Search Results (3,232)

Background: Peritonitis in peritoneal dialysis and cirrhosis remains common and leads to morbidity. Neutrophil gelatinase-associated lipocalin (NGAL) has been evaluated as a rapid adjunctive biomarker. Methods: Following PRISMA-DTA and PROSPERO registration (CRD420251105563), we searched MEDLINE, Embase, Cochrane Library, LILACS, Scopus, and Web of Science from inception to 31 December 2024, and ran an update on 30 June 2025 (no additional eligible studies). Diagnostic accuracy studies measuring NGAL in peritoneal/ascitic fluid against guideline reference standards were included. When 2 × 2 data were not reported, we reconstructed cell counts from published metrics using a prespecified, tolerance-bounded algorithm (two studies). Accuracy was synthesized with a bivariate random effects (Reitsma) model; 95% prediction intervals (PIs) were used to express heterogeneity; small-study effects were assessed by Deeks’ test. Results: Thirteen studies were included qualitatively and ten were entered into a meta-analysis (573 cases; 833 controls). The pooled sensitivity was 0.95 (95% CI, 0.90–0.97) and specificity was 0.86 (0.70–0.94); likelihood ratios were LR+ ≈7.0 and LR− 0.06. Between-study variability was concentrated on specificity: the PI for a new setting was 0.75–0.98 for sensitivity and 0.23–0.99 for specificity. Deeks’ test showed evidence of small-study effects in the primary analysis; assay/platform and thresholding contributed materially to heterogeneity. Conclusions: NGAL in peritoneal/ascitic fluid demonstrates high pooled sensitivity but variable specificity across settings. Given the wide prediction intervals and the signal for small-study effects, NGAL should be interpreted as an adjunct to guideline-based criteria—not as a stand-alone rule-out test. Standardization of pre-analytics and assay-specific, locally verified thresholds, together with prospective multicenter validations and impact/economic evaluations, are needed to define its clinical role. Full article

Ensuring food quality and safety is a growing challenge in the food industry, where early detection of contamination or spoilage is crucial. Using gas sensors combined with Artificial Intelligence (AI) offers an innovative and effective approach to food identification, improving quality control and minimizing health risks. This study aims to evaluate food identification strategies using supervised learning techniques applied to data collected by the BME Development Kit, equipped with the BME688 sensor. The dataset includes measurements of temperature, pressure, humidity, and, particularly, gas composition, ensuring a comprehensive analysis of food characteristics. The methodology explores two strategies: a neural network model trained using Bosch BME AI-Studio software, and a more flexible, customizable approach that applies multiple predictive algorithms, including DT, LR, kNN, NB, and SVM. The experiments were conducted to analyze the effectiveness of both approaches in classifying different food samples based on gas emissions and environmental conditions. The results demonstrate that combining electronic noses (E-Noses) with machine learning (ML) provides high accuracy in food identification. While the neural network model from Bosch follows a structured and optimized learning approach, the second methodology enables a more adaptable exploration of various algorithms, offering greater interpretability and customization. Both approaches yielded high predictive performance, with strong classification accuracy across multiple food samples. However, performance variations depend on the characteristics of the dataset and the algorithm selection. A critical analysis suggests that optimizing sensor calibration, feature selection, and consideration of environmental parameters can further enhance accuracy. This study confirms the relevance of AI-driven gas analysis as a promising tool for food quality assessment. Full article

