Search Results (703)

Background/Objectives: Pediatric intussusception, a condition where part of the intestine telescopes into an adjacent segment, predominantly affects children aged 6–18 months. Prompt diagnosis and management are crucial to prevent serious complications such as ischemia or necrosis. This systematic review aims to comprehensively evaluate and synthesize existing research on predictive and prognostic biomarkers associated with pediatric intussusception that can aid in early diagnosis, severity assessment, outcome prediction, and treatment. Methods: A comprehensive literature search was conducted across PubMed, Scopus, and Web of Science using specific MeSH and free-text terms related to intussusception, biomarkers, and the pediatric population. The review followed PRISMA guidelines, with independent screening, data extraction, and quality assessment using the Joanna Briggs Institute critical appraisal tools. A total of 47 studies, mostly retrospective cohorts from diverse countries, with over 20,000 patients, were included. Results: The studies identified numerous biomarkers associated with disease severity, including hematological markers and indices (e.g., WBC counts and neutrophil-to-lymphocyte ratio), inflammatory markers (CRP and cytokines), biochemical markers (serum lactate, D-dimer, and electrolytes), and novel molecular markers (I-FABP, MCP-1, and transfer RNA fragments). Elevated inflammatory markers and derived ratios consistently predicted bowel necrosis, ischemia, and need for surgery. Biochemical markers like serum lactate and D-dimer correlated with ischemic severity. Emerging molecular biomarkers show promise for early, non-invasive risk stratification. However, heterogeneity in study designs, assay methods, and cutoff values currently limits immediate clinical application. Conclusions: Biomarker research offers valuable tools for improving pediatric intussusception management, with the potential to enhance early diagnosis and outcome prediction. While traditional markers are useful, novel molecular and protein biomarkers hold promise for more specific and rapid assessment. Validation through multicenter, prospective studies and standardized protocols is essential before routine implementation. Integrating biomarkers with clinical and imaging data could refine decision-making, ultimately reducing morbidity and improving prognosis in affected children. Full article

Background/Objectives: Infantile cardiomyopathy is a rare but often life-threatening condition in which monogenic causes are particularly relevant, especially when cardiac disease is preceded by hypotonia or multisystem involvement. Among sarcomeric genes, MYL2, encoding the ventricular regulatory myosin light chain, plays a critical role in myocardial contractility. However, biallelic MYL2-associated disease remains exceptionally rare, and its clinical spectrum is not fully defined. This study aims to describe a novel case and further delineate the phenotype of recessive MYL2-related cardiomyopathy. Methods: We report a male infant with congenital hypotonia and delayed motor development who underwent extensive metabolic, neuromuscular, and neuroimaging evaluation. Trio-based whole-exome sequencing was performed to identify a potential genetic etiology, followed by variant interpretation using standard bioinformatic and ACMG/AMP criteria. Results: The patient developed acute decompensated heart failure at approximately 10 months of age, with severe left ventricular systolic dysfunction and multiorgan failure, and died at 12 months despite maximal intensive care support. Whole-exome sequencing identified a homozygous MYL2 c.413T>A (p.Met138Lys) missense variant. The variant is absent or extremely rare in population databases, affects a highly conserved residue, is predicted to be deleterious by multiple in silico tools, and is compatible with autosomal recessive inheritance, with both parents confirmed as heterozygous carriers. In the context of a phenotype consistent with recessive MYL2-associated disease, these findings support a likely pathogenic interpretation. Conclusions: This case expands the allelic and phenotypic spectrum of recessive MYL2-associated cardiomyopathy and highlights the value of early genomic testing in infants with unexplained hypotonia and rapidly progressive cardiac dysfunction. Molecular diagnosis may aid in prognosis, clinical decision-making, and genetic counseling. Full article

