Search Results (7,810)

High levels of low-density lipoprotein cholesterol (LDL-c) have been recognized as the main causal factor of atherosclerotic cardiovascular disease (ASCVD) and are influenced by both genetic and environmental factors. Among genetic determinants, Familial Hypercholesterolemia (FH) is the most common monogenic disorder, caused by rare high-impact variants in genes involved in LDL uptake. Other monogenic causes of hypercholesterolemia include sitosterolemia, cerebrotendinous xanthomatosis and lysosomal acid lipase deficiency (LALD). However, monogenic disorders only account for a small proportion of inherited hypercholesterolemia. In many individuals, increased LDL-c levels are caused by the contemporary presence of different single-nucleotide polymorphisms (SNPs) with a moderate/low impact. These SNPs could be summarized through polygenic risk scores (PRS) that attribute relative weight to each of these. Another genetic determinant of hypercholesterolemic phenotypes is high levels of lipoprotein(a)—Lp(a). Lp(a) is an LDL particle modified by the binding of apolipoprotein(a)—apo(a)—which represents an independent risk factor for ASCVD. Lp(a) levels are mainly genetically determined by variation in the number of kringle IV type 2 (K-IV₂) repeats, as well as by several SNPs, and remain stable throughout life. The aim of this narrative review is to report an updated overview of the genetic mechanisms underlying hypercholesterolemia, including monogenic disorders, PRS and Lp(a), focusing on their potential repercussion in clinical practice by the integration into cardiovascular risk stratification beyond traditional clinical assessment. This integration could lead to a more comprehensive and individualized approach to cardiovascular prevention, with emerging perspectives including the possible use of artificial intelligence (AI). Full article

Transportation hub cities serve as pivotal nodes within integrated transport systems. This study reveals the corridor-oriented characteristics of comprehensive transportation system, confirms the progress of its transportation hub city development, and identifies future improvement directions based on diagnostic evaluation, taking Gansu Province, China as the research subject. To address hierarchical differentiation and structural constraints in the development of integrated transportation hubs, this study develops an evaluation framework integrating the entropy-weighted TOPSIS method, a coupling coordination model, and indicator-based diagnostic analysis. This framework was applied to 14 prefecture-level cities and autonomous prefectures in Gansu, classifying them into four hub tiers according to the comprehensive evaluation index. The results reveal a pronounced hierarchical and corridor-oriented spatial structure: Lanzhou is identified as the only Tier 1 core hub, five cities are classified as Tier 2 backbone hubs, seven cities and prefectures as Tier 3 general hubs, and Pingliang as Tier 4 terminal hub. Lanzhou exhibits the highest development level, with a comprehensive evaluation index of 0.9640, which is substantially higher than the provincial mean of 0.3867, but its radiation-driving capacity still needs to be strengthened. In terms of subsystem coordination, Lanzhou reaches the primary coordination stage with a coupling coordination degree of 0.532, while Jiuquan, Jiayuguan, and Tianshui are classified into the near-coordination stage with D values of 0.353, 0.351, and 0.321, respectively; the remaining ten units are classified as uncoordinated relatively. Based on the combined perspectives of development level and subsystem coordination, the study identifies future development directions for hub operational organization, multimodal transport integration, feeder connectivity, and industry-logistics coupling. The findings reveal the corridor-oriented characteristics and development progress of Gansu’s transportation hub system, highlight the analytical value of distinguishing hub development level from subsystem coordination, and provide empirical evidence for understanding hierarchical and functional differentiation in corridor-oriented inland regions. Full article

Permutation flow shop scheduling is an important production planning problem handled in different contexts. Just-in-time measures have been significant in the optimization of real problems and one is specifically addressed here: the total earliness and tardiness of jobs. The most used approach in the literature to mathematically express this measure is to sum them up using unit weights thus obtainning a mono-objective function. In this paper it is shown that this is a simplification of a problem that is inherently multi-objective, highlighting how a more comprehensive approach can better support decision-making. A bi-objective mathematical optimization model and tools capable of analyzing the mono-objective solution within the multi-objective perspective are proposed. A computational study to analyze the benefits and difficulties of the solution using the bi-objective approach is presented. The results show that for large-scale instances in which the tardiness factor is small, the conflict between the objectives of minimizing the total earliness and minimizing the total tardiness of jobs increases significantly. Specifically, the mono-objective solution is unbalanced in 50.00% of the analyzed instance structures. However, in 48.12% of the instances, alternative Pareto-optimal trade-offs can be achieved with zero increase to the mono-objective optimal value. Therefore, the multi-objective approach has a greater potential to support decision-makers. Furthermore, we show that the choice of the solution method must be carefully considered, since the Pareto frontier associated with most instances has many non-supported points, representing up to 66.71% of the non-dominated set. Full article

