Search Results (12,804)

Pythiosis is a neglected infectious disease caused by the aquatic oomycete Pythium insidiosum and remains underrecognized in cattle, particularly in tropical regions. Here, we report the first molecularly confirmed outbreak of bovine pythiosis in the Amazon biome, affecting more than 400 animals raised under extensive production systems and areas with prolonged exposure to standing water. Clinically affected cattle presented ulcerative and exudative cutaneous lesions, predominantly involving the distal limbs. Given the diagnostic challenges associated with pythiosis, etiological confirmation was achieved through quantitative PCR (qPCR) targeting the internal transcribed spacer (ITS) region of P. insidiosum, providing rapid and specific molecular detection during the outbreak investigation. Therapeutic interventions were implemented as part of routine field management, including intramuscular triamcinolone combined with topical copper sulfate; this regimen was associated with clinical improvement in a substantial proportion of affected animals, though treatment efficacy was not formally evaluated. The outbreak occurred in flood-prone pastures during the rainy season, highlighting the role of aquatic environments in pathogen transmission. These findings expand the current understanding of bovine pythiosis in tropical ecosystems and underscore the importance of molecular diagnostics, outbreak surveillance, and a One Health approach for the identification and management of water-associated pathogens in livestock. Full article

Atmospheric refractivity governs the propagation behavior of electromagnetic waves in the lower troposphere. Accurate spatial characterization of this parameter is essential for optimizing communication, radar, and navigation systems. This study presents a geostatistical framework for generating high-resolution refractivity maps using Universal Kriging (UK) applied to meteorological observations from a dense network of automatic weather stations in the Galician region (NW Spain). The methodology explicitly models the non-stationary vertical structure of the atmosphere by decomposing the refractivity field into a deterministic altitude-dependent drift and a stochastic residual component characterized by an exponential variogram. Validation, performed using independent test stations bounding the regional vertical profile, demonstrates that the UK approach significantly outperforms Ordinary Kriging (OK). UK not only reduces mean errors and improves linear agreement, but critically minimizes systematic bias and extreme outlier occurrences ($P_{95}$). Beyond accurate spatial interpolation, the dynamically estimated vertical drift retrieves the macroscopic refractivity gradient, serving as a direct, real-time diagnostic tool to classify anomalous radio-frequency (RF) propagation regimes (e.g., super-refraction and ducting) and supporting robust decision-making in complex topographies. Full article

Background/Objectives: Persistent symptoms following SARS-CoV-2 infection pose a substantial burden in occupational settings. This study aimed to characterize symptoms following work-related SARS-CoV-2 infection and to assess their associations with sociodemographic and clinical factors. Methods: Data were obtained from a multicenter clinical registry of insured individuals referred for persistent symptoms 12 weeks after laboratory-confirmed work-related SARS-CoV-2 infection. Participants were assessed within a standardized post-COVID diagnostic program at six specialized clinics for occupational accident insurance in Germany. Persistent symptoms reported by ≥50% of participants were analyzed using generalized linear mixed models with random intercepts for center. Results: A total of 1511 participants (76.7% women; median age 54 years) were included, with a median interval of 16 months between infection and assessment. On average, participants reported ten persistent symptoms. The most frequent complaints were limited physical capacity (95.6%), concentration difficulties (78.8%), dyspnea (70.5%), exhaustion/tiredness (68.9%), and memory difficulties (67.5%). Individuals reporting more than ten acute symptoms had increased odds of persistent complaints (ORs between 2.1 and 4.66). Hospitalization was independently associated with persistent dyspnea (OR 1.62; 95%CI 1.17–2.25). Reinfections were linked to exhaustion and cognitive fatigue. Compared with Omicron, wild-type infection was associated with higher odds of concentration difficulties (OR 1.65; 95%CI 1.17–2.33). Comorbidities demonstrated symptom-specific associations. Conclusions: Among individuals with work-related SARS-CoV-2 infection, limited physical capacity and cognitive impairments were the most frequently reported symptoms, and higher acute symptom burden was strongly associated with the development of persistent symptoms. These findings support course-oriented evaluation and symptom-specific approaches in occupational disease assessment and management. Full article

