Search Results (18,144)

There have been reports of an association between the gut microbiota and the development of chronic liver disease, fibrosis, and carcinogenesis; however, it is not yet possible to reach a definite conclusion. In this cross-sectional study, we examined the association between the presence or absence of hepatocellular carcinoma (HCC) and the gut microbiota in patients with chronic hepatitis B virus (HBV) infection. The study subjects consisted of 62 consecutive HBV patients admitted to our hospital who provided informed consent to participate in the study. We performed 16S rRNA analysis using DNA extracted from fecal pellets. The sequencing depth per sample was 80,000 to 90,000 reads. We calculated the proportion of each bacterial genus so that the total for each sample added up to 100%. The male-to-female ratio was 49/13, the median age was 67 years, and 46 of the patients had HCC. Twenty microbial phyla spanning 41 classes, 79 orders, 163 families, and 431 genera were identified. Receiver operating characteristic (ROC) analysis was performed on the identified bacterial taxa, from the level of phylum down to genus, to assess their ability to distinguish between patients with and without HCC. Several bacteria with an area under the curve (AUC) > 0.65 were identified as follows: TM7 phylum TM7-3 class (AUC = 0.700); Firmicutes phylum Clostridiales class Lachnobacterium genus, Dialister genus, Ruminococcus genus, and Roseburia genus (AUC = 0.670, 0.668, 0.667, and 0.660, respectively); and Firmicutes phylum Erysipelotrichi class (AUC = 0.656). Combining three of these taxa resulted in high discriminative power (p = 0.000585) with a sensitivity and specificity of 0.761 and 0.750, respectively. A similar trend was observed in the subgroup analysis based on liver reserve capacity. Even after adjusting for factors related to liver reserve capacity in the multivariate analysis, an association between these bacterial genera and HCC was confirmed. Our results suggest that gut microbiota may be associated with the prevalence of HCC in HBV patients. Full article

Background/Objectives: Blood-based biomarkers may improve risk stratification of indeterminate pulmonary nodules detected on low-dose computed tomography (LDCT). We evaluated the diagnostic performance and independent predictive value of an aptamer-based blood assay, AptoDetect™-Lung, in a high-risk Korean screening population. Methods: This multicenter prospective cohort study enrolled adults with Lung Imaging Reporting and Data System (Lung-RADS) category 3 or 4 pulmonary nodules identified on LDCT across ten tertiary hospitals in South Korea between June 2023 and December 2024. Analyses focused on a predefined high-risk subgroup meeting Korean screening criteria (age 54–74 years and ≥30 pack-years of smoking). Baseline serum AptoDetect™-Lung scores were measured. Associations with lung cancer diagnosis were assessed using univariate and multivariable logistic regression, adjusting for clinical and radiologic variables. Diagnostic performance was evaluated using receiver operating characteristic analysis. Results: Among 1084 participants with histopathologic confirmation, 319 met high-risk criteria, of whom 260 (81.5%) were diagnosed with lung cancer. In this subgroup, the AptoDetect™-Lung score was independently associated with malignancy after adjustment (adjusted odds ratio of 1.14 per unit; 95% confidence interval of 1.02–1.27; p = 0.020). Discriminative performance was higher in the high-risk subgroup than in the overall cohort (area under the curve [AUC] of 0.639 vs. 0.570; p = 0.025). Performance was higher for squamous cell carcinoma and small-cell lung cancer than for adenocarcinoma. A multivariable model incorporating biomarker score, Lung-RADS category, age, and family history achieved an AUC of 0.710. Conclusions: An aptamer-based blood biomarker may provide modest adjunctive value for risk stratification in high-risk individuals. Full article

