Search Results (17,529)

Introduction: Although anterior non-infectious uveitis affects the structures of the anterior segment of the eye, (inflammatory) disruption of the hemato–ocular barrier may lead to changes in the structures of the posterior segment of the eye. Objective: To evaluate functional retinal changes using multifocal electroretinography (mfERG) and their relationship with structural optical coherence tomography (OCT) parameters in patients with acute anterior non-infectious uveitis (AANU). Methods: This prospective study included 38 eyes of 19 patients diagnosed with unilateral AANU and age-matched healthy fellow eyes as controls. All subjects underwent comprehensive ophthalmological examination, including best-corrected visual acuity (BCVA), spectral-domain OCT, and mfERG testing at baseline, 3 months, and 6 months. mfERG parameters (amplitude and implicit times) were analyzed alongside central field thickness (CFT), macular volume (MV), and average macular thickness (AMT). Results: Eyes affected by AANU demonstrated a significant reduction in mfERG response amplitude in the central retinal region compared with control eyes, particularly during the acute phase. Although OCT parameters showed partial structural normalization during follow-up, functional recovery was less pronounced in selected retinal regions. Latency values showed minimal variation over time. These findings indicate a potential dissociation between electrophysiological function and structural morphology during disease resolution. Conclusions: Acute anterior uveitis is associated with measurable macular functional impairment detectable by mfERG, even when structural OCT parameters appear relatively stable. These results suggest that inflammatory processes in AAU may extend beyond the anterior segment and transiently affect retinal function. mfERG may therefore serve as a sensitive adjunct tool for detecting and monitoring subclinical macular dysfunction in AANU. Clinical Relevance: Functional retinal impairment may persist despite apparent structural recovery in acute anterior uveitis. Incorporating mfERG into clinical evaluation may improve the detection of subtle macular involvement and enhance understanding of disease dynamics beyond conventional imaging findings. Full article

This study investigates how CEO demographic characteristics, including age, gender, and nationality, and cognitive characteristics, including tenure, education, and multiple directorships, influence firms’ ESG performance, with a focus on the moderating role of Worldwide Governance Indicators (WGIs). Using a regime/smooth transition approach with panel data from STOXX Europe 600 firms spanning the years 1999 and 2023, the results show that demographic characteristics exert a more consistent effect than cognitive effects in the full sample and in non-sensitive industries. In sensitive industries, however, both demographic and cognitive CEO traits significantly affect ESG performance. Older and female CEOs enhance ESG performance under strong worldwide governance indicators (WGIs) in the full sample and sensitive industries, whereas foreign CEOs perform better under weaker worldwide governance conditions. In non-sensitive industries, the patterns for female and foreign CEOs are reversed. Cognitive traits such as tenure and multiple directorships show limited influence, while higher educational qualifications improve ESG outcomes under weak governance but reduce them under strong governance across all samples. Overall, the findings highlight the importance of aligning CEO characteristics with the institutional governance environment to enhance corporate sustainability performance. This study contributes by examining how CEO demographic and cognitive characteristics affect ESG performance under varying country-level governance conditions. It also highlights sectoral differences between sensitive and non-sensitive industries and, by using a nonlinear (PSTR) approach, uncovers regime-dependent effects with implications for governance-aware CEO selection and ESG strategy. This study extends upper echelons and institutional theories by showing that the effect of CEO characteristics on ESG performance depends on country governance quality, offering insights for boards and policymakers seeking to align leadership selection with governance contexts to strengthen sustainability and accountability. Full article

C₁₃-Norisoprenoids are important contributors to the aroma of Riesling wine. Their quantification is analytically challenging due to their low concentrations, the lack of commercial standards and their pronounced sensitivity to analytical conditions, reflecting their chemical lability, as well as the dynamic nature of the wine matrix, leading to high reactivity and, consequently, remarkable structural diversity. Here, we developed an assay for the analysis of C₁₃-norisoprenoids in wine using headspace solid-phase microextraction coupled to gas chromatography–mass spectrometry (HS-SPME–GC-MS/MS). After evaluating different fiber materials, a statistical design of experiments (DoE) approach was employed to systematically optimize key HS-SPME parameters, including incubation, extraction and desorption conditions. Selected reaction monitoring (SRM) transitions were established for all targeted C₁₃-norisoprenoids, allowing the assay to provide relative quantification of more than 40 compounds using representative labeled and unlabeled standards to generate linear calibration curves. Following method validation, this approach was applied to a young German Riesling wine to investigate the effect of various acidic hydrolysis conditions on the norisoprenoid profile as well as on specific compounds. A central composite design (CCD) was used to systematically study the impact of pH, temperature, and hydrolysis time. Quantitative data were obtained for 22 C₁₃-norisoprenoids demonstrating that hydrolysis conditions strongly affected the norisoprenoid composition. pH and temperature showed a greater influence than reaction time. Response surface models (RSM) indicated that TDN, Vitispirane and TPB in particular are predominantly formed under strongly acidic and high-temperature conditions, whereas others such as Riesling acetal and actinidols are formed under milder conditions. The results indicate that hydrolysis conditions should be tailored to the specific norisoprenoid under investigation and the research question, particularly when simulating conditions of accelerated wine ageing for analytical purposes. Full article

