Search Results (10,034)

Heart failure with reduced ejection fraction (HFrEF) is increasingly recognized as a systemic metabolic disorder. The aim of this study was to characterize amino acid-related metabolic differences between heart failure with moderately reduced ejection fraction (HFmrEF) (LVEF 40–49%) and HFrEF (LVEF < 40%) and to derive a biologically interpretable composite metabolomic index capable of discriminating between these two stages of systolic dysfunction. We conducted a cross-sectional metabolomic analysis of 42 patients stratified by left ventricular ejection fraction (LVEF < 40% vs. 40–49%). The reference group comprised patients with mildly reduced ejection fraction (LVEF 40–49%), without inclusion of individuals with preserved or normal cardiac function. Targeted amino acid profiling was performed using liquid chromatography-tandem mass spectrometry (LC–MS/MS). Metabolites were standardized and analyzed individually and in combination. A composite index (Heart Failure Amino Acid-Derived Systolic Index: HASI-40), integrating markers of proteolysis and metabolic resilience, was derived to distinguish patients with HFrEF from those with HFmrEF. Discrimination was assessed using receiver operator curve (ROC) analysis with internal validation and multivariable adjustment. Patients with LVEF < 40% exhibited a coordinated metabolic phenotype characterized by reduced methionine, sarcosine, serine, and taurine. While individual metabolites did not retain significance after multiple-testing correction, the composite HASI-40 index remained strongly associated with HFrEF (OR 5.56, 95% CI: 1.70–18.14; p = 0.004), although the wide confidence interval indicates limited precision due to sample size. The index demonstrated good discrimination with an area under the curve (AUC) of 0.862, which improved when combined with age (AUC 0.932). The index represents a standardized composite measure and does not define a diagnostic cutoff for individual patients. These findings suggest that HFmrEF and HFrEF exhibit partially distinct metabolic phenotypes despite overlapping clinical characteristics. These findings suggest that HASI-40 captures metabolic differences between patients with HFmrEF (LVEF 40–49%) and those with HFrEF (LVEF < 40%), reflecting progression toward more advanced systolic dysfunction. However, due to the absence of a control group with preserved ejection fraction, small sample size, and lack of external validation, the index should be considered exploratory and hypothesis-generating rather than clinically applicable. Full article

Objectives: To validate claims-based algorithms for identifying thyroid eye disease (TED) cases and assess whether machine learning can improve case identification in a large, linked real-world dataset. Methods: Using a large, linked database from Komodo Health^® and Academy IRIS^® Registry, we evaluated six rule-based algorithms incorporating Graves’ disease (GD), eye symptoms and signs. The IRIS Registry’s curated data, based on confirmed TED diagnoses from medical notes, served as the reference standard. Additionally, we developed supervised machine learning models using demographic, diagnostic, procedural, and medication data. Feature selection was performed using recursive feature elimination to rank predictive codes and construct a simplified, interpretable model. Cross-validation was used to assess model performance and compare performance with the rule-based algorithms. Results: The rule-based algorithms demonstrated a trade-off between sensitivity and specificity, with some achieving high specificity but limited sensitivity. Algorithm 1 had the highest sensitivity (48.7%) but lower specificity (59.9%) and PPV (75.8%). Algorithms 2–5 demonstrated higher specificity (87.2–93.5%) but lower sensitivity (17.8–27.0%). Algorithm 6 improved sensitivity (33.4%) compared to Algorithms 2–5 while maintaining high specificity (86.8%) and a strong PPV (86.7%). Machine learning models demonstrated similar trade-offs. One model achieved improved specificity (77.2%) with sensitivity of 49.3%, outperforming Algorithm 1 in specificity while matching its sensitivity. Another model maximized specificity (91.7%) and PPV (89.8%) at a reduced sensitivity of 28.5%. These results highlight the flexibility of machine learning models in adjusting performance to address different research objectives. Conclusions: This study evaluated existing rule-based algorithms for identifying TED cases in claims data, revealing trade-offs between sensitivity and specificity. Machine learning models provide additional flexibility, allowing performance to be tailored to specific research use cases. While no single method consistently outperformed others across all metrics, both rule-based and machine learning approaches demonstrated value in improving TED case identification using real-world data sources. Full article