Lost circulation in fractured formations is a major challenge during drilling operations, while conventional plugging evaluation methods relying solely on pressure-bearing curves and fluid-loss data often fail to accurately distinguish effective internal plugging from ineffective plugging behavior. To address this issue, a visualized plugging evaluation apparatus with high pressure-bearing capacity and large-window observation capability was developed to directly observe the plugging process and evaluate plugging performance under different fracture conditions. Based on the Ideal Packing Theory and the D₉₀ rule, plugging formulations were systematically evaluated under different fracture-width coefficients, slurry concentrations, and fracture-width conditions. The results showed that excessively large fracture-width coefficients or excessively high slurry concentrations could lead to premature “external plugging,” in which plugging materials accumulated near the fracture entrance without forming effective internal plugging structures. Although such cases exhibited rapid pressure buildup, visual observations confirmed that the fracture itself remained insufficiently sealed. Under the present experimental conditions, the optimized formulation with a fracture-width coefficient of 0.8 W and a slurry concentration of 25% exhibited the best overall plugging performance. The formulation reached 10 MPa in approximately 2650 s and successfully formed stable internal plugging structures under different fracture-width conditions, with the maximum variation in plugging time remaining within 7%. Field applications in Well BD-X further validated the effectiveness of the proposed method and optimized formulations under real drilling conditions. The developed apparatus and evaluation method provide a reliable experimental approach for optimizing plugging formulations and preventing lost circulation in fractured formations. Full article

Background/Objectives: The accuracy and reliability of automated clinical analyzers are fundamental to patient safety and effective medical decision-making. Traditional quality control (QC) methods, which rely on periodic manual calibration and reactive maintenance, are inherently limited by high latency and labor costs; furthermore, they fail to provide continuous, real-time monitoring. This paper introduces a novel hybrid ensemble learning framework for the automated quality inspection of medical devices through the analysis of time-series reaction curves. Methods: Our system integrates three heterogeneous anomaly detection paradigms: an Enhanced Dynamic Time Warping (DTW) detector for robust non-linear pattern matching, a Shape Template Matching (STM) detector that mimics expert clinical logic by analyzing morphological features in a normalized shape space, and a specialized Time-series Variational Autoencoder (TimeVAE) for deep representation learning. The outputs of these detectors are fused using a weighted ensemble strategy, which is specifically designed to prioritize the minimization of false negatives—a critical requirement in medical diagnostics. Results: We evaluate our framework on a comprehensive, multi-center real-world dataset comprising seven distinct biochemical assays. Experimental results demonstrate that our proposed method achieves superior performance, attaining a $0\%$ false negative rate on CRE and DBIL assays and outperforming all baseline methods on the other five datasets. An ablation study confirms the model’s robustness even with limited training data, and a comparative analysis against eight state-of-the-art baseline methods further validates the effectiveness of our domain-optimized ensemble approach. Conclusions: The system provides a robust, interpretable, and highly automated solution for transitioning from reactive maintenance to proactive, real-time quality assurance in clinical laboratories. Full article

The constitutive analysis model and hot processing map of the ZM6 alloy across various deformation conditions were investigated during hot compression experiments. True stress-strain curves within 300–450 °C and 0.0001–0.1 s⁻¹ were obtained from compression tests on a Gleeble-1500 platform. The results showed that higher strain rates (e.g., 0.1 s⁻¹) induced pronounced work hardening, whereas high temperatures (300–400 °C) combined with low strain rates (10⁻⁴ s⁻¹) promoted conditions conducive to dynamic recrystallization (DRX), leading to a softening tendency of steady-state flow stress. Additionally, a modified strain-compensated constitutive model was built for flow stress prediction. Material constants were plotted as fifth-order polynomial functions of strain (0.025–0.80) for precise stress predictions. The derived activation energy (Q = 182.38 kJ/mol) falls within the typical range for Mg-RE alloys. Leave-one-temperature-out cross-validation showed average AARE values of 7.2–9.8%, demonstrating the model’s interpolation capability and its sensitivity to extrapolation. Cross-validation within the training dataset showed reasonable consistency between experimental and predicted stresses (R > 0.997, AARE < 4.35%). Using the dynamic materials model, hot processing maps identified safe deformation zones and instability zones of the ZM6 alloy. Flow instability was observed at strain rates >0.01 s⁻¹, particularly at low temperatures (300–350 °C). Optimal processing windows appeared in high-energy dissipation (η > 30%) regions, e.g., 400–450 °C/10⁻⁴–10⁻³ s⁻¹. Optical microscopy confirmed that at high temperatures (≥400 °C) and low strain rates (≤0.001 s⁻¹), a uniform, fine-grained, fully recrystallized structure can be obtained, whereas low temperatures (350 °C) and high strain rates (0.1 s⁻¹) produce coarse elongated grains with limited DRX, consistent with the instability regime predicted by the processing maps. Under intermediate conditions (e.g., 400 °C, 0.01 s⁻¹), a bimodal grain distribution indicates incomplete recrystallization. Although EBSD analysis was not performed in this study, the optical microstructures directly validate the predicted safe and unstable windows. Together, all these findings provide preliminary model-based guidance for optimizing hot working parameters to balance microstructural stability and processing efficiency. Full article

