Search Results (12,723)

Background/Objective: Timely identification of bloodstream pathogens is critical for sepsis management; however, PCR inhibitors such as sodium polyanetholesulfonate (SPS) in blood culture broth compromise nucleic acid recovery and long read sequencing. We assessed whether coupling a benzyl alcohol SPS-removal step to the fully automated LabTurbo AIO extractor improves Oxford Nanopore-based pathogen detection. Methods: Thirteen positive blood culture broths were pre-treated with benzyl alcohol and divided: half volumes were purified on the LabTurbo AIO; paired aliquots underwent manual QIAamp extraction. DNA purity was evaluated by NanoDrop and Qubit. Barcoded libraries were sequenced on MinION R9.4.1 flow cells for 6 h. Results: Automated eluates showed a median A₂₆₀/A₂₈₀ of 1.92 and A₂₆₀/A₂₃₀ of 1.96, versus 1.80 and 1.48 for manual extracts. The automated workflow generated 1.69 × 10⁶ total reads compared with 3.9 × 10⁵ reads for manual extraction. The median N50 read length increased from 5.9 kb to 8.7 kb, and the median proportion of reads classified to species increased from 62% to 84%. The hands-on time was <5 min and the sample-to-answer turnaround was <8 h, compared with >9 h and 90 min for the manual protocol, respectively. Conclusions: Benzyl alcohol SPS removal integrated into the LabTurbo AIO extractor yielded purer, longer, and higher read counts, enhancing nanopore sequencing depth and accuracy while compressing diagnostic turnaround to a single working day. This represents a practical advance for rapid blood culture pathogen identification in critical care settings. Full article

Background: Papilledema, an ophthalmic finding associated with increased intracranial pressure, is often induced by dermatological medications, including corticosteroids, isotretinoin, and tetracyclines. Early detection is crucial for preventing irreversible optic nerve damage, but access to ophthalmologic expertise is often limited in rural settings. Artificial intelligence (AI) may enable the automated and accurate detection of papilledema from fundus images, thereby supporting timely diagnosis and management. Objective: The primary objective of this study was to explore the diagnostic capability of ChatGPT-4o, a general large language model with multimodal input, in identifying papilledema from fundus photographs. For context, its performance was compared with a ResNet-based convolutional neural network (CNN) specifically fine-tuned for ophthalmic imaging, as well as with the assessments of two human ophthalmologists. The focus was on applications relevant to dermatological care in resource-limited environments. Methods: A dataset of 1094 fundus images (295 papilledema, 799 normal) was preprocessed and partitioned into a training set and a test set. The ResNet model was fine-tuned using discriminative learning rates and a one-cycle learning rate policy. GPT-4o and two human evaluators (a senior ophthalmologist and an ophthalmology resident) independently assessed the test images. Diagnostic metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and Cohen’s Kappa, were calculated for each evaluator. Results: GPT-4o, when applied to papilledema detection, achieved an overall accuracy of 85.9% with substantial agreement beyond chance (Cohen’s Kappa = 0.72), but lower specificity (78.9%) and positive predictive value (73.7%) compared to benchmark models. For context, the ResNet model, fine-tuned for ophthalmic imaging, reached near-perfect accuracy (99.5%, Kappa = 0.99), while two human ophthalmologists achieved accuracies of 96.0% (Kappa ≈ 0.92). Conclusions: This study explored the capability of GPT-4o, a large language model with multimodal input, for detecting papilledema from fundus photographs. GPT-4o achieved moderate diagnostic accuracy and substantial agreement with the ground truth, but it underperformed compared to both a domain-specific ResNet model and human ophthalmologists. These findings underscore the distinction between generalist large language models and specialized diagnostic AI: while GPT-4o is not optimized for ophthalmic imaging, its accessibility, adaptability, and rapid evolution highlight its potential as a future adjunct in clinical screening, particularly in underserved settings. These findings also underscore the need for validation on external datasets and real-world clinical environments before such tools can be broadly implemented. Full article

Defect detection in textile manufacturing is critically hampered by the inefficiency of manual inspection and the dual constraints of deep learning (DL) approaches. Specifically, DL models suffer from poor generalization, as the rapid iteration of fabric types makes acquiring sufficient training data impractical. Furthermore, their high computational costs impede real-time industrial deployment. To address these challenges, this paper proposes a texture-adaptive fabric defect detection method. Our approach begins with a Dynamic Subspace Feature Extraction (DSFE) technique to extract spatial luminance features of the fabric. Subsequently, a Light Field Offset-Aware Reconstruction Model (LFOA) is introduced to reconstruct the luminance distribution, effectively compensating for environmental lighting variations. Finally, we develop a texture-adaptive defect detection system to identify potential defective regions, alongside a probabilistic ‘OutlierIndex’ to quantify their likelihood of being true defects. This system is engineered to rapidly adapt to new fabric types with a small number of labeled samples, demonstrating strong generalization and suitability for dynamic industrial conditions. Experimental validation confirms that our method achieves 70.74% accuracy, decisively outperforming existing models by over 30%. Full article