Background and Clinical Significance: Retroperitoneal urinomas are uncommon complications that can arise following trauma, particularly in the context of congenital anomalies such as pyeloureteral duplication. These conditions pose significant diagnostic and therapeutic challenges, requiring a comprehensive and multidisciplinary approach to ensure optimal patient outcomes. Case Presentation: Here, we report the case of a 22-year-old male who presented to the emergency department with right lumbar and flank pain, nausea, and abrasions following a fall from a height. Initial imaging revealed a right-sided retroperitoneal urinoma and a rare congenital anomaly: complete pyeloureteral duplication with the upper pole draining into the right seminal vesicle. The patient underwent two surgical interventions, including the insertion of a ureteral stent and reimplantation of the ureter using a latero-terminal U trans U technique. Conclusions: This case highlights the complexity of managing traumatic retroperitoneal urinomas associated with congenital anomalies such as complete pyeloureteral duplication. It emphasizes the importance of timely surgical intervention to prevent complications and improve patient outcomes. Full article

Background/Objectives: Antimicrobial resistance (AMR) is one of the greatest health threats affecting humans, animals and the environment. Antimicrobial use (AMU) in the livestock sector contributes to the development and spread of AMR, highlighting the need to understand the current situation, to target knowledge gaps and non-prudent practices with tailored interventions, and improve antimicrobial stewardship. This is especially important in low- and middle-income countries (LMICs), where data on AMU and AMR are currently limited. This study assessed knowledge, attitudes and practices (KAP) among farmers, veterinarians, veterinary pharmacy personnel and feed mill personnel related to AMU (particularly considering the use of antibiotics) and AMR in seven former Soviet countries, Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Tajikistan and Ukraine. Methods: Face-to-face interviews were conducted between 2020 and 2025 with 3012 participants, with results analyzed using an aggregated regional approach. Results: The interviews revealed common regional knowledge gaps and practices among livestock sector stakeholders related to antimicrobials, AMR, antimicrobial residues, and prudent AMU. Non-prudent practices, such as the purchase of antimicrobials without a prescription, the use of antimicrobials as growth promoters, the inappropriate disposal of antimicrobials, and the frequent use of highest priority critically important antimicrobials (HPCIAs) were reported. Another factor that may hinder prudent AMU was the limited access of veterinarians to diagnostic laboratories. Conclusions: Despite significant global efforts to tackle AMR, there is an ongoing need to address knowledge gaps and non-prudent practices of livestock sector stakeholders in former Soviet countries. The findings highlight the importance of antimicrobial stewardship interventions that address system-level drivers of improper AMU beyond stakeholder trainings. Full article

Infectious complications remain a principal determinant of late morbidity and mortality following major thermal injury, reflecting a convergence of barrier disruption, microbial adaptation, and host immune dysfunction. The post-burn environment creates a uniquely permissive niche for pathogen persistence, characterized by altered tissue perfusion, biofilm formation, and dynamic shifts in microbial ecology toward multidrug-resistant organisms. Concurrently, profound and evolving changes in host immunity and metabolism reshape both susceptibility to infection and response to therapy. This review integrates current evidence across pathophysiology, microbiology, diagnostics, and treatment, with a focus on challenges that limit effective infection control in burn patients. Particular attention is given to diagnostic uncertainty arising from overlap between sterile inflammation and true infection, the clinical implications of biofilm-associated tolerance, and the impact of burn-specific pharmacokinetic variability on antimicrobial efficacy. We further examine emerging diagnostic and therapeutic innovations, including host-response profiling, rapid molecular detection platforms, and next-generation anti-infective strategies targeting microbial virulence, biofilm structure, and host immune pathways. Despite substantial scientific advances, translation into clinical practice remains constrained by limited burn-specific trials, heterogeneous definitions, and systemic barriers to antimicrobial development. Collectively, these challenges underscore the need for integrated, precision-based approaches that combine early source control, individualized antimicrobial optimization, and advanced diagnostic frameworks. Future progress will depend on coordinated efforts to standardize definitions, generate high-quality multicenter data, and align innovation with clinical applicability across diverse healthcare settings. Full article

