Search Results (1,965)

Background: Diabetic ketoacidosis (DKA) in patients with kidney failure receiving dialysis presents a formidable clinical challenge. Standard DKA protocols, designed for patients with preserved renal function, often fail in this cohort and can be unsafe when applied without modification. Patients are at risk of iatrogenic fluid overload, dyskalaemia, and hypoglycaemia due to altered insulin kinetics, impaired gluconeogenesis, and the absence of osmotic diuresis. Purpose: This narrative review aims to synthesise current understanding of DKA pathophysiology in dialysis patients, delineate distinct clinical phenotypes, and propose individualised management strategies grounded in physiology-based reasoning, comparative guideline insights, and consensus-supported literature. Methods: We searched PubMed/MEDLINE, Embase, and Google Scholar (January 2004–June 2024) for adult dialysis populations, using terms spanning DKA, kidney failure, insulin kinetics, fluid balance, and cerebral oedema. Reviews, observational cohorts, guidelines, consensus statements, and physiology papers were prioritised; case reports were used selectively for illustration. Evidence was weighted by physiological plausibility and practice relevance. Nephrology-led authors aimed for a pragmatic, safety-first synthesis, seeking and integrating contradictory recommendations. Conclusions: Our findings highlight the critical need for a nuanced approach to fluid management, a tailored insulin strategy that accounts for glucose-insulin decoupling and prolonged insulin half-life, and careful consideration of potassium and acidosis correction. We emphasise the importance of recognising specific volume phenotypes (hypovolaemic, euvolaemic, hypervolaemic) to guide fluid therapy, and advocating the judicious use of variable-rate insulin infusions (‘dry insulin’) to mitigate fluid overload. We also show that service-level factors are critical. Dialysis-specific pathways, interdisciplinary training, and quality improvement metrics can reduce iatrogenic harm. By linking physiology with workflow adaptations, this review provides a physiologically sound, bedside-oriented map for navigating this complex emergency safely and effectively. In doing so, it advances an individualised model of DKA care for dialysis-dependent patients. Full article

Sodium-glucose cotransporter-2 (SGLT2) inhibitors, also known as gliflozins, are a class of antidiabetic agents that act independently of insulin by promoting renal glucose excretion. They modulate glucose reabsorption in proximal renal tubules. Initially, they were used for the treatment of type 2 diabetes mellitus (T2DM); however, numerous pleiotropic benefits beyond glycemic control were observed. Large clinical trials confirmed their efficacy in reducing cardiovascular mortality, heart failure hospitalizations, and progression of chronic kidney disease. SGLT2 inhibitors reduce oxidative stress and inflammation and induce favorable metabolic adaptations, including lowering ketosis and upregulation of erythropoiesis. They also exert protective effects on hepatic and cognitive function. Additionally, SGLT2 inhibitors lower serum uric acid and reduce adipose tissue mass, which usually results in weight loss. Although generally well-tolerated, they are associated with increased risk of urogenital infections, euglycemic ketoacidosis, and a potentially enlarged amputation risk. Current guidelines worldwide recommend their use not only for T2DM but also for heart failure and chronic kidney disease, marking a paradigm shift toward organ-protective therapies. This review provides a comprehensive synthesis of current evidence on the mechanisms, clinical benefits, and safety profile of SGLT2 inhibitors, highlighting their expanding role in cardiometabolic and multisystem disease management. Full article

Cotton’s susceptibility to low temperatures makes it a crucial raw resource for the world’s textile industry, yet its cultivation in temperate regions is severely limited. Although plant growth and stress responses depend on receptor-like kinases (RLKs), the functions of the MEDOS (MDS) gene family, which includes genes that encode RLK, are still poorly understood in cotton. In this study, we conducted a genome-wide analysis to systematically investigate the distribution of MDS gene family members in four cotton species. Phylogenetic analysis identified five evolutionary clades of the MDS gene family in cotton. The role of promoter cis-acting elements in hormone signaling and abiotic stress responses was suggested by analysis. Collinearity analysis demonstrated that segmental duplication was the primary driver of family expansion. Gene expression profiling showed that GhMDS11 was significantly upregulated under cold stress. Functional validation through silencing GhMDS11 compromised cold tolerance, confirming its role in stress adaptation. Comparative transcriptome study of silenced plants demonstrated substantial enrichment in pathways associated with hormone signal transduction and fatty acid breakdown. It is speculated that the chain of “hormone synthesis → signal transduction → secondary metabolism” completely presents the transcriptional regulation network and functional response of plants after receptor kinase VIGS. Silencing the GhMDS11 gene in cotton initiates regulatory effects through hormone synthesis, which is amplified via a signal transduction cascade, ultimately affecting secondary metabolism. This comprehensive pathway clearly demonstrates the downstream transcriptional reprogramming and functional changes. This work thoroughly examined the evolutionary traits of the MDS family across four cotton species and clarified the functional and molecular processes of GhMDS11 in improving low-temperature tolerance, laying a solid foundation for further clarifying multidimensional regulatory networks and breeding cold-resistant cotton materials. Simultaneously, our findings pave the way for future research to develop molecular markers, which could potentially shorten the breeding cycle and facilitate the targeted enhancement of cold tolerance in cotton. Full article