Acute Lymphoblastic Leukemia (ALL) is a highly prevalent hematological malignancy, especially in children, for whom precise and prompt subtype identification is essential to establish suitable treatment protocols. Current deep learning-based computer-aided diagnosis (CAD) methods for identifying ALL are hindered by numerous drawbacks, such as a dependence on solely spatial feature depictions, elevated feature dimensions, computationally extensive deep learning architectures, inadequate multi-layer feature utilization, and poor interpretability. This paper introduces LUMINA-Net, a custom, lightweight, and interpretable deep learning CAD for the automated identification and subtype diagnosis of ALL using microscopic blood smear pictures. LUMINA-Net makes four principal contributions: first, it integrates a self-attention module within a lightweight custom Convolution Neural Network (CNN) to effectively capture long-range spatial relationships across clinically pertinent cytological patterns while preserving a compact design. Second, it employs a Discrete Wavelet Transform (DWT)-based wavelet pooling layer that decreases feature dimensions by up to 96.875% while enhancing the obtained depictions with spatial-spectral information. Third, it utilizes a multi-layer feature fusion strategy that combines wavelet-pooled features from two deep layers with a third fully connected layer to create a discriminating multi-scale feature vector. Fourth, it incorporates Gradient-weighted Class Activation Mapping as a dedicated explainability process to furnish clinicians with apparent visual explanations for each classification decision. Withoit the need for image enhancement or segmentation preprocessing, LUMINA-Net outperforms the competing state-of-the-art methods on the same dataset, achieving a peak accuracy of 99.51%, specificity of 99.84%, and sensitivity of 99.51% on the publicly available Kaggle ALL dataset. This demonstrates that LUMINA-Net has the potential to be a dependable, effective, and clinically interpretable CAD tool for ALL diagnosis. Full article

Background: Shared decision-making (SDM) is particularly relevant in rheumatoid arthritis (RA), where multiple treatment options with distinct benefit–risk profiles require alignment with patient values and preferences. This study describes the development of a web-based PtDA to support treatment decision-making in RA and represents the first structured, standards-aligned PtDA in the Greek healthcare context. Methods: Guided by the Ottawa Decision Support Framework and the International Patient Decision Aid Standards, a multistage, user-centered methodology was applied, including evidence synthesis, iterative prototyping, and alpha and beta testing. Qualitative methods, including focus group discussions, semi-structured interviews, and think-aloud protocols, were used, while usability was assessed with the System Usability Scale (SUS). Methodological quality was evaluated using IPDASi v3 and UCD-11 criteria. Results: The final PtDA provides a three-step pathway supporting values clarification, comparison of medication options, and reflection on decisional confidence. It was developed as a publicly accessible, web-based tool compatible with multiple devices, with core elements also available in printable format. The tool showed good usability (mean SUS: 75.93) and strong alignment with IPDASi (83.3/100), and user-centered design criteria (11/11). Conclusions: Developing digital PtDAs is inherently complex, underscoring the importance of established methodological frameworks. The findings demonstrate acceptable usability and alignment with established standards within this early-stage development study. Further research is required to examine the tool’s impact on decision-making processes, value–choice concordance, and longer-term clinical outcomes. Full article

Background: Artificial intelligence-based computer-aided detection (AI-CAD) systems are increasingly being used in endovascular practice to support time-sensitive detection, triage and prioritization tasks in imaging and procedural workflows. Despite rapid technological advancements and expanding regulatory clearances, the translation to lasting clinical benefit varies. Objective: This narrative review synthesizes AI-CAD applications in endovascular interventions and proposes an evaluation-oriented framework to support responsible clinical translation; this framework emphasizes detection-specific metrics, external validation, bias-aware assessment, and workflow integration. Methods: A structured narrative review was conducted using targeted searches in PubMed, Google Scholar, and IEEE Xplore (2020–2026); this review was supported by an examination of US FDA device databases and citation tracking. Evidence was assessed using a pragmatic hierarchical classification framework based on regulatory status and validation rigor. Results: AI-CAD applications were mapped across four main endovascular domains: neurovascular interventions (e.g., large vessel occlusion triage), coronary interventions (CCTA-based stenosis detection and intravascular imaging support), aortic interventions/EVAR (endoleak detection and sac monitoring), and peripheral interventions (lesion detection and angiographic decision support). Across the domains, performance reporting was heterogeneous and often relied on retrospective, single-center assessments. Key barriers to clinical readiness included acquisition variability and dataset shift due to artifacts, limited multicenter validation, annotation variability, and human–AI workflow factors. Evaluation priorities included whether to assess at the lesion level or case level, false positive burden and calibration, external validation under real-world heterogeneity, and clinical impact measures such as treatment timing and procedural decision-making. Conclusions: AI-CAD systems hold significant potential for improving endovascular care; however, clinical readiness depends on rigorous, endovascular feature-specific assessment and transparent reporting, beyond retrospective accuracy. The proposed evidence level framework and assessment checklist provide practical tools for distinguishing mature technologies from research prototypes and guiding future validation, implementation, and post-market monitoring. Full article