Diterpenoid diester alkaloids (DDAs) are the primary toxic constituents in aconite herbs, while also being the key pharmacologically active components. Consequently, establishing rapid enrichment and highly sensitive analytical methods for DDAs is of critical importance. Herein, we developed and constructed a gas–liquid microextraction (GLME) device, which enables the rapid and selective enrichment of DDAs from complex matrices. The enriched extract can be directly analyzed by high-resolution Orbitrap mass spectrometry without requiring any further pretreatment. A comparative analysis of six commonly used Aconitum herbs medicines and their processed derivatives was conducted. Notably, GLME enhanced the mass spectrometric signals of DDAs by 3–4 orders of magnitude, facilitating the identification of 27 alkaloids, including 3 potential new compounds (15-Ethyl-13-deoxyanhydroaconitine, 13-Hydroxy-15-ethylanhydroaconitine and 8-eicosapentaenoic-benzoylmesaconine). It was found that among the tested samples, the DDAs response intensity of raw Caowu was the highest, and the DDA signals decreased significantly after processing. This result chemically validates the detoxification efficacy of traditional methods. The proposed GLME-MS strategy has the advantages of being green, economical, easy to operate, and highly selective (>1000-fold), which provides a technical reference for the rapid detection, safety assessment, and quality control of Aconitum herbs. Full article

Most biomarkers exhibit abnormal expression in more than one disease, making conventional single-biomarker detection strategies prone to false-negative results. Detecting multiple biomarkers associated with a single disease can therefore substantially improve diagnostic accuracy. Accordingly, recent research has focused on precise multiplex detection, leading to the development of sensors employing various readout methods, including electrochemical, fluorescence, Raman, and colorimetric approaches. This review focuses on optical sensing applications, such as fluorescence, Raman spectroscopy, and colorimetry, which offer rapid and straightforward detection and are well suited for point-of-care testing (POCT). These optical sensors exploit nanoscale phenomena derived from the intrinsic properties of nanomaterials, including metal-enhanced fluorescence (MEF), Förster resonance energy transfer (FRET), and surface-enhanced Raman scattering (SERS), which can be tailored through modifications in material type and structure. We summarize the types and properties of commonly used nanomaterials, including plasmonic and carbon-based nanoparticles, and provide a comprehensive overview of recent advances in multiplex biomarker detection. Furthermore, we address the potential of these nanosensors for clinical translation and POCT applications, highlighting their relevance for next-generation disease diagnostic platforms. Full article

With the rapid development of cybersecurity technologies, cybersecurity testing has played an increasingly critical role in scientific research, new technology validation, system performance evaluation, and talent development. A central challenge in this domain lies in efficiently and rapidly constructing testing environments while ensuring the reliability and reproducibility of test results. To address this issue, this paper proposes an integrated evaluation method specifically designed for cybersecurity testing, combining anomaly detection and predictive analysis techniques. The method first employs an autoencoder (AE) to perform dimensionality reduction on the raw data collected from a testbed environment, followed by anomaly detection using the Isolation Forest (IF) algorithm. To assess the impact of cyberattacks on the stability of the testbed system, the steady-state data of the environment was treated as the ideal reference. The degree of disruption was then quantified by calculating the Euclidean distance between the dimensionally reduced experimental data and the reference state. Finally, a specific case study was conducted to validate the feasibility and effectiveness of the proposed method, and a percentage-based scoring mechanism was introduced to quantitatively evaluate the security level of the system. Full article

Nitrate, as one of the important nutrients in seawater, influences the constant ratio of nitrogen to phosphorus, which is closely related to phytoplankton survival. In this work, a Cu-nanosphere-modified gold microwire electrode was used as the working electrode for determining nitrate in an artificial seawater sample with salinity of 35‰ by a differential pulse voltammetry technique. Under the optimized conditions, the detection linear range is from 1 μM to 2000 μM, the limit of detection is 0.33 μM, and the response time for a single sample is 5 min. Then, to reduce the influence of factors such as temperature, humidity, and microbial environment during sample transporting on the nitrate concentration in real seawater, a portable electrochemical system was introduced for on-site detection. Rapid field determination results show that nitrate levels correlate with tides, proving the portable system’s reliability. Full article

