Search Results (34,558)

Benzene, a well-established human carcinogen and major industrial pollutant, poses significant health risks through occupational exposure due to its no-threshold effect, leading to multi-system damage involving the hematopoietic, nervous, and immune systems. This makes the investigation of its toxic mechanisms crucial for precise prevention and control of its health impacts. Programmed cell death (PCD), an orderly and regulated form of cellular demise controlled by specific intracellular genes in response to various stimuli, has emerged as a key pathway where dysfunction may underlie benzene-induced toxicity. This review systematically integrates evidence linking benzene toxicity to PCD dysregulation, revealing that benzene and its metabolites induce abnormal subtypes of PCD (apoptosis, autophagy, ferroptosis) in hematopoietic cells. This occurs through mechanisms including activation of Caspase pathways, regulation of long non-coding RNAs, and epigenetic modifications, with recent research highlighting the IRP1-DHODH-ALOX12 ferroptosis axis and oxidative stress–epigenetic interactions as pivotal. Additionally, this review describes a comprehensive monitoring system for early toxic effects comprising benzene exposure biomarkers (urinary t,t-muconic acid (t,t-MA), S-phenylmercapturic acid (S-PMA)), PCD-related molecules (Caspase-3, let-7e-5p, ACSL1), oxidative stress indicators (8-OHdG), and genetic damage markers (micronuclei, p14ARF methylation), with correlative analyses between PCD mechanisms and benzene toxicity elaborated to underscore their integrative roles in risk assessment. Furthermore, the review details analytical techniques for these biomarkers, including direct benzene detection methods—direct headspace gas chromatography with flame ionization detection (DHGC-FID), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and portable headspace sampling (Portable HS)—alongside molecular imprinting and fluorescence probe technologies, as well as methodologies for toxic effect markers such as live-cell imaging, electrochemical techniques, methylation-specific PCR (MSP), and Western blotting, providing technical frameworks for mechanistic studies and translational applications. By synthesizing current evidence and mechanistic insights, this work offers novel perspectives on benzene toxicity through the PCD lens, identifies potential therapeutic targets associated with PCD dysregulation, and ultimately establishes a theoretical foundation for developing interventional strategies against benzene-induced toxicity while emphasizing the translational value of mechanistic research in occupational and environmental health. Full article

Ischemic stroke triggers a dynamic immune response that influences both acute damage and long-term recovery. This review synthesizes a decade of evidence on immunological and inflammatory biomarkers in ischemic stroke, emphasizing their prognostic and therapeutic significance. Following ischemic insult, levels of pro-inflammatory cytokines, such as interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α), and chemokines like interleukin-8 (IL-8) rapidly rise, promoting blood–brain barrier disruption, leukocyte infiltration, and neuronal death. Conversely, anti-inflammatory mediators such as interleukin-10 (IL-10) and transforming growth factor-β (TGF-β) facilitate repair, neurogenesis, and immune regulation in later phases. The balance between these pathways determines outcomes and is reflected in circulating biomarkers. Composite hematological indices including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) offer accessible and cost-effective prognostic tools. Several biomarkers correlate with infarct size, neurological deterioration, and mortality, and may predict complications like hemorrhagic transformation or infection. Therapeutic strategies targeting cytokines, especially IL-1 and IL-6, have shown promise in modulating inflammation and improving outcomes. Future directions include personalized immune profiling, real-time cytokine monitoring, and combining immunotherapy with neurorestorative approaches. By integrating immune biomarkers into stroke care, clinicians may enhance risk stratification, optimize treatment timing, and identify candidates for novel interventions. This review underscores inflammation’s dual role and evolving therapeutic and prognostic relevance in ischemic stroke. Full article