The discharge of strongly acidic industrial wastewater containing high concentration of chloride ions (Cl⁻) has become one of the major environmental challenges faced globally. For the removal of extremely stable Cl⁻ in acidic aqueous conditions, precipitation method possesses major advantages of strong adaptability and simple operation. This study proposed a novel cuprous oxide (Cu₂O) method assisted by ascorbic acid (VC) for the removal of Cl⁻ from strongly acidic wastewater. First, liquid-phase reduction was chosen as the optimal Cu₂O synthesis method based on product purity and composition. Then, parameter optimization results show that increased reagent dosage and acidity significantly enhanced Cl⁻ removal efficiency, while other factors had negligible impacts. After treatment with the sole addition of Cu₂O, the dosed Cu₂O existed in four forms, including cuprous chloride (CuCl), copper ion (Cu²⁺), elemental copper (Cu⁰), and Cu₂O, among which the generation of Cu²⁺ and Cu⁰, through the oxidation and disproportionation of cuprous ion (Cu⁺), served as the main reason for the unsatisfactory efficiency in the removal of Cl⁻. Fortunately, VC is precisely capable of inhibiting the side reactions of Cu⁺, and under the assistance of 0.10 g VC, the removal of Cl⁻ by Cu₂O was greatly improved with the multiple of theoretical reagent dosage decreasing from 12 to 3, the residual concentration of Cu²⁺ decreasing from 1197 to 18.4 mg/L and the residual concentration of Cl⁻ decreasing from 88.4 to 53.8 mg/L, thus validating the feasibility of this method. Full article

The purpose of this research is to systematically map and analyze the use of AI technologies in project management, identifying themes, research gaps, and practical implications. This study conducts a systematic literature review (SLR) that combines bibliometric analysis with qualitative content evaluation to explore the present landscape of AI in project management. The search covered literature published until November 2024, ensuring inclusion of the most recent developments. Studies were included if they examined AI methods applied to project management contexts and were published in peer-reviewed English journals as articles, review articles, or early access publications; studies unrelated to project management or lacking methodological clarity were excluded. It follows a structured coding protocol informed by inductive and deductive reasoning, using NVivo (version 12) and Biblioshiny (version 4.3.0) software. From the entire set of 1064 records retrieved from Scopus and Web of Science, 27 publications met the final inclusion criteria for qualitative synthesis. Bibliometric clusters were derived from the entire set of 885 screened records, while thematic coding was applied to the 27 included studies. This review highlights the use of Artificial Neural Networks (ANN), Case-Based Reasoning (CBR), Digital Twins (DTs), and Large Language Models (LLMs) as central to recent progress. Bibliometric mapping identified several major thematic clusters. For this study, we chose those that show a clear link between artificial intelligence (AI) and project management (PM), such as expert systems, intelligent systems, and optimization algorithms. These clusters highlight the increasing influence of AI in improving project planning, decision-making, and resource management. Further studies investigate generative AI and the convergence of AI with blockchain and Internet of Things (IoT) systems, suggesting changes in project delivery approaches. Although adoption is increasing, key implementation issues persist. These include limited empirical evidence, inadequate attention to later project stages, and concerns about data quality, transparency, and workforce adaptation. This review improves understanding of AI’s role in project contexts and outlines areas for further research. For practitioners, the findings emphasize AI’s ability in cost prediction, scheduling, and risk assessment, while also emphasizing the importance of strong data governance and workforce training. This review is limited to English-language, peer-reviewed research indexed in Scopus and Web of Science, potentially excluding relevant grey literature or non-English contributions. This review was not registered and received no external funding. Full article