The rapid emergence of antimicrobial resistance has intensified the need for advanced therapeutic platforms capable of improving the efficacy, stability, and targeted delivery of antimicrobial agents. Electrospun nanofibers have emerged as highly promising materials for biomedical applications due to their large surface area, high porosity, tunable morphology, and ability to incorporate a broad range of bioactive compounds. This review provides a comprehensive overview of the design, fabrication, and biomedical applications of electrospun bioactive nanofibers functionalized with antimicrobial drugs. It presents the main nanofiber fabrication techniques, with particular emphasis on electrospinning and the influence of solution, process, and environmental parameters on fiber morphology and drug-loading efficiency. Natural, synthetic, and hybrid polymer systems commonly employed in electrospun antimicrobial nanofibers are analyzed in relation to their physicochemical properties, biocompatibility, and therapeutic performance. In addition, the review highlights different drug incorporation strategies, including encapsulation, immobilization, and surface coating, as well as the mechanisms of action of antimicrobial agents. Recent advances in nanotechnology-based antimicrobial systems and their role in overcoming analytical, biopharmaceutical, and drug-delivery limitations are also examined. Furthermore, the review addresses current challenges related to scalability, reproducibility, stability, and clinical translation of electrospun nanofibers. Finally, future perspectives focusing on multifunctional, stimuli-responsive, and personalized antimicrobial nanofiber systems are discussed as promising directions for combating bacterial infections and reducing the global burden of antimicrobial resistance. Full article

Aqueous zinc–manganese dioxide (Zn–MnO₂) batteries are undergoing a paradigm shift from traditional ion-insertion mechanisms to a reversible deposition/dissolution process. By leveraging a two-electron transfer (Mn²⁺/MnO₂), this electrolytic system achieves a high theoretical capacity of 616 mAh g⁻¹ and a theoretical operating voltage of 1.99 V. However, the accumulation of dead Mn, electrically isolated inactive phases, and dynamic interfacial pH fluctuations remain critical barriers to cycle life and practical energy density. This review systematizes a trinitarian strategy to overcome these bottlenecks, focusing on interfacial engineering, redox mediator-assisted recovery, and advanced electrode architectures. We evaluate how anion engineering and pH-buffering stabilize reaction pathways, and how diverse mediators (e.g., halogens, metal ions, and organic molecules) chemically rescue inactive manganese. Furthermore, we examine the integration of 3D carbon networks and low-cost hybrid electrodes to sustain high-areal-capacity deposition. To elucidate these complex mechanisms, we highlight multiscale analytical approaches combining synchrotron X-ray techniques and density functional theory (DFT). Finally, we outline a roadmap for applications ranging from grid-scale flow batteries to flexible wearable electronics. This work provides a comprehensive perspective on realizing sustainable, safe, and high-performance zinc-based energy storage. Full article

Aqueous magnesium-ion batteries (AMIBs) are promising for next-generation energy storage technologies due to their high safety, low cost, high theoretical energy density, and environmental friendliness. In particular, manganese-based oxides have attracted much attention due to the abundant resources, high theoretical capacity, and environmental friendliness. This paper provides a comprehensive overview of manganese-based oxide cathode materials for AMIBs, including the crystal structure, electrochemical performance, optimization strategies, and electrode reaction mechanisms. Meanwhile, recent research progress of AMIB full cells based on Mn-based oxide cathode materials is summarized. Finally, the challenges and future perspectives of Mn-based oxide cathode materials for AMIBs are discussed. This review will provide a valuable reference and source of inspiration for future research of manganese-based oxide cathode materials for AMIBs. Full article

