Visual Detection Research Articles

Highly efficient removal and real-time visual detection of fluoride ions using ratiometric CAU-10-NH2@RhB: Probe design, sensing performance, and practical applications

The extensive use of fluoride in agriculture, industry, medicine, and daily necessities has raised growing concerns about fluoride residue. To date, real-time visual detection and efficient removal of fluoride ions from water remain greatly desirable. Herein, nano-CAU-10-NH2@RhB is introduced as a ratiometric fluorescent probe and efficient scavenger for the intelligent detection and removal of fluoride ions. CAU-10-NH2@RhB is readily obtained through one-pot synthesis and exhibits high sensitivity and selectivity for real-time fluoride ion detection, with a naked-eye distinguishable color change from pink to blue. A portable device for point-of-care testing was developed based on color hue analysis readout using a smartphone. A quantitative response was achieved across a wide concentration range, with a detection limit of 54.2 nM. Adsorption experiments suggest that nano-CAU-10-NH2@RhB serves as an efficient fluoride ion scavenger, with a fluoride adsorption capacity of 49.3 mg/g. Moreover, the mechanistic study revealed that hydrogen bonds formed between fluoride ions and amino groups of CAU-10-NH2@RhB are crucial for the detection and adsorption of fluoride ions. This analysis platform was also used for point-of-care quantitative visual detection of fluoride ions in food, water, and toothpaste.

Identification of differentially expressed genes and screening for key genes involved in ovarian cancer prognosis: An integrated bioinformatics and network analysis approach

Objectives: Ovaries are important and essential organs of animals in producing and releasing eggs. Ovarian cancer (OvCa) is one of the most prevalent lethal gynecological malignancies with a lack of distinct biomarkers. Advances in high-throughput genomic data and the continued refinement of bioinformatics tools enable the identification of potential biomarkers. Leveraging these insights, we can employ systems biology approaches to enhance the accuracy of diagnosis and prognosis. Material and Methods: A comparative analysis was conducted between normal and tumor samples, employing bioinformatics software and tools. Differential expression analysis utilized fold-change statistics, while DAVID 6.8 software was used to perform gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. The protein-protein interaction (PPI) network was constructed differentially expressed genes (DEGs) using Search Tool for the Retrieval of Interacting Genes database, and Cytoscape 3.9.1, along with its Molecular Complex Detection and CytoHubba plugins, facilitated network visualization, analysis, and module detection. Hub gene expression and overall survival were explored through the Kaplan–Meier plotter, while Gene Expression Profiling Interactive Analysis 2 analyzed the tumor stage of OvCa patients. Hub genes protein expression was analyzed using the human protein atlas database through immunostaining results. The NetworkAnalyst program and Cytoscape were employed to analyze and visualize the transcription factor-hub gene associations. Subsequently, single-nucleotide variation, methylation, and pathway activity of hub genes were examined. Validation of hub genes messenger RNA expression was done using quantitative real-time polymerase chain reaction analysis. Results: 607 DEGs, including 248 upregulated and 359 downregulated genes, were identified. The top 20 candidate genes were screened out through PPI network analysis. We discovered that the genes BUB1 Mitotic Checkpoint Serine/Threonine Kinase B (BUB1B), Cyclin A2 (CCNA2), Mitotic Arrest Deficient 2 Like 1 (MAD2L1), Protein Regulator of Cytokinesis 1 (PRC1), Thyroid Hormone Receptor Interactor 13 (TRIP13), and ZW10 Interacting Kinetochore Protein (ZWINT) exhibited significant importance in OvCa prognosis. Conclusion: Six genes, BUB1B, CCNA2, MAD2L1, PRC1, TRIP13, and ZWINT (identified as functional hub genes), are probably playing tumor-promotive roles, except TRIP13. All genes product is functionally related to the cell cycle. These can be targeted in quest of potential therapeutics for OvCa treatment.