This study proposes a standards-based Reference AI Business Model Canvas (Reference AI-BMC) that translates the use-case descriptors of ISO/IEC TR 24030 into the nine blocks of the Business Model Canvas, addressing the lack of a structured translation layer between AI standards and business-model design. Using ten selected fields of the ISO/IEC TR 24030 use-case template, a two-round Delphi process derives consensus-based mapping rules from expert judgments; Latent Dirichlet Allocation is used as a field-level semantic analysis to provide interpretive context for the Delphi-derived mappings. Primary mappings are reported as default translation references that met the 80% strict-consensus threshold, secondary mappings as context-dependent relations, and the adjudicated dual-mapping exception A5 (Threats/Challenges → Cost Structure) as a separately documented case. After converting the finalized primary mapping rules into a coding manual, three independent coders applied them to 81 AI use cases; the Layer 1 coding yielded Krippendorff’s α = 1.000, descriptively indicating no observed coder disagreement under the specified coding conditions. The Reference AI-BMC contributes a standards-based, consensus-derived translation layer for systematically organizing AI use cases in business-model terms, offering a structured starting point for early use-case workshops, preliminary portfolio screening, and standards-aware AI service design discussions. Together, these results position the Reference AI-BMC as a standards-based, consensus-derived reference layer for organizing AI use cases in BMC terms, with its applicability bounded by the ISO/IEC TR 24030 descriptor structure and the specified mapping procedure. Full article

With the global emphasis on sustainable development, supply chain management is facing new challenges and opportunities. Enterprises often face a large number of suppliers when selecting suppliers, which makes the selection process complex. Considering the crucial role of supplier selection in sustainable supply chains, a sustainable supplier selection model based on multi-attribute utility analysis and a fuzzy approximation ideal solution ranking method is proposed to reduce carbon emissions and environmental pollution. This model helps companies scientifically evaluate and select suppliers by comprehensively considering three aspects: environment, economy, and society. Meanwhile, the study utilizes an optimized genetic algorithm-based order allocation model to raise the efficacy and fairness of order allocation. Reducing procurement costs often relies on improving resource utilization and reducing production waste, which directly lowers the energy consumption and carbon emission intensity per unit of product. At the same time, reducing product damage and delivery delay rates can avoid additional greenhouse gas emissions caused by rework, abandonment, and emergency transportation. By improving supplier productivity and optimizing order allocation, the developed model can not only reduce economic costs but also control environmental pollution and carbon footprints from the source of the supply chain. The outcomes indicate that technological level is a crucial factor influencing supplier selection, with a significant positive impact on supplier willingness to choose, and its standard path coefficient is 0.199, with a significance level of 0.001. Meanwhile, the optimized genetic algorithm exhibits strong stability and convergence in order allocation. This optimization model has high efficiency in handling large-scale orders. This provides strong support for the decision-making of enterprises in sustainable supply chain management and a valuable reference for China’s exploration and practice in the field of sustainable development. Full article

