In recent years, misdiagnosis or delayed diagnosis of Chlamydia psittaci (C. psittaci) infections has led to frequent outbreaks of severe public health events, such as severe pneumonia and respiratory distress, drawing increasing attention. Rapid and simple detection methods are vital for early intervention to reduce severity and mortality. In this study, we designed highly specific RPA primers and crRNA (CRISPR RNA) based on the highly conserved CPSIT_0429 gene in the C. psittaci genome, and preliminarily established a nucleic acid detection method for C. psittaci using the RPA-CRISPR/Cas12a system. In the two-step assay, the combination of the CPSIT_0429-F1/R1 primer pair and CPSIT_0429-crRNA2 achieved a detection limit of 2×10° copies/μL. Incorporating 20% glycerol enabled a one-tube assay with a limit of 2×102 copies/μL. Furthermore, the method showed no cross-reactivity with common respiratory pathogens such as Influenza virus, SARS-CoV-2, and Streptococcus pneumoniae, demonstrating excellent specificity. Both the two-step and one-tube methods were compared with qPCR-verified C. psittaci positive samples. The results indicated that both assays showed high consistency with qPCR results. The RPA-CRISPR/Cas12a detection method is rapid, accurate, highly sensitive, and specific, providing a reliable platform for early diagnosis and clinical management of C. psittaci infections.

Gold nanoparticles have long been explored for their potential in medical diagnostics, drug delivery, and imaging, particularly in oncology. However, successful translation to clinical applications requires a deep understanding of their biomolecular interactions and transport mechanisms across cellular barriers and within cells. In this review, we examine the current understanding of the journey of gold nanoparticles from systemic administration to tumour infiltration. Specific focus is placed on the biological barriers crossed and the mechanisms involved in traversing those barriers, including active and selective transport pathways, like transcytosis, increasingly recognised as critical for nanoparticle translocation across endothelial and tumour barriers. We stratify the nanoparticle journey into smaller stages and critically discuss the most relevant in vitro models used to study each stage in isolation. Although traditional 2D cell cultures have provided some fundamental insights, more advanced tissue culture models outlined in this review offer enhanced physiological relevance. Monitoring nanoparticle behaviour within these models cannot be achieved without sophisticated imaging and quantification techniques. Herein, we have identified the most appropriate detection methods and their suitability for being used on each in vitro model for the detection of label-free gold nanoparticles. Using label-free nanoparticles preserves their native physicochemical properties and avoids potential artefacts introduced by fluorescent or radioactive tags, and conveniently, gold lends itself well to label-free detection due to its unique optical and electronic properties. By integrating insights from advanced in vitro modelling and cutting-edge detection strategies, this review highlights the current landscape and future directions for optimising the study of gold nanoparticle delivery across barriers in cancer nanomedicine.

Smart contracts have been widely applied in various fields. Due to the immuta-bility of data on the blockchain, it is of great significance to conduct smart con-tract vulnerability detection before data is uploaded to the chain. To address the problems of low accuracy and single vulnerability type in traditional detection methods, a blockchain smart contract vulnerability detection method based on Graph Neural Network (GNN) is proposed. This method abstracts the functions and key code segments in smart contracts into nodes in a graph, and constructs edges by leveraging data and control dependencies during code execution, thereby accurately depicting the specific graph structures of reentrancy attacks and timestamp-dependent vulnerabilities. To further enhance the model’s sensi-tivity to key vulnerability patterns, the multi-head attention mechanism is in-novatively introduced, which can effectively screen out the nodes and edges that contribute the most to vulnerability detection, suppress irrelevant or noisy information, and significantly improve the accuracy and robustness of vulnera-bility detection. Experimental results show that the proposed method achieves an accuracy of 85.19% in reentrancy vulnerability detection and 82.37% in timestamp-dependent vulnerability detection, demonstrating excellent vulner-ability identification capability.

This study aims to optimize the protection of photovoltaic (PV) systems against electrical disturbances, particularly voltage dips. The objective is to develop new methods for analyzing and managing these disturbances, which affects the power quality in electrical networks and causes the automatic disconnection of PV systems, leading to production losses and plant malfunctions. In addition, voltage dips represent a major challenge for industrial sectors, where they can cause production interruptions and process malfunctions, leading to economic losses and product quality degradation. This research proposes a method for real-time detection of voltage dips, by integrating the recloser settings within the monitoring system. This approach makes it possible to distinguish temporary outages, related to reclosing, from prolonged outages, thus avoiding unnecessary disconnections of PV systems. The method's performance has been validated by simulations carried out in the MATLAB/SIMULINK environment.

