Search Results (14,359)

Alpha-fetoprotein (AFP) is widely utilized for auxiliary diagnosis of primary hepatocellular carcinoma. Therefore, the development of a facile immunosensor is essential for clinical applications. This study aims to develop a simple immunoassay for AFP detection. By incorporating silicon quantum dots (SiQDs) into etching hollow gold nanoshells (EHGNs) via precise nanomanipulation, we designed molecular probes based on SiQDs@EHGNs complex immobilized capture antibodies, which can convert the antigen/antibody binding process into fluorescent divergence signals for AFP measurement. This strategy enabled one-step fluorescence sensing for AFP detection with a linear range of 3.125–200.0 ng/mL and LOD of 0.234 ng/mL. The detection results of 15 clinical serum real samples demonstrated a 93.7% correlation with the market-accepted ECLIA method. The proposed method take advantages of simplicity and rapid response, offering a novel approach for tumor marker analysis with significant potential. Full article

Traditional malware classification approaches primarily address fixed sets of well-studied malware types and therefore struggle to accommodate the continual emergence of novel or previously unseen malware strains. While visualization-based strategies have shown promise in few-shot malware classification, existing methods often produce representations with limited semantic richness. In parallel, few-shot learning models frequently converge with suboptimal solutions, limiting their ability to generalize effectively to new classes. To address these challenges, we propose MetaWave, a unified framework that jointly optimizes both data representation and model learning for few-shot malware classification. Rather than treating feature representation and learning strategy as largely independent stages, MetaWave is formulated as an explicit representation–adaptation integration framework that combines multi-view malware encoding with meta-learning-based optimization. At the data level, we propose a Wavelet Transform-based Malware Representation method that leverages multi-scale frequency analysis and complementary views to generate semantically enriched representations. At the model level, we adopt Model-Agnostic Meta-Learning (MAML) to optimize model initialization for rapid adaptation to unseen tasks under limited data conditions. Extensive experiments are conducted on two benchmark datasets, EMBER and Malicia, under a 5-way 5-shot protocol with disjoint class splits to ensure evaluation on previously unseen malware families. The proposed framework achieves superior performance, reaching 97.8% accuracy on EMBER and 96.2% on Malicia, consistently outperforming state-of-the-art methods. These results indicate that jointly enhancing representation quality and model adaptability can improve classification accuracy and unseen-family performance under the evaluated 5-way 5-shot protocol. Overall, MetaWave provides an effective framework for few-shot malware classification and offers a promising basis for detecting emerging malware under limited-data conditions, while robustness to adversarial perturbation, obfuscation, and polymorphism remains to be validated through dedicated future evaluation. Full article

Heart rate variability (HRV) has emerged as a key biomarker for assessing autonomic nervous system activity, stress, fatigue, and emotional states. With the rapid development of wearable sensor technologies, HRV analysis has expanded from clinical environments to real-world, continuous monitoring. This review summarizes current applications of HRV metrics in wearable devices, including fitness tracking, mental stress assessment, sleep quality evaluation, and early detection of physiological or psychological disorders. Recent advances in photoplethysmography (PPG)-based HRV estimation have enabled noninvasive and user-friendly measurement, though challenges remain in accuracy under motion and variable environmental conditions. We also discuss methodological considerations, such as artifact correction, data segmentation, and the integration of HRV with other biosignals for multimodal analysis. Emerging research suggests that combining HRV with metrics such as respiration rate, skin conductance, and accelerometry can enhance robustness and interpretability in dynamic settings. Finally, future directions are proposed toward personalized health analytics, emotion-aware computing, and real-time adaptive feedback systems. This review highlights the growing potential of wearable HRV analysis as a foundation for preventive healthcare and human–machine symbiosis. Full article

African swine fever (ASF), caused by African swine fever virus (ASFV), is a highly destructive transboundary disease in domestic pigs. The circulating virus in this study belonged to ASFV genotype II, commonly associated with high virulence. In endemic regions such as Thailand, limited vaccine availability and shortages of naïve breeding stock necessitate reliance on early detection, surveillance, and the retention of convalescent sows, thereby raising concerns regarding viral persistence and reproductive performance. This study evaluated the long-term reproductive performance of convalescent sows compared with naïve cohorts under co-habitation conditions, while assessing the efficacy of passive surveillance and strict biosecurity in preventing viral transmission from both internal and external sources. Convalescent sows showed reproductive performance comparable to naïve cohorts across two parities. Long-term co-habitation with naïve sentinel pigs was not associated with detectable viral transmission, although low-level viral persistence or intermittent shedding cannot be excluded. From a disease control perspective, the transition from delayed detection to enhanced passive surveillance facilitated early clinical recognition and targeted removal (“tooth extraction”) of infected animals, effectively limiting intra-herd transmission without full depopulation. Importantly, irrespective of the uncertain carrier status, strict biosecurity and rapid response protocols appeared effective in mitigating both external introduction and within-farm transmission of ASFV. These findings suggest that, under appropriate management and biosecurity conditions, convalescent sows may be reintegrated into production systems with caution. Full article