The construction industry faces persistent productivity shortfalls and rising carbon dioxide emissions, which drives a shift toward the use of low-carbon materials and higher degrees of automation. Timber, a renewable and carbon-sequestering material, becomes especially compelling when combined with robotic fabrication. Although rapid advances have been implemented in the last decade, research and practice remain fragmented, and systematic evaluations of technological readiness are scarce. This gap is addressed in this review through critical literature synthesis of robotic timber construction, combining bibliometric analysis with a comparative evaluation of twelve representative case studies from 2020 to 2025. Computational and robotic tools are mapped across the design to fabrication pipeline, and emerging advancements are identified such as digital twins, real-time adaptive workflows, and machine learning driven fabrication, alongside discrete and circular strategies. Barriers to scale up are also assessed, including mid-level technology readiness, regulatory and safety obligations for human–robot interaction, evidence on cost and productivity, and workforce training needs. By clarifying the current level of robotization and specifying both research gaps and industrial prerequisites, this study provides a structured foundation for the next phase of development. It helps scholars by consolidating methods and metrics for rigorous evaluation, and it helps practitioners by highlighting pathways to scalable, certifiable, and circular deployment that align cost, safety, and training requirements. Full article

The widespread presence of bisphenol A (BPA), a persistent endocrine-disrupting pollutant, in aquatic environments poses significant ecological and health risks, necessitating its effective removal. However, conventional remediation technologies are often hampered by catalysts with narrow pH adaptability and poor stability. In this study, a novel catalyst, Zeolite-supported Cu₃Mn-layered double hydroxide (LDH), was fabricated using the co-precipitation method. The synthesized catalyst was applied to activate peroxymonosulfate (PMS), effectively enabling decomposition of BPA by advanced oxidation processes. The composite material was characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and transmission electron microscopy (TEM), which confirmed the successful synthesis of the zeolite-supported Cu₃Mn-LDH. The catalyst exhibited high activity in both neutral and strongly alkaline environments, achieving complete degradation of 10 mg⋅L⁻¹ bisphenol A (BPA) within 40 min and a 98% total organic carbon (TOC) removal rate when both the PMS and catalyst were dosed at 0.15 g⋅L⁻¹. Singlet oxygen was detected as the primary reactive species responsible for BPA degradation, as verified by quenching experiments and EPR analysis, which also identified the presence of sulfate (SO₄^•−), hydroxyl (•OH), and superoxide (•O₂⁻) radicals. The catalyst exhibited excellent reusability, maintaining high catalytic efficiency over two consecutive cycles with minimal performance loss. Gas chromatography-mass spectrometry (GC-MS) analysis revealed five intermediate products, enabling the proposal of potential BPA degradation pathways. This work not only presents a novel synthetic approach for zeolite-supported LDH composites, but also offers a promising strategy for the efficient removal of BPA from aqueous systems through AOPs. Full article

Objectives: Elastic band training is a popular alternative to traditional resistance methods, but its effects on sport-specific performance in team athletes remain inconsistent. This systematic review and meta-analysis aim to evaluate the efficacy of elastic band training on muscular strength, linear sprint, change of direction (COD), and jump height in team sport athletes. Methods: Following PRISMA guidelines, a comprehensive search was conducted in PubMed, Web of Science, and Scopus for randomized controlled trials and quasi-experimental studies. The quantitative synthesis included studies comparing elastic band training interventions with control groups receiving routine training, habitual physical activity, or no additional resistance training intervention. Data were extracted using a standardized form, and a meta-analysis was performed using a random-effects model. Standardized mean differences (SMDs) and 95% confidence intervals (CIs) were calculated to determine the pooled effect of the intervention on key performance indicators. A total of 729 athletes were included. Results: The meta-analysis showed a statistically significant positive effect of elastic band training on lower limb explosive power (SMD = 1.43, p = 0.01), change of direction performance (SMD = −2.54, p = 0.01), and sprint performance (SMD = −1.64, p = 0.01). Conclusions: Elastic band training is a highly effective and practical method for significantly improving key physical performance indicators, including explosive power, COD, and sprint ability, in team sport athletes. Its portability and adaptability make it a valuable alternative or complement to conventional resistance training. Full article

Industrial heritage has emerged in recent decades as a distinctive category within cultural heritage, though its aesthetic significance remains underexplored. Unlike traditional monuments with long historical resonance, industrial remains are often recent, standardized, and seemingly devoid of unique cultural symbolism. Yet, in China—where industrial production expanded massively under both demographic pressures and the Maoist planned economy—these sites now constitute one of the world’s largest inventories of heritage. This study builds on earlier discussions of heritage aesthetics by systematically analyzing the foundations of aesthetic value in industrial heritage, combining historical, functional, and identity-driven perspectives. Drawing on long-term field research, archival documentation, and policy analysis, it examines how adaptive reuse projects—from Beijing’s 798 Art District to Shougang Park and the reconfigured factories of Shanghai and Wuhan—redefine the visual and social significance of former industrial sites. The methodology integrates heritage aesthetic theory with case-based evidence to assess three key components: technological–historical traces, landscape transformation, and collective memory. Results indicate that aesthetic value rarely arises from static preservation but is constructed through refunctionalization, where industrial ruins acquire renewed meaning as cultural parks, creative hubs, or community spaces. Moreover, large-scale Chinese practices reveal that industrial heritage possesses not only visual appeal but also profound identity-based resonance for generations shaped by the “factory managing community.” By situating industrial heritage within the broader aesthetic system of cultural heritage, this research demonstrates that its value lies in the synthesis of function, memory, and landscape, and that China’s experience provides a compelling framework for rethinking global approaches to industrial heritage aesthetics. Full article