The shift toward lightweight powertrain architectures necessitates a detailed characterization of polymer gears to verify their efficiency and durability. This study investigated the effectiveness of non-contact structured-light 3D scanning for evaluating the surface topography and dimensional tolerance quality of polymer gears produced via distinct manufacturing technologies. A structured-light 3D scanner was used to capture dense point clouds (exceeding 6 million points) of gears produced by three methods: conventional hobbing (POM-C), Material Extrusion (MEX) with carbon fiber reinforcement, and Selective Laser Sintering (SLS). The manufactured parts were compared against the nominal Computer Aided Design (CAD) models to evaluate their geometrical deviations in accordance with DIN 3961 and surface roughness parameters per ISO 25178. The experimental results revealed a consistent ranking of manufacturing quality. The conventionally hobbed POM-C gear exhibited superior precision, achieving DIN quality grades of Q9–Q10 and the smoothest surface finish (S_a = 5.0 µm). Among additive manufacturing techniques, SLS-printed PA 12 showed intermediate quality (Q11, S_a = 12 µm), whereas MEX-printed PPS-CF exhibited significant deviations (exceeding Q12) and the highest surface irregularity (S_a = 25 µm) due to stair-stepping effects. These findings indicate that while additive manufacturing offers geometric flexibility, conventional hobbing retains a decisive advantage in dimensional precision. The optical scanning methodology demonstrated here constitutes an efficient metrological framework for gear quality control, with potential applications extending to the quality assurance of additively manufactured adaptive fixtures and assembly tooling, including automotive assembly operations. Full article

Architects and interior design (AID) practitioners have a professional responsibility to advocate and design for minority occupants, yet it is not always possible to consult with all future users due to commercial project constraints. In lieu of occupant engagement, this paper asks what self-directed inquiry might guide more inclusive strategic decision-making in AID practice? Taking a systems perspective, a novel framework for interpreting the occupant–building system is proposed. By deductively extending Shove, Panzar and Watson’s existing Social Practices Theory (SPT) operationalisation, their omission of space is remedied through integrating Reckwitz’s affective spaces of social practices. The framework changes the unit of analysis from the physical by describing occupancy as a social practice with three elements: material, the physical assemblage including human bodies and space; competences, the rules and habits of using the space; and meanings of space for occupant cohorts. The revised theory elevates the social to equal status of material, thus reinforcing their reciprocal relationship and making this explicit for AID practice. The framework is proposed as an interpretive sensemaking tool for AID practitioners to identify different spatial occupations beyond stereotypical expectations. It also offers a framework for AID practitioners to critically reflect on their agency in stabilising or evolving the spatialisation of culture. Three interpretations are demonstrated for contemporary Australian multicultural and inclusion scenarios. It is argued that this theory offers a framework for practice to enable strategic inclusive outcomes in projects with or without user consultation. Furthermore, in addressing the social practices of the built environment, this organising framework offers broader and holistic future built environment research and education. Full article

