Rather, AI improves productivity and work capacity while creating new costs related to paid tools, data preparation, integration, output verification, and governance. The interviews show that AI implementation follows a staged path: from curiosity-driven experimentation, through cognitive work augmentation, to workflow integration and, in selected cases, AI-enabled business model innovation. The transition from ad hoc use to strategic implementation depends less on firm size alone and more on process maturity, capabilities, and data readiness. Barriers also change with maturity: early-stage firms face a lack of knowledge, time, and clear use cases, whereas advanced users encounter data quality, hallucinations, security, integration, and governance problems. The study finds that sustainability considerations, particularly environmental impacts and ESG-related implications of AI, remain largely unperceived in SME decision-making. Entrepreneurs primarily interpret sustainability through the lenses of organizational resilience, long-term competitiveness, adaptability, and responsible digital transformation rather than through formal environmental metrics. The findings suggest that SME managers should implement AI gradually, link adoption to measurable process-level outcomes, and invest in AI literacy and governance. They should also integrate responsible AI principles into organizational strategy to support sustainable digital transformation. The study contributes to the literature by showing that AI adoption in SMEs should be understood not only as a productivity-enhancing process but also as a broader organizational transition shaping long-term sustainability and resilience. Full article

Radiation-based treatments have emerged as important environmental and postharvest regulatory tools for improving the quality of edible mushrooms. Visible light, ultraviolet (UV) radiation, gamma irradiation, and pulsed-light treatments influence mushroom growth, morphogenesis, nutrient accumulation, antioxidant capacity, and storage performance through distinct physiological and molecular mechanisms. However, current findings remain fragmented, and a comprehensive synthesis of their regulatory effects and underlying mechanisms is lacking. This systematic review was conducted following the PRISMA 2020 framework. A structured literature search was performed in the Web of Science, PubMed, and CNKI databases. After screening and eligibility assessment, 111 studies were included in the qualitative synthesis. The available evidence indicates that radiation-based treatments exert stage-dependent and species-specific effects on edible mushrooms. Visible light primarily regulates morphogenesis through photoreceptor-mediated signaling pathways, whereas UV radiation promotes vitamin D₂ biosynthesis and antioxidant accumulation through photochemical and reactive oxygen species (ROS)-related mechanisms. Gamma irradiation and pulsed-light treatments are mainly applied during postharvest handling to suppress microbial contamination, delay browning and senescence, and extend shelf life. Based on the available evidence, a unified mechanistic framework linking signal perception, ROS regulation, transcriptional reprogramming, metabolic responses, and quality formation is proposed. Despite these advances, substantial challenges remain, including limited mechanistic understanding, insufficient integration of multi-omics evidence, lack of standardized treatment protocols, and difficulties in industrial-scale implementation. Future research should focus on multi-radiation synergistic strategies, precision environmental regulation, and intelligent cultivation systems. Overall, this review provides a comprehensive synthesis of current evidence regarding radiation-mediated quality regulation in edible mushrooms and offers a theoretical basis for optimizing mushroom production and developing sustainable postharvest preservation technologies. Full article

In the era of precision medicine, managing non-small cell lung cancer (NSCLC) requires pathological confirmation, accurate nodal staging, and comprehensive biomarker profiling performed rapidly and concurrently. In the 9th edition of the TNM classification, the N2 category is subdivided into single-station (N2a) and multistation (N2b) subcategories, highlighting the clinical importance of precise mediastinal staging. This refinement necessitates systematic nodal evaluation using minimally invasive modalities such as endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) and endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) to appropriately stratify patients for surgery, neoadjuvant therapy, and definitive chemoradiotherapy. Concurrently, although N1 has not been formally subclassified because of the lack of standardized clinical diagnostic criteria, the increasing use of sublobar resection for early-stage NSCLC requires more precise hilar and intrapulmonary nodal assessments, which can potentially be facilitated by emerging technologies such as thin convex-probe EBUS. Concurrently, adequate tissue acquisition is essential for timely biomarker testing. Before administering neoadjuvant immune checkpoint inhibitors, actionable driver alterations, such as epidermal growth factor receptor (EGFR) mutations and anaplastic lymphoma kinase (ALK) rearrangements, must be identified to select appropriate treatment and prevent severe sequential toxicities associated with subsequent targeted therapies. This review highlights the indispensable role of endoscopic nodal staging and multidisciplinary collaboration in adapting to the updated TNM classification and optimizing personalized treatment strategies for patients with NSCLC. Full article