The rapid dissemination of health misinformation on the Internet and social media has become a growing challenge for public health, particularly in terms of health information credibility. Promising efforts have been made to detect misinformation using generative AI and large language models (LLMs). However, such tools still lack domain-specific knowledge that limits their performance. In this study, we examine the use of predefined knowledge data structures in the forms of debunk lists to augment existing LLMs’ capabilities. We evaluate five different LLMs, including Llama-3.1-8B-instruct, Mistral-large, GPT-4o-mini, Claude-3.5-haiku, and Gemini-1.5-flash, under three experimental settings: zero-shot and debunk-augmented (50 and 100 entities). Results show that external knowledge, in the form of debunk lists, can notably improve LLMs’ performance in detecting misinformation. While Llama shows minimal benefit, the F1 score improvement ranges from 2.63% (GPT-4o) to 11% (Claude). In addition, analysis of model justifications shows that frequent use of debunk lists does not necessarily relate to accurate predictions. This highlights the importance of a model’s ability in effectively using the debunk list rather than reporting superficial integration of external knowledge. Moreover, the proposed framework is generalizable to other misinformation domains and provides key insights for applying external knowledge and evaluating LLMs’ reasoning reliability. Full article

SPCEs are crucial for electrochemical sensing because of their portability, low cost, disposability, and ease of mass production. This study details their manufacture, surface modifications, electrochemical characterization, and use in chemical and biosensing. SPCEs integrate working, reference, and counter electrodes on PVC or polyester substrates for compact sensor design. Surface modifications, such as plasma treatment (O₂, Ar), nanomaterial addition (AuNPs, GO, CNTs), polymer coatings, and MIPs, enhance performance. These changes improve sensitivity, selectivity, stability, and electron transport. Electrochemical methods such as CV, DPV, SWV, and EIS detect analytes, including biomolecules (glucose, dopamine, and pathogens) and heavy metals (Pb²⁺, As³⁺). Their applications include healthcare diagnostics, environmental monitoring, and food safety. Modified SPCEs enable rapid on-site analysis and offer strong potential to transform our understanding of the physical world. Full article

Rapid and accurate detection of trapped victims is vital in disaster rescue operations, yet most existing object detection methods cannot simultaneously deliver high accuracy and fast inference under resource-constrained conditions. To address this limitation, we propose the LightSeek-YOLO, a lightweight, real-time victim detection framework for disaster scenarios built upon YOLOv11. Our LightSeek-YOLO integrates three core innovations. First, it employs HGNetV2 as the backbone, whose HGStem and HGBlock modules leverage depthwise separable convolutions to markedly reduce computational cost while preserving feature extraction. Secondly, it introduces Seek-DS (Seek-DownSampling), a dual-branch downsampling module that preserves key feature extrema through a MaxPool branch while capturing spatial patterns via a progressive convolution branch, thereby effectively mitigating background interference. Third, it incorporates Seek-DH (Seek Detection Head), a lightweight detection head that processes features through a unified pipeline, enhancing scale adaptability while reducing parameter redundancy. Evaluated on the common C2A disaster dataset, LightSeek-YOLO achieves 0.478 AP@small for small-object detection, demonstrating strong robustness in challenging conditions such as rubble and smoke. Moreover, on the COCO, it reaches 0.473 mAP@[0.5:0.95], matching YOLOv8n while achieving superior computational efficiency through 38.2% parameter reduction and 39.5% FLOP reduction, and achieving 571.72 FPS on desktop hardware, with computational efficiency improvements suggesting potential for edge deployment pending validation. Full article

Cardiovascular diseases (CVDs) are increasingly being defined not only in terms of metabolic or purely vascular disorders, but also as complex immunometabolic disorders. One of the most groundbreaking discoveries in recent years is the role of neutrophil extracellular networks (NETs/NENs) as a key link between chronic vascular wall inflammation and thrombotic processes. In this article, we present a synthetic overview of the latest data on the biology of NETs/NENs and their impact on the development of atherosclerosis, endothelial dysfunction, and the mechanisms of immunothrombosis. We highlight how these structures contribute to the weakening of atherosclerotic plaque stability, impaired endothelial barrier integrity, platelet activation, and the initiation of the coagulation cascade. We also discuss the modulating role of classic risk factors such as hypertension, dyslipidemia, and exposure to tobacco smoke, which may increase the formation or hinder the elimination of NETs/NENs. We also focus on the practical application of this knowledge: we present biomarkers associated with the presence of NETs/NENs (cfDNA, MPO–DNA complexes, CitH3, NE), which may be useful in diagnostics and risk stratification, and we discuss innovative therapeutic strategies. In addition to classic methods for indirectly inhibiting NET/NEN formation (antiplatelet, anti-inflammatory, and immunometabolic agents), we present experimental approaches aimed at their neutralization and removal (e.g., DNase I, elastase, and myeloperoxidase inhibitors). We pay particular attention to the context of cardiac and cardiac surgical procedures (Percutaneous Coronary Intervention-PCI, coronary artery bypass grafting-CABG), where rapid NET/NEN bursts can increase the risk of acute thrombotic complications. The overall evidence indicates that NETs/NENs represent an innovative and promising research and therapeutic target, allowing us to view cardiovascular diseases in a new light—as a dynamic interaction of inflammatory, atherosclerotic, and thrombotic processes. This opens up new possibilities in diagnostics, combination treatment and personalisation of therapy, although further research and standardization of detection methods remain necessary. Full article