Background: Radon, a naturally occurring radioactive gas, is increasingly recognized as a major risk factor for lung cancer (LC), especially among non-smokers. The objective of this study was to identify serum biomarkers for the early detection of LC in individuals at high risk due to prolonged residential radon exposure in Chiang Mai, Thailand, and to assess whether the use of single or combined biomarkers improves the sensitivity and specificity of detection. Methods: A total of 15 LC patients and 30 healthy controls (HC) were enrolled. The HC group was further stratified into two subgroups: low radon (LR, n = 15) and high radon (HR, n = 15) exposure. All participants were non-smokers or former smokers. Serum levels of cytokeratin 19 fragment (CYFRA 21-1), carcinoembryonic antigen (CEA), interleukin-6 (IL-6), interleukin-8 (IL-8), transforming growth factor-alpha (TGF-alpha), and indoleamine 2,3-dioxygenase-1 (IDO-1) were measured using the Milliplex^® Kit on a Luminex^® Multiplexing Instrument (MAGPIX^® System). Results: Serum CEA, IL-6 and IL-8 levels were significantly higher in LC patients compared to the HC group (p < 0.05). Among analyzed biomarkers, only IL-8 was significantly elevated in LC patients compared to the HR group (p = 0.04). Notably, CYFRA 21-1 was the only biomarker that significantly differed between LR and HR groups (p = 0.004). The diagnostic potential of these biomarkers was evaluated using receiver operating characteristic (ROC) analysis. Individually, IL-6 showed the highest discriminative ability for differentiating LC patients from both HC and HR groups, with high specificity but moderate sensitivity. Combining IL-6 and IL-8 improved specificity and increased the area under the ROC curve (AUC), though it did not enhance sensitivity for distinguishing LC from HC. For distinguishing LC from HR individuals, IL-6 and CYFRA 21-1 exhibited strong diagnostic performance. Their combination significantly improved diagnostic accuracy, yielding the highest AUC, sensitivity, and specificity. In contrast, CEA, IL-8, TGF-alpha, and IDO-1 demonstrated limited diagnostic utility. Conclusions: Based on the available literature, this is the first study to evaluate the combined use of IL-6 and CYFRA 21-1 as potential biomarkers for LC screening in individuals with high residential radon exposure. Our findings highlight their utility, particularly in combination, for improving diagnostic accuracy in this high-risk population. Full article

In this article, the classification of signals arising from the movements of the lower limb of the leg (LLL) based on electromyography (EMG) (walking, sitting, up and down the stairs) was carried out. In the data collection process, 25 athletes aged 15–22 were involved, and two types of data sets (DS-dataset) were formed using FreeEMG and Biosignalsplux devices. Six important time and frequency domain features were extracted from the EMG signals—RMS (Root Mean Square), MAV (Mean Absolute Value), WL (Waveform Length), ZC (Zero Crossing), MDF (Median Frequency), and SSCs (Slope Sign Changes). Several classification algorithms were used to detect and classify movements, including RF (Random Forest), NN (Neural Network), SVM (Support Vector Machine), k-NN (k-Nearest Neighbors), and LR (Logistic Regression) models. Analysis of the experimental results showed that the RF algorithm achieved the highest accuracy of 98.7% when classified with DS collected via the Biosignalsplux device, demonstrating an advantage in terms of performance in motion recognition. The results obtained from the open systems used in signal processing enable real-time monitoring of athletes’ physical condition, which plays a crucial role in accurately and rapidly determining the degree of muscle fatigue and the level of physical stress experienced during training sessions, thereby allowing for more effective control of performance and timely prevention of injuries. Full article

This study explored the use of Borassus fruit fiber as reinforcement for earthen matrices (BFRC). The experimental results of the testing carried out on the structural properties were used to generate a primary dataset for training and testing machine learning (ML) models. Linear regression (LR), Decision tree regressor (DTR), and gradient boosting regression (GBR) were used to build an ensemble learning (EL) model during the prediction of the hygroscopic properties, Young’s modulus, and compressive strength of the BFRC. Fiber content, activation concentration, curing days, dry weight, saturated weight, mass, flexural vibration, longitudinal vibration, correction factor, maximum load, and cross-sectional area were the various inputs considered in the structural properties prediction. The performance of both EL and single models (SMs) was appraised via three performance metrics—mean square error (MSE), root mean square (RMSE), and the coefficient of determination (R²)—to comparatively ascertain the model’s efficiency. Results showed that all models exhibited high accuracy in predicting Young’s modulus and compressive strength. Ensemble learning outperformed single models in predicting these properties, with MSE, RMSE, and R² of 0.01 MPa, 0.1 MPa, and 99% and 3,923,262.5 MPa, 1980.7 Pa, and 99% for compressive strength and Young’s modulus, respectively. However, for hygroscopic behavior, linear regression (LR) demonstrated superior performance compared to other models, with MSE, RMSE, and R² values of 0.13%, 0.36%, and 99%. Full article