Objective: Continuous pressure-flow cerebral bio-signals are critical for monitoring cerebrovascular dynamics but are often disrupted by missing data segments caused by artifacts from a variety of sources. These missing segments refer to segments of the signal that do not contain any valid physiological data. Such interruptions fragment the signals, resulting in discontinuities that compromise their overall integrity. Therefore, reconstructing missing values and preserving signal continuity are essential for ensuring the stable computation of signal trajectories and the accuracy of derived cerebrovascular indices. Methods: To address this issue, this systematic scoping review aimed to identify and synthesize existing interpolation and imputation strategies for handling missing segments in continuous pressure-flow cerebral bio-signals. Following the Cochrane and Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a comprehensive search of five electronic databases was conducted from their inception to 24 September 2024, and updated on 16 June 2025, using a detailed search string. Results: The initial searches yielded 19,403 results, and 8 studies were filtered and included in the review. All included studies employed interpolation techniques, such as linear and spline interpolation algorithms, to correct distorted signal segments. However, none of the included studies directly utilized interpolation or imputation strategies to reconstruct or completely fill missing data segments. Conclusions: This reveals a critical knowledge gap, as no study has systematically addressed the utilization of interpolation or imputation strategies for missing segments in pressure-flow cerebral bio-signals. Therefore, this systematic review emphasizes the need for specialized methodologies and standardized frameworks to enable reliable recovery of missing data segments in pressure-flow cerebral bio-signals, which is critical for advancing real-time neurocritical care monitoring and experimental neuroscience/psychological research. Significance: This systematic review lays the groundwork for future research into physiologically informed interpolation and imputation strategies for pressure-flow cerebral bio-signals in clinical and research applications. Full article

Reinforced concrete (RC) slabs with internal voids are increasingly used to improve material efficiency; however, their residual structural performance after fire exposure remains insufficiently understood. This study presents a numerical investigation of RC slabs with different void geometries using a three-dimensional nonlinear Finite Element (FE) model. A sequential thermal–structural approach was adopted, in which fire exposure was simulated through transient thermal analysis, and the resulting spatial distribution of maximum temperatures was used to assign residual material properties to each FE based on its local peak temperature, followed by structural analysis under ambient conditions. A parametric study was conducted on seven slab configurations, including two solid slabs and five voided slabs with spherical, elliptical, ellipsoidal, capsule, and biaxial capsule geometries. To ensure a consistent evaluation, two reference solid slabs were considered: a 230 mm thick slab to enable comparison under identical geometric conditions, and a 160 mm thick slab representing equivalent concrete volume to assess material efficiency. Fire exposure was applied according to the ISO 834 standard fire curve for durations of 30, 60, and 90 min. The results indicate that voided slabs exhibit higher deflections than the solid slab of identical thickness due to reduced stiffness, while achieving comparable performance relative to the solid slab with equivalent concrete volume. These findings highlight the trade-off between structural stiffness and material efficiency under increasing fire exposure time. Full article

Accurate paper fiber identification is essential for cultural heritage conservation. Traditional staining methods are destructive, while macroscopic AI models often lack physicochemical interpretability. This study explores the feasibility of a non-destructive analytical approach using micro-hyperspectral imaging (Micro-HSI) to overcome both limitations. Three traditional Japanese bast fibers, Kozo, Mitsumata, and Gampi, were analyzed as standard reference samples. Relative reflectance spectra were extracted from microscopic fiber regions using Micro-HSI. Dynamic normalization and Savitzky–Golay first-derivative filtering were applied to suppress scattering effects and baseline drift. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied in parallel for dimensionality reduction and supervised classification, respectively. The results indicated that unsupervised PCA exhibited substantial inter-class overlap because of the shared cellulose matrix among the fiber types. In contrast, supervised LDA amplified subtle chemical differences and achieved clear separation among the three fibers. Feature-loading analysis indicated that the classification was mainly associated with visible range reflectance characteristics, lignin π→π* absorption bands in the 400–450 nm region, and near-infrared O−H and C−H overtone vibrations near 835 nm. Leave-One-Specimen-Out Cross-Validation yielded an overall accuracy of 77.8%, with error-free classification of Kozo (F1 = 1.00) and misclassification limited to the chemically similar Gampi and Mitsumata pair. This proof-of-concept study demonstrates that combining Micro-HSI with chemometric analysis enables non-destructive fiber discrimination while retaining physicochemically interpretable spectral features. The findings also establish a microscopic spectral reference framework for future non-destructive analysis of historical paper materials. Full article