Flavonoids are a structurally diverse class of plant-derived polyphenolic compounds widely recognized for their pleiotropic biological activities, including antioxidant, anti-inflammatory, and anticancer effects. In oncology, these compounds have demonstrated the ability to modulate key signaling pathways involved in cell proliferation, apoptosis, angiogenesis, and metastasis, highlighting their potential as multitarget therapeutic agents. However, their clinical translation remains significantly limited by unfavorable pharmacokinetic properties, such as poor aqueous solubility, extensive first-pass metabolism, rapid systemic clearance, and consequently low oral bioavailability. In this context, nanotechnology has emerged as a promising strategy to overcome these limitations. This review provides a comprehensive and critical analysis of current nanocarrier-based delivery systems for flavonoids, including polymeric nanoparticles, lipid-based nanocarriers (liposomes, solid lipid nanoparticles, and nanoemulsions), micelles, and cyclodextrin complexes, emphasizing their role in improving drug stability, enhancing cellular uptake, and enabling targeted delivery to tumor tissues through both passive mechanisms, such as the enhanced permeability and retention effect, and active targeting approaches. In addition, recent in vitro and in vivo studies demonstrating the superior antitumor efficacy of nanoencapsulated flavonoids compared to free compounds are discussed. Finally, the major translational challenges, safety considerations, and future perspectives for the clinical application of flavonoid-based nanomedicines in cancer therapy are highlighted. Full article

Intestinal fungal overgrowth (IFO) is an increasingly recognized yet underexplored component of gut dysbiosis with potential implications for gastrointestinal and systemic disease. While bacterial microbiota have historically garnered research attention, recent advances in sequencing technologies have highlighted the importance of the gut mycobiome in maintaining intestinal homeostasis. Disruption of fungal–bacterial balance, particularly involving Candida albicans, C. tropicalis, and C. glabrata, may contribute to symptom generation through immune activation, epithelial barrier dysfunction, biofilm formation, and the production of toxic metabolites such as acetaldehyde and candidalysin. Emerging clinical evidence suggests that IFO is associated with persistent gastrointestinal symptoms, including bloating, abdominal discomfort, and altered bowel habits, particularly in patients who do not respond to conventional therapies targeting bacterial overgrowth. Furthermore, fungal dysbiosis involving Malassezia restricta and Saccharomyces cerevisiae has been associated with inflammatory bowel disease, metabolic disorders, and systemic immune dysregulation; however, the nature and directionality of these relationships remain incompletely understood. Despite increasing recognition, the diagnosis of IFO remains challenging due to a lack of standardized criteria and validated non-invasive tools. Therapeutic strategies, including antifungal agents such as fluconazole and nystatin, as well as microbiome-targeted interventions, show promise but require further validation. This review provides a comprehensive synthesis of current evidence regarding the epidemiology, pathophysiology, clinical manifestations, diagnostic challenges, and therapeutic implications of IFO, with particular emphasis on species-specific mechanisms. Recognition of the intestinal mycobiome as a potentially important component of gut health may provide new perspectives for understanding gastrointestinal disorders and inform future precision medicine approaches. Full article

Runway systems serve as the critical interface between airports and terminal airspace, and their efficient operation is essential for balancing air traffic demand and airport capacity. With the continuous growth of air traffic, intelligent runway demand and capacity management has become increasingly important for mitigating congestion and delays. This paper presents a comprehensive review of runway capacity–demand management from both supply-side and demand-side perspectives. On the supply side, runway configuration selection is reviewed, including runway configuration capacity envelopes, influencing factors, and existing optimization methodologies, such as prescriptive models, descriptive models, and reinforcement learning approaches. On the demand side, flight runway sequencing for arrivals, departures, and integrated arrival–departure operations is systematically analyzed. Problem analogies, operational characteristics, optimization objectives, and solution algorithms are discussed in detail. A critical comparison of existing methodologies is conducted from the perspectives of solution quality, real-time capability, human interpretability, technology readiness, trust requirements, and human–AI collaboration. Finally, future research directions are identified, including integrated runway management, multi-airport coordination, uncertainty-aware optimization, human–AI decision support, AI-enabled runway management, and integrated manned–unmanned operations. The review provides a reference for researchers, airport operators, air navigation service providers, and decision-support system developers seeking to improve runway operational efficiency and safety. Full article