Design and preparation of a fluorescent molecularly imprinted membrane for the selective detection of pepsin enzyme as a biomarker for gastroesophageal reflux disease

A novel fluorescent molecularly imprinted polymer membrane (FMIM) has been developed for the selective binding and qualitative detection of pepsin enzyme, a biomarker indicative of gastroesophageal reflux disease (GERD). This study utilises the high selectivity offered by molecular imprinting techniques to capture pepsin enzyme within complex biological matrices, such as human saliva. Additionally, fluorescent carbon dots integrated into the membrane matrix provide instant visual detection of pepsin. Various combinations of functional monomers and cross-linkers were systematically evaluated to investigate their impact on the binding capacity and mechanical stability of the resultant FMIMs. The optimum performance was achieved with a mixture of two hydrophilic monomers, namely N-(hydroxymethyl)acrylamide and acrylamide, in conjunction with N,N-methylenebis(acrylamide) as the cross-linking agent. The developed FMIM demonstrated a binding capacity of 21.56 mg g-1, surpassing that of the fluorescent non-imprinted membrane (FNIM) at 8.49 mg g-1. Moreover, the binding of FMIM to pepsin was tested against other competitor enzymes to verify its selectivity. Furthermore, comprehensive characterisation of both FMIM and FNIM was conducted using various analytical techniques to ensure their structural integrity and functionality. Ultimately, the developed FMIM exhibited effective binding of pepsin in standard solutions and samples, enabling enrichment and visual detection of the biomarker enzyme.

Visual object detection system based on augmented reality and steady-state visual evoked potential

This study investigates a brain-computer interface (BCI) system based on an augmented reality (AR) environment and steady-state visual evoked potentials (SSVEP). The system is designed to facilitate the selection of real-world objects through visual gaze in real-life scenarios. By integrating object detection technology and AR technology, the system augmented real objects with visual enhancements, providing users with visual stimuli that induced corresponding brain signals. SSVEP technology was then utilized to interpret these brain signals and identify the objects that users focused on. Additionally, an adaptive dynamic time-window-based filter bank canonical correlation analysis was employed to rapidly parse the subjects' brain signals. Experimental results indicated that the system could effectively recognize SSVEP signals, achieving an average accuracy rate of 90.6% in visual target identification. This system extends the application of SSVEP signals to real-life scenarios, demonstrating feasibility and efficacy in assisting individuals with mobility impairments and physical disabilities in object selection tasks.

Multifunctional intelligent indication labels featuring antibacterial properties based on pectin/carboxymethyl chitosan incorporated with porous microspheres loaded with anthocyanins: A new approach for salmon preservation and freshness detection

Multifunctional intelligent indication labels tend to prolong the quality guarantee period and monitor the freshness of food. In this study, indication labels were prepared with carboxymethyl chitosan (CMCS) and pectin as substrate, and porous microspheres loaded with anthocyanins (ACN@PGMs) as indicators and antioxidant as well as clove essential oil as an antibacterial agent. The interaction between pectin and CMCS and the addition of ACN@PGMs improved the physical properties and structural density of indication labels due to electrostatic complexing and hydrogen-bonding interactions, which exhibited optimal performance when the added anthocyanins equivalent was 0.25 mg/mL. Meanwhile, the water contact angle and antibacterial activity reached maximum values of 78.8 ± 2.3°, 11.8 ± 0.88 cm (E.coli), and 17.00 ± 0.71 cm (S.aureus), respectively. In addition, indication labels displayed sensitive color responses to different pH and ammonia environments. In application, labels could extend the shelf life of refrigerated salmon from 4 days to 9 days and showed significant color changes when salmon spoiled. Overall, indication labels played a positive role in extending shelf life and visual freshness detection, which could be employed in food quality evaluation and food preservation.