Background: Cerebral arteriovenous malformations (AVMs) are high-flow shunts in which abnormal arteriovenous connections expose draining veins to venous hypertension, arterialization, and altered oxygenation. While digital subtraction angiography (DSA) remains the reference standard for dynamic angioarchitecture, it does not directly characterize venous oxygenation or microhemorrhagic tissue changes. Objective: To synthesize current evidence on susceptibility-based MRI-susceptibility-weighted imaging (SWI) and quantitative susceptibility mapping (QSM) for characterization, risk-related features, and treatment monitoring in cerebral AVMs. Methods: Narrative review of the foundational and contemporary literature on AVM pathophysiology, SWI and QSM technical principles, and clinical applications including venous drainage depiction, microhemorrhage detection, oxygenation-related biomarkers, and post-treatment surveillance. Results: SWI provides high-resolution, non-contrast depiction of venous drainage and perinidal hemorrhagic/calcific components, improving visualization of draining veins and microhemorrhages compared with conventional MRI and complementing TOF-MRA. Arterialized draining veins may show altered SWI signal consistent with elevated venous oxygen saturation, though interpretation is indirect and influenced by flow and orientation. QSM extends susceptibility imaging by quantifying tissue susceptibility and enabling indirect estimation of venous oxygenation (SvO₂), offering a potential physiological biomarker of shunt severity and treatment response after radiosurgery or embolization. Key limitations include lack of dynamic flow timing, flow-related artifacts, orientation dependence, confounding from hemorrhage/calcification, and limited standardization and prospective validation. Conclusions: Susceptibility-based MRI does not replace DSA but meaningfully enriches multimodal AVM assessment by adding structural and physiological information-particularly venous mapping, microhemorrhage detection, and oxygenation-sensitive biomarkers. Standardized acquisition/reconstruction and prospective studies are needed to validate susceptibility-derived metrics for risk stratification and longitudinal monitoring. Full article

Schistosomiasis, a neglected tropical disease (NTD), affects humans and leads to considerable clinical morbidity and severe long-term sequelae. Laboratory diagnostics for Schistosoma mansoni are mainly based on microscopic identification of eggs in stool, but sensitivity varies with infection intensity. Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) is the gold standard for bacterial identification in high-income countries. Here, we first evaluate the capacity of MALDI-TOF MS and our existing ‘in-house helminths’ database for the identification of S. mansoni worms and eggs. A subset of adult worms and egg samples was used to generate MALDI reference spectra, which were added to the database and evaluated by blind-test identification. Subsequently, egg-free human stool was spiked with purified S. mansoni eggs and analyzed by MALDI-TOF MS. Log score values (LSVs) were employed to assess the reliability of identification. A total of 62/90 (68.9%, 95% confidence interval (CI): 58.3–78.2%) adult samples were correctly identified. After database expansion, 90/90 (100%, 95% CI: 96.0–100%) and 59/60 (98.3%, 95% CI: 91.1–100%) were correctly identified for adult worms and purified eggs, respectively. In contrast, the analysis of 35 human stool samples spiked with S. mansoni as eggs did not yield identifiable spectra. MALDI-TOF MS can be applied for the identification of isolated adult S. mansoni worms and eggs. Further investigations and optimization are necessary before potential application to clinical samples (e.g., for egg detection in stool). Full article