Although greenhouse vegetable production is rapidly shifting toward innovative soilless systems, soil-based conventional cultivation still dominates globally. This production system faces growing pressure to transition to sustainable practices. However, introducing biofertilisers into intensive systems often yields inconsistent results. Specifically, their effects on different lettuce traits vary due to complex relationships between genotype, biofertiliser, environmental conditions, and market demands. Single-parameter evaluations fail to balance conflicting criteria, necessitating multi-criteria decision-making (MCDM) methods for selecting optimal choices. This study aims to overcome these inconsistencies through an integrated fuzzy MCDM-based optimisation model. Three lettuce cultivars (‘Carmesi’, ‘Aquino’, and ‘Gaugin’) were grown in an unheated Surčin (Serbia) greenhouse during a 58-day autumn experiment using a complete block design. Four treatments were applied: a control (without fertilisation), effective microorganisms, a Trichoderma-based fertiliser, and their combination. Biofertilisers were applied before transplanting and four times foliarly during the vegetation period via battery sprayer. This defined 12 production models (cultivar–fertiliser pairs), evaluated across 10 criteria: agronomic (core ratio, number of leaves), quality (nitrate content, total antioxidant capacity, total soluble solids, and chlorogenic acid), sensory (overall taste, overall quality), and economic (total variable costs, total income). Four decision-making experts from the Faculty of Agriculture and the ready-to-eat salad industry assessed weighting coefficients using the fuzzy PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) method. The fuzzy MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution) method was used to rank the alternatives. To confirm the stability of the obtained ranking with the fuzzy MARCOS method, we performed sensitivity analysis through 20 different scenarios. Applied fuzzy methods identified alternative A11—‘Aquino’ cultivar with combined biofertilisers—as the best-ranked option, followed by A6 and A7. This study validates fuzzy PIPRECIA and fuzzy MARCOS as effective tools for optimising lettuce production models. They support farmers in selecting the most favourable solution based on multiple criteria, aiding the shift from mineral fertilisers to sustainable biofertiliser-based systems in intensive production—especially helpful for producers making this transition. Full article

Background/Objectives: The transition from the intensive care unit (ICU) to the hospital ward is a critical high-risk period for patients. Early ICU discharge reduces costs and frees up ICU resources but can lead to readmission or unexpected death if patients are discharged prematurely. Despite the availability of risk stratification tools such as the Stability and Workload Index for Transfer (SWIFT) score, predicting ICU readmission remains challenging and inconsistent. However, artificial intelligence (AI) and machine learning (ML) techniques have recently shown promise in improving clinical decision support systems, particularly in the ICU. This study aimed to identify the risk factors and assess the performance of AI models in predicting readmission or death within seven days of ICU discharge using the MIMIC-IV (between 2008 and 2019) and Kangwon National University Hospital (KNUH, between 1 January 2016 and 28 February 2023) databases. Methods: This retrospective cohort study utilized the MIMIC-IV database for model training and internal validation and the KNUH database for external validation. Various machine learning and deep learning models have been developed to predict ICU readmission or death within seven days of discharge. The performance of the primary model, GRU-D++, was compared to the SWIFT score. Statistical analysis focused on the area under the receiver operating characteristic curve (AUROC) data to evaluate model accuracy. Results: The GRU-D++ model outperformed the SWIFT score, achieving AUROC of 0.802 and 0.756 for internal and external validations, respectively. Both datasets demonstrated that the GRU-D++ model provided better predictive performance for ICU readmission or death within seven days than the traditional SWIFT score. Conclusions: Our findings suggest that the GRU-D++ deep learning model is a valuable tool for the early detection of patient deterioration after ICU discharge, potentially aiding the prevention of ICU readmission. This study highlights the potential of AI to improve clinical decision-making in intensive care settings. Full article

The Copernicus Sentinel-2 (S2) data are a crucial resource for urban policymakers in land-cover classification, offering a freely accessible alternative to expensive commercial data sources. While medium spatial resolution often limits the applicability of data-intensive machine learning approaches, the Geographic Object-Based Image Analysis (GEOBIA) framework could be an effective, operational alternative for urban land-cover classification using S2 data. This study applies the Geographic Object-Based Image Analysis (GEOBIA) approach to classify land cover in Hue, Vietnam, using Sentinel-2 data processed through the eCognition interface. The study’s findings emphasize the potential of GEOBIA and S2 data in enhancing decision-making processes for city authorities, ensuring better resource allocation, environmental protection, and infrastructure development. The results indicate that the method performs reliably for mesoscale and spatially continuous classes, such as vegetation and built-up surfaces, while accuracy is lower for small or spectrally heterogeneous features, particularly shallow water bodies and fragmented rice paddies, due to mixed-pixel effects inherent in 10–20 m resolution imagery. The results demonstrate an Overall Accuracy (OA) of 91%, highlighting the method’s effectiveness in extracting and classifying urban land-cover classes. This study demonstrates a replicable model for urban land monitoring that can be adapted across various geographic contexts. Furthermore, this approach fosters a more data-driven governance model, where urban expansion and land-use changes can be monitored in real time, allowing for proactive interventions. With urbanization accelerating worldwide, particularly in rapidly developing regions, such a cost-effective and accessible classification method can significantly aid in achieving long-term urban sustainability. The findings illustrate the relevance of GEOBIA as a feasible tool for supporting data-driven urban governance, enabling systematic tracking of land-use change, informed infrastructure planning, and sustainable urban management in both developed and rapidly urbanizing regions. Full article