Intelligent visual detection method for the early surface damage of mine hoisting wire ropes

Intelligent visual detection method for the early surface damage of mine hoisting wire ropes

Visual Detection of Traffic Incident through Automatic Monitoring of Vehicle Activities

Intelligent transportation systems (ITSs) derive significant advantages from advanced models like YOLOv8, which excel in predicting traffic incidents in dynamic urban environments. Roboflow plays a crucial role in organizing and preparing image data essential for computer vision models. Initially, a dataset of 1000 images is utilized for training, with an additional 500 images reserved for validation purposes. Subsequently, the Deep Simple Online and Real-time Tracking (Deep-SORT) algorithm enhances scene analyses over time, offering continuous monitoring of vehicle behavior. Following this, the YOLOv8 model is deployed to detect specific traffic incidents effectively. By combining YOLOv8 with Deep SORT, urban traffic patterns are accurately detected and analyzed with high precision. The findings demonstrate that YOLOv8 achieves an accuracy of 98.4%, significantly surpassing alternative methodologies. Moreover, the proposed approach exhibits outstanding performance in the recall (97.2%), precision (98.5%), and F1 score (95.7%), underscoring its superior capability in accurate prediction and analyses of traffic incidents with high precision and efficiency.

Audio–visual deepfake detection using articulatory representation learning

Advancements in generative artificial intelligence have made it easier to manipulate auditory and visual elements, highlighting the critical need for robust audio–visual deepfake detection methods. In this paper, we propose an articulatory representation-based audio–visual deepfake detection approach, ART-AVDF. First, we devise an audio encoder to extract articulatory features that capture the physical significance of articulation movement, integrating with a lip encoder to explore audio–visual articulatory correspondences in a self-supervised learning manner. Then, we design a multimodal joint fusion module to further explore inherent audio–visual consistency using the articulatory embeddings. Extensive experiments on the DFDC, FakeAVCeleb, and DefakeAVMiT datasets demonstrate that ART-AVDF obtains a significant performance improvement compared to many deepfake detection models.

A tiny sample rapid visual detection technology for imidacloprid resistance in Aphis gossypii by CRISPR/Cas12a

Insecticide resistance monitoring is essential for guiding chemical pest control and resistance management policies. Currently, rapid and effective technology for monitoring the resistance of tiny insects in the field is absent. Aphis gossypii Glover is a typical tiny insect, and one of the most frequently reported insecticide-resistant pests. In this study, we established a novel CRISPR/Cas12a-based rapid visual detection approach for detecting the V62I and R81T mutations in the β1 subunit of the nAChR in A. gossypii, to reflect target-site resistance to imidacloprid. Based on the nAChR β1 subunit gene in A. gossypii, the V62I/R81T-specific RPA primers and crRNAs were designed, and the ratio of 10 μM/2 μM/10 μM for ssDNA/Cas12a/crRNA was selected as the optimal dosage for the CRISPR reaction, ensuring that Cas12a only accurately recognizes imidacloprid-resistance templates. Our data show that the field populations of resistant insects possessing V62I and R81T mutations to imidacloprid can be accurately identified within one hour using the RPA-CRISPR/Cas12a detection approach under visible blue light at 440–460 nm. The protocol for RPA-CRISPR detection necessitates a single less than 2 mm specimen of A. gossypii tissues to perform RPA-CRISPR detection, and the process only requires a container at 37 °C and a portable blue light at 440–460 nm. Our research represents the first application of RPA-CRISPR technology in insecticide resistance detection, offers a new method for the resistance monitoring of A. gossypii or other tiny insects, helps delay the development of resistance to imidacloprid, improves the sustainability of chemical control, and provides theoretical guidance for managing pest resistance.