Circadian desynchronization is increasingly linked to metabolic, immune, neurocognitive, and oncological disease, but integrated clinical phenotyping across endocrine, microbiome, metabolic, and neuroimaging domains remains limited. We conducted a prospective, single-centre, observational study in 179 symptomatic patients referred for chronic multisystem features consistent with circadian dysregulation and 107 practically healthy controls. Circadian melatonin status was assessed using fractionated 24 h urinary 6-sulfatoxymelatonin (aMT6s) and standardized dim-light plasma sampling at daytime (14:00–16:00) and nocturnal (02:00–04:00) windows. Microbiome composition was assessed by 16S rRNA sequencing, urolithin A by targeted metabolomics, and putative glymphatic/perivascular clearance by MRI-derived DTI-ALPS index, perivascular space scoring, and white-matter-hyperintensity (WMH) volumetry. Patients showed markedly reduced nocturnal melatonin output and loss of day–night contrast (night aMT6s 10.2 vs. 40.6 ng/mL; urinary aMT6s day/night ratio 0.81 vs. 0.14; plasma nocturnal melatonin 12.7 vs. 54.4 pg/mL; all p < 0.0001), accompanied by altered cortisol day–night patterning. Patients also showed reduced microbiome diversity, depletion of Gordonibacter and Ellagibacter, lower plasma urolithin A, higher WMH volume and perivascular space scores, and a lower DTI-ALPS index. Age distributions broadly overlapped in the individual-level dataset, and key biomarkers were not significantly correlated with chronological age within the patient cohort; however, this finding is interpreted as an exploratory absence of detectable age gradient within the symptomatic cohort, not as proof of biological age-independence. Overall, the data support a coherent cross-sectional association among blunted nocturnal melatonin rhythmicity, dysbiosis/urolithin depletion, and MRI markers compatible with impaired perivascular clearance. The MGM axis framework should be regarded as hypothesis-generating; causal direction, melatonin receptor involvement, and AQP4-related mechanisms require longitudinal and mechanistic validation. Full article

Background/Objectives: An intraoperative pathological assessment is essential for surgical decision-making but is limited by artifacts and a suboptimal image quality in frozen section analysis (FSA). Rapid fresh pathology (RFP), enabled by whole-mount tissue staining and a digital H&E transformation, has emerged as a potential alternative. This study aims to clinically validate the feasibility and diagnostic performance of RFP in lung and breast surgical specimens. Methods: Fresh surgical specimens, including tumor and adjacent non-neoplastic tissues, from patients with lung and breast lesions were processed using a rapid staining protocol and optical imaging via PATHOscope PS100 (Mesoview, New Taipei City, Taiwan) without slicing tissues. Diagnoses based on RFP images were independently evaluated by pathologists and compared with reference diagnoses obtained from formalin-fixed paraffin-embedded FFPE tissue histology to assess diagnostic concordance. Results: RFP demonstrated a high concordance with standard histopathology in both lung and breast specimens. The technique preserved the tissue architecture and provided a clear visualization of the cellular morphology, similar to conventional FFPE processing. In addition, RFP significantly reduced the time required for the tissue evaluation compared with FFPE workflows. Conclusions: RFP is a feasible and reliable approach for the rapid evaluation of lung and breast surgical specimens. Its ability to provide high-quality morphological information while preserving tissue integrity highlights its potential for future clinical applications, including intraoperative and time-sensitive pathological assessments. Full article

This study evaluates the mechanical properties of Hinoki (Chamaecyparis obtusa) from Japan to determine reliable design values for its application as structural dimension lumber species in the United States. A comprehensive experimental program was conducted on 1464 (approximately 240 per grade/size) dimension lumber in-grade specimens sourced from prominent Hinoki-growing regions of Japan. These specimens were tested in bending, compression perpendicular to the grain, and horizontal shear. Tests were conducted, and the results were subjected to statistical analysis and adjustment factors to determine base reference values in accordance with ASTM International standards. The four-point bending tests showed moderate numerical variation across growing regions; however, one-way ANOVA confirmed no statistically significant regional effect on MOR or MOE. Compression parallel to grain and tensile strength were estimated from the MOR values using empirical relationships per ASTM D1990. The base design values after adjustments for 15% moisture content, specimen size, and volume effects fall within the expected range for high-quality structural species and support the acceptability of Hinoki as a load-carrying wood species. The results constitute the first complete, statistically verified dataset for Hinoki, and provide a basis for its use in wood design specifications such as the National Design Specification (NDS) for Wood Construction (NDS). Wider recognition of Hinoki as a viable structural species could expand its commercial use and support sustainable forest management practices in Japan. Full article

