Search Results (1,104)

Knee disorders are a major indication for musculoskeletal imaging, yet MRI reliability remains constrained by signal nonuniformity, motion artefacts, protocol variability, and reader-dependent effects. This study presents an integrated two-vector framework that couples (i) a fuzzy diagnostic control-risk model with (ii) a quantitative digital-maturity assessment to strengthen MRI-based diagnosis of knee pathology. The vertical vector characterizes organizational readiness through a weighted fuzzy aggregation of six capability agents (technical, information and analytical, mathematical/model, metrological, human resources, and software support). The horizontal vector estimates producer’s and consumer’s risks as misclassification probabilities relative to an acceptance boundary, driven by measurement/interpretation uncertainty, variability of the decision threshold, and the ratio of instrumental to physiological dispersion. Simulation results indicate that error probabilities increase sharply when threshold uncertainty exceeds 20–25% and rise by approximately 15–20% as the standard-deviation ratio approaches unity. To connect diagnostic reliability with downstream mechanics, a FE analysis of the tibial insert in TKA under F = 1150 N at 0° flexion predicts a peak contact pressure of 85.449 MPa and a maximum UHMWPE von Mises stress of 43.686 MPa, identifying wear-critical contact zones. Overall, the proposed framework provides interpretable quantitative targets for QA, protocol refinement, and resource allocation in radiology services undergoing digital transformation, and offers a reproducible pathway for linking imaging reliability to biomechanical risk. Full article

Using a quantile–frequency connectedness framework, this study analyzes the regime-contingent and horizon-specific transmission of shocks among AI assets, FinTech markets, and Blue Economy financial instruments. The empirical results reveal a distinctly asymmetric connectedness structure, whereby high-frequency spillovers intensify in upper-quantile states associated with liquidity stress and sentiment-driven trading, while low-frequency connectedness remains comparatively muted, thereby preserving cross-segment diversification potential. AI assets emerge as dominant net transmitters in short-horizon dynamics, reflecting rapid innovation cycles and speculative adjustments. FinTech markets exhibit stabilizing properties under median regimes but transition into net propagation roles when risk conditions escalate. Blue finance instruments act as conditional net absorbers, attenuating volatility originating from digital innovation-driven markets, particularly during adverse market states. By decomposing spillover intensities across quantiles and spectral bands, the analysis highlights a structural differentiation between innovation-sensitive digital assets and the comparatively stable behavior of blue-themed financial assets. These findings advance the understanding of nonlinear dependence, asymmetric contagion, and state-dependent co-movements in emerging financial ecosystems. The results provide actionable insights for systemic-risk measurement, cross-market shock diagnostics, and multi-asset portfolio construction in an increasingly interconnected global financial system. Full article

Multi-way analysis has become one of the most powerful and versatile chemometric approaches for dealing with the increasing complexity of data generated in modern analytical chemistry. Advances in instrumentation, the widespread use of hyphenated techniques, and the inherently multidimensional nature of many experimental designs require methods capable of preserving structural relationships within datasets. In this context, multi-way tools such as Tucker 3, PARAFAC, or other supervised variants provide rigorous and interpretable descriptions of variability across multiple modes (samples, variables, conditions), enabling the extraction of meaningful patterns, improved noise handling, and enhanced robustness, compared with traditional bilinear approaches. This review offers a critical overview of the most commonly applied multi-way algorithms and their practical use in fields such as environmental chemistry, food science, clinical diagnostics, industrial process monitoring, and pharmaceutical analysis. The essential steps of the workflow, from data acquisition and preprocessing to model selection and interpretation, are discussed, highlighting their impact on model reliability. A dedicated section summarizes the software environments available for performing multi-way analyses, guiding readers in selecting the most suitable tools for their needs. Overall, this review emphasizes how multi-way chemometrics is becoming increasingly crucial for converting complex, high-dimensional data into reliable and actionable chemical knowledge. Full article