Introduction: Ambiguous loss is a profound yet underexplored phenomenon in the lives of aging migrants. Older adults who have experienced migration often face disruptions to their sense of belonging, identity, and continuity across borders. These losses are compounded by aging, health challenges, and social isolation. Despite its significance, ambiguous loss among aging migrants has not been conceptually analyzed in depth, limiting the development of culturally responsive care practices. Aim: This concept analysis aimed to identify the defining attributes of ambiguous loss among aging migrants and to develop a conceptual definition that enhances our understanding of the phenomenon and informs future research and practice. Method: Walker and Avant’s eight-step concept analysis framework was applied to examine the concept of ambiguous loss in the context of aging migrants. A systematic keyword search was conducted across four databases (CINAHL, Medline, SCOPUS, PsycINFO), Google Scholar, and relevant gray literature, covering the years of 2010–2024. Covidence software supported the screening process. From 367 records identified, 146 underwent full-text review, and 74 met inclusion criteria. The analysis drew on literature synthesis, case exemplars, antecedents, consequences, and empirical referents. This review followed PRISMA (2020) reporting guidelines. Results: Four defining attributes of ambiguous loss among aging migrants were identified: (a) physical, social, and emotional loss; (b) displacement and loss of homeland; (c) erosion of social identity and agency; and (d) cultural and transnational bereavement. A conceptual definition emerged, describing ambiguous loss as a multifaceted experience of disconnection, intensified by aging, illness, economic hardship, and social isolation. The analysis also highlighted antecedents such as forced migration and health decline, as well as consequences including diminished well-being, resilience challenges, and barriers to integration. Conclusions: Ambiguous loss among aging migrants is a complex construct encompassing intertwined physical, social, and cultural dimensions of loss. This conceptual clarity provides a foundation for developing culturally responsive care models that promote adaptation, resilience, and social inclusion among older migrants. Full article

With the rapid advancement of artificial intelligence and robotics, the integration of Large Language Models (LLMs) with 3D vision is emerging as a transformative approach to enhancing robotic sensing technologies. This convergence enables machines to perceive, reason, and interact with complex environments through natural language and spatial understanding, bridging the gap between linguistic intelligence and spatial perception. This review provides a comprehensive analysis of state-of-the-art methodologies, applications, and challenges at the intersection of LLMs and 3D vision, with a focus on next-generation robotic sensing technologies. We first introduce the foundational principles of LLMs and 3D data representations, followed by an in-depth examination of 3D sensing technologies critical for robotics. The review then explores key advancements in scene understanding, text-to-3D generation, object grounding, and embodied agents, highlighting cutting-edge techniques such as zero-shot 3D segmentation, dynamic scene synthesis, and language-guided manipulation. Furthermore, we discuss multimodal LLMs that integrate 3D data with touch, auditory, and thermal inputs, enhancing environmental comprehension and robotic decision-making. To support future research, we catalog benchmark datasets and evaluation metrics tailored for 3D-language and vision tasks. Finally, we identify key challenges and future research directions, including adaptive model architectures, enhanced cross-modal alignment, and real-time processing capabilities, which pave the way for more intelligent, context-aware, and autonomous robotic sensing systems. Full article