The widespread prevalence of Salmonella underscores the urgent need for rapid, sensitive, and reliable detection methods to ensure food safety and protection of public health. In this study, we successfully developed an integrated detection system that combines immunomagnetic separation with surface plasmon resonance (SPR) analysis. This system achieved high capture efficiencies, exceeding 96.04% in phosphate-buffered saline and over 91.66% in milk samples artificially spiked with S. Typhimurium at concentrations below 4.2 × 10⁴ CFU/mL. However, direct SPR detection of the isolated S. Typhimurium showed limited sensitivity, with a limit of detection (LOD) of 4.2 × 10⁷ CFU/mL. Incorporating a sandwich assay with antibody-conjugated gold nanoparticles significantly enhanced sensitivity, lowering the LOD by six orders of magnitude to 4.2 × 10¹ CFU/mL. The whole integrated process, integrating immunomagnetic separation with SPR analysis, was completed within 50 min. These results demonstrate that this AuNP-enhanced SPR platform offers both the rapidity and sensitivity essential for effective monitoring of food safety and traceability in Salmonella-related foodborne outbreaks, particularly in products such as milk. Full article

The structural reliability of agricultural machinery is critically dependent on bolted joints, with loosening being a significant and prevalent failure mode. Harsh operational environments (intense vibration, impact, corrosion) severely exacerbate loosening risks, compromising machinery performance and safety. Traditional periodic inspections are inadequate for preventing sudden failures induced by loosening, leading to impaired efficiency and safety hazards. This review comprehensively analyzes the unique challenges and opportunities in monitoring bolted joint reliability within agricultural machinery. It covers the following: (1) the status of bolted joint reliability issues (failure modes, impacts, maintenance inadequacies); (2) environmental challenges to joint integrity; (3) evaluation of conventional detection methods; (4) principles and classifications of modern detection technologies (e.g., vibration-based, acoustic, direct measurement, vision-based); and (5) their application status, limitations, and techno-economic hurdles in agriculture. This review identifies significant deficiencies in current technologies for agricultural machinery bolt loosening surveillance, underscoring the pressing need for specialized, dependable, and cost-effective online monitoring systems tailored for agriculture’s demanding conditions. Finally, forward-looking research directions are outlined to enhance the reliability and intelligence of structural monitoring for agricultural machinery. Full article

The rise in antimicrobial resistance and strict milk withdrawal regulations drive the search for safe, non-antibiotic intramammary therapies. This pilot field study focused on clinical parameters, including the somatic cell count (SCC) and the assessment of changes, as well as overall safety, which together enabled a prospective evaluation of whether the substance exerted any therapeutic effect. In this study, 48 Holstein–Friesian cows with naturally occurring clinical mastitis (somatic cell count > 400,000 cells/mL; single quarter) were randomized to receive either seven daily infusions of 10% pectin (n = 24) or two standard intramammary doses of a licensed multi-component antibiotic formulation (n = 24). The clinical severity scores (0–3) and SCC were monitored from 72 h before to 168 h after treatment initiation; the bacteriological cultures, milk TNF-α, milk yield, and blood hematology/biochemistry were also assessed. Both groups exhibited comparable and significant reductions in the mastitis scores and log₂-transformed SCC by 48 h post-treatment, with equivalent bacteriological cure rates and pathogen profiles (predominantly Streptococcus uberis, coagulase-negative staphylococci, and E. coli) and no local irritation, systemic adverse effects, or alterations in the milk yield, TNF-α, or blood parameters. These findings indicate that intramammary pectin at a 10% concentration is safe and well tolerated and that it provides efficacy equivalent to standard antibiotic therapy, supporting its potential as an alternative mastitis treatment that avoids antibiotic residues and contributes to antimicrobial stewardship. Full article