Label free immunochromatographic sensor based on TCBPE for the detection of Acidovorax citrulli in Cucurbitaceae plants

The presence of Acidovorax citrulli (Ac), the etiological agent responsible for bacterial fruit blotch (BFB), exerts a profound impact on the growth and development of cucurbit plants. Therefore, it is imperative to promptly detect Ac to effectively mitigate the widespread dissemination of this disease among cucurbit crops. The present study describes the development of a novel label-free immunochromatographic strip (ICTS) sensor for the detection of Ac in cucurbit samples. This was achieved by utilizing 4’,4’’’,4’’’’’,4’’’’’’’-(ethene-1,1,2,2-tetrayl) tetrabiphenyl-4-carboxylic acid (TCBPE) carboxyl activation product (CA-TCBPE) as a fluorescent probe. The CA-TCBPE@Ac complex is captured at the detection site through the immobilization of a monoclonal antibody monoclonal antibody (mAb) targeting Ac in this sensor. Under the excitation of 365 nm ultraviolet (UV) light, the detection of Ac in the concentration range of 105-108 CFU/mL was successfully achieved, with a visual detection limit of 105 CFU/mL. The accuracy of the sensor was further validated through experimentation with real samples, including watermelon, cantaloupe, cucumber, and zucchini. This study presents the pioneering development of a label-free ICTS sensor, utilizing TCBPE material markers, for rapid and sensitive detection of Ac. This innovative approach offers a robust solution for the swift detecting of Ac in cucurbits and other samples, thereby holding great promise for future applications.

Deep learning-based computer vision in project management: Automating indoor construction progress monitoring

Progress monitoring is crucial for effective project management, particularly in construction projects. The adoption of computer vision with deep learning expedites automation, accuracy, and efficiency in construction progress monitoring by overcoming the challenges of laborious, and error prone manual methods. While there is growing attention on developing computer vision based deep learning models for construction progress monitoring, deployment platforms for project managers are lacking. Using computer vision, this study develops a Mask Recurrent Convolutional Neural Network deep learning model. It utilizes progress images of drywall construction from two indoor construction sites and tests the model on a third indoor site in Sydney, Australia. The model is capable of automated as-built visual detection and work-in-progress measurement. The study also provides an understanding on the deployment process of the deep learning model on a cloud-based platform called Streamlit. By developing a model tailored for automatically quantifying work-in-progress of indoor construction elements and detailing the process of deploying that model on a cloud-based platform, this study significantly advances digitalization of construction project management. Project managers, stand to benefit from these advancements by gaining access to more accurate and automated construction progress monitoring for better decision-making.

Xenografting Human Musculoskeletal Sarcomas in Mice, Chick Embryo, and Zebrafish: How to Boost Translational Research.

Musculoskeletal sarcomas pose major challenges to researchers and clinicians due to their rarity and heterogeneity. Xenografting human cells or tumor fragments in rodents is a mainstay for the generation of cancer models and for the preclinical trial of novel drugs. Lately, though, technical, intrinsic and ethical concerns together with stricter regulations have significantly curbed the employment of murine patient-derived xenografts (mPDX). In alternatives to murine PDXs, researchers have focused on embryonal systems such as chorioallantoic membrane (CAM) and zebrafish embryos. These systems are time- and cost-effective hosts for tumor fragments and near-patient cells. The CAM of the chick embryo represents a unique vascularized environment to host xenografts with high engraftment rates, allowing for ease of visualization and molecular detection of metastatic cells. Thanks to the transparency of the larvae, zebrafish allow for the tracking of tumor development and metastatization, enabling high-throughput drug screening. This review will focus on xenograft models of musculoskeletal sarcomas to highlight the intrinsic and technically distinctive features of the different hosts, and how they can be exploited to elucidate biological mechanisms beneath the different phases of the tumor's natural history and in drug development. Ultimately, the review suggests the combination of different models as an advantageous approach to boost basic and translational research.