Background/Objectives: Preoperative differentiation among benign thyroid nodules, follicular variant papillary thyroid carcinoma (FV-PTC), and classical papillary thyroid carcinoma (C-PTC) remains clinically challenging. FV-PTC is particularly difficult to identify due to its substantial sonographic and cytological overlap with both benign nodules and other malignant subtypes, frequently resulting in overtreatment or delayed diagnosis. This study aimed to develop and validate an interpretable multimodal model for accurate three-class discrimination using routine ultrasound images, with a specific focus on improving the preoperative identification of FV-PTC. Methods: This retrospective study included 479 pathologically confirmed thyroid nodules from 462 patients. Conventional ultrasound features and radiomics features extracted from grayscale ultrasound and color Doppler flow imaging were used to construct three predictive models: a Conventional Ultrasound model (conventional ultrasound features only), a Radiomics model (radiomics features only), and an Integrated model (combined features). Each model was trained using four machine learning classifiers. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Model interpretability was assessed using SHapley Additive exPlanations (SHAP) analysis, and clinical usefulness was evaluated using decision curve analysis (DCA). Results: The support vector machine (SVM)-based Integrated Model achieved the best overall performance. In the independent testing cohort, the AUCs were 0.853 for FV-PTC, 0.882 for C-PTC and 0.928 for benign nodules. The Integrated Model showed the greatest improvement for FV-PTC, with a ΔAUC of 0.141 compared with the Conventional Ultrasound Model. SHAP (SHapley Additive exPlanations) analysis identified wavelet-HL_gldm_Dependence and wavelet-HH_glcm_InverseVariance as the two most important radiomics predictors in both the Radiomics Model and the Integrated Model, demonstrating robust cross-model stability and high discriminative power. Conclusions: The SVM-based Integrated Model demonstrated promising performance for three-class classification of thyroid nodules and enhanced the preoperative identification of FV-PTC. This approach may provide an interpretable and noninvasive decision-support tool for refining subtype-specific risk stratification and supporting individualized clinical management. Full article

Typhoon-induced landslides in coastal mountainous regions are controlled by the coupled effects of rainfall, wind, topography, and storm-track geometry. However, conventional static susceptibility models have limited ability to represent event-scale forcing under extreme weather conditions. This study develops a physics-guided dynamic landslide susceptibility framework and retrospectively applies it to the 2017 Tropical Cyclone Cempaka event in Pacitan Regency, Indonesia, where 743 landslides were identified. The framework integrates static terrain factors, antecedent wetness, event-scale rainfall accumulation and intensity, maximum wind speed, and a typhoon geometric exposure index derived from IBTrACS best-track information that represents track proximity, topographic shielding, rainfall-favored quadrant effects, and storm-motion effects. Under spatial block cross-validation, model performance improved progressively from the static baseline to the full-factor model, with the receiver operating characteristic area under the curve (ROC-AUC) increasing from 0.648 to 0.751, the precision–recall area under the curve (PR-AUC) reaching 0.826, and the F1-score reaching 0.744. The full-factor model also reduced missed landslide cases from 328 to 205 and concentrated predicted high-susceptibility zones along the typhoon exposure corridor. Additional parameter-sensitivity analyses further indicate that the event-based E_geo setting produced positive performance increments under the event-consistent quadrant convention. These results indicate that physically meaningful typhoon-exposure information can improve the spatial discrimination and interpretability of event-scale landslide susceptibility assessment. Full article