Accurate soil moisture estimation is critical for precision agriculture and water resource management, yet traditional sampling methods are time-consuming, destructive, and provide limited spatial coverage. Ground Penetrating Radar (GPR) offers a promising non-destructive alternative, but optimal machine learning approaches for GPR-based soil moisture prediction remain unclear. This study presents a comparative analysis of regression tree and boosted tree algorithms for predicting soil moisture content from Ground Penetrating Radar (GPR) histogram features across 21 sites in Eastern Thailand. Soil moisture content was measured at multiple depths (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 m) using samples collected during Standard Penetration Test procedures. Feature extraction was performed using 16-bin histograms from processed GPR radargrams. A single regression tree achieved a cross-validation RMSE of 5.082 and an R² of 0.761, demonstrating superior training accuracy and interpretability. In contrast, the boosted tree ensemble achieved significantly better generalization performance, with a cross-validation RMSE of 4.7915 and an R² of 0.708, representing a 5.7% improvement in predictive performance. Feature importance analysis revealed that specific histogram bins effectively captured moisture-related variations in GPR signal amplitude distributions. A comparative evaluation demonstrates that while single regression trees offer superior interpretability for research applications, boosted tree ensembles provide enhanced predictive performance that is essential for operational deployment in precision agriculture and hydrological monitoring systems. Full article

The increasing demand for high-throughput, real-time, and single-molecule protein analysis in precision medicine has propelled the development of novel sensing technologies. Among these, nanopore-based methods have garnered significant attention for their unique capabilities, including label-free detection, ultra-sensitivity, and the potential for miniaturization and portability. Originally designed for nucleic acid sequencing, nanopore technology is now being adapted for peptide and protein analysis, offering promising applications in biomarker discovery and disease diagnostics. This review examines the latest advances in biological, solid-state, and hybrid nanopores for protein sensing, focusing on their ability to detect amino acid sequences, structural variants, post-translational modifications, and dynamic protein–protein or protein–drug interactions. We critically compare these systems to conventional proteomic techniques, such as mass spectrometry and immunoassays, discussing advantages and persistent technical challenges, including translocation control and signal deconvolution. Particular emphasis is placed on recent advances in protein sequencing using biological and solid-state nanopores and the integration of machine learning and signal-processing algorithms that enhance the resolution and accuracy of protein identification. Nanopore protein sensing represents a disruptive innovation in biosensing, with the potential to revolutionize clinical diagnostics, therapeutic monitoring, and personalized healthcare. Full article

Measuring-while-drilling (MWD) techniques have great potential for use in geotechnical engineering research. This study first addresses the current use of MWD, which consists of recording data using sensors in a drilling machine operating on site. It then addresses the currently unsolved problems of quality control in drilled piles and assessments of their interaction with the soil under load. Next, an original method of drilling displacement piles using a special EGP auger (Electro-Geo-Probe) is presented. The innovation of this new drilling system lies in the placement of the sensors inside the EGP auger in the soil. These innovative sensors form an integrated measurement system, enabling improved real-time control during pile drilling. The most original idea is the use of a Cone Penetration Test (CPT) probe that can be periodically and remotely inserted at a specific depth below the pile base being drilled. This new MWD-EGP system with cutting-edge sensors to monitor the soil’s impact on piles during drilling revolutionizes pile drilling quality control. Furthermore, implementing this in-auger sensor system is a step towards the development of digital drilling rigs, which will provide better pile quality thanks to solutions based on the results of real-time, on-site soil testing. Finally, examples of measurements taken with the new sensor-equipped auger and a preliminary interpretation of the results in non-cohesive soils are presented. The obtained data confirm the usefulness of the new drilling system for improving the quality of piles and advancing research in geotechnical engineering. Full article