Background and purpose: Chloramphenicol (CAP) is a broad-spectrum antibiotic whose unregulated presence in pharmaceuticals and food products raises significant health concerns, underscoring the need for rapid, reliable detection methods. This study aimed to develop a sensitive and economical electrochemical sensing platform based on a novel gadolinium tungstate (Gd2(WO4)3) and sulphur-doped graphitic carbon nitride (S-g-C3N4) nanocomposite for the efficient determination of CAP. Experimental approach: The Gd2(WO4)3/S-g-C3N4 nanocomposite was synthesized via a simple co-precipitation method and characterized using XRD, XPS, EDS, and TEM to confirm structural and morphological integration. A glassy carbon electrode modified with the composite was evaluated by cyclic and linear sweep voltammetry, along with analyses of interference, repeatability, stability, and real samples in eye-drop formulations and milk. Key results: The modified electrode exhibited significantly enhanced electrocatalytic oxidation of CAP compared with bare and individually modified electrodes, demonstrating high sensitivity, good selectivity against common interferents, and strong operational stability and reproducibility. A low detection limit was achieved, and the electrode effectively quantified CAP in real matrices with satisfactory recovery. Conclusion: The findings establish the Gd2(WO4)3/S-g-C3N4 nanocomposite as an efficient sensing material, offering a reliable, stable, and cost-effective platform for routine monitoring of antibiotic residues. While minor optimization may further expand its applicability, the study advances electrochemical sensing by introducing a robust nanocomposite with improved analytical performance for CAP detection.

Accurate histopathological image classification plays a crucial role in cancer detection and diagnosis. In automated cancer detection methods, extraction of histological features of malignant and benign tissues is a challenging task. This paper presents a modified approach on octave convolution to extract high and low-frequency features which help to provide a comprehensive representation of histopathological images. Proposed octave convolution model is used to perform histopathological image classification using three different optimization strategies. Firstly, an optimal alpha value of 0.5 is used to give equal importance to both high-frequency and low-frequency feature maps. This balanced approach ensures that the model effectively considers critical high-frequency features as well as low-frequency features of cancerous tissues. Secondly, high-frequency and low-frequency feature maps are extracted and down sampled into half the spatial dimension size to reduce the computational cost compared to standard CNN. Thirdly, training and validation was conducted using ReLU, PReLU, LeakyReLU, ELU, GELU and Swish activation functions. From the experiment, it was concluded that PReLU is the best activation function for capturing intricate patterns inherent in cancer-related histopathological images. Combining all these optimization strategies, the proposed method proved to provide a classification accuracy of 93% and also to reduce the computational cost by 50%. Performance validation against pre-trained models, CNN variants and vision transformer-based models has also been conducted, which proved superior performance of the proposed model.

Diabetes mellitus (DM) and its complications pose a major global health burden, while currently used biomarkers such as HbA1c and microalbuminuria remain limited for early diagnosis and individualized management. Klotho, originally identified as an anti-aging protein, has recently gained increasing attention due to its roles in mineral metabolism, oxidative stress regulation, inflammation, and fibrosis. This review discusses the biological functions of Klotho and its involvement in the pathogenesis of DM, with a particular emphasis on its regulatory mechanisms in glucose metabolism and oxidative stress. We comprehensively summarize recent findings on the role of Klotho in diabetic complications, including diabetic kidney disease, retinopathy, neuropathy, and cardiovascular disease, and highlight evidence on Klotho gene polymorphisms that influence susceptibility to DM and its complications across different populations. Furthermore, we analyze the limitations of current studies, including inconsistent findings on circulating Klotho levels, lack of standardized detection methods, and insufficient large-scale clinical trials. Finally, this review explores the potential of Klotho as both a biomarker and therapeutic target, and outlines future research directions focusing on standardization, mechanistic studies, and translational applications to advance precision medicine in diabetes management.