Background: In clinical practice, LDL-dominant familial hypercholesterolemia (FH) may overlap phenotypically with triglyceride-dominant or mixed familial dyslipidemia. Rule-based diagnostic approaches like the Dutch Lipid Clinic Network (DLCN) and Simon Broome (SB) criteria are frequently used in countries with limited genetic testing, but their concordance with molecular confirmation is inconsistent. In a large Turkish tertiary-care cohort, we studied phenotype-related discordance between clinical criteria and molecular data and tested whether machine learning (ML) models could improve the prediction of reportable pathogenic/likely pathogenic variant positivity among patients with a clinical FH phenotype. Methods: Patients referred for suspected familial hyperlipidemia underwent targeted next-generation sequencing with a 9-gene panel. For the ML analysis, we focused on FH cases with a definitive molecular status (pathogenic/likely pathogenic vs. no reportable variant; variants of uncertain significance were excluded) and applied an 80/20 stratified split (n = 200; 82 molecular-positive cases). Elastic-net logistic regression, random forest, and XGBoost models trained on routinely available clinical variables were compared with dichotomized SB and DLCN classifications. Results: SB positivity was significantly more frequent in triglyceride-dominant phenotypes than in FH (68.4% vs. 52.3%, p = 0.041), despite the substantially lower molecular positivity (14.0% vs. 36.9%, p = 0.002), indicating FH-like false-positive clinical classification in mixed dyslipidemia. In the FH test set, the ML models showed higher discrimination for reportable pathogenic/likely pathogenic variant positivity than dichotomized rule-based criteria (AUC: XGBoost 0.808; random forest 0.769; elastic-net 0.747 vs. SB 0.639; and DLCN 0.598). Thirteen novel variants absent from gnomAD were identified, predominantly in LDLR. Conclusions: In this real-world Turkish cohort, within clinically defined FH cases, ML models performed better at predicting LP/P variant positivity than dichotomized DLCN and Simon Broome criteria. ML-based risk stratification may support prioritization for genetic testing; however, external validation is warranted. Full article

Against the backdrop of intensifying global population aging, ensuring the sustainable provision of age-friendly spaces has become an important domain of urban policy intervention. A close examination of the supply–demand matching of age-friendly spaces is therefore essential for policymakers seeking to achieve social and environmental sustainability in an aging society. However, existing approaches to assessing this alignment primarily rely on quantitative analyses of geographical spatial distribution, lacking methods to evaluate the structural alignment of spatial functional types. To address this gap, this study proposes and validates a type-based quantitative approach to examining the alignment between policy supply and everyday demands for age-friendly spaces. By integrating policy text analysis, questionnaire surveys, activity logs, and behavior snapshots, the study identifies the types of age-friendly spaces mentioned by policies and those demanded in daily life, and quantitatively evaluates their alignment using a matching model. The results show that the older adults’ spatial demands shift progressively from life-oriented spaces to survival-oriented spaces as age increases and health declines. More importantly, a significant structural imbalance is evident: survival-oriented spaces are oversupplied, while life-oriented spaces remain in short supply. This study provides a diagnostic method for assessing the provision of age-friendly spaces and provides practical implications for local governments in formulating more balanced, responsive, and sustainable supply strategies. Full article

Artificial intelligence (AI) is increasingly used to support tuberculosis (TB) screening and diagnosis, particularly through computer-aided detection (CAD) applied to chest radiography (CXR). However, the programmatic value of AI depends not only on diagnostic accuracy but also on implementation context, threshold calibration, and integration into diagnostic pathways. We conducted a narrative, state-of-the-art review of AI applications across the TB diagnosis pathway. Evidence was synthesized from World Health Organization policy documents, independent validation initiatives, and peer-reviewed studies published between 2010 and 2026, with a structured selection process aligned with PRISMA principles. CAD for CXR is the most mature AI application and is recommended by WHO for TB screening and triage among individuals aged ≥15 years in specific contexts. Across studies, CAD-CXR demonstrates sensitivity comparable to human readers, although performance varies by product, population, and imaging conditions, necessitating local threshold calibration. Evidence from implementation studies suggests improvements in screening efficiency and potential cost-effectiveness in high-burden settings. Other AI modalities, including computed tomography (CT)-based imaging analysis, point-of-care ultrasound interpretation, cough or stethoscope sound analysis, clinical risk models, and genomic resistance prediction show promising but heterogeneous results, with most requiring further independent validation and prospective evaluation. AI has the potential to strengthen TB screening and diagnostic pathways, but its impact depends on integration into health systems and evaluated using patient- and program-level outcomes rather than accuracy alone. A differentiated approach is needed, with responsible scale-up of policy-endorsed tools alongside rigorous evaluation of emerging technologies to support effective and equitable TB care. Full article