As calibration errors have a direct impact on epipolar consistency, rectification accuracy, and metric 3D reconstruction performance, stereo camera calibration is a fundamental requirement for high-accuracy 3D modeling and reliable digital twin data acquisition. Because current calibration workflows (based on pairwise calibration methods) lack systematic data-quality checks mechanisms, there is a clear need for more robust data selection strategies. The novelty of the approach consists in the development of a new outlier-aware stereo calibration algorithm (OutAw) that introduces a unified multi-stage approach that integrates hard geometric selection, candidate subset generation, multi-criterion ranking, bootstrap stability analysis, and triangulation assessment into a comprehensive and systematic calibration framework. Unlike conventional approaches, OutAw (through its mechanism of detecting and rejecting inconsistent pairs) redefines the calibration strategy from arbitrary to criterion-based data selection. Also, the proposed algorithm is compared with BSC (a baseline OpenCV all-pairs calibration algorithm) and InterFil (an intermediate filtered variant) using 49 stereo pairs (at 1280 × 720 resolution) captured using a planar checkerboard. OutAw algorithm achieved (using only nine image pairs) superior results (epipolar error 0.5119 px, stereo RMS 0.7666 px) to the BSC ones (epipolar error 1.3687 px, stereo RMS 1.9385 px), representing statistically significant improvements (60.5%, respectively 62.3%). OutAw geometric consistency was validated by triangulation-based metrics (square-length standard deviation 0.1140 mm and square absolute error 0.1097 mm). Contamination analysis revealed that as the outlier rate increases, the calibration process degrades progressively. Also, the results obtained highlight that geometric quality-driven image selection is critical for achieving a reliable stereo calibration for DT applications. Full article

CLEC3B (C-type lectin domain family 3 member B), also known as tetranectin (TN), is a secreted trimeric protein containing a C-type lectin-like domain (CTLD). Located on chromosome 3p21.31. CLEC3B maintains organismal homeostasis through roles in immune regulation, angiogenesis, and musculoskeletal biology. Genetic studies demonstrate that CLEC3B deficiency impairs tissue repair, bone mineralization, and fibrinolytic balance. Altered CLEC3B expression is linked to cardiovascular disease progression, autoimmune susceptibility, and cancer prognosis. This review synthesizes CLEC3B’s biological functions and evaluates its translational potential: circulating CLEC3B as a prognostic and diagnostic biomarker; tissue-resident CLEC3B as a predictive marker for therapeutic response; and CLEC3B-related pathways as candidate therapeutic targets for potential amenable to replacement or inhibition strategies. We identify critical research gaps to guide future investigations, including limited structural data, ambiguous glycan specificity, incomplete proteolytic network mapping, and lack of validated disease models. Collectively, these gaps currently preclude definitive therapeutic claims. Full article

Background: Contraceptive counseling is a critical component of reproductive healthcare, directly influencing method uptake, continuation, and user satisfaction. While global health guidelines increasingly emphasize person-centered, rights-based approaches to counseling, wide variations in practice persist, with significant implications for equity and autonomy. Objective: This narrative review aims to synthesize current evidence on the strengths, limitations, and future opportunities of contraceptive counseling within person-centered care frameworks, with particular attention to adolescents and other populations facing structural or sociocultural barriers to equitable care. Methods: A comprehensive literature search was conducted across six indexed databases (PubMed, Scopus, Embase, Web of Science, CINAHL, and PsycINFO) for peer-reviewed articles published between January 2010 and April 2025. Eligible studies included original quantitative, qualitative, and mixed-methods research examining contraceptive counseling practices, user experiences, provider–client communication, counseling interventions, or implementation strategies in reproductive healthcare settings. Results: Emerging strengths in the field include the increasing adoption of shared decision-making, motivational interviewing, and culturally tailored counseling approaches, all of which contribute to improved client satisfaction and method adherence. Digital tools and mHealth platforms have expanded the reach of counseling and show promise in supplementing in-person care. However, significant gaps remain. Provider bias, limited training, communication barriers, and a lack of socio-cultural tailoring frequently undermine the quality of care, especially for adolescents, migrants, women with disabilities, and socially vulnerable populations. Ethical challenges—such as coercion, inadequate informed consent, and structural inequities—persist in many healthcare settings. Moreover, contraceptive counseling is often treated as a one-time event rather than an ongoing, adaptive process. Conclusions: To maximize its impact, contraceptive counseling must be reframed as a longitudinal, relational, and ethically grounded practice. Future efforts should prioritize the development of structured training programs, integration into broader health services, and qualitative research that centers patient experiences. Embedding counseling within reproductive justice frameworks will be essential for advancing equity and autonomy. High-quality contraceptive counseling, when informed by evidence and empathy, is a strategic tool for reproductive empowerment and public health advancement. Full article