Background: Rotary instrument fracture during third molar extraction is rare but clinically relevant, presenting diagnostic and therapeutic challenges. Aim: This scoping review summarizes available evidence on bur breakage and displacement during third molar surgery, focusing on causes, clinical manifestations, and management strategies. Materials and Methods: A systematic search of PubMed, Virtual Health Library, and Google Scholar was conducted for studies published from January 2008 to March 2025 reporting rotary instrument fracture during third molar extraction. Extracted data were qualitatively analyzed. Results: Eight studies reporting eleven clinical cases were included. All fractures occurred during mandibular third molar extractions. Pain was the most frequent symptom (45%), followed by swelling (27%) and trismus (18%). Management varied from immediate surgical retrieval to conservative observation. Conclusions: Although uncommon, rotary bur fracture during third molar extraction requires preventive attention and accurate reporting. Adherence to manufacturer recommendations, single-use bur policies, and adequate irrigation should be considered. Prospective multicenter and mechanical studies are needed to establish standardized management protocols. Full article

Rapid detection of β-lactamase activity is becoming increasingly important as β-lactam resistance spreads at an alarming rate and conventional diagnostics often require several hours to deliver actionable results. Over the past decade, methods based on luminescence, bioluminescence, chemiluminescence, and fluorescence have become powerful tools for the functional assessment of resistance resulting from β-lactamase activity. These approaches provide highly sensitive, activity-based readouts, often within minutes, and frequently rely on simple optical instrumentation. In this review, we summarize recent developments in luminescent probe design between 2015 and 2025, with emphasis on reaction mechanisms, analytical performance, and the ability of these systems to discriminate between different β-lactamases, including narrow-spectrum enzymes, AmpC, ESBL, and carbapenemases. We also discuss their applications in bacterial cultures, clinical isolates, complex biological matrices and, in some cases, in vivo models. While luminescent assays are not yet positioned to replace standard susceptibility testing, they offer a practical and increasingly robust complement to culture-based and molecular methods. The emerging trends highlighted here, such as self-immobilizing fluorogenic probes, chemiluminescent relay systems, nanomaterial-based sensors and AI-assisted mobile platforms, suggest that luminescent β-lactamase detection could play a meaningful role in future rapid diagnostics and resistance surveillance. Full article

This paper presents the development of an Intelligent Virtual Instrument (VI) for detecting and characterizing corrosion on aluminum and steel surfaces. Implemented within the LabVIEW^® environment, the system utilizes colorimetric computer vision techniques tailored for the metalworking industry. The methodology integrates colorimetric and roughness analysis with Artificial Intelligence, specifically employing Fuzzy Logic for decision-making and Deep Learning algorithms for image processing. This system enables personnel without specialized training to perform rapid, objective diagnostics. The results demonstrate a high correlation between the color spectra of processed images and standard industry patterns, validating the instrument as an efficient and reliable alternative for diverse industrial environments. Full article

Background: Fungal infections are considered a global health concern, resulting in high morbidity and mortality rates, especially in immunocompromised individuals. Traditional diagnostic techniques, such as microscopy, culture, and polymerase chain reaction (PCR), suffer from low sensitivity, long processing time, and accessibility challenges, especially in resource-limited settings. Artificial intelligence (AI) and machine learning (ML) tools have demonstrated potential to enhance diagnostic accuracy and efficiency. This systematic study assesses the progress, precision, and efficacy of AI-driven diagnostic tools for fungal infections within various clinical contexts in comparison to traditional procedures. Methods: A systematic review was conducted according to PRISMA principles. Literature searches were conducted in PubMed, ScienceDirect, Web of Science, and Ovid, focusing on research employing AI models to diagnose fungal infections. The inclusion criteria were research that compared AI-based tools with conventional diagnostic methods in terms of sensitivity, specificity, and accuracy. Data extraction and quality evaluation were performed utilizing validated instruments, such as the Methodological Index for Non-Randomized Studies (MINORS). Results: Eleven research studies met the inclusion criteria: six retrospective and five prospective investigations. AI models, such as convolutional neural networks (CNNs), Faster R-CNN, VGG19, and MobileNet, have improved diagnostic accuracy, sensitivity, and specificity compared to traditional methods. However, differences in dataset quality, model validation, and real-world applicability remain as limitations. Conclusions: AI-driven diagnostic technologies provide significant benefits in identifying fungal infections, improving the speed and accuracy of diagnoses. However, additional extensive investigations and clinical validation are required to improve model generalizability and facilitate smooth incorporation into healthcare systems. Full article