Rainfall-induced slope failures are among the most frequent and destructive natural hazards in Japan’s mountainous regions, often causing severe loss of life and damage to infrastructure. This study presents an integrated statistical framework for regional-scale landslide hazard mapping, with a focus on 0-order basins. To enhance spatial prediction accuracy, both bivariate and multivariate statistical models are employed. Bivariate models efficiently assess the relationship between individual conditioning factors and landslide occurrences but assume variable independence. Conversely, multivariate models account for multicollinearity and the combined effects of interacting factors, although they often require more complex data processing and may lack spatial clarity. To leverage the strengths of both approaches, two hybrid models were developed and applied to a 242.94 km² area in Fukuoka Prefecture, Japan. Model validation was performed using a matrix-based evaluation supported by a threshold optimization algorithm. Among the models tested, the hybrid Frequency Ratio–Logistic Regression (FR + LR) model demonstrated the highest predictive performance, achieving a success rate of 84.30%, a false alarm rate of 17.88%, and a miss rate of 12.30%. It effectively identified critical slip surfaces within zones classified as ‘High’ to ‘Very High’ susceptibility. This integrated approach offers a statistically robust, scalable, and interpretable solution for landslide hazard assessment in geomorphologically complex terrains. It provides valuable support for regional disaster risk reduction and contributes directly to achieving the Sustainable Development Goals (SDGs). Full article

Image super-resolution (SR) techniques can significantly enhance visual quality and information density. However, existing methods often rely on large amounts of paired low- and high-resolution (LR-HR) data, which limits their generalization and robustness when faced with data scarcity, distribution inconsistencies, and missing high-frequency details. To tackle the challenges of image reconstruction in data-scarce scenarios, this paper proposes a semi-supervised learning-driven multi-fidelity fusion (SSLMF) method, which integrates multi-fidelity data fusion (MFDF) and semi-supervised learning (SSL) to reduce reliance on high-fidelity data. More specifically, (1) an MFDF strategy is employed to leverage low-fidelity data for global structural constraints, enhancing information compensation; (2) an SSL mechanism is introduced to reduce data dependence by using only a small amount of labeled HR samples along with a large quantity of unlabeled multi-fidelity data. This framework significantly improves data efficiency and reconstruction quality. We first validate the reconstruction accuracy of SSLMF on benchmark functions and then apply it to image reconstruction tasks. The results demonstrate that SSLMF can effectively model both linear and nonlinear relationships among multi-fidelity data, maintaining high performance even with limited high-fidelity samples. Finally, its cross-disciplinary potential is illustrated through an audio restoration case study, offering a novel solution for efficient image reconstruction, especially in data-scarce scenarios where high-fidelity samples are limited. Full article

In supervised fine-tuning (SFT) for Text2SQL tasks, particularly for databases with numerous tables, encoding schema features requires excessive tokens, escalating GPU resource requirements during fine-tuning. To bridge this gap, we propose LR-SQL, a general dual-model SFT framework comprising a schema linking model and an SQL generation model. At the core of our framework lies the schema linking model, which is trained on a novel downstream task termed slice-based related table filtering. This task dynamically partitions a database into adjustable slices of tables and sequentially evaluates the relevance of each slice to the input query, thereby reducing token consumption per iteration. However, slicing fragments destroys database information, impairing the model’s ability to comprehend the complete database. Thus, we integrate Chain of Thought (CoT) in training, enabling the model to reconstruct the full database context from discrete slices, thereby enhancing inference fidelity. Ultimately, the SQL generation model uses the result from the schema linking model to generate the final SQL. Extensive experiments demonstrate that our proposed LR-SQL reduces total GPU memory usage by 40% compared to baseline SFT methods, with only a 2% drop in table prediction accuracy for the schema linking task and a negligible 0.6% decrease in overall Text2SQL Execution Accuracy. Full article

The proliferation of fake news, particularly in sensitive domains like religious texts, necessitates robust authenticity verification methods. This study addresses the growing challenge of authenticating Hadith, where traditional methods relying on the analysis of the chain of narrators (Isnad) and the content (Matn) are increasingly strained by the sheer volume in circulation. To combat this issue, machine learning (ML) and natural language processing (NLP) techniques, specifically through transfer learning, are explored to automate Hadith classification into Genuine and Fake categories. This study utilizes an imbalanced dataset of 8544 Hadiths, with 7008 authentic and 1536 fake Hadiths, to systematically investigate the collective impact of both linguistic and contextual features, particularly the chain of narrators (Isnad), on Hadith authentication. For the first time in this specialized domain, state-of-the-art pre-trained language models (PLMs) such as Multilingual BERT (mBERT), CamelBERT, and AraBERT are evaluated alongside classical algorithms like logistic regression (LR) and support vector machine (SVM) for Hadith authentication. Our best-performing model, AraBERT, achieved a 99.94% F1score when including the chain of narrators, demonstrating the profound effectiveness of contextual elements (Isnad) in significantly improving accuracy, providing novel insights into the indispensable role of computational methods in Hadith authentication and reinforcing traditional scholarly emphasis. This research represents a significant advancement in combating misinformation in this important field. Full article