Plant essential oils are widely utilized as natural preservatives, flavoring agents, and nutritional supplements owing to their remarkable antibacterial, antioxidant, and aroma-enhancing properties. However, their low abundance in plant matrices, together with the compositional complexity and thermal sensitivity of volatile constituents, poses significant challenges for efficient extraction and purification. In recent years, membrane separation technology has emerged as a promising green strategy for the extraction, purification, and concentration of plant essential oils. Membrane-based processes, including microfiltration, ultrafiltration, nanofiltration, reverse osmosis, and pervaporation, enable selective separation under mild operating conditions based on differences in molecular size, polarity, and diffusivity. Compared with conventional thermal- and solvent-based methods, membrane processes offer lower energy consumption, reduced solvent usage, and superior retention of thermolabile bioactive compounds and natural aroma profiles. Moreover, recent advances in membrane materials and surface modification strategies have significantly improved membrane selectivity, permeability, and fouling resistance, thereby enhancing process stability and industrial applicability. This review systematically summarizes the theoretical principles, separation mechanisms, membrane classifications, and recent applications of membrane technologies in plant essential oil processing. Based on a comparative analysis of more than 120 published studies, the performance of different membrane processes is evaluated in terms of flux, selectivity, energy consumption, and product quality. Particular attention is given to current challenges, including the lack of standardized performance metrics and comprehensive techno-economic assessments. Recent advances in membrane materials and surface modification strategies, together with future research directions and industrial prospects, are also discussed. This review provides valuable guidance for membrane selection, process optimization, and sustainable industrial implementation in plant essential oil extraction and purification. Full article

Lactic acid bacteria (LAB), particularly members of the genus Lactobacillus, have emerged as promising biological agents with antimicrobial and anti-biofilm properties. While numerous individual studies have reported their inhibitory effects against pathogenic microorganisms, a systematic understanding that integrates their functional components, molecular mechanisms, and material-based applications remains lacking. In this review, we provide a comprehensive and component-oriented overview of LAB-mediated antimicrobial strategies. We first summarize secreted factors, including organic acids, bacteriocins, hydrogen peroxide, and extracellular vesicles, which collectively contribute to direct pathogen inhibition and environmental modulation. We then discuss cell-associated components such as surface-layer proteins and exopolysaccharides, highlighting their roles in adhesion interference and competitive exclusion. In addition, we examine whole-cell effects, including niche competition, quorum sensing disruption, and host immune modulation. Importantly, we place particular emphasis on the anti-biofilm activity of lactobacilli, detailing mechanisms involved in the prevention of the pathogen initial adhesion, disruption of extracellular polymeric substance matrices, and destabilization of mature biofilms. Finally, we explore emerging strategies that integrate lactobacilli with biomaterials, particularly hydrogel-based systems, to achieve controlled delivery, enhanced stability, and sustained antimicrobial activity. These biohybrid approaches represent a promising direction for the development of next-generation antimicrobial materials. These findings support the concept of LAB-based living antimicrobial materials as a next-generation strategy to combat biofilm-associated infections. Overall, this review aims to bridge the gap between molecular functions and translational applications of lactobacilli, providing new insights into its potential as a versatile platform for antimicrobial and anti-biofilm interventions. Full article