This study aimed to validate the 9-point body condition score (BCS) system in sled dogs by assessing its reliability and by comparing it with objective measures including real-time ultrasonography, plicometry, and anthropometry. Twenty-seven Siberian Huskies (11 females, 16 males) from three sled dog teams were assessed for BCS by three trained veterinarians and their respective mushers. Intra-observer reliability was substantial (Krippendorff’s α = 0.734), while agreement between expert raters (Kα = 0.580) and between the expert rater and mushers (Kα = 0.691) was moderate, with mushers tending to overestimate the BCS of their own dogs (median difference = −0.5). BCS showed positive correlations with body mass index (BMI) and subcutaneous fat at the chest and flank via plicometry (for all: p < 0.05). Ultrasonography showed weak correlations with BCS, likely due to the different anatomical layers evaluated and the distinctively high muscle-to-fat ratio typical of sled dogs. Both univariate and multivariate analyses revealed sex- and neutering-related differences in body composition, with males generally presenting larger skeletal dimensions and neutering influencing patterns of fat distribution. These findings support the reliability and field applicability of the BCS system when used by trained evaluators, highlighting the importance of considering sex and anatomical site when assessing body condition in athletic dogs. The 9-point BCS, combined with accessible objective tools, represents a consistent, cost-effective method for monitoring body condition in long-distance performance sled dogs. Full article

Intensified human activities in recent years, such as wastewater discharge and agricultural non-point source pollution have led to a decline in lake water quality, especially in the middle and lower reaches of the Yangtze River Basin, which threaten the stability of lake water ecosystems. Therefore, it is necessary to conduct a scientific assessment of the water eco-environmental quality of shallow lakes and implement targeted management measures. Considering the characteristics of shallow lakes, major ecological and environmental issues, and current standards and guidelines, an indicator system method was employed to establish a water eco-environmental quality evaluation system tailored for typical shallow lakes in the middle and lower reaches of the Yangtze River Basin. This evaluation system comprises three criteria layers (aquatic organism, habitat quality, and water quality) and 10 indicator layers. Using survey data from 2022 to 2024 for evaluation, the results showed that the water eco-environmental quality of Lake Gehu was rated as poor, with the lowest score for macrophyte coverage and the highest score for riparian vegetation coverage. This indicates that the shoreline restoration project in Lake Gehu was effective, while the lake water quality still needs improvement. Remedial measures include increasing aquatic vegetation coverage, reducing nitrogen and phosphorus pollution loads, and controlling the occurrence of algal blooms. This evaluation system combines field surveys with remote sensing monitoring data, fully considering historical and current conditions, and can guide local authorities in evaluating lake water environmental quality. The constructed evaluation system is applicable for the assessment of shallow lakes in the middle and lower reaches of Yangtze River Basin. It provides a scientific basis for the continuous improvement of eco-environmental quality and the construction of Beautiful Lakes Initiative, contributing to the management and protection of lake ecosystems. Full article

The treatment of chronic wounds and pressure sores is an important challenge in the context of public health and the effectiveness of patient treatment. Therefore, new methods are being developed to reduce or, in extreme cases, to initiate and conduct the wound healing process. This article presents an innovative smart bandage, programmable using a smartphone, which generates small amplitude impulse vibrations. The communication between the smart bandage and the smartphone is realized using BLE. The possibility of programming the smart bandage allows for personalized therapy. Owing to the built-in MEMS sensor, the smart bandage makes it possible to monitor work during rehabilitation and implement an auto-calibration procedure. The flexible, openwork mechanical structure of the dressing was made in 3D printing technology, thanks to which the solution is easy to implement and can be used together with traditional dressings to create hybrid ones. Miniature electronic circuits and actuators controlled by the PWM signal were designed as replaceable elements; thus, the openwork structure can be treated as single-use. The smart bandage containing six actuators presented in this article generates oscillations in the range from about 40 Hz to 190 Hz. The system generates low-amplitude vibrations, below 1 g. The actuators were operated at a voltage of 1.65 V to reduce energy consumption. For comparison, the actuators were also operated at the nominal voltage of 3.17 V, as specified by the manufacturer. Full article