Escherichia coli (E. coli) is a microorganism commonly found in water and food matrices, and its rapid and accurate detection is crucial for maintaining public health and ensuring food safety. However, traditional molecularly imprinted polymer (MIP) sensors often face challenges such as tedious template removal and prolonged sensing times. This study develops a label-free bacterial molecularly imprinted sensor that utilizes the synergistic effect of polypyrrole (PPy) and multi-walled carbon nanotubes (MWCNTs) to achieve highly sensitive detection of E. coli. Based on the large specific surface area and superior conductivity of MWCNTs, as well as the favorable electrochemical polymerization properties of PPy, a PPy/MWCNTs composite film was fabricated via a one-step electropolymerization process. The prepared sensor exhibited excellent kinetic characteristics, with a template removal time of only 15 min, and could be regenerated and used for subsequent detection within 30 min. Under optimized conditions, the biosensor showed a satisfactory linear response over the concentration range of 10²–10⁸ CFU/mL, with a low detection limit of 65 CFU/mL (3σ/S). Furthermore, recovery experiments conducted in tap water and lemon juice samples yielded satisfactory recoveries ranging from 87.1% to 114.8%, demonstrating the reliability and practical applicability of the proposed sensor for bacterial detection in real samples. This sensor offers advantages such as simple preparation, low material cost, and high sensitivity, providing a reliable and practical analytical platform for the rapid and reliable detection of bacteria. Full article

The impact of environmental disturbances and sensor deployment variations on damage identification represents a critical bottleneck that constrains the practical effectiveness of structural health monitoring. Existing methods addressing these challenges often suffer from poor interpretability due to information loss during feature extraction or exhibit insufficient sensitivity in identifying early-stage minor damage. This paper proposes a damage identification method based on the Shapelet Transform and Random Forest classifier, which extracts highly interpretable local shape features from vibration response signals to achieve robust identification of structural state changes. The study utilizes measured random vibration response data from a timber truss bridge. The dataset comprises four reference states collected on different dates and five damage states simulated by additional masses ranging from +23.5 g to +193.7 g, with sensors deployed in both vertical and horizontal directions. The Shapelet Transform selects local subsequences with high information gain from the original time series as features, which are subsequently classified using the Random Forest algorithm. The experimental design systematically investigates the influence of different damage severities, sensor locations, and environmental variations on method performance. The results demonstrate that with a Shapelet extraction time of 10 min, the method achieves 100% identification accuracy across multiple operating conditions comprehensively considering environmental variations, sensor location differences, and varying damage severities. When the extraction time is reduced to 5 min, 3 min, and 1 min, the average accuracies are 93.98%, 89.51%, and 58.48%, respectively. The method effectively identifies the minimum simulated damage (+23.5 g), which represents only 0.07% of the total structural mass, while maintaining stable performance under varying sensor locations and environmental conditions. Compared to traditional methods based on global frequency-domain features or statistical characteristics, the proposed method extracts physically meaningful local Shapelet features, offering significant advantages in interpretability. In contrast to deep learning approaches, this method demonstrates greater robustness under limited sample conditions. This study confirms that the combined framework of the Shapelet Transform and Random Forest can effectively address multiple real-world challenges in structural health monitoring, delivering high accuracy, strong robustness, and excellent interpretability, thereby providing a novel approach for developing practical real-time damage identification systems. Full article