Pesticide residues in food and agricultural products continue to constitute a significant concern for food safety, particularly when rapid decision-making is required across production and supply chains. Although chromatographic methods such as GC-MS and LC-MS/MS remain essential for confirmatory analysis, their dependence on central laboratories limits their applicability for field screening. Consequently, portable biosensor-based detection platforms have attracted increasing attention as rapid screening tools. This review synthesizes 26 peer-reviewed studies published between 2010 and 2025 on portable biosensor-based screening tools for pesticide detection in food and agricultural matrices, including electrochemical sensors, immunoassays, aptamer-based systems, paper-based lateral flow devices, and smartphone-assisted platforms. Given the heterogeneity of analytes, sensing mechanisms, and study designs, a narrative synthesis approach was applied. Overall, the evidence suggests a shift from laboratory-centered detection toward field-deployable technologies that may support preliminary screening within food safety monitoring frameworks. Paper-based lateral flow assays are widely reported as deployable formats, while electrochemical and affinity-based platforms are often positioned as intermediate solutions for mobile or semi-controlled testing environments. However, most platforms remain at the proof-of-concept or early validation stage, and challenges related to matrix interference, long-term stability, reproducibility, standardization, and large-scale implementation persist. This review highlights the potential role of portable biosensor technologies as complementary tools within tiered food safety monitoring systems and outlines key priorities for further development before wider regulatory integration can be considered. Full article

Urban districts along major rivers are undergoing rapid transformation, yet long-term evidence on how redevelopment reshapes land cover and vegetation structure remains limited in post-socialist cities. This study examines the spatio-temporal evolution of land use and land cover (LULC) and vegetation dynamics along the Sava River corridor in Belgrade from 1990 to 2024. CORINE Land Cover (CLC) datasets were combined with Landsat-derived NDVI and MSAVI time series, while high-resolution Esri Wayback imagery was used for visual interpretation and qualitative corroboration of the detected land-cover and vegetation patterns. Beyond conventional NDVI/LULC assessments, the study integrates multi-decadal spectral trends with functional vegetation structure classification to evaluate canopy continuity and ecological configuration under contrasting redevelopment models. Results reveal a pronounced divergence between the two riverbanks. The left bank (New Belgrade) maintains stable land-cover composition and consistently higher NDVI and MSAVI values, indicating preserved green infrastructure and sustained canopy continuity. In contrast, the right bank (Belgrade Waterfront) experienced substantial land-cover conversion after 2006, with a statistically significant decline in vegetation greenness (NDVI −0.020 dec⁻¹, p < 0.001) and a marked increase in impervious surfaces. MSAVI-based functional classes indicate a shift from mixed low vegetation to predominantly sealed land, while tree canopy remained persistently low throughout redevelopment. The findings demonstrate measurable ecological simplification and canopy loss, even where nominal green areas remain present. By providing a rare multi-decadal, spatially explicit comparison of two contrasting planning paradigms within the same river corridor, the study contributes new empirical evidence on how governance and redevelopment models shape riparian ecological trajectories and sustainable urbanism in post-socialist cities. Strengthening blue-green infrastructure and restoring native riparian vegetation are essential for enhancing climate resilience and ensuring long-term riverfront sustainability. Full article