Unmanned Aerial Vehicles (UAVs) face collision risks due to Beyond Visual Line of Sight operations. Therefore, UAV Traffic Management (UTM) systems are used to manage and monitor UAV flight paths. However, centralized UTM systems are susceptible to various security attacks and are inefficient in managing flight data from different service providers. It further fails to provide low-latency communication required for UAV real-time operations. Thus, this paper proposes to integrate Drone-to-Drone (D2D) communication protocol into a secure public blockchain-based UTM system to enable direct communication between UAVs for efficient collision avoidance. The D2D protocol is designed using SHA256 hash function and bitwise XOR operations. A proof of concept has been built to verify that the UTM system is secure by enabling authorized service providers to view sensitive flight data only using legitimate secret keys. The security of the protocol has been analyzed and has been proven to be secure from key disclosure, adversary-in-the-middle, replay, and tracking attacks. Its performance has been evaluated and is proven to outperform existing studies by having the lowest computation cost of 0.01 ms and storage costs of 544–800 bits. Full article

Despite the widespread deployment of inclinometers and GPS, an engineering gap remains for a low-cost, seepage-sensitive landslide early-warning technique. To explore the application of self-potential (SP) in landslide monitoring and early warning, a series of physical simulations were conducted, focusing on slope rainfall and slope cracking conditions. The self-potential signals were monitored using a custom-built STM32-based acquisition system, which provided continuous, real-time data with minimal noise. The relationship between self-potential signals and internal changes within the landslide body was analyzed, revealing that SP signals are highly sensitive to seepage, saturation, and structural changes within the slope. During slope rainfall simulations, the self-potential signals responded rapidly to changes in rainfall intensity, capturing the dynamic nature of seepage and saturation changes. A dynamic early-warning model was developed based on statistical methods, including sliding t-tests/Pettitt mutation tests and Mahalanobis distance test, to detect early signs of landslide instability. The model successfully identified significant changes in SP signals that corresponded to the onset of landslide movement, demonstrating the potential of self-potential for real-time landslide monitoring and early warning. This study highlights the effectiveness of self-potential monitoring in detecting early signs of landslide instability and suggests that SP signals can be a valuable addition to existing landslide monitoring systems. Full article

Dam displacement serves as a critical visual indicator for assessing structural integrity and stability in dam engineering. Data-driven displacement forecasting has become essential for modern dam safety monitoring systems, though conventional approaches—including statistical models and basic machine learning techniques—often fail to capture comprehensive feature representations from multivariate environmental influences. To address these challenges, a bidirectional gated recurrent unit (BiGRU)-enhanced neural network is developed, incorporating sliding window mechanisms to model time-dependent hysteresis characteristics. The BiGRU’s architecture systematically integrates historical temporal patterns through overlapping window segmentation, enabling dual-directional sequence processing via forward–backward gate structures. Validated with four instrumented measurement points from a major concrete gravity dam, the proposed model exhibits significantly better performance against three widely used recurrent neural network benchmarks in displacement prediction tasks. These results confirm the model’s capability to deliver high-fidelity displacement forecasts with operational stability, establishing a robust framework for infrastructure health monitoring applications. Full article

Water quality deterioration poses a critical threat to ecological security and sustainable development, particularly in rapidly urbanizing regions. To enable proactive environmental management, this study develops a novel hybrid deep learning model, the NGO-CNN-GRU, for high-precision time-series water quality prediction in the Xijiang River Basin, China. The model integrates a Convolutional Neural Network (CNN) for spatial feature extraction and a Gated Recurrent Unit (GRU) for temporal dependency modeling, with hyperparameters optimized via the Northern Goshawk Optimization (NGO) algorithm. Using historical water quality (pH, DO, CODMn, NH3-N, TP, TN) and meteorological data (precipitation, temperature, humidity) from 11 monitoring stations, the model achieved exceptional performance: test set R² > 0.986, MAE < 0.015, and RMSE < 0.018 for total nitrogen prediction (Xiaodong Station case study). Across all stations and indicators, it consistently outperformed baseline models (GRU, CNN-GRU), with average R² improvements of 12.3% and RMSE reductions up to 90% for NH3-N predictions. Spatiotemporal analysis further revealed significant pollution gradients correlated with anthropogenic activities in the Pearl River Delta. This work provides a robust tool for real-time water quality early warning systems and supports evidence-based river basin management. Full article