A ratiometric nanoplatform for fluorimetric and visual detection of Cu2+ and biothiols based on Eu MOFs and carbon dots

Herein, a ratiometric nanoplatform was established for fluorimetric and visual detection of Cu2+ and biothiols based on europium-based metal–organic frameworks (Eu MOFs) and carbon dots (CDs). Fluorescent Eu MOFs with red emission was facilely prepared via the coordination of Eu3+ with tetracycline (TC) and 2-[4-(2-hydroxyethyl)piperazin-1-yl] ethanesulfonic acid (Hepes). However, with addition of Cu2+, the fluorescence of Eu MOFs significantly decreased due to higher affinity of Cu2+ with TC, whereas the green fluorescence of CDs was not influenced by Cu2+, which served as an internal standard of the sensing platform. Interestingly, after introducing biothiols, the stronger coordination between Cu2+ and the sulfydryl of biothiols induced the release of TC to coordinate with Eu3+, thus recovering the fluorescence of Eu MOFs. The ratio changes of these two emissions accompanying by the distinct color variations of the solution under a 365 nm ultraviolet lamp could be used as output signals for fluorimetric and visual detection of Cu2+ and biothiols. The nanoplatform exhibited excellent sensing performance with limit detection as low as 0.57 μM for Cu2+, 0.54 μM for glutathione, 0.14 μM for homocyeteine and 0.42 µM for cysteine, respectively. In addition, it was successfully applied to detecting Cu2+ in environment water samples and biothiols in human serum samples, suggesting great potential for Cu2+ and biothiols determination in environmental and biological samples.

From Lab to Home: Ultrasensitive Rapid Detection of SARS-CoV-2 with a Cascade CRISPR/Cas13a-Cas12a System Based Lateral Flow Assay.

Currently, CRISPR/Cas-based molecular diagnostic techniques usually rely on the introduction of nucleic acid amplification to improve their sensitivity, which is usually more time-consuming, susceptible to aerosol contamination, and therefore not suitable for at-home molecular testing. In this research, we developed an advanced CRISPR/Cas13a-Cas12a-based lateral flow assay that facilitated the ultrasensitive and rapid detection of SARS-CoV-2 RNA directly from samples, without the need for nucleic acid amplification. This method was called CRISPR LFA enabling at-home RNA testing (CLEAR). CLEAR used a novel cascade mechanism with specially designed probes that fold into hairpin structures, enabling visual detection of SARS-CoV-2 sequences down to 1 aM sensitivity levels. More importantly, CLEAR had a positive coincidence rate of 100% and a negative coincidence rate of 100% for clinical nasopharyngeal swabs from 16 patients. CLEAR was particularly suitable for at-home molecular testing, providing a low-cost, user-friendly solution that can efficiently distinguish between different SARS-CoV-2 variants. CLEAR overcame the common limitations of high sensitivity and potential contamination associated with traditional PCR-based systems, making it a promising tool for widespread public health application, especially in environments with limited access to laboratory resources.

Visual detection of aldehyde gases using a silver-loaded paper-based colorimetric sensor array

The small molecule aldehydes are volatile organic compounds (VOCs), possessing cytotoxicity and carcinogenicity. Long-term exposure can pose a serious threat to human health. Based on an in-situ reduction colorimetric method to generate silver nanoparticles and induce colorimetric response, we proposed a silver-loaded paper-based colorimetric sensor array for visually detecting and differentiating five relatively common trace small molecule aldehyde gases. The silver ions are immobilized onto a porous filter paper and stabilized by complexing agents of branched polyethyleneimine, ethylenediamine, and 1,6-diaminohexane, respectively. The as-fabricated sensor array expresses remarkable stability and capacity to resist humidity. The qualitative analysis reveals that the sensor array has excellent selectivity for aldehyde gases and displays remarkable anti-interference ability. The quantitative analysis indicates that the sensor array exhibits superior sensitivity for five aldehyde gases, with limits of detection (LODs) of 9.0 ppb for formaldehyde (FA), 3.1 ppm for acetaldehyde (AA), 3.5 ppm for propionaldehyde (PA), 23.8 ppb for glutaric dialdehyde (GD), and 71.5 ppb for hydroxy formaldehyde (HF), respectively. Importantly, these LODs are all comfortably below their respective permissible exposure limits. A unique colorimetric response fingerprint is observed for each analyte. Standard chemometric methods illustrate that the sensor array has excellent clustering capability for these aldehyde gases. Additionally, the sensor array's response is irreversible and possesses outstanding performance for cumulative monitoring. This colorimetric sensor array based on silver ions reduced to silver nanoparticles offers a novel detection method for the continuous, ultrasensitive, and visual detection of trace airborne pollutants.