Background/Objectives: Chronic non-specific low back pain (CNLBP) is a leading cause of disability worldwide. Although several randomized trials have evaluated treatment effectiveness, less attention has been given to the responsiveness of outcome measures used to assess clinical change. This study aimed to evaluate the internal and external responsiveness of commonly used outcome measures in individuals with CNLBP. Methods: This study is a secondary analysis of a randomized controlled trial. Participants were analyzed as active and placebo groups and assessed at baseline, post-intervention, and follow-up. Internal responsiveness was evaluated using standardized mean differences (SMD) and standardized response means (SRM). External responsiveness was assessed using anchor-based approaches, including correlations with the Global Rating of Change Scale (GRCS) and receiver operating characteristic (ROC) curve analysis. Results: Outcome measures demonstrated moderate to high internal responsiveness, with large effect sizes observed for pain intensity (NRS) and quality of life (EQ-5D-3L). However, external responsiveness was limited, with all instruments presenting area under the curve (AUC) values below 0.70. The Bournemouth Questionnaire showed the highest discriminative performance among the instruments. Conclusions: The evaluated instruments were sensitive to detecting change at the group level but showed limited ability to discriminate clinically meaningful improvement at the individual level. These findings support the use of combined outcome measures to improve clinical interpretation and decision-making in CNLBP. Full article

The present study aimed to estimate the heritabilities of behavioral, growth, carcass, and growth curve traits and to determine the genetic and phenotypic relationships among these traits in Japanese quail (Coturnix japonica). A total of 500 quail chicks originating from a pedigree population were evaluated for growth performance, carcass characteristics, growth curve parameters, and behavioral traits. Genetic parameters were estimated using multi-trait animal models based on pedigree information. Heritability estimates for body weight traits ranged from 0.19 to 0.71, indicating substantial additive genetic variation for growth performance. Feed conversion ratio traits showed low to moderate heritability estimates (0.06–0.45), whereas growth curve parameters exhibited heritabilities ranging from 0.13 to 0.44. Among behavioral traits, feather pecking (0.35), wing stretching (0.30), and aggressive pecking (0.28) displayed the highest heritability estimates. Favorable genetic and phenotypic relationships were observed between feeding and inactivity behaviors and growth and carcass traits, while walking behavior showed moderate negative genetic associations with BW42. In contrast, welfare-related behavioral traits generally exhibited weak relationships with production traits. Genetic correlations among several behavioral traits suggested the existence of common biological mechanisms influencing behavioral expression. These findings demonstrated that behavioral traits possess exploitable genetic variation and should be considered alongside conventional production traits in future poultry breeding programs aimed at improving both productivity and animal welfare. Full article

Background and Aims: The “quantity” of bone can be evaluated by dual-energy X-ray absorptiometry (DXA) scans, but not its “quality. We aim to study the clinical relevance of urinary-N-terminal telopeptide (NTx) in a retrospective exploratory study. Patients and Methods: The medical records of patients with osteoporosis, osteopenia with or without fractures, and with available urinary NTx were retrospectively reviewed; those on anti-osteoporotic medication before the start of the study were excluded. In all NTx levels, bone-specific alkaline phosphatase (BSAP), parathormone, serum calcium, and vitamin D were measured. In all cases, a recent DXA scan and fracture risk assessment (FRAX) had been performed. Appropriate statistics were applied using SPSS. 15. Results: Included were 93 patients (17.2% males); thirty-one (33.33%) had osteoporosis, 56 (60.21%) osteopenia, whereas 36 (38.7%) had prior or existing fractures. Older participants had lower NTx levels, and females had higher NTx levels, albeit NS. A negative correlation was found between the T-score of the left hip and NTx levels (p = 0.015) but not of the right hip or lumbar spine. In multivariate analysis, NTx levels (p = 0.013) and FRAX (p = 0.001) were significantly associated with fractures. Patients with osteoporosis had higher NTx levels when compared to patients with osteopenia (p = 0.015). NTx at a cut-off value of 207.4 showed a sensitivity of 80.6% and a specificity of 56.1% for the diagnosis of previous fracture with an area under the curve (AUC) of 0.72 (95% CI: 0.61, 0.83). Conclusions: Elevated NTx levels were significantly associated with existing or prior fractures. Combining DXA scan and FRAX, with NTx testing, may provide a comprehensive approach to osteoporosis assessment and treatment. Further prospective studies are warranted to validate its clinical utility. Full article