Vitamin K (VK), traditionally recognized for its role in coagulation, is increasingly implicated in extrahepatic processes, including glucose metabolism and calcium regulation. A suboptimal VK status is common in the general population and may limit these functions, yet evidence linking VK to glucose metabolism and other endocrine axes remains heterogeneous and incompletely synthesized. This narrative review integrates mechanistic, observational, and interventional evidence to examine the role of VK across the endocrine system, with particular emphasis on glucose metabolism. Mechanistic studies indicate that VK supports pancreatic β-cell function, modulates peripheral insulin sensitivity, and facilitates proper calcium distribution. Observational studies consistently associate a higher VK status with a lower risk of type 2 diabetes, while interventional studies suggest that VK supplementation may improve glucose metabolism, primarily in metabolically impaired populations. In bone and mineral metabolism, VK acts synergistically with calcitriol, with combined supplementation showing more consistent benefits in skeletal outcomes than either vitamin alone. Evidence for VK involvement in other endocrine axes, including reproductive and inflammatory pathways, remains limited and largely mechanistic. Overall, the available evidence supports a context-dependent role for VK in glucose metabolism, influenced by baseline nutritional and metabolic status and outcome selection, as well as a synergistic interaction with calcitriol and parathormone in calcium regulation. Future clinical studies should incorporate baseline VK status stratification, dynamic measures of insulin sensitivity, and adequately powered designs to clarify the therapeutic relevance of VK across endocrine and metabolic outcomes. Full article

In this study, Ag-functionalized porous ZnO nanocomposites were successfully synthesized via pyrolysis of Ag-loaded ZIF-8 precursors. The structural and surface properties of the materials were systematically characterized using XRD, XPS, FESEM, and HRTEM analyses. A gas sensor fabricated from the optimized 3.0 wt% Ag–ZnO sample exhibited a significantly enhanced response (Ra/Rg = 103) toward 100 ppm acetone at an operating temperature of 275 °C, which is approximately 2.51 times greater than that of pristine ZnO. The sensor also demonstrated rapid response/recovery times (6 s/7 s), excellent linearity over a wide concentration range (500 ppb–200 ppm), good selectivity against common interfering VOCs, and stable performance, with over 95% response retention after 30 days. The improved sensing performance is attributed to the hierarchical porous structure derived from ZIF-8 and the increased oxygen vacancy concentration and chemisorbed oxygen species induced by Ag loading, which collectively increase surface reaction activity. This work provides an effective strategy for constructing noble metal-modified porous ZnO materials for sensitive and reliable acetone detection. Full article

Disruption management is critical for airline operations, yet existing recovery models often assume deterministic disruption durations, limiting their effectiveness in real-world, uncertain environments. This paper addresses the integrated airline recovery problem under uncertain disruptions. To capture this uncertainty, delay times are modeled as fuzzy variables and a fuzzy chance-constrained programming model is developed, aimed at minimizing total recovery costs. The model is transformed into a deterministic equivalent using trapezoidal fuzzy numbers. An improved Greedy Randomized Adaptive Search Procedure (GRASP) algorithm is designed to efficiently solve the problem, balancing solution quality and computational efficiency through insert, exchange, and cancel. The local search process is enhanced by incorporating the acceptance criteria of the simulated annealing algorithm. The proposed method is validated using real-world airline data. Results show that, compared to the traditional GRASP algorithm, the improved GRASP algorithm can obtain better solutions in a shorter time; the solutions in deterministic scenarios tends to be more conservative, leading to resource waste; the proposed method can achieve airline recovery at the minimum recovery cost. Sensitivity analysis reveals that selecting an appropriate confidence level significantly influences recovery costs. This paper provides a robust framework for enhancing operational resilience and passenger satisfaction under uncertain conditions, offering practical insights for real-world application. 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

The rapid expansion of bibliometric research has generated a large volume of descriptive “snapshot” studies that map publication trends but offer limited strategic or policy-relevant insight. Although improved database access and visualization tools have broadened participation in bibliometric analysis, methodological variability, limited reproducibility, and insufficient evaluative framing constrain its utility for research governance. We argue that bibliometric studies should not be conducted as ends in themselves, but as methods for addressing clearly defined, decision-relevant questions. We define evaluative bibliometrics as decision-oriented analysis grounded in explicit research questions, theoretically aligned indicator selection, temporal sensitivity, robustness assessment, and contextual interpretation. Key methodological considerations are examined, including database selection, search strategy design, attribution bias, normalization approaches, and science mapping parameters. We further synthesize emerging reporting frameworks and propose an evaluative extension framework that integrates decision-context specification with structured transparency requirements. By reframing bibliometrics as a decision-support discipline rather than a descriptive genre, this paper provides a methodological roadmap for researchers, editors, and institutions seeking to enhance the rigor, interpretability, and strategic relevance of bibliometric evidence. Full article