Background and Objectives: Shortages of dermatologists create significant barriers to care, particularly for inflammatory and history-dependent conditions where image-only artificial intelligence (AI) classifiers have limited applicability. Current teledermatology solutions largely focus on single-task, morphology-based neoplasm classifiers, leaving the vast majority of dermatologic presentations underserved. This study evaluated the diagnostic accuracy and management plan quality of Dermflow (Prava Medical, Delaware, USA), a proprietary dermatology-focused Multimodal Image-and-Data Assistant (MIDA) that autonomously gathers dermatology-specific history, integrates data with patient-submitted images, and outputs structured differential diagnoses and management summaries. Materials and Methods: Two AI systems, Dermflow and Claude Sonnet 4 (Claude, a leading vision–language model), analyzed 87 clinical images from the Skin Condition Image Network and Diverse Dermatology Images databases, representing 10 inflammatory dermatoses and 9 neoplastic conditions stratified across Fitzpatrick Skin Tone (FST) categories (I–II, III–IV, V–VI). For the diagnostic comparison, Dermflow received images and autonomously gathered clinical history, while Claude received identical images without history. For the management plan comparison, both systems received the correct diagnosis and the clinical histories gathered by Dermflow. The primary outcome was diagnostic accuracy. The secondary outcome was management plan quality, assessed by two blinded dermatologists across eight clinical dimensions using 5-point Likert scales. Chi-square tests compared diagnostic accuracy between models; t-tests and ANOVA compared management quality scores. Results: Dermflow achieved markedly superior diagnostic accuracy compared to Claude (86.2% vs. 24.1%, p < 0.001). Both models maintained consistent diagnostic performance across FST categories without significant within-model differences (Dermflow p = 0.924; Claude p = 0.828). Management plan quality showed no significant overall differences between models. However, composite management quality scores declined significantly for darker skin tones across both systems: Dermflow scored 4.20 (FST I–II), 3.99 (FST III–IV), and 3.47 (FST V–VI); Claude scored 4.35, 3.97, and 3.44, respectively (p < 0.001 for most pairwise FST comparisons within each model). Conclusions: Multimodal AI integrating targeted history with image analysis achieves substantially higher diagnostic accuracy than image-only approaches across both inflammatory and neoplastic dermatologic conditions. Autonomous history gathering addresses fundamental limitations of morphology-only classifiers and enables scalable, patient-facing triage across the full spectrum of dermatologic disease. However, both models demonstrated reduced management plan quality for darker skin tones despite receiving the correct diagnosis, suggesting persistent training data limitations that require targeted bias-mitigation strategies beyond domain-specific instruction. Full article

Background: Miscarriage is the most common complication of pregnancy. Current trends in medicine point to the increasing importance of evidence-based personalization in diagnostic and therapeutic processes. Purpose: The aim of this study was to develop an innovative prenatal screening panel and treatment strategy for miscarriage prevention. Results: Previous studies have demonstrated an imbalance between oxidative and anti-oxidant mechanisms, resulting in systemic oxidative stress in women with a history of miscarriage. The importance of monitoring toxic metal concentrations as potential risk factors in early pregnancy was confirmed. The involvement of NETs in the pathogenesis of miscarriages was demonstrated, while identifying early biomarkers of this process. The effect of BPA on the activation of NETs and the development of an inflammatory response in the female participants was demonstrated. Furthermore, a mechanism of NO-dependent oxidative–anti-oxidative imbalance and NLRP3 inflammasome activation during pregnancy loss was identified in a pathway independent of NET formation, excluding apoptosis. The participation of certain microRNA molecules in reproductive failure and their value in minimally invasive diagnostics in the early stages of pregnancy have been proven. Conclusions: The proposed screening panel accounts for the above parameters, represents a novel approach in modern prenatal care, and prioritizes miscarriage prevention strategies. Full article