Background and Objective: Positive surgical margins (PSMs) remain a major challenge during radical prostatectomy, particularly in patients with high-risk prostate cancer (HR-PCa), where extracapsular extension, multifocal disease, and aggressive tumor biology substantially increase the likelihood of incomplete resection. In this setting, PSMs are strongly associated with early biochemical recurrence and frequently prompt adjuvant or salvage treatments, potentially exposing patients to overtreatment and added morbidity. Materials and Methods: To review and critically appraise established and emerging intraoperative technologies for surgical margin assessment during radical prostatectomy, with a specific focus on their potential role and relevance in patients with HR-PCa. Evidence Acquisition: A non-systematic literature review was performed using Pubmed, MEDLINE, Web of Science, and Google Scholar, focusing on preoperative, intraoperative ex vivo, and intraoperative in vivo technologies for margin assessment. Emphasis was placed on techniques with potential applicability to HR-PCa, where real-time intraoperative decision-making is particularly consequential. Evidence Synthesis: Preoperative tools, including multiparametric MRI, PSMA-PET imaging, and predictive nomograms, aid surgical planning but show limited sensitivity for microscopic extracapsular extension, especially in high-risk disease. Intraoperative frozen section analysis reduces positive surgical margin rates while enabling selective nerve-sparing (defined as a side-specific, risk-adapted preservation strategy); however, its widespread adoption is constrained by substantial logistical and resource requirements, and robust oncological outcome data in high-risk populations remain limited. Novel ex vivo approaches, such as fluorescence confocal microscopy and specimen-based PSMA PET/CT imaging, offer rapid whole-gland or targeted margin assessment with reduced dependency on dedicated pathology workflows. In parallel, emerging in vivo technologies, particularly PSMA-targeted near-infrared-fluorescence-guided surgery, enable real-time detection of residual tumor and facilitate selective re-resection, representing a biology-driven approach that may be especially suited to HR-PCa. Conclusions: In high-risk prostate cancer, intraoperative margin assessment technologies may extend beyond functional preservation and play a central role in optimizing oncological radicality and multimodal treatment sequencing. While NeuroSAFE remains the reference standard, PSMA-based ex vivo and in vivo technologies are particularly promising in HR-PCa due to their ability to integrate tumor biology into surgical decision-making. Prospective studies focusing on high-risk-specific oncological and patient-reported outcomes are needed before widespread clinical implementation. Full article

Regional energy self-sufficiency based on microgeneration from clean, local energy sources is essential and strategic for meeting growing electricity demand. In this context, initiatives driven by local governments are decisive in achieving such progress. This study proposes a methodology for sizing photovoltaic (PV) capacity at the parish level, which is the basic political–administrative unit in Ecuador. Rooftop-based microgeneration and self-supply are considered to entail minimal environmental impact while offering significant potential to meet the basic energy demands of buildings in the Andean equatorial climate. The results demonstrate that, using accessible tools such as drones, computer-aided design software, and Agisoft Metashape, and through low-labour processes, it is feasible to estimate the PV potential of buildings at the parish scale. A total of 1698 rooftops were surveyed, and after discarding those with precarious construction materials, the estimated solar potential was found to be between ten and twenty-three times higher than the electrical demand of the analysed parishes. The estimated annual generation potential reaches 28,101 MWh, compared to an annual demand of 1827 MWh for both parishes combined. The proposed process enables the incorporation of rooftop-based technological capacity, relying on a low-technology, affordable methodological approach and instruments for low-income parish governance offices, with low-density populated areas as the main novelty, providing clear information to both authorities and the local population. Full article