Gold Nanocluster-Based Self-Assembly Fluorescence Microbeads for Sensor Array Discrimination of Multicomponent Metal Ions.

Benefiting from easy visualization and simultaneous detection of multiple targets, fluorescence microbeads are commonly used as fluorescence-sensing elements to detect pollutants in the environment. However, the application of fluorescence microbead-based sensor arrays is still limited because fluorescence dyes always suffer from self-quenching, photobleaching, and spectral overlap. Herein, three kinds of gold nanoclusters (Au NCs) were assembled with polystyrene microspheres (PS NPs) by electrostatic interaction to prepare fluorescence microbeads (PS-Au NCs), developing a sensor array for the simultaneous analysis of multiple metal ions. In this work, different PS-Au NCs showed an enhancing or quenching fluorescence response to various metal ions, owing to distinct binding capacities. Combined with the recognition algorithm from linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA), this sensor assay could realize single-component and multicomponent qualitative detection for 8 kinds of heavy metal ions (HMIs) including Cu2+, Co2+, Pb2+, Hg2+, and Ce3+. Particularly, the large surface area of PS NPs could provide a direct reaction microenvironment to improve the efficiency of the detection process. Meanwhile, the fluorescence property of Au NCs could also be enhanced by a partially effective aggregation-induced emission (AIE) effect to give better fluorescence signal output. Under optimal conditions, 8 kinds of heavy metals and their multicomponent mixtures could be identified at concentrations as low as 0.62 μM. Meanwhile, the analytical performance of this sensor assay in water samples was also verified, meeting the requirement of actual analysis. This study provides a great potential and practical example of single-batch, multicomponent identification for HMIs.

The human fluorescence discrimination as precondition for the use of fluorescence-aided identification techniques (FIT)

ObjectivesThe fluorescence-aided identification technique (FIT) is based on the fluorescence properties of dental materials, specifically the intensity of their fluorescence compared to the autofluorescence of hard dental substances; this creates a perceived contrast between dental material and tooth. However, no studies to date have determined the extent to which the fluorescence intensity of tooth-colored dental materials must differ from that of natural autofluorescence to ensure reliable visual detection. The aim of this study was therefore to determine, for the first time, how pronounced the difference between fluorescence intensity and autofluorescence must be to reliably identify tooth-colored material. MethodsTen dentists assessed six different resin-based composite (RBC) samples of varying fluorescence intensity placed in the cavities of ten extracted teeth under standardized fluorescence-exciting illumination. The outcome variable was fluorescence perceptibility. Their assessments of the outcome variable were compared with measurements of the fluorescence intensities of the RBCs and the surrounding dental hard tissues, which were expressed as a fluorescence intensity ratio. Demographic data of the participants, including age, gender, and professional experience, were also recorded. ResultsNo significant differences were found for visual fluorescence perceptibility in relation to the explanatory variables of gender (p = 0.14), age (p = 0.13), and professional experience (p = 0.34). In contrast, the fluorescence intensity ratio was significantly different (p < 0.0001). SignificanceFor both clinicians and manufacturers, fluorescence intensity levels are important when selecting or developing FIT-compatible materials. Our results suggest that the fluorescence intensity levels of dental materials should be no more than 75% and no less than 200% of tooth natural autofluorescence to ensure reliable detection of tooth-colored materials.

Optimizing Corn Tar Spot Measurement: A Deep Learning Approach Using Red-Green-Blue Imaging and Stromata Contour Detection Algorithm for Leaf-Level Disease Severity Analysis.