Toxoplasmosis is a widely distributed zoonosis caused by the protozoan parasite Toxoplasma gondii. Infection during pregnancy is a major public health concern due to its potential impact on both maternal health and fetal development. Early detection of maternal infection is critical to prevent adverse outcomes; however, maternal signs are often subtle, non-specific or absent, complicating timely diagnosis. This scoping review aimed to map and synthesise existing evidence on early maternal signs, pregnancy and foetal outcomes, frequently assessed risk factors, and diagnostic approaches of toxoplasmosis in expectant mothers in Africa. The review was done in accordance with the PRISMA-ScR guidelines. A literature search of PubMed, Scopus, ResearchGate, and Google Scholar was performed to identify studies published between 2000 and 2025. Retrieved records were managed using Zotero (version 8.0.4) for deduplication and screening. Only English-language studies conducted in Africa and reporting relevant maternal or clinical data were included. A total of 28 cross-sectional studies were included. Lymphadenopathy (25.0%) was the most frequently reported maternal early sign, followed by flu-like illness, asymptomatic infection, low-grade or mild fever, and fatigue or malaise (each 10.7%). Congenital anomalies (50.0%) and miscarriage or spontaneous abortion (42.9%) were the most commonly reported foetal and pregnancy outcomes. Frequently reported risk factors were exposure to cat faeces (57.1%) and ingestion of undercooked or raw meat (42.9%). Diagnostic approaches were commonly enzyme-based immunoassays (78.6%), with limited use of RDTs and molecular methods. These findings suggest the need for improved early detection and prevention strategies in high-risk, low-resource African settings. Enhancing routine screening, health education, and access to appropriate diagnostics are considered. Future studies should consider adopting standardised reporting and integrating sensitive, affordable, rapid diagnostic approaches to enhance early detection and reduce the burden of congenital toxoplasmosis. Full article

This study proposes a robust hybrid framework for iris segmentation in covert biometric systems, specifically addressing the challenge of non-ideal images featuring fully or nearly closed eyes. To overcome the limitations of traditional geometric methods, this study implements a SqueezeNet-based Deep Convolutional Neural Network (DCNN) for rapid eye-state classification. Comparative analysis with various pretrained DCNN models indicates that SqueezeNet provides an optimal balance of accuracy and efficiency, requiring only 1.24 million parameters and a minimal memory footprint of 5.2 MB. For iris contour demarcation, the proposed algorithm combines the Circular Hough Transform (CHT) with global gray-level statistics and anatomical constraints to facilitate reliable iris localization. Utilizing image decimation, percentile-based thresholding, and Canny edge detection, it systematically delineates the limbic and pupillary boundaries. This improved search methodology ensures precise contour delineation, even under sub-optimal imaging circumstances. The proposed algorithm was validated on a novel dataset encompassing challenging conditions such as specular reflections, blur, non-uniform illumination, and varying degrees of occlusion, including nearly or fully closed eyes. Experimental results demonstrate superior segmentation accuracy and significant computational efficiency, underscoring the model’s potential for real-time biometric applications in unconstrained environments. Full article

Background/Objectives: Wound infections cause significant morbidity, yet current diagnostics rely on time-consuming microbial culture. Volatile organic compounds (VOCs) from bacterial metabolism offer potential for early diagnosis. This study aimed to validate the volatile metabolites profiled by gas chromatography–ion mobility spectrometry (GC-IMS) combined with machine learning for rapid identification of wound infections and certain bacterial infections. Methods: Headspace of clinical wound samples were analyzed using GC-IMS. Volatile metabolite profiles were compared between infected and non-infected groups and between Escherichia coli (E. coli)-positive and negative samples. Partial least squares discriminant analysis (PLS-DA) and Mann–Whitney U test were used for preliminary screening with variable importance in projection (VIP) > 1 and p-value < 0.05. Three machine learning algorithms, namely support vector machine (SVM), logistic regression (LR), and random forest (RF), were trained on the selected features for classification, using 5-fold cross-validation with 10 repeated runs. Model performance was assessed using key evaluation metrics, including accuracy, sensitivity, specificity, the area under the curve (AUC) and feature importance ranking to identify the most relevant biomarkers. Results: A total of 19 volatile metabolites associated with clinical wound samples were identified. The RF model achieved 90.15% sensitivity and 0.91 AUC for bacterial infection detection. For E. coli identification, LR reached 85.35% sensitivity and 0.89 AUC. Potential volatile metabolic biomarkers including elevated 3-methyl-1-butanol, 2-methyl-1-butanol, and ethyl hexanoate for identifying bacterial infection were selected through the cross-validation results of the three algorithms. Conclusions: Untargeted metabolomics by GC-IMS effectively captures infection-specific volatile metabolic signatures in complex wound samples. Integration with machine learning enables rapid, high-accuracy diagnosis of bacterial infections and E. coli identification at point of care. This approach addresses clinical metabolomics translational challenges by providing a portable and cost-effective method, potentially reducing antibiotic misuse through more timely and targeted therapy. Full article