Storage of Blood units determines the accumulation of harmful substances, such as malondialdehyde (MDA) and free hemoglobin (fHb). These may lead to several complications, including cardiovascular, neurodegenerative, and metabolic disorders in recipients. The objective of this study was to evaluate the concentrations of MDA and fHb in canine leukoreduced (LR) and non-leukoreduced (NLR) packed red blood cells (pRBC) during the storage period of six weeks. Blood samples were collected from six healthy adult Weimaraner dogs (three females and three males). Whole blood was stored in citrate-phosphate-dextrose saline-adenine-glucose-mannitol additive solution (CPD-SAGM) bags and, for each donor, two pRBC units (one NLR and one LR) were produced and stored at 4 °C for 42 days. Samples were collected on days 0, 7, 14, 21, 28, 35, and 42, and analyzed for malondialdehyde (MDA) using a canine-specific ELISA method, and for free hemoglobin (fHb) using the Harboe direct spectrophotometric method. The results demonstrated a statistically significant reduction in MDA accumulation in LR-pRBC compared to NLR-pRBC blood units and lower values of fHb in LR at T6. However, no significant difference in fHb levels were demonstrated. These findings suggest that leukoreduction may limit oxidative stress during blood storage, reducing the potential adverse effects of transfusions related to oxidative damage. Full article

Optimizing metadata indexing remains critical for enhancing distributed file system performance. The Traditional Log-Structured Merge-Trees (LSM-Trees) architecture, while effective for write-intensive operations, exhibits significant limitations when handling massive metadata workloads, particularly manifesting as suboptimal read performance and substantial indexing overhead. Although existing learned indexes perform well on read-only workloads, they struggle to support modifications such as inserts and updates effectively. This paper proposes SwiftKV, a novel metadata indexing scheme that combines LSM-Tree and learned indexes to address these issues. Firstly, SwiftKV employs a dynamic partition strategy to narrow the metadata search range. Secondly, a two-level learned index block, consisting of Greedy Piecewise Linear Regression (Greedy-PLR) and Linear Regression (LR) models, is leveraged to replace the typical Sorted String Table (SSTable) index block for faster location prediction than binary search. Thirdly, SwiftKV incorporates a load-aware construction mechanism and parallel optimization to minimize training overhead and enhance efficiency. This work bridges the gap between LSM-Trees’ write efficiency and learned indexes’ query performance, offering a scalable and high-performance solution for modern distributed file systems. This paper implements the prototype of SwiftKV based on RocksDB. The experimental results show that it narrows the memory usage of index blocks by 30.06% and reduces read latency by 1.19×~1.60× without affecting write performance. Furthermore, SwiftKV’s two-level learned index achieves a 15.13% reduction in query latency and a 44.03% reduction in memory overhead compared to a single-level model. For all YCSB workloads, SwiftKV outperforms other schemes. Full article

High-risk (HR) neuroblastoma (NBL) patients often receive standardized treatment despite wide variations in clinical outcomes, underscoring the need for improved stratification tools. A distinguishing feature of NBL is the patient-specific expression of gangliosides (GGs), particularly GD2, which may serve as biomarkers. We analyzed GG profiles in 18 patient-derived tumors and 11 NBL cell lines using thin-layer chromatography and mass spectrometry. Expression of 0-, a-, and b-series GGs was examined and correlated with clinical risk, outcome, and gene expression data. Low-risk (LR) tumors expressed higher levels of complex b-series GGs. In HR tumors, five GG profiles (A–E) were identified. Profile A featured complex b-series GGs; B showed GD2 dominance; C showed synthesis arrest at GM3 or GD3 due to low expression of the GM2/GD2 synthase, encoded by the B4GALNT1 gene; D included complex a- and b-series GGs; and E was marked by GM2 and GD1a prevalence. B4GALNT1 expression served as a prognostic marker. Relapsed tumors following anti-GD2 therapy typically exhibited reduced GD2 levels, except for one profile A tumor that displayed a ceramide anchor shorter than those found in LR tumors. Astonishingly, the ceramide anchor composition of GD2 itself appears to separate LR and HR NBL, hinting at a role of ceramide synthases in NBL biology. All cell lines expressed GM2, but exhibited very low levels of complex b-series GGs. Profile C was found only in cell lines of the mesenchymal subtype. These findings support further investigation of GG composition and associated enzyme expression as potential biomarkers for risk stratification and treatment response in NBL. Full article