Dysthyroid optic neuropathy (DON) is a severe complication of thyroid eye disease (TED) with limited early detection methods. This study aimed to investigate the clinical characteristics of patients with TED who developed DON and to establish a predictive model for early identification of high-risk cases. Herein, 257 TED patients were prospectively included, of whom 68 (26.5%) developed DON. All patients were divided into derivation and validation cohorts, and Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression analyses were applied to identify clinical factors and construct a prediction model. In the derivation cohort (185 TED patients), 49 (26.5%) developed DON. DON patients showed significantly higher prevalence of pretibial myxedema (PTM) (22.4% vs. 5.9%, p=0.001), diabetes mellitus (18.4% vs. 7.4%, p=0.029), older age (58.04±11.30 years vs. 47.99±10.65 years, p < 0.001), higher CAS (5 vs. 4, p < 0.001), elevated triglyceride (TG) levels (1.44 mmol/L vs. 1.15 mmol/L, p=0.042) and lower visual functioning (VF) (43.75 vs. 62.50, p < 0.001). LASSO regression analysis identified age, PTM, TG, VF and CAS as independent predictors of DON. The developed nomogram presented AUCs of 0.853 (95%CI: 0.792-0.914) and 0.856 (95%CI:0.762-0.950) in the derivation and validation cohorts respectively. Altogether, the findings of this study identify advanced age, elevated CAS, increased TG, lower VF and PTM as significant predictors of DON in patients with TED. The proposed nomogram offers a practical clinical tool for risk stratification, providing clinicians with an approach for individualized risk assessment and timely therapeutic intervention.

Transfer RNA-derived small RNAs (tsRNAs) have emerged as critical regulators in cancer biology, influencing gene expression, protein synthesis, and cellular signaling. Their unique expression patterns and functional diversity highlight their potential for diagnostic, prognostic, and therapeutic potential, particularly in hepatocellular carcinoma (HCC), a major cause of cancer-related mortality worldwide. This review provides the first integrative overview of tsRNAs in HCC, encompassing their biogenesis, classification, molecular functions, and involvement in tumor hallmarks, including proliferation, metastasis, apoptosis, metabolism, angiogenesis, and immune modulation. We also summarize current advances in detection methods and databases and highlight the translational potential of tsRNAs as diagnostic, prognostic, and therapeutic targets. This work emphasizes unexplored dimensions of tsRNA-mediated regulation, connecting mechanistic insights with clinical applications. Although tsRNAs show great promise in HCC diagnosis, prognosis, and therapy, clinical translation remains hindered by gaps in mechanistic understanding, technical challenges in detection, and a lack of large-scale validation. Overcoming these limitations through standardized methodologies and multi-omics integration could unlock their full potential in precision cancer medicine.

Implementing real-time driving anger detection and appropriate intervention are crucial for road safety. A naturalistic driving study (NDS) was conducted to further validate the anger detection method tested in simulated experiments. Thirty-four drivers participated in the tests, each lasting one to two weeks. A smartphone with a self-developed application was used to record the encountered anger-provoking events, drivers' facial expressions, and vehicle kinematic data. Drivers' anger-related traits were collected through questionnaires. A total of 570 events were collected. Abnormal lane-changing, being blocked, and slow driving were the most common anger triggers, mainly occurring during morning rush hours on ring roads and inbound/outbound highways/main roads. The driving anger detection model with the collected data achieved 93.6% accuracy and 93.4% F1 score. Anger-sensitive features and their variations during anger were presented. These findings may enhance drivers' emotional experience in intelligent vehicles and facilitate the development of emotion detection and intervention systems.

Accurate structural variant detection from short-read sequencing data remains challenged by false positives, particularly for heterozygous deletions where reduced allelic support and coverage-based detection methods are ambiguous. Existing SV genotyping and filtering approaches suffer from significant recall reductions, dependencies on additional pre-computed resources, or restriction to depth-based signals that overlook read level evidence. Here we present SVhet, a novel computational framework that leverages the heterozygosity patterns detected from different read evidences to identify false heterozygous deletions. Comprehensive benchmarking using 31 Human Genome Structural Variation Consortium Phase 3 samples demonstrated SVhet's ability to further reduce false positives while maintaining baseline recall. Hybrid approach of duphold and SVhet achieved up to 60% reduction in false positive counts while preserving recall. We also showed SVhet to be computationally efficient that can complete a whole genome structural variant callset under 5min using 4 CPU cores. SVhet is available under a permissive MIT license via https://github.com/snakesch/SVhet. SVhet provides an accurate and efficient solution for evaluating heterozygous deletions derived from short read sequencing data. SVhet can be used as a standalone tool or in conjunction with other filtering tools such as duphold. Importantly, it does not require additional variant sets, and can operate with minimal compute. Altogether, SVhet adds to the current effort to achieve accurate structural variant detection using short reads.