As the number of elderly individuals living alone increases, the risk of fall-related accidents correspondingly rises, underscoring the need for rapid fall detection systems. Because falls are difficult to predict in terms of location, detection systems must be deployed in a distributed manner, which in turn requires compact and low-power implementations. Unlike camera sensors, radar sensors do not raise privacy concerns and are not limited by line-of-sight constraints. Moreover, compared with wearable sensors, radar enables continuous monitoring without user intervention. However, prior radar-based approaches incur high computational complexity, leading to increased power consumption and larger hardware area, thereby necessitating efficient hardware design. This paper proposes a lightweight fall detection system based on continuous-wave (CW) radar and a binarized convolutional neural network (BCNN). Radar signals are preprocessed using short-time Fourier transform (STFT) to generate binary spectrograms, which are then fed into a BCNN-based classification network. The proposed system performs binary classification of five fall activities and seven non-fall activities with an accuracy of 96.1%. The preprocessing module and classification network were implemented as hardware accelerators and integrated with a microprocessor in a system-on-chip (SoC) architecture on a field-programmable gate array (FPGA). Compared with the software implementation, the proposed hardware achieved speedups of 387.5× and 86.7× for the preprocessing and classification modules, respectively. Furthermore, the overall system processing time was 2.58 ms, corresponding to an 89.5× speedup over the software baseline. Full article

Occupational sedentary behavior presents a public health risk, yet current interventions often rely on subjective self-reports or context-blind prompts. This study validates a privacy-preserving, edge-computing time-of-flight (ToF) sensor that detects postural states and quantifies therapeutic exercise gestures in real time. The dual-sensor architecture distinguishes between sitting, standing, and absence, while capturing rapid sit-to-stand repetitions suitable for active-break interventions. In this paper, a laboratory study (N = 7) evaluated the system against ground truth comprising activPAL3 accelerometry and video analysis. Across 378 postural events, the sensor achieved high temporal fidelity (mean absolute error < 1.6 s) and 100% sensitivity in counting exercise repetitions. The system differentiated workstation occupancy from physical absence. These findings demonstrate that ToF sensing matches the accuracy of video analysis without privacy concerns while offering the contextual awareness required for just-in-time, adaptive workplace interventions. Full article

Enzootic nasal adenocarcinoma is a contagious neoplasm of goats for which early antemortem diagnosis remains challenging under field conditions. This study evaluated the diagnostic accuracy of infrared nasal thermography for detecting the disease using histopathology as the reference standard. Eighty-six goats from a dairy herd with confirmed enzootic nasal adenocarcinoma were examined by infrared thermography one day prior to slaughter under standardized environmental conditions. Thermal images of the ethmoidal region were qualitatively assessed for asymmetry or focal hyperthermia. Following slaughter, all heads underwent systematic necropsy and bilateral histopathological examination. Twenty-three goats (26.7%) were histologically confirmed as positive with confirmation by RT-PCR (Reverse Transcriptase Polymerase Chain Reaction) from tissue samples. Infrared thermography showed a sensitivity of 82.6% and a specificity of 90.5%, with an overall diagnostic accuracy of 88.4%. Positive and negative predictive values were 76.0% and 93.4%, respectively. Agreement between thermography and histopathology was substantial (Cohen’s κ = 0.711; p < 0.001). Although thermography did not achieve the specificity of macroscopic post-mortem examination, its non-invasive and rapid nature supports its potential as a preliminary complementary antemortem screening approach, although its applicability at herd level requires validation in broader and more representative populations. Full article

CeO₂ is widely studied in catalysis owing to its Ce⁴⁺/Ce³⁺ redox couple and oxygen storage capacity (OSC), but its low-temperature redox activity remains a challenge. To address this, this study investigates the effects of saturated In³⁺ doping (1 mol.%) on the structural, redox, and catalytic properties of nano-octahedral CeO₂. Structural and chemical analyses reveal that In³⁺ doping induces lattice contraction from 5.4171 to 5.4129 Å, increases oxygen vacancy concentration from 29.7% to 39.8%, and raises surface Ce³⁺ fraction from 27.6% to 30.0%. Consequently, H₂-TPR measurements show that the surface reduction peak temperature decreases from 548 to 406 °C and the onset reduction temperature shifts from 309 °C to 183 °C. Quantitative OSC analysis further demonstrates that the low-temperature OSC increases from 13.17 to 20.57 mmol O₂/mol and the high-temperature OSC from 53.36 to 59.38 mmol O₂/mol upon doping. As a result of these enhancements, CO-TPSR tests reveal improved low-temperature CO oxidation performance, with the CO₂ light-off temperature decreasing from 99 to 72 °C and the rapid oxidation temperature from 153 to 96 °C. Notably, H₂O and H₂ signals are detected during CO-TPSR, and FTIR analysis confirms the enrichment of surface hydroxyl groups in the doped sample, offering new mechanistic insights into the involvement of surface species in the reaction pathway. Overall, saturated In³⁺ doping effectively enhances the oxygen vacancy concentration, surface reducibility, and CO oxidation activity of nano-octahedral CeO₂. Full article