Aiming at the inherent bottlenecks of traditional smoke detection technologies in high and large-volume building scenarios, this paper conducts research on an early fire smoke detection method for high and large-volume spaces based on Light Detection and Ranging (LiDAR). A special experimental platform was independently designed to obtain the physical characteristics of smoke particles from standard smoldering fires. Combined with the optical scattering and reflection interaction mechanism between laser and particulate matter, the theoretical feasibility of LiDAR for smoke detection was systematically verified. Smoke irradiation experiments were carried out in the full detection distance, and the LiDAR point cloud characterization characteristics of smoldering smoke were clarified. A special smoke detection algorithm based on point cloud features was designed, a LiDAR smoke detection system was built, and multi-condition comparative experiments with traditional photoelectric smoke detection methods were carried out in a full-scale laboratory. The experimental results show that the LiDAR-based smoke detection method proposed in this paper has significant advantages over traditional detection methods in terms of alarm response speed, detection coverage, and height adaptability. This research provides a brand-new technical path and reference for the theoretical research and engineering application of early fire warning technology for high and large-volume buildings. Full article

Antimicrobial resistance in bovine mastitis pathogens poses interconnected threats to dairy productivity and public health; however, longitudinal evidence on the association between veterinary oversight and lower resistance to critically important antimicrobials in tropical smallholder systems remains limited. This study analyzed 1347 quarter milk samples from 47 dairy farms in Saraburi Province, Thailand, submitted consecutively over five years (2020–2025), to evaluate whether participation in a veterinary-led herd health (HH) program was independently associated with reduced vancomycin resistance and multidrug resistance (MDR). Bacterial identification was conducted using standard methods, and susceptibility was determined using the Kirby–Bauer disk diffusion method. Among 1069 culture-positive samples, Streptococcus spp. (25.3%) and coagulase-negative staphylococci (16.8%) predominated. Vancomycin resistance declined from 15.9% to 5.4% between early (2020–2022) and recent (2023–2025) periods. Of the 686 isolates tested against ≥3 antibiotic classes, 61 (8.9%) were MDR; vancomycin resistance co-occurred in 34.4% of these samples. Repeated logistic regression identified non-HH farm status (OR = 4.035; p = 0.027) and early-year groups (OR = 4.611; p = 0.002) as independent risk factors for vancomycin resistance. As HH participation and year group showed a significant interaction, their joint effect for MDR was modeled: compared with non-HH farms in the early period (reference), all other combinations—including HH farms in 2023, HH farms in 2020, and non-HH farms in 2023—were associated with significantly lower odds of MDR. These findings provide the first evidence from Thailand that structured veterinary herd health programs are independently associated with lower critically important antimicrobial resistance, supporting their integration into a One-Health stewardship policy. Full article

Methylomics has emerged as a central framework for understanding gene regulation in development and disease, yet the rapid expansion of profiling technologies, computational integration methods, and clinical applications has outpaced comprehensive synthesis. This review addresses that gap by systematically examining current advances across the full methylomics pipeline, from data generation to clinical translation. We draw on evidence from large-scale consortium datasets and benchmarking studies of multi-omics integration methods including MOFA, DIABLO, and deep learning architectures, single-cell and spatial methylomic technologies, long-read sequencing platforms (Oxford Nanopore, PacBio HiFi), and cell-free DNA (cfDNA) liquid biopsy approaches. The review further surveys methylation dysregulation across major disease domains, including cancer, cardiovascular disease, neurological disorders, and autoimmune conditions. Integrating methylomic data with transcriptomic and chromatin accessibility layers, particularly in spatial and single-cell contexts, substantially improves the resolution of disease-associated regulatory mechanisms. cfDNA methylation profiling emerges as a cross-disease, non-invasive monitoring platform with broad diagnostic potential, supported by machine learning-based deconvolution. We conclude that while technological barriers are diminishing, standardization of analytical workflows, population diversity in reference datasets, and regulatory alignment remain the principal challenges for translating methylomics advances into broadly accessible precision medicine. Full article