Plant-derived exosome-like nanoparticles (PELNs), a subset of extracellular vesicle (EV) secreted by plant cells, have emerged as revolutionary biomaterial with broad applications in biomedicine, agriculture, and nanotechnology. Structurally, PELNs feature a phospholipid bilayer homologous to plant cell membranes, encapsulating bioactive components such as proteins, nucleic acids, lipids, and secondary metabolites. The native structure of PELNs endows them with enhanced bioavailability, reduced immunogenicity, and improved barrier penetration for precise tissue delivery. Recent studies highlight the cross-kingdom therapeutic potential of PELNs in mammals, including antitumor, anti-inflammatory, tissue repair, immunomodulation and so on. This review comprehensively summarized recent advancements in PELN research, including innovative isolation techniques, molecular characterization, their roles in drug delivery and disease therapy. We also discussed challenges in standardization, scalability, and regulatory frameworks which could provide future perspectives for translating PELNs into clinical and industrial applications. Full article

Solar-driven interfacial evaporation (SIE) has emerged as a transformative, off-grid technology that confines heat at the air–liquid interface, enabling high-efficiency vapor generation for decentralized water purification. Here, we present a comprehensive and critical review of the field, charting its evolution from fundamental photothermal principles to integrated multifunctional systems. We first elucidate the thermodynamics of interfacial heat localization and the resultant enhancement in evaporation efficiency. We then systematically analyze material innovation strategies—including broadband-absorbing photothermal agents and tailored evaporator architectures—designed to overcome persistent challenges such as salt crystallization, fouling, and thermal losses. Moving beyond freshwater production, we highlight emerging pathways for extending SIE platforms toward water–energy cogeneration, selective resource recovery, and zero-liquid-discharge wastewater treatment. We further identify and objectively assess the key bottlenecks that currently hinder the transition from laboratory-scale prototypes to real-world deployment, with a focus on long-term material robustness under harsh environments, adaptability to fluctuating water chemistries, and techno-economic viability. Finally, we outline forward-looking research directions, including stimulus-responsive smart evaporators, elucidation of multi-field coupling mechanisms, and the establishment of standardized performance evaluation protocols. This review aims to provide both a tutorial for newcomers and a critical assessment for experienced researchers, offering a balanced perspective on the current state-of-the-art and a roadmap for translating SIE from academic research into sustainable, impactful technologies. Full article

Recent advancements in Vision-Language Models (VLMs) have enabled robotic systems to leverage model-based understanding and reasoning over visual and linguistic inputs, offering a promising approach for interpreting user intent in human–robot interaction (HRI). In particular, deictic expressions commonly used in object handovers, such as “take this” and “give me that”, cannot be fully interpreted through language alone and require a comprehensive understanding of the speaker’s perspective and the environment. This study proposes a prompt-driven vision-language framework for deictic interpretation in human–robot handover. The system integrates a pre-trained VLM with a hierarchical prompt that decomposes reasoning into intent classification, spatio-temporal grounding, and output self-validation, enabling accurate identification of target objects and goal locations without model fine-tuning. Experimental results demonstrate 100% command interpretation accuracy across multiple interaction scenarios, including pick-and-place tasks, robot-to-human and human-to-robot handovers, and temporal deictic commands. Notably, the system operates under a prompt–command language mismatch, accurately interpreting Korean commands while being guided by English-based prompts. Analysis across progressive system configurations further demonstrates that structured prompting plays a critical role in reasoning performance. These results highlight the effectiveness of a prompt-driven approach for deictic interpretation and spatio-temporal grounding, providing a practical training-free framework for HRI. Full article