With the rapid advancement in Generative Artificial Intelligence (GenAI), AI-generated content (AIGC) lacking human cognitive oversight is increasingly permeating open web environments and academic communication systems. This study integrates longitudinal retraction data (Retraction Watch Database, 1990–2026), web-scale analyses of AI-content penetration (Common Crawl, 2013–2026), and bibliometric mapping of governance scholarship (Web of Science Core Collection, Scopus, Google Scholar, 2020–2026) to diagnose the cross-level misalignment between synthetic-content diffusion, AI-related misconduct pressure, and governance attention. On this basis, it proposes a Normalized Coverage Index (NCI) to measure the relative relationship between scholarly attention to AI-related academic misconduct governance and the level of misconduct pressure observed through retraction data across disciplines. The results reveal pronounced asymmetries at the disciplinary level. Fields such as chemistry (0.04), physics, mathematics & statistics (0.11), and life sciences & biology (0.34) exhibit clear governance gaps, whereas Education shows a comparatively excessive level of attention (NCI = 29.26). Since 2022, AIGC has expanded rapidly across open web corpora, accompanied by a sharp rise in AI-related retractions, which also exhibit a longer detection lag than traditional forms of misconduct (2.77 years vs. 1.91 years). Although the volume of academic governance-related research has grown rapidly, its proportion within the broader body of AI-related research has declined, suggesting that scholarly attention to governance has not kept pace with technological diffusion. Consequently, a structural misalignment in governance—closely tied to the allocation of attention—has emerged within the academic system in the era of GenAI. This misalignment may pose potential risks to the robustness of the knowledge production system. Addressing it requires rebuilding epistemic infrastructure through provenance transparency, auditable workflows, and governance-aware seed corpora aligned with empirically concentrated risks. Full article

This study systematically compared the induction and resuscitation characteristics of the viable but non-culturable (VBNC) state in Klebsiella pneumoniae FY170-1 following sublethal exposure to sodium hypochlorite (NaClO) or glutaraldehyde (GA). Treatment with 30 mg/L NaClO or 60 mg/L GA for 60 min reduced culturability to below the detection limit (<1 CFU/mL). However, CTC staining showed that 50.80% and 63.44% of cells, respectively, retained respiratory activity, while SYTO 9/PI staining indicated that membrane integrity was largely preserved, consistent with induction of the VBNC state. Scanning electron microscopy revealed distinct morphological alterations in the two groups. NaClO-induced VBNC cells showed surface depressions and wrinkling, consistent with oxidative damage, whereas GA-induced cells exhibited filamentous and net-like surface structures, consistent with aldehyde-mediated cross-linking. Among the tested additives, sodium succinate showed the strongest resuscitation-promoting effect under the experimental conditions, with OD600 increasing after approximately 2 h of incubation. Post-resuscitation analysis further revealed marked differences between the two VBNC states. In resuscitated NaClO-induced VBNC cells, ATP partially recovered, but reactive oxygen species remained elevated and catalase activity showed little recovery. In contrast, resuscitated GA-induced VBNC cells exhibited lower ATP recovery but more rapid normalization of ROS and better recovery of oxidative stress-related parameters. Total protein analysis and SDS-PAGE further supported distinct patterns of protein-level alteration between the two treatments. Overall, these findings suggest that NaClO and GA induce phenotypically distinct VBNC states in K. pneumoniae, with different recovery behaviors and stress response profiles. Sodium succinate was identified as the most effective recovery-promoting additive under the tested conditions. These results highlight the risk of underestimating bacterial survival when culturability is used as the sole indicator of disinfection efficacy and support the need for more comprehensive viability assessment. Full article