Multiphoton endomicroscopy (MPEM) has recently become a key development in optical biomedical diagnostics, providing histologically relevant in vivo images that are eliminating both the need for tissue damage during biopsy sampling and the need for dye injections. Due to its ability to visualize structures at the epithelial, extracellular matrix, and subcellular levels, MPEM offers a promising diagnostic method for precancerous conditions and early forms of gastrointestinal (GI) cancer. The high specificity of multiphoton signals—the two-photon fluorescence response of endogenous fluorophores (NADH, FAD), the second-harmonic generation signal from collagen, and others—makes this method a promising alternative to both traditional histology and confocal endoscopy, enabling real-time assessment of metabolic status, intestinal epithelial cell status, and stromal remodeling. Despite the promising prospects of multiphoton microscopy, its practical implementation is progressing extremely slowly. The main factors here include the difficulty of delivering ultrashort pulses with high peak power, which is necessary for multiphoton excitation (MPE), and obtaining these pulses at the required wavelengths to activate the autofluorescence mechanism. One of the most promising solutions is the use of specialized multimode optical fibers that can both induce beam self-cleaning (BSC), which allows for the formation of a stable beam profile close to the fundamental mode, and significantly broaden the optical spectrum, which can ultimately cover the entire region of interest. This review presents the biophysical foundations of multiphoton microscopy of GI tissue, existing endoscopic architectures for MPE, and an analysis of the potential for using novel nonlinear effects in multimode optical fibers, such as the BSC effect and supercontinuum generation. It is concluded that the use of optical fibers in which the listed effects are realized in the tracts of multiphoton endomicroscopes can become a key step in the creation of a new generation of high-resolution instruments for the early detection of malignant neoplasms of the GI tract. Full article

Artificial intelligence (AI) is rapidly reshaping adult cardiac surgery, enabling more accurate diagnostics, personalized risk assessment, advanced surgical planning, and proactive postoperative care. Preoperatively, deep-learning interpretation of ECGs, automated CT/MRI segmentation, and video-based echocardiography improve early disease detection and refine risk stratification beyond conventional tools such as EuroSCORE II and the STS calculator. AI-driven 3D reconstruction, virtual simulation, and augmented-reality platforms enhance planning for structural heart and aortic procedures by optimizing device selection and anticipating complications. Intraoperatively, AI augments robotic precision, stabilizes instrument motion, identifies anatomy through computer vision, and predicts hemodynamic instability via real-time waveform analytics. Integration of the Hypotension Prediction Index into perioperative pathways has already demonstrated reductions in ventilation duration and improved hemodynamic control. Postoperatively, machine-learning early-warning systems and physiologic waveform models predict acute kidney injury, low-cardiac-output syndrome, respiratory failure, and sepsis hours before clinical deterioration, while emerging closed-loop control and remote monitoring tools extend individualized management into the recovery phase. Despite these advances, current evidence is limited by retrospective study designs, heterogeneous datasets, variable transparency, and regulatory and workflow barriers. Nonetheless, rapid progress in multimodal foundation models, digital twins, hybrid OR ecosystems, and semi-autonomous robotics signals a transition toward increasingly precise, predictive, and personalized cardiac surgical care. With rigorous validation and thoughtful implementation, AI has the potential to substantially improve safety, decision-making, and outcomes across the entire cardiac surgical continuum. Full article

Residential energy renovation remains a central pillar of climate mitigation and social sustainability strategies, yet renovation rates persistently lag behind policy targets, particularly in older urban neighborhoods. This study investigates the underlying causes of renovation inertia using a neighborhood-scale mixed-methods approach that combines door-to-door household surveys, façade infrared thermography, and expert focus groups. Using a post-industrial residential district in Liège, Belgium, as an exploratory case, the study jointly analyzes building conditions, household characteristics, and renovation contexts. The results reveal that renovation failure cannot be explained solely by technical deficiencies. Instead, three interacting socio-technical mechanisms emerge: adaptive occupant behaviors that mask poor building performance, a constrained renovation agency shaped by tenure and income asymmetries, and the stratification of energy awareness along social lines. Together, these mechanisms reinforce a form of renovation lock-in in which technical degradation, behavioral adaptation, and institutional fragmentation mutually sustain inaction. By integrating physical diagnostics with social and experiential data, the study explains why conventional incentive-based renovation policies systematically underperform in comparable urban contexts. Rather than treating energy renovation as a purely technical or economic decision, the findings highlight the need for policy instruments that explicitly address agency constraints, behavioral compensation, and unequal exposure to energy-related risks. The proposed mixed-method framework is transferable to other urban neighborhoods and offers a replicable approach for diagnosing renovation barriers, supporting more socially sustainable energy transition strategies. Full article