A robust detection method of apple leaf spectral image Starscream based on CRS-YOLO algorithm

Separation and enrichment of active components in Toddalia asiatica (L.) Lam.by carboxylated chitosan-modified magnetic metal-organic frameworks.

Meat adulteration poses significant challenges to public health and consumer trust, necessitating the development of effective detection methods. Isothermal nucleic acid amplification (NAA) techniques, such as loop-mediated isothermal amplification (LAMP) and polymerase spiral reaction (PSR), have emerged as rapid and cost-effective alternatives to conventional polymerase chain reaction (PCR). These techniques simplify infrastructure requirements and can enable naked-eye detection (NED) through visible color changes with specific dyes. This study highlights the potential and suitability of various dyes for the NED of IA-based meat adulteration detection. Intercalating dyes, such as SYBR Green I, bind to the amplified DNA and emit fluorescence. pH-sensitive dyes, including phenol red, neutral red, and xylenol orange, change color with pH shifts during amplification. Triphenylmethane dyes, such as crystal violet and malachite green, directly interact with DNA, showing no pH dependence. Intercalating dyes, such as SYBR Green I, achieve superior sensitivity, often reaching 10fg of target DNA. Conversely, other dyes like Hydroxynaphthol blue and other pH-sensitive and pH-independent dyes provide slightly lesser sensitivity, typically ranging from 100fg to 1 pg. Dye-based NED combined with IA offers advantages such as rapid results, high sensitivity and specificity, suitability for field testing, and potential integration into lab-on-chip systems. However, further research is required to optimize dye formulations, develop multiplex assays, enhance sample preparation for complex food matrices, and investigate novel isothermal methods and primer designs. Accurate standardization and validation of these techniques are crucial for their widespread adoption to ensure food safety and consumer trust in the meat industry.

Backscatter interferometry (BSI) is a refractive index detection method for capillary electrophoresis that is inexpensive, flexible, and easily miniaturized. Interestingly, unlike most detectors that respond exclusively to analyte concentration, the BSI signal is sensitive to both refractive index (analyte concentration) and the separation voltage. The latter is linked to zone conductivity and leads to improved BSI signals and lower detection limits with increasing field strengths. Enhanced BSI signals can also be generated using a photothermal mechanism, where resonantly excited analytes release heat into their surroundings to increase the BSI signal amplitude. Both voltage-based and photothermal signal enhancement mechanisms can lead to a change in the polarity of the BSI signal, which can be either positive or negative depending on the specific analyte, its concentration, and the separation conditions. Here, we show that this leads to a significant increase in apparent peak efficiency. At the transition in peak polarity, both mechanisms result in over a 10-fold increase in apparent peak efficiency, improving from approximately 105 plates/m to over a million plates/m. Simultaneously measured BSI and fluorescence electropherograms confirm that the efficiency increase is unique to the BSI signal and not due to changes in zone dispersion, and can be tuned to optimize separation resolution. The origin of the efficiency increase is discussed in terms of the refractive index and zone conductivity contributions to the BSI signal.

Per- and polyfluoroalkyl substances (PFAS) are a large family of synthetic chemicals known for their exceptional stability, strong surface activity, and ability to repel both water and oil. Due to these characteristics, PFAS have been widely used since the 1950s across multiple industries. However, over the decades, these substances have emerged as persistent and bioaccumulative contaminants. While it is evident that PFAS pose adverse effects on both ecosystems and human well-being, the mechanisms underlying their toxicities are yet to be fully understood. To better examine the thematic evolution of PFAS research, this review divides the literature into four distinct eras: before 2000s, from 2000 to 2010, from 2010 to 2020, and from 2020 onwards. Since the latter half of the 20th century, the rapid development and mass production of PFAS resulted in the manufacture of thousands of industrial and household products. After decades of concerns regarding their toxic impacts, major phase-outs in the early 2000s shifted attention towards environmental studies and biomonitoring. Throughout the 2010s, extensive studies were conducted to assess the PFAS toxicities, especially perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA), the two widely detected compounds on human populations. Since 2020, research efforts have increasingly progressed toward molecular-level studies, advancements in analytical detection methods, and remediation technologies. Additionally, this review examines regulatory changes, highlights current knowledge gaps, and outlines directions for future research.