Fibromyalgia (FM) is a complex chronic syndrome marked by widespread musculoskeletal pain, neurocognitive dysfunction (“fibro-fog”), and autonomic disturbances. Clinical management remains challenging due to subjective symptom reporting and the lack of definitive diagnostics. Emerging evidence points to a multifactorial origin involving central sensitization, neuroendocrine imbalance, and systemic immune-inflammatory alterations. A wide array of candidate biomarkers has been reported in FM, encompassing neurotransmitters (serotonin, norepinephrine), excitatory and inhibitory amino acids, metabolic and glycolytic enzymes, stress-related proteins, autoantibodies, oxidative stress markers and pro-inflammatory cytokines. This molecular heterogeneity reflects the systemic and multidimensional nature of FM. However, most of these biomarkers have been primarily investigated in serum or plasma, where analytical validation and reference ranges are more established. In contrast, the exploration of salivary biomarkers—although highly attractive due to its non-invasive, stress-free, and repeatable collection—remains comparatively limited. Saliva contains a reduced concentration range of many systemic markers and is strongly influenced by circadian rhythms, stress, flow rate, and oral health conditions. While promising candidates such as α-amylase, cortisol, calgranulins, and selected metabolic enzymes have shown potential in saliva, many proposed FM-related biomarkers lack full analytical validation, standardized protocols, and clinically defined reference intervals in this matrix. In this context, non-invasive electrochemical biosensors represent a transformative technological approach. Advanced electrode architectures incorporating nucleic acid probes, redox reporters, and nanostructured materials offer high sensitivity in low-volume and low-concentration biofluids such as saliva. The integration of multiplexed biomarker panels into portable platforms could enable real-time, longitudinal monitoring of FM pathophysiology, supporting phenotype stratification, personalized therapeutic adjustment, and objective disease activity tracking. Full article

Preeclampsia (PE), pregnancy-associated high blood pressure linked to organ damage, affects 3–8% of all pregnancies and results worldwide in 70,000 maternal and 500,000 perinatal deaths each year. Untreated PE may progress to eclampsia with long-term health implications for both mother and child. Non-invasive prenatal diagnosis or screening applies cell-free DNA approaches and offers a less invasive and more economical method for early diagnosis and prediction of various pregnancy complications. Recently, cell-free assays, particularly blood-based cell-free DNA and RNA analysis, have shown great potential in early PE prediction and diagnosis. Here, we provide an updated review of the current understanding and discoveries of PE, focusing on recent publications (1 January 2019–30 December 2025) of liquid biopsy-derived circulating fetal cells (circulating trophoblasts and fetal nucleated red blood cells), cell-free DNA, cell-free RNA and small extracellular vesicles (i.e., exosomes). We aim to discuss the conceptual framework and technical evolution of liquid biopsy applications in preeclampsia pathogenesis, prediction and diagnosis. Progressing novel screening and diagnostic molecular biomarkers have high potential to facilitate early detection for patients at risk of PE. Liquid biopsy-based screening strategies may aid in providing timely intervention and treatment. Full article

This review provides a comprehensive overview of the most adopted 3D surveying techniques in Cultural Heritage, offering practical guidance for the selection of appropriate methods when three-dimensional documentation of artworks is required. The analysis focuses on the most effective technologies for the 3D documentation of sites and objects of artistic value, with selection criteria primarily centred on non-invasiveness, given the uniqueness and cultural significance of the case studies, and the instrument flexibility, a crucial requirement for non-transportable items. A broad spectrum of 3D techniques is currently available for the multiscale diagnostic investigation of artworks, providing information at both macroscopic and microscopic levels. This review reports on the state of the art of such systems and evaluates the main characteristics of each technology in relation to its applicability in the heritage field. Particular attention is given to highlighting advantages and limitations, and to assessing performance in terms of resolution, gauge volume/area, acquisition time, and cost. In addition, the review discusses exemplary cases in which 3D methods are integrated with other analytical techniques to enable a more comprehensive understanding of the object under investigation. Finally, recent studies are examined to identify the most suitable approaches and the specific requirements for the digitization of real-world heritage assets. Full article

Driven by the large-scale deployment of renewable electricity, water electrolysis has emerged as a leading pathway for high-efficiency hydrogen production. Under practical operating conditions, gas evolution and gas–liquid two-phase flow inside electrolyzers substantially reshape electrode interfacial states and the in-cell mass transfer environment and have been reported to cause performance losses on the order of 10–30% under unfavorable conditions. This review summarizes the evolution of electrode-generated bubbles during nucleation, growth, detachment, and coalescence, and consolidates the fundamental features of two-phase hydrodynamics and phase-distribution patterns in electrolyzer channels. Progress and limitations of major two-phase modeling approaches are then assessed with respect to their capability to resolve the relevant interfacial and transport processes. The impacts of gas evolution and two-phase flow on electrochemical performance, stability, and durability are subsequently discussed. Finally, recent advances in two-phase-flow management—through flow-field organization and structural design, as well as the introduction of external physical fields—are reviewed, together with experimental and diagnostic methods used to quantify bubble behavior and phase distributions. This review aims to provide a coherent understanding of the governing behaviors, research tools, and performance implications of gas evolution and two-phase flow in water electrolysis, and to inform electrode/transport-layer design, flow-field management, and the development of predictive numerical models. Full article