Background/Objectives: Bereavement in childhood, adolescence, and young adulthood is associated with a range of grief responses, and a subset of bereaved individuals develop persistent or severe grief symptoms. Understanding the prevalence and risk factors of prolonged grief symptoms is important for guiding supportive care. Methods: We systematically searched PubMed, MedLine, Embase and PsycINFO for all studies comparing the prevalence and prognostic factors of prolonged grief-related symptoms among young individuals following parental or sibling death from cancer. Young individuals were defined as those not more than 25 years old before losing a parent or sibling to any cancer. Prolonged grief-related symptoms were defined as the presence of grief symptoms at least six months following the death of a parent or sibling of the bereaved person. Retrospective cross-sectional studies were included for evaluating prognostic factors affecting prolonged grief-related symptoms, but were not used for meta-analyses. Random-effects meta-analyses were conducted for the primary analysis. Results: From 1561 records identified, thirteen studies were included with five for quantitative pooling in meta-analysis. The pooled prevalence of self-reported prolonged grief-related symptoms was 48% (95% CI: 29–67%). Stratified analyses suggested a prevalence of 28% (95% CI: 7–65%) after parental death and 59% (95% CI: 45–72%) after sibling death. Factors associated with elevated prolonged grief-related symptoms included pre-existing depression, emotional difficulties, and insomnia. As no included studies conducted diagnostic clinical interviews, prolonged grief disorder according to the ICD-11 or DSM-5-TR criteria could not be assessed. Conclusions: Prolonged grief-related symptoms appear common among young individuals bereaved by loss of a parent or sibling to cancer, especially after sibling loss. However, interpretation remains limited by substantial heterogeneity, such as outcome measures, symptom thresholds, assessment time window, non-validated symptom measures, and predominance of cross-sectional studies. Future larger and methodologically rigorous studies using validated grief instruments across diverse settings are needed to clarify grief trajectories and guide developmentally appropriate intervention strategies. Full article

Background: Persistent pain following orthopaedic trauma is common, often disproportionate to structural healing, and increasingly interpreted as reflecting centrally mediated pain mechanisms. However, the mechanisms, clinical features, diagnostic approaches, prognostic indicators, and management strategies relevant to trauma-related central sensitisation (CS) remain poorly understood. Objective: To map and synthesise existing evidence on CS following orthopaedic trauma, addressing mechanistic pathways, clinical manifestations, epidemiology, assessment methods, management approaches, and health system implications. Methods: A scoping review was conducted in accordance with PRISMA-ScR. Twenty-one studies met the eligibility criteria, comprising nine primary trauma cohorts and 12 contextual mechanistic or review studies relevant to trauma-associated CS. Data were charted across six prespecified domains of mechanistic processes, clinical presentation and diagnostic features, epidemiology and prognosis, assessment tools and outcome measures, interventions, and health system and care delivery considerations. Results: Mechanistic studies demonstrated trauma-induced neuroimmune activation, altered cortical and spinal excitability, and molecular pathways consistent with sensitisation. Clinical studies have identified neuropathic features, widespread pain, and heightened sensory responsiveness following fractures and other injuries. Neurophysiological evidence has indicated early cortical disinhibition following upper limb trauma, whereas epidemiological cohorts have reported persistent pain and disability years after major trauma. Measurement studies have highlighted the limited reliability and specificity of current tools in trauma populations, including quantitative sensory testing and self-report instruments. Early predictors of adverse trajectories include severe acute pain, neuropathic descriptors, psychological distress, and opioid-dominant analgesia. Evidence regarding early intervention, rehabilitation strategies, and system-level screening pathways remains limited. Conclusions: Central sensitisation (CS)–consistent mechanisms after orthopaedic trauma are suggested by convergent mechanistic, neurophysiological, and clinical findings. However, trauma-specific diagnostic criteria, prognostic models, and management frameworks remain underdeveloped. High-quality longitudinal research is needed to clarify early trajectories, refine assessment methods, and establish targeted interventions to reduce long-term pain and disability. Full article