Classifying human locomotor activities from wearable sensor data is an important high-level component of control schemes for many wearable robotic exoskeletons. In this study, we evaluated three machine learning models for classifying activity type (walking, running, jumping), speed, and surface incline using input data from body-worn inertial measurement units (IMUs) and e-textile insole pressure sensors. The IMUs were positioned on segments of the lower limb and pelvis during lab-based data collection from 16 healthy participants (11 men, 5 women), who walked and ran on a treadmill at a range of preset speeds and inclines. Logistic Regression (LR), Random Forest (RF), and Light Gradient-Boosting Machine (LGBM) models were trained, tuned, and scored on a validation data set (n = 14), and then evaluated on a test set (n = 2). The LGBM model consistently outperformed the other two, predicting activity and speed well, but not incline. Further analysis showed that LGBM performed equally well with data from a limited number of IMUs, and that speed prediction was challenged by inclusion of abnormally fast walking and slow running trials. Gyroscope data was most important to model performance. Overall, LGBM models show promise for implementing locomotor activity prediction from lower-limb-mounted IMU data recorded at different anatomical locations. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is the most common primary malignant tumour of the liver. In a cirrhotic liver, each nodule larger than 10 mm demands further work-up using CT or MRI. The Liver Imaging Reporting and Data System (LI-RADS) is still based on visual assessment and measurements. The purpose of this study was to evaluate whether semi-automated quantification of visual LR-5 lesions is appropriate and can objectify HCC classification for personalized radiomic research. Methods: A total of 52 HCC patients (median age 67 years, 17% females, 83% males) from a retrospective data collection were evaluated visually and compared by the results using an oncology software with features of LI-RADS-based structured tumour evaluation and documentation, semi-automated tumour segmentation, and texture analysis. Results: Software-based evaluation of non-rim arterial-phase hyperenhancement (APHE) and non-peripheral washout, as well as the LI-RADS-score, showed no statistically significant differences compared with visual assessment (p = 0.2, 0.7, 0.17), with a consensus between a human reader and the software approach in 98% (APHE), 89% (washout), and 93% (threshold growth) of cases, respectively. The software provided automated LI-RADS classification, structured reporting, and quantitative features for HCC registries and radiomic research. Conclusions: The presented work may serve as an outlook for LI-RADS-based automated qualitative and quantitative evaluation. Future research may show if texture analysis can be used to foster personalized medical approaches in HCC. Full article

In 2024, Brazil experienced record-breaking wildfire activity, underscoring the escalating influence of climate change. This study examines the long-range transport of wildfire-generated aerosol plumes to São Paulo, combining multi-platform observations to trace their origin and properties. During August and September—a period marked by intense fire outbreaks in Pará and Mato Grosso do Sul—lidar measurements performed at São Paulo detected pronounced aerosol plumes. To investigate their source and characteristics, we integrated data from the Earth Cloud Aerosol and Radiation Explorer (EarthCARE) satellite, HYSPLIT back-trajectory modeling, and ground-based AERONET and Raman lidar measurements. Aerosol properties were derived from aerosol optical depth (AOD), Ångström exponent, and lidar ratio (LR) retrievals. Back-trajectory analysis identified three transport pathways originating from active fire zones, with coinciding AOD values (0.7–1.1) and elevated LR (60–100 sr), indicative of dense smoke plumes. Compositional analysis revealed a significant black carbon component, implicating wildfires near Corumbá (Mato Grosso do Sul) and São Félix do Xingu (Pará) as probable emission sources. These findings highlight the efficacy of satellite-based lidar systems, such as Atmospheric Lidar (ATLID) onboard EarthCARE, in atmospheric monitoring, particularly in data-sparse regions where ground instrumentation is limited. Full article