In industrial production, defect detection for automotive headlight lenses is an essential yet challenging task. Transparent glass defect detection faces several difficulties, including a wide variety of defect shapes and sizes, as well as the challenge of identifying transparent surface defects. To enhance the accuracy and efficiency of this process, we propose a computer vision-based inspection solution utilizing multi-angle lighting. For this task, we collected 2000 automotive headlight images to systematically categorize defects in transparent glass, with the primary defect types being spots, scratches, and abrasions. During data acquisition, we proposed a dataset augmentation method named SWAM to address class imbalance, ultimately generating the Lens Defect Dataset (LDD), which comprises 5532 images across these three main defect categories. Furthermore, we propose a defect detection network named the Transparent Glass Defect Network (TGDNet), designed based on common transparent glass defect types. Within the backbone of TGDNet, we introduced the TGFE module to adaptively extract local features for different defect categories and employed TGD, an improved SK attention mechanism, combined with a spatial attention mechanism to boost the network’s capability in multi-scale feature fusion. Experiments demonstrate that compared to other classical defect detection methods, TGDNet achieves superior performance on the LDD, improving the average detection precision by 6.7% in mAP and 8.9% in mAP50 over the highest-performing baseline algorithm.

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) pose one of the world's most prominent chemical health threats and are detected in virtually everything from the Antarctic environment to human blood. Due to the extreme half-lives and omnipresent distribution of the chemical class, the threat cannot be eliminated but rather must be continuously monitored and managed through widespread sample testing and targeted remediation long term. Unfortunately, the current standard detection method, liquid chromatography/tandem mass spectrometry (LC/MS/MS), is expensive, time-consuming, and limited to use by highly trained professionals in centralized laboratories. For this reason, there is an urgent need for a field-deployable, affordable, and easy-to-use device for PFAS detection. This paper addresses the issue with a protein-based electrochemical sensor for the point-of-need detection of perfluorooctanoic acid (PFOA), which is one commonly regulated PFAS compound of particular concern. Using two proteins, lubricin (LUB, proteoglycan 4) and human liver fatty acid binding protein engineered with a methylene blue redox tag (FABP1-MB), a response to PFOA is detected at 0.41 ng/L and 0.41 μg/L concentrations. Detection of PFOA is also demonstrated in real water and whole blood samples, demonstrating the sensors' possible future application for both environmental and biomedical monitoring.

Antibiotic resistance genes (ARGs) are emerging pollutants that pose significant threats to both the environment and human health. Rapid detection of ARGs is essential for monitoring their levels and controlling their spread. However, traditional detection methods are often time-consuming and require specialized equipment, leading to delays. To address this issue, this study developed a novel method for visual microarray detection of ARGs, including sul1, tetA, qepA, macB, and vanR. This method employs ARG-specific dual probes combined with silver staining signal cascade amplification technology. A centrifuge-free concentration device has been developed that can directly enrich nucleic acid fragments from environmental samples, with traditional nucleic acid extraction and PCR amplification being eliminated. Using a dual probe combined with silver staining enhancement dual probe amplification technology, rapid high-throughput detection of low-concentration ARG is achieved. No specialized equipment is required, and on-site visual detection of ARG was realized. The detection method can detect target genes (sul1, tetA, qepA, macB, and vanR) within a concentration range of 0.411 µg mL-1 to 55.9 µg mL-1, with the lowest detectable target gene concentration being 0.411 µg mL-1. This study marks the first integration of a centrifuge-free concentration device, specific dual probes, and silver staining signal cascade amplification technology to establish a rapid visual detection method for ARGs using a microarray. The developed detection method holds significant potential not only for the detection and monitoring of ARGs but also as a prototype for devising future detection strategies.

Cardiac lymphatic system and coronary heart disease: associations, mechanisms and therapeutic strategies.