Visual detection of stromata (brown-black, elevated fungal fruiting bodies) is a primary method for quantifying tar spot early in the season, as these structures are definitive signs of the disease and essential for effective disease monitoring and management. Here, we present Stromata Contour Detection Algorithm version 2 (SCDA v2), which addresses the limitations of the previously developed SCDA version 1 (SCDA v1) without the need for empirical search of the optimal Decision Making Input Parameters (DMIPs), while achieving higher and consistent accuracy in tar spot stromata detection. SCDA v2 operates in two components: (i) SCDA v1 producing tar-spot-like region proposals for a given input corn leaf Red-Green-Blue (RGB) image, and (ii) a pre-trained Convolutional Neural Network (CNN) classifier identifying true tar spot stromata from the region proposals. To demonstrate the enhanced performance of the SCDA v2, we utilized datasets of RGB images of corn leaves from field (low, middle, and upper canopies) and glasshouse conditions under variable environments, exhibiting different tar spot severities at various corn developmental stages. Various accuracy analyses (F1-score, linear regression, and Lin's concordance correlation), showed that SCDA v2 had a greater agreement with the reference data (human visual annotation) than SCDA v1. SCDA v2 achievd 73.7% mean Dice values (overall accuracy), compared to 30.8% for SCDA v1. The enhanced F1-score primarily resulted from eliminating overestimation cases using the CNN classifier. Our findings indicate the promising potential of SCDA v2 for glasshouse and field-scale applications, including tar spot phenotyping and surveillance projects.

Portable and Visual Detection of Cytochrome c with Graphene Quantum Dots–Filter Paper Composite

As a significant biomarker during the apoptosis process, cytochrome c (Cyt c) is considered as a critical component in the inherent apoptotic pathway, but the simple and portable detection still remains challengeable. In this work, a portable and visual sensing platform for Cyt c was developed based upon the fluorescence quenching of graphene quantum dots (GQDs), which could be finished within a few seconds. Herein, the absorption spectrum of Cyt c matched the emission spectrum of GQDs well, which could cause the fluorescence quenching of GQDs via the inner filter effect (IFE) in the range of 1–50 μg/mL with the limit of detection as low as 0.1 μg/mL. Furthermore, the intracellular Cyt c was imaged to observe the apoptosis process of cancer cells induced by staurosporine. To achieve the portable and visual detection of Cyt c, GQDs were deposited on the filter paper to form the solid platform, which displayed a gradual fluorescence quenching when different concentrations of Cyt c were present. Compared to the conventional methods, the proposed assay is low-cost, fast, portable, and visual, which will be useful for the investigation of mitochondrial dysfunction and apoptotic cell death.

Development of a Gold Nanoparticle-Based Sensor for Authentication of Organic Milk Based on Differential Levels of miRNA.

Dairy production systems significantly impact environmental sustainability, animal welfare, and human health. Intensive farming maximizes output through high-input practices, raising concerns about environmental degradation, animal welfare, and health risks from antibiotic residues. Conversely, organic farming emphasizes sustainable practices, animal welfare, and minimal synthetic inputs, potentially enhancing biodiversity, soil health, and milk quality. MicroRNAs (miRNAs), non-coding RNAs regulating gene expression, are promising biomarkers due to their response to various conditions. In this study, miRNAs bta-miR-103 and bta-miR-155, which are abundant in milk from pasture-fed cows, were selected. Additionally, bta-miR-215, which is abundant in milk fat from intensive systems, was also studied, in order to differentiate dairy production systems. A novel, cost-effective gold nanoparticle (AuNP)-based sensor was developed for miRNA detection, leveraging the unique plasmonic properties of AuNPs for visual detection. The method involves functionalizing AuNPs with complementary RNA probes and detecting miRNA-induced aggregation through colorimetric changes. This rapid, results in 30 min, and sensitive, visual limit of detection of 200 nM, assay requires minimal instrumentation and can be easily interpreted, offering significant advantages for field implementation in characterizing dairy production systems. This study demonstrates the successful application of this sensor in detecting miRNAs in 350 nM miRNA spiked raw milk, highlighting its potential for in situ dairy industry applications.