Acute respiratory distress syndrome (ARDS) has traditionally been managed with population-based, protocolized mechanical ventilation strategies designed to limit ventilator-induced lung injury. While these approaches have improved outcomes, they fail to account for the pronounced biological, mechanical, radiological, and temporal heterogeneity that characterizes ARDS. Accumulating evidence shows that patients differ markedly in functional lung size, recruitability, chest wall mechanics, inflammatory burden, and tolerance to ventilatory stress, making uniform ventilatory targets physiologically imprecise and, at times, harmful. This narrative review examines the evolution from conventional lung-protective ventilation toward a precision-based paradigm that aligns ventilatory support with individual patient physiology. We conceptualize ARDS not as a static syndrome but as a dynamic spectrum, viewing the injured lung as a heterogeneous mechanical system susceptible to regionally amplified stress and strain. Within this framework, we discuss key principles underlying precision ventilation, including functional lung size (the “baby lung”), driving pressure, mechanical power, patient–ventilator interaction, spontaneous breathing-associated injury, and the time-dependent evolution of lung mechanics. We synthesize current evidence supporting mechanical, biological, and radiological subphenotyping as complementary strategies to individualize ventilatory management, while critically appraising their current limitations. This review also evaluates bedside tools that may operationalize precision ventilation in clinical practice, including esophageal pressure monitoring, lung ultrasound, and electrical impedance tomography, and examines the role of artificial intelligence as a clinician-directed decision-support aid rather than a prescriptive substitute for physiological reasoning. Implications for clinical trial design, ethical considerations, and future directions toward predictive and adaptive ventilation strategies are also addressed. Precision mechanical ventilation represents a shift from rigid thresholds toward proportional, physiology-guided intervention across the disease trajectory. By integrating evolving lung mechanics, ventilatory load, and patient effort over time, this approach provides a coherent framework for safer and more effective mechanical ventilation in ARDS while preserving the core principles of lung protection. Full article

Soil erosion, particularly ephemeral gully (EG) erosion, poses a significant threat to agricultural sustainability and ecosystem health. Despite their substantial impact on soil degradation, EGs have been relatively understudied, primarily due to their temporary nature and the limitations of existing measurement techniques. This study introduces an integrated approach for quantifying and analyzing EGs, addressing the critical need for accurate and scalable measurement methods. Our methodology combines three key components: (1) an updated portable field tool (Gulliometer), which improves upon existing designs to enhance data collection in diverse field conditions; (2) a standardized image acquisition protocol that ensures consistent, high-quality data capture; and (3) an image processing technique leveraging easy repetitive analysis of gully cross-sections. Laboratory validation using known geometric shapes demonstrated the high precision of our methodology, with error rates below 1%. Field applications in two distinct locations in Ontario, Canada, further confirmed the practicality and effectiveness of our approach under varied environmental conditions. This approach not only advances our understanding of ephemeral gully erosion but also aids in the development of effective soil conservation strategies and informed decision-making in land management. Full article

Climate change affects flood frequency and intensity throughout the world, leading to a research gap in the traditional management framework. Furthermore, traditional frameworks often rely on complex hydrological patterns and one-way communication, demonstrating urgent needs for adaptive and two-way communication approaches. For this purpose, the current systematic literature review (SLR) fills this gap by analyzing the widely reported literature on the role of an artificial intelligence (AI)-based framework. This SLR provides conceptual and theoretical insight into the potential role of generative AI and an OpenAI-based theoretical framework for effective flood management. Therefore, 77 peer-reviewed articles published between 2010 and 2025 in reputed sources such as ScienceDirect, Springer Nature, MDPI, Wiley, and others were analyzed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. According to the results of this paper, four hypothetical applications of generative AI are described, namely: (i) a knowledge translator to provide simplified hydrological information, (ii) a decision-support assistant that aids real-time strategic analysis, (iii) a community engagement tool to increase the participation and understanding of people, and (iv) an interface to harmonize and synthesize various sources of information. The discussion indicates that there is a lot of potential in terms of generative AI improving the inclusiveness, real-time sensitivity, and cost-effectiveness of flood risk management practice. Nevertheless, the research also presents significant issues that are connected to data integrity, algorithm bias, digital equity, and ethical governance. The results indicate that generative AI has a significant potential of developing robust, more accessible, and more communicative flood risk management systems, and that additional studies on the responsible and ethical use of the technology are necessary. Full article