The zygomaticus major and minor (ZMa/ZMi) are key determinants of smile dynamics and midface contour, yet they exhibit substantial morphological variability—including bifid or multibellied bellies, accessory slips, and atypical insertions. Such variants can alter force vectors, fat-compartment boundaries, and SMAS planes, increasing the risk of asymmetry, contour irregularities, or “joker smile” following facelifts, fillers, thread lifts, and smile reconstruction. To our knowledge, this is the first review to integrate the Landfald classification of ZMa/ZMi variants with a standardized dynamic imaging-based workflow for aesthetic and reconstructive midface procedures. We conducted a narrative literature synthesis of anatomical and imaging studies. Bifid or multibellied variants have been reported in up to 35% of cadaveric specimens. We synthesize anatomical, biomechanical, and imaging evidence (MRI, dynamic US, 3D analysis) to propose a practical protocol: (1) focused history and dynamic examination, (2) US/EMG mapping of contraction vectors, (3) optional high-resolution MRI for complex cases, and (4) individualized adjustment of surgical vectors, injection planes, and dosing. Procedure-specific adaptations are outlined for deep-plane releases, thread-lift trajectories, filler depth selection, and muscle-transfer orientation. We emphasize that standardizing preoperative dynamic mapping and adopting a “patient-specific mimetic profile” can enhance safety, predictability, and preservation of authentic expression, ultimately improving patient satisfaction across diverse midface interventions. Full article

The dynamic growth in the dependence of numerous industrial sectors, businesses, and critical infrastructure on infocommunication technologies necessitates the enhancement of their resilience to cyberattacks and radio-frequency threats. This article addresses a relevant scientific and applied issue, which is to formulate prospective directions for improving the effectiveness of cybersecurity approaches for infocommunication networks through a comparative analysis and logical synthesis of the state-of-the-art of applied research on cyber threats to the information security of mobile and satellite networks, including those related to the rapid development of quantum computing technologies. The article presents results on the systematisation of cyberattacks at the physical, signalling and cryptographic levels, as well as threats to cryptographic protocols and authentication systems. Particular attention is given to the prospects for implementing post-quantum cryptography, hybrid cryptographic models and the integration of threat detection mechanisms based on machine learning and artificial intelligence algorithms. The article proposes a classification of current threats according to architectural levels, analyses typical protocol vulnerabilities in next-generation mobile networks and satellite communications, and identifies key research gaps in existing cybersecurity approaches. Based on a critical analysis of scientific and applied literature, this article identifies key areas for future research. These include developing lightweight cryptographic algorithms, standardising post-quantum cryptographic models, creating adaptive cybersecurity frameworks and optimising protection mechanisms for resource-constrained devices within information and digital networks. Full article

Analyzing the biochemical and physiological activities of food microbes using molecular and bioinformatics tools is important, offering profound insights into their safety, functional, and applicational roles in food. In this study, Lactiplantibacillus plantarum NMGL2, a well-documented beneficial lactic acid bacteria (LAB) strain, was investigated for its genomic, metabolic, and transcriptomic characteristics. Whole-genome sequencing revealed that this strain possesses a chromosome and two plasmids, with 3320 annotated genes, showcasing pathways involved in carbohydrate metabolism, stress adaptation, and bioactive compound synthesis. Growth studies under various nutritional conditions, including fructose, lactose, exogenous exopolysaccharide (EPS), and soy peptone, demonstrated that nitrogen source alteration significantly enhanced bacterial growth and EPS production. Transcriptomic analysis showed the addition of EPS and soy peptone resulted in similar regulatory patterns, suggesting shared modulation of metabolic pathways, although distinct gene regulation patterns were involved. In contrast, fructose and lactose primarily regulated carbohydrate metabolism without increasing EPS yield. Prophage gene clusters were consistently down-regulated across all experimental conditions, reflecting the strain’s adaptive response. These findings highlight L. plantarum NMGL2’s ability to dynamically adjust its metabolism and gene expression in response to environmental and nutritional changes, offering valuable insights for its application in functional foods and probiotics. These results also imply the potential of LAB strains in bioactive compound production and health-related applications through metabolic engineering. Full article

Credit card fraud remains a significant concern for financial institutions due to its low prevalence, evolving tactics, and the operational demand for timely, accurate detection. Machine learning (ML) has emerged as a core approach, capable of processing large-scale transactional data and adapting to new fraud patterns. However, much of the literature modifies the natural class distribution through resampling, potentially inflating reported performance and limiting real-world applicability. This systematic literature review examines only studies that preserve the original class imbalance during both training and evaluation. Following PRISMA 2020 guidelines, strict inclusion and exclusion criteria were applied to ensure methodological rigor and relevance. Four research questions guided the analysis, focusing on dataset usage, ML algorithm adoption, evaluation metric selection, and the integration of explainable artificial intelligence (XAI). The synthesis reveals dominant reliance on a small set of benchmark datasets, a preference for tree-based ensemble methods, limited use of AUC-PR despite its suitability for skewed data, and rare implementation of operational explainability, most notably through SHAP. The findings highlight the need for semantics-preserving benchmarks, cost-aware evaluation frameworks, and analyst-oriented interpretability tools, offering a research agenda to improve reproducibility and enable effective, transparent fraud detection under real-world imbalance conditions. Full article