Serum contains diverse proteins whose concentrations vary with pathological conditions such as cancer, liver disease, neurological disorder, and infections. Conventional methods like serum protein electrophoresis (SPEP) and enzyme-linked immunosorbent assay (ELISA) are gold standards for protein identification; however, they are time-consuming and can miss abnormal serum protein levels. Inspired by chemical nose sensing based on selective sensor–analyte interactions, we synthesized five pyrene-conjugated fluorescent polymers (PFPs) with distinct side-chain head groups to construct a multichannel fluorescence sensor array. These polymers were screened for sensitivity to changes in serum protein levels using linear discriminant analysis (LDA), a machine learning method. This process led to the successful discovery of two PFPs that effectively detect protein level fluctuations. These PFPs provided a sensitive sensor array capable of generating a high-content response pattern (fingerprint) with six fluorescence channels. This sensor array successfully discriminated protein level fluctuations in serum with 98% jackknife classification accuracy and 95% unknown identification accuracy. This polymer sensor array holds strong potential as a diagnostic tool for serum-based samples and can be extended to other applications related to protein identification. Full article

Endoscopic bariatric and metabolic therapies (EBMTs) offer minimally invasive treatment options for obesity and related metabolic disorders such as type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated steatotic liver disease (MASLD). These therapies are broadly categorized into gastric and small bowel interventions. Gastric EBMTs, including intragastric balloons and endoscopic sleeve gastroplasty, promote weight loss primarily through mechanical restriction and delayed gastric emptying, thereby improving metabolic outcomes. Small bowel therapies target the proximal intestine to modulate nutrient-sensing and hormonal pathways, providing metabolic benefits that may occur independently of weight loss. Techniques such as duodenal mucosal resurfacing, electroporation-based re-cellularization, and duodenal-jejunal bypass liners demonstrate promising effects on glycemic control, insulin sensitivity, and liver health. Emerging technologies utilizing thermal, vapor, and laser ablation further expand therapeutic possibilities. While these interventions show favorable safety profiles and potential as standalone or adjunctive treatments, further long-term studies and randomized trials are necessary to optimize patient selection and procedural protocols. Collectively, EBMTs represent an evolving paradigm in the management of obesity and metabolic diseases, bridging the gap between conservative medical therapies and bariatric surgery. Full article

Campylobacter spp. is one of the most common pathogens responsible for gastroenteritis in developed countries and is raising public health concerns worldwide. This work optimized a label-free electrochemical genosensor based on screen-printed gold electrodes (SPAuEs) for the rapid detection of Campylobacter jejuni, C. coli, C. lari and C. upsaliensis. SPAuEs were functionalized with a specific thiolated DNA probe and tested with a ferrocyanide solution for signal production. The optimization of the conditions was obtained using DNA extracted from pure cultures of Campylobacter spp. and negative controls such as Escherichia coli, Listeria innocua, Salmonella spp., and Helicobacter pylori. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were compared to assess sensitivity and specificity. The relative change in intensity of the ferrocyanide anodic peak (Ipa) was proportional to the value of Campylobacter spp. DNA concentrations in the range of 1 pg/µL to 10⁴ pg/µL. The limit of detection of our optimized system was 1.06 pg/μL. After optimization, the method was applied to chicken meat samples from the market. The proposed electrochemical DNA biosensor was able to detect Campylobacter jejuni, C. coli, C. lari and C. upsaliensis after selective enrichment and DNA isolation within 60 min of DNA extraction, demonstrating its usefulness for routine analyses. Full article

Feature weighting plays a central role in medical classification by enhancing predictive accuracy, interpretability, and clinical trust through the explicit quantification of variable relevance. Despite their widespread use, existing filter-, wrapper-, and embedded-based feature weighting methods are predominantly deterministic and exhibit pronounced sensitivity to label noise and outliers, which are pervasive in real-world medical data. This often results in unstable importance estimates and unreliable clinical interpretations. In this work, we introduce a novel Bayesian feature weighting model that fundamentally departs from existing approaches by jointly integrating simplex-constrained Dirichlet priors for global feature weights, hierarchical shrinkage priors for coefficient regularization, and contamination-aware priors for explicit modeling of label noise within a single coherent probabilistic framework. Unlike conventional Bayesian feature selection or robust classification models, the proposed formulation yields globally interpretable feature weights defined on the probability simplex, while simultaneously providing full posterior uncertainty quantification and robustness to both mislabeled observations and aberrant feature values through principled influence control. Comprehensive simulation studies across diverse contamination scenarios, together with applications to multiple real-world medical datasets, demonstrate that the proposed model consistently outperforms classical and state-of-the-art baselines in terms of discrimination, probabilistic calibration, and stability of feature-importance estimates. These results highlight the practical and methodological significance of the proposed framework as a robust, uncertainty-aware, and interpretable solution for medical decision making under noisy data conditions. 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