Background/Objectives: Inflammation plays a central role in the pathophysiology and prognosis of non-ST-segment elevation myocardial infarction (NSTEMI). This study aimed to investigate the clinical and prognostic significance of the Cumulative Inflammatory Index (IIC) in patients with NSTEMI. Methods: This single-center, retrospective study included 2274 individuals, comprising 1172 patients with NSTEMI and 1102 angiographic controls without acute coronary syndrome or obstructive coronary artery disease. IIC was calculated using mean corpuscular volume, red cell distribution width, neutrophil count, and lymphocyte count. The primary outcome was 360-day all-cause mortality in the NSTEMI cohort. Logistic regression, receiver operating characteristic curve analysis, and DeLong testing were performed. Results: Patients with NSTEMI had significantly higher IIC values than controls [9.08 (4.05–15.03) vs. 1.90 (1.45–2.89), p < 0.001]. Among NSTEMI patients, non-survivors had significantly higher IIC levels than survivors [14.25 (8.56–26.59) vs. 8.57 (3.73–14.06), p < 0.001]. In multivariable logistic regression analysis, IIC remained independently associated with 360-day all-cause mortality after adjustment for age, diabetes mellitus, estimated glomerular filtration rate, hemoglobin, albumin, and C-reactive protein (OR: 1.045, 95% CI: 1.029–1.060; p < 0.001). IIC showed a modestly higher area under the curve among the evaluated indices (AUC: 0.704). Conclusions: IIC was significantly elevated in patients with NSTEMI and was independently associated with 360-day all-cause mortality. IIC may serve as a simple adjunctive marker for risk stratification in patients with NSTEMI. Full article

Background: Given the limitations of available predictive tools for poor anticoagulation control, we aimed to build and validate a novel tool and compare its predictive ability with the SAMe-TT₂R₂ score, using the patients from the OBJETIVO 2024 study. Methods: The original sample was randomly assigned into a training group (70%, n = 1982) for model development and a validation group (30%, n = 909) for model validation. Stratification of patients was performed based on the presence of diabetes and functional dependence on daily living activities (N with available data = 2891). The model was developed through binary logistic regression, with poor international normalized ratio (INR) control (time in therapeutic range <65% using the Rosendaal method) as a dependent variable. Independent variables included renal insufficiency (glomerular filtration rate < 60 mL/min), chronic obstructive pulmonary disease, diabetes, active smoker, alcohol abuse, previous ablation, hemoglobin level, HbA1c, functional dependence in daily living activities, and number of treatments received in the last 6 months. Results: The receiver operating characteristic area under the curve (ROC AUC) was 0.579. The optimal cut-off point was 0.474 (sensitivity: 47.5%; specificity: 65.0%). Overall quality of the model for the training and validation groups was 0.49 and 0.55, respectively. The mean SAMe-TT₂R₂ in patients from the OBJETIVO 2024 study was 2.3. The ROC AUC for the SAMe-TT₂R₂ tool was 0.530. Overall quality of SAMe-TT₂R₂ for the present population was 0.51. Conclusions: None of the models presently tested reached the minimum threshold considered acceptable for discriminative ability. To date, utility of different models to predict poor anticoagulation control seems far from optimal in clinical practice. Full article

Background: The lactate-to-albumin ratio (LAR) and C-reactive protein-to-albumin ratio (CAR) are biomarkers for metabolic stress and inflammation. However, their prognostic significance after return of spontaneous circulation (ROSC) in out-of-hospital cardiac arrest (OHCA) remains unclear. Therefore, this study aims to investigate the association between serial LAR/CAR measurements and in-hospital mortality. Methods: This retrospective observational cohort study included adult comatose patients with OHCA treated with targeted temperature management between January 2022 and December 2025. Serum lactate, albumin, and C-reactive protein levels were measured at admission and at 24, 48, and 72 h after ROSC. The primary outcome was in-hospital mortality. Multivariable logistic regression analyses were performed to assess independent associations of LAR and CAR with in-hospital mortality, and discriminatory performance was assessed using the area under the receiver operating characteristic curve (AUC). Results: Of the 284 eligible patients, 253 were included in the final analysis. Of these, 80 patients died in hospital, corresponding to an in-hospital mortality rate of 31.6%. LAR and CAR were significantly higher in non-survivors than in survivors at admission and at 24, 48, and 72 h after ROSC. After adjustment for potential confounders, LAR was associated with in-hospital mortality at all assessed time points. CAR was independently associated with in-hospital mortality at admission and at 48 and 72 h after ROSC, but not at 24 h. The AUCs of LAR for predicting in-hospital mortality ranged from 0.702 to 0.734, whereas those of CAR ranged from 0.640 to 0.690. Conclusions: In this single-center retrospective cohort of post-ROSC OHCA patients, sequential tracking of LAR and CAR profiles during the first 72 h after ROSC provided meaningful insights into in-hospital mortality. LAR showed a more consistent independent association with mortality and fair discriminatory performance, whereas CAR demonstrated limited prognostic value despite its association with mortality. Full article