Establishing a simple and effective method for the visual detection of formaldehyde plays an important role in environmental emergency monitoring. In this work, L-cysteine-stabilized copper clusters were synthesized via a green, mild, and facile one-step preparation method. Through the optimization of reaction conditions, including reactant concentration and pH, the clusters exhibited stable red fluorescence. Upon exposure to formaldehyde, the fluorescence intensity of copper clusters gradually quenched with increasing formaldehyde concentration, enabling the development of a visual detection method that was successfully applied to analyze formaldehyde samples in air. Furthermore, by immobilizing the copper clusters into hydrogels, the visual detection performance and portability of the material were significantly enhanced. This method offers the advantages of simple preparation and rapid and accurate determination, demonstrating potential for semi-quantitative field detection of formaldehyde in emergency scenarios. Full article

Amid the increasing frequency and severity of major disasters, the rapid spread of online misinformation poses substantial risks to public safety, effective crisis management, and long-term societal sustainability. Current methods for managing disaster-related rumors rely on static, rule-based approaches that lack scalability, fail to capture nuanced misinformation, and are limited to reactive responses, hindering effective disaster management. To address this gap, this study proposes a novel framework that leverages large language models (LLMs) and event knowledge graphs (EKGs) to facilitate the sustainable agile identification and adaptive control of disaster-related online rumors. The framework follows a multi-stage process, which includes the collection and preprocessing of disaster-related online data, the application of Gaussian Mixture Wasserstein Autoencoders (GMWAEs) for sentiment and rumor analysis, and the development of EKGs to enrich the understanding and reasoning of disaster events. Additionally, an enhanced model for rumor identification and risk control is introduced, utilizing Graph Attention Networks (GATs) to extract node features for accurate rumor detection and prediction of rumor propagation paths. Extensive experimental validation confirms the efficacy of the proposed methodology in improving disaster response. This study contributes novel theoretical insights and presents practical, scalable solutions for rumor control and risk management during crises. Full article

Electrochemical sensors have emerged as powerful analytical tools for the detection of anti-inflammatory and antibiotic drugs due to their high sensitivity, rapid response, and cost-effectiveness compared to conventional chromatographic and spectrophotometric methods. This review highlights recent advances in electrode materials, surface modification strategies, and signal amplification approaches for quantifying nonsteroidal anti-inflammatory drugs (NSAIDs) and various antibiotic classes, including sulfonamides, tetracyclines, macrolides, and quinolones. Particular attention is given to nanostructured carbon-based materials, metal nanoparticles, and polymer composites that enhance electron transfer, improve selectivity, and lower limits of detection (LODs). The analytical performance of different electrochemical techniques such as cyclic voltammetry, differential pulse voltammetry, and square-wave voltammetry is critically compared across various drug targets. Trends indicate that hybrid nanomaterial-modified electrodes consistently achieve sub-micromolar detection limits in biological and environmental samples, offering potential for point-of-care diagnostics and environmental monitoring. Current challenges include improving sensor stability, mitigating fouling effects, and ensuring reproducibility in complex matrices. Future research should focus on integrated, miniaturized sensing platforms capable of multiplex detection, paving the way for rapid, portable, and sustainable analytical solutions in pharmaceutical and biomedical applications. Full article

We report a novel tablet-based reverse titration system for rapid, point-of-use measurement of water hardness, overcoming key limitations of conventional EDTA titration. Reagents are encapsulated in pullulan matrix giving two separate tablets. The first tablet contains the Eriochrome black T (EBT) and N-cyclohexyl-3-aminopropanesulfonic acid (CAPS) buffer, while the second encapsulates ethylenediaminetetraacetic acid (EDTA) disodium salt dihydrate. The system employs a trimodal detection strategy: qualitative screening via immediate color change with the EBT tablet, semi-quantitative estimation through combined tablet dissolution and adjusting the sample volume to a reference level, and quantitative determination using reverse titration, where water is gradually added until the red wine endpoint appears. This approach enhances interference tolerance from competing metal ions and improves accuracy over traditional methods. Testing with real water samples showed excellent agreement with standard titration. The tablets remain stable for over seven months, and the system eliminates the need for skilled personnel, laboratory equipment, or bulky instrumentation. This low-cost, user-friendly, and interference-tolerant platform enables rapid and accurate water hardness assessment at the point of use. Full article