The traditional Chinese medicinal herb Gynura segetum is increasingly recognized for its hepatotoxic potential, primarily attributed to its pyrrolizidine alkaloid (PA) content. PAs are a leading cause of herb-induced liver injury (HILI) in China and are strongly linked to hepatic sinusoidal obstruction syndrome (HSOS). This review systematically summarizes the pathogenesis, diagnostic advancements, and therapeutic strategies for PA-induced HSOS. Molecular mechanisms of PA metabolism are detailed, encompassing cytochrome P450-mediated bioactivation and the subsequent formation of pyrrole-protein adducts, which trigger sinusoidal endothelial cell injury and hepatocyte apoptosis. Advances in diagnostic criteria, including the Nanjing Criteria and the Roussel Uclaf Causality Assessment Method (RUCAM)-integrated Drum Tower Severity Scoring System, are discussed. Furthermore, emerging biomarkers, such as circulating microRNAs and pyrrole-protein adducts, are examined. Imaging modalities, such as contrast-enhanced computed tomography (CT) and gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) magnetic resonance imaging (MRI), have evolved from descriptive tools into quantitative and prognostic instruments. Therapeutic approaches have evolved from supportive care to precision interventions, including anticoagulation, transjugular intrahepatic portosystemic shunt (TIPS), and autophagy-modulating agents. A comprehensive literature review, utilizing databases such as PubMed and Web of Science, was conducted to summarize progress since the introduction of the “Nanjing Guidelines”. Ultimately, this review underscores the critical need for integrated diagnostic and therapeutic frameworks, alongside enhanced public awareness and regulatory oversight, to effectively mitigate PA-related liver injury. Full article

Urban heat islands are among the most intense and unequal climate impacts in Mediterranean cities, with direct effects on health, thermal comfort, and habitability. This reality calls for the incorporation of binding and verifiable climate criteria into spatial planning and urban planning law. This article examines the extent to which the Spanish legal framework—at national, regional, and municipal levels—incorporates measurable standards to mitigate urban heat islands and how it might evolve towards operational climate-responsive urbanism. A legal–analytical and comparative methodology is applied, based on multilevel normative content analysis and a comparison of four autonomous communities, four Spanish cities, and four international reference cases with consolidated metrics. The results show that, despite progress in recognising adaptation, territorial asymmetries persist, enforceable parameters remain scarce, and there is a prevailing reliance on strategic or voluntary instruments. In response to these gaps, the study proposes a coherent set of urban climate standards (urban vegetation, functional soil permeability, roof albedo/cool roofs, green roofs and façades, plot-scale performance indices, urban ventilation, and thermal diagnostics) and a multilevel integration model aimed at guiding legislative reforms and strengthening cities’ adaptive capacity and thermal equity. Full article

Background: Primary Biliary Cholangitis (PBC) requires early diagnosis and specialized management to prevent progression to cirrhosis. This study evaluates the awareness levels of Turkish physicians from various specialties regarding the clinical features, diagnostic criteria, and current treatment protocols of PBC. Methods: A multi-regional cross-sectional survey was conducted with 269 physicians across Türkiye. Knowledge levels were assessed through a 28-item instrument covering epidemiology, diagnosis and therapy. Data distribution was non-normal (Skewness: −1.296, Kurtosis: 2.857), necessitating the use of the Kruskal–Wallis H test and Dunn–Bonferroni post hoc procedure for inter-group comparisons. Internal consistency was confirmed with a Cronbach’s alpha of 0.80. Results: The overall mean awareness score was 62.6%. Item-level analysis revealed a near-universal understanding of the non-mandatory role of liver biopsy in diagnosis (99.1%) yet identified a critical knowledge gap regarding second-line therapies, particularly the use of steroids (6.8%). Significant disparities were observed among specialties (p < 0.001). Gastroenterologists (Median: 91.07%) and gastroenterology fellows (Median: 85.71%) exhibited significantly higher proficiency compared to general practitioners (64.29%) and family medicine residents (67.86%). Internal medicine specialists outperformed primary care providers, while no significant differences were found among other subgroups after Bonferroni adjustment. Conclusions: Professional specialization is the primary determinant of PBC awareness. While core diagnostic knowledge is stable, significant gaps exist in pharmacological management among non-specialists. Targeted medical education for primary care physicians is essential to ensure timely referral and optimize patient outcomes. Full article