High-temperature structural components are susceptible to creep deformation, which can ultimately lead to failure. In this work, an AI-based framework was developed capable of predicting the creep time of 316 austenitic stainless steel. Here, creep time refers to both the time to reach specific strain levels and the time to rupture. However, the scope of the present work is limited to rupture-time prediction, while the application of the framework to strain-level prediction will be reported in future work. The dataset consisted of creep strain curves from four heats, including both rupture and non-rupture curves. Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Support Vector Regressor (SVR), Gaussian Process Regressor (GPR), and Neural Network (NN) were employed. The effects of square-root and cube-root transformations on data distribution and model learning behaviour were analysed using model learning curves. An Optuna (version 4.3.0)-based hyperparameter tuning strategy was employed. The cube-root transformation improved the learning performance of SVR, GPR, and NN, whereas RF, GB, and XGB remained unaffected. Learning curves revealed mild overfitting for RF, GB, and XGB, and very minimal overfitting for SVR, GPR, and NN. NN achieved the best predictive performance (${\mathrm{R}}^2=0.92,\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=0.195$, deviation factor of 1.57). The findings demonstrated that the combined useof creep strain curves, data transformation, learning curve guided model selection, and rigorous hyperparameter tuning can improve the prediction accuracy under a limited dataset. Full article

Artemisia campestris is a medicinal plant species endemic to Algeria, particularly abundant in the southern regions and the central Sahara. Its long-standing use in traditional medicine has recently gained scientific attention, prompting further investigation into its bioactive potential. This study focuses on the phytochemical composition and biological activity of its hydromethanolic extract, with a particular emphasis on its ability to inhibit neural enzymes associated with insect physiology with particular relevance to Aphis gossypii (Glover), a major polyphagous agricultural pest. Preliminary screening revealed a diverse array of secondary metabolites, including tannins (catechic and gallic), flavonoids, quinones, glycosides, terpenoids, saponins, coumarins, and alkaloids; however, anthocyanins were not detected. Quantitative analysis confirmed high concentrations of total phenolics (80.91 ± 1.58 mg GAE/g), flavonoids (60.45 ± 2.02 mg RE/g), phenolic acids (4.24 ± 0.38 mg CAE/g), and condensed tannins (2.26 ± 0.29 mg CE/g). Enzyme inhibition assays were performed using Ellman’s method, and IC₅₀ values were calculated by nonlinear regression analysis based on dose–response curves. The extract demonstrated significant in vitro inhibitory activity against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), with IC₅₀ values of 13.79 ± 0.79 µg/mL and 8.34 ± 0.58 µg/mL, respectively. Molecular docking analyses further confirmed strong binding affinities of cyanidin-3-O-glucoside, malvidin-3-O-glucoside, and apigenin (−8.20 to −8.50 kcal/mol) with the AChE active site, stabilized by hydrogen bonding and π–π interactions with key residues. These results were benchmarked against galantamine, a reference inhibitor, which exhibited IC₅₀ values of 1.50 ± 0.12 µg/mL under the same conditions. Although galantamine showed superior potency, the relatively low IC₅₀ values of the A. campestris extract support its potential as a natural cholinesterase-inhibitory agent warranting further investigation. These findings suggest that A. campestris may represent a promising source of natural cholinesterase inhibitors with potential relevance for eco-friendly insect control. These in vitro and in silico findings provide a mechanistic rationale warranting future in vivo bioassay validation against A. gossypii and related agricultural pests. Full article