Head and neck squamous cell carcinoma (HNSCC) remains one of the most aggressive and heterogeneous malignancies worldwide, characterized by high rates of locoregional recurrence, metastatic dissemination, and therapeutic resistance. Angiogenesis plays a central role in tumor progression by supporting vascular remodeling, hypoxia adaptation, invasion, immune evasion, and metastatic spread. In HNSCC, angiogenic activation is regulated through complex interactions involving hypoxia-inducible factors, vascular endothelial growth factor (VEGF) signaling, stromal remodeling, inflammatory pathways, and epigenetic mechanisms within the tumor microenvironment. Recent evidence has also highlighted the role of non-coding RNAs, particularly microRNAs, and exosome-mediated communication in modulating angiogenic and immune-related signaling pathways. Although antiangiogenic therapies, including monoclonal antibodies and tyrosine kinase inhibitors, have demonstrated biological activity in HNSCC, their clinical efficacy remains limited by tumor heterogeneity, adaptive resistance mechanisms, toxicity, and the lack of validated predictive biomarkers. Several emerging therapeutic strategies are under preclinical or early clinical investigation in HNSCC, including miRNA-based approaches, nanoparticle-assisted delivery systems, vascular normalization concepts, and combinations with immune checkpoint inhibitors; however, robust clinical evidence for most of these strategies remains limited, and their translation to routine practice requires further validation. This review provides a comprehensive overview of the molecular mechanisms regulating angiogenesis in HNSCC and critically discusses current and emerging pharmacological strategies targeting these pathways. Particular emphasis is placed on VEGF/VEGFR signaling, the integration of miRNA and exosome biology, resistance mechanisms, and translational perspectives for biomarker-guided personalized therapy. The novelty of this review lies in the systematic integration of miRNA- and exosome-mediated angiogenic regulation, therapeutic resistance pathways, and precision medicine strategies into a unified pharmacological framework, addressing gaps not fully covered by prior reviews focused primarily on VEGF-targeted agents. Full article

Students’ reading preferences and voices are increasingly relevant for informing teaching practices and strengthening students’ motivation and engagement with reading, thus making their reading experiences meaningful. However, in Chile, there is still little evidence regarding the reading preferences and perspectives of primary school students. Therefore, this study analyzes students’ preferences and perceptions of the texts assigned by their teachers, as well as the pedagogical practices for reading mediation applied across various subjects in the La Araucanía Region of southern Chile. To this end, using a qualitative, multiple-case study design, we conducted 9 discussion groups on reading mediation and discourse genres with 96 students in grades 3–6, each connected to one of 6 outstanding teachers. Thus, we applied an inductive content analysis, constructing categories through initial coding, focused coding, and interpretive analysis, all of which underwent triple review and calibration by team members. The findings show that, in general, students value the support and scaffolding their teachers provide to facilitate reading, comprehension, and participation. However, they express a desire for greater agency in selecting texts and for more opportunities to engage in dialogue around these texts, especially in subjects other than Language Arts. These results highlight the importance of reading mediation across subjects, including student text selection and dialogic interaction, to promote motivation and sustained reading practices in primary education. Full article

Background: Respiratory diseases (RDs), including asthma, COVID-19, chronic obstructive pulmonary disease (COPD), and pneumonia, remain a major global health challenge, contributing substantially to global morbidity and mortality. Conventional diagnosis relies heavily on clinicians’ expertise to interpret respiratory sounds and radiographic images, a process that can be subjective, time-consuming, and prone to inter-observer variability. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled automated diagnostic approaches that can improve the efficiency, consistency, and scalability of respiratory disease detection. However, existing research remains fragmented across different data modalities. Methods: This review systematically analyzes recent studies on AI-based respiratory disease detection using both visual modalities (e.g., chest X-rays, computed tomography (CT) scans, and ultrasound) and audio modalities (e.g., cough and breath sounds). To provide a comprehensive perspective, the reviewed literature is organized using a unified taxonomy that categorizes existing approaches into three main groups: audio-based, visual-based, and audio–visual-based methods. In addition, two conceptual frameworks are proposed to illustrate representative pipelines for audio-based and visual-based respiratory disease classification. Results: The analysis reveals that most existing studies focus on single-modality approaches, while multimodal integration remains relatively underexplored. Only a limited number of studies combine audio and visual data within unified frameworks, primarily due to the scarcity of synchronized multimodal datasets collected from the same patients. The proposed taxonomy and conceptual frameworks provide a structured basis for comparing existing methods, identifying methodological trends, and highlighting key research gaps in multimodal respiratory disease detection. Conclusions: Future research should prioritize the development of multimodal datasets, robust evaluation protocols, and interpretable and lightweight AI models suitable for real-world clinical deployment. Advancing multimodal integration has the potential to significantly enhance the accuracy, reliability, and clinical applicability of AI-driven respiratory disease diagnosis systems. Full article