Sensitive Identification Research Articles

Construction of silver-doped graphitic carbon nitride integrated zeolitic imidazolate framework as an ultra-sensitive probe for colorimetric detection application

Abstract Malachite green, and phloxine B are highly effective antifungal, and antibacterial agents utilized primarily in aquaculture. It is classified within the Triarylmethane dye. Numerous studies have indicated that malachite green, and phloxine B are significantly toxic to both terrestrial and aquatic organisms. More critically, it poses substantial risks to human health and safety. As a result, several governments have prohibited its application in aquaculture and food production. There is a pressing need to develop a novel colorimetric probe for the detection of malachite green, and phloxine B. In response, a composite material was synthesized, combining Zeolitic Imidazolate Framework (ZIF-8) with silver-doped graphitic carbon nitride (Ag-g-C3N4), to serve as a colorimetric probe for the sensitive and selective identification of malachite green, and phloxine B. The Ag-g-C3N4/ZIF-8 composite demonstrated exceptional colorimetric sensing capabilities, attributed to the synergistic interactions between ZIF-8 and Ag-g-C3N4, achieving a detection limit as low as 1.073 nM, and 1.214 nM, for malachite green, and phloxine B, respectively. This composite represents a significant advancement in environmental sensing technology, providing a versatile and effective approach for detecting and addressing contamination in water resources.

Increasing awareness of invasive species in coastal ecosystems and early detection through DNA-based tools: a study with middle school students

One of the most significant challenges facing humanity today is the loss of biodiversity, which plays a crucial role in sustaining all life on Earth. This study implemented a “hands-on” activity using DNA barcoding to identify marine invertebrate species, including invasive species, among middle school students. Invasive species are a primary driver of biodiversity loss, making the adoption of more sensitive and effective identification methods a priority for enhancing local management efforts. The increase in correct answers and positive feedback from students highlight the effectiveness of this educational activity in significantly improving their knowledge of marine biodiversity and invasive species management.

Colorimetric sensor array for sensitive detection and identification of bacteria based on the etching of triangular silver nanoparticles regulated by Cl- at various concentrations.

Acolorimetric sensor array isproposed for ultrasensitive detection and identification of bacteria by using Cl- at various concentrations as sensing elements and triangular silver nanoparticles (T-AgNPs) as a single sensing nanoprobe. T-AgNPs are easily etched by Cl-. However, in the presence of bacteria, the etching process will be hindered. Different bacteria have differential protective effects on T-AgNPs due to their interactions, resulting in different etching degrees of T-AgNPs by Cl-, and visual color changes. By adjusting the antagonistic action between bacteria protection on T-AgNPs and the etching by using Cl- at various concentrations, differentbacteria had their own color response patterns. Combined with linear discriminant analysis (LDA) and orthogonal partial least squares-discriminant analysis(OPLS-DA), the bacteria could be identified. Themethod was also used for bacteria mixtures identification and showed high sensitivity (OD600 = 1.0 × 10-6) for V. parahaemolyticus detection. Finally, the sensor array was successfully utilized in the identification of bacteria in pure and mineral bottled water. Themethod is low-cost, simple, sensitive, visual, and has potential application in point-of-care testing of bacteria.

Practical SERS Substrates by Spray Coating of Silver Solutions for Deep Learning-Assisted Sensitive Antigen Identification

Surface-enhanced Raman spectroscopy (SERS) has long been recognized for its rapid and sensitive detection capabilities; however, challenges persist in practical fabrication of the substrates and interpreting complex data. Herein, we propose a deep learning (DL) assisted SERS approach to enable rapid and sensitive detection of analytes on a practical yet highly effective substrates prepared by direct spray-coating of a nanoparticle-free true solution of a reactive Ag ink and on-site thermal annealing mediated generation of nanostructures. This design ensured homogeneous distribution of Ag nanostructures throughout the entire substrate, significantly increasing the number of hotspots and enhancing the Raman signals, thereby achieving an impressive analytical enhancement factor of ~1010 in a reproducible and consistent manner. The diagnostic utility of this platform was demonstrated by detecting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein in both buffer and saliva, with detection limits of 74.3pg/mL and 7.43ng/mL, respectively. The DL-assisted SERS not only accurately identified the presence or absence of viral antigen, but also automatically quantified the viral load. This automatic identification achieved an outstanding accuracy of ~99.9%, highlighting the exceptional performance of the proposed platform. This simple, cost-effective, scalable, and ultra-sensitive DL-assisted SERS platform offers significant opportunities for early and precise detection in a range of analytical scenarios.

Validation of plasmonic-based biosensors for rapid and in depth characterization of monoclonal antibodies directed against rabbit haemorrhagic and foot-and-mouth disease viruses in biological samples.

ELISA and RT-PCR represent the standard tools for the sensitive identification of viruses in biological samples, but they lack the capacity to finely characterize the binding of viruses or viral antigens to monoclonal antibodies (MAbs). Biosensing technologies are gaining increasing importance as powerful MAb characterization tools in the field of virology. Surface plasmon resonance (SPR) is an optical biosensing technology already used for the in depth characterization of MAbs of diagnostic and therapeutic value. Rabbit haemorrhagic disease virus (RHDV) and foot-and-mouth disease virus (FMDV) are top veterinary issues for which the development of novel methods aimed at the characterization of antiviral MAbs represents a priority with important livestock healthcare and economic implications. With these premises in mind, here we prepared a series of SPR biosensors by immobilizing RHDV2 or its 6S subunit by different strategies that were then used to characterize the binding capacity of a panel of anti-RHDV2 MAbs. From the comparison of the results obtained, the biosensor composed of intact RHDV2 captured with catcher-MAb covalently immobilized to the surface showed the best analytical performances. To evaluate the versatility of the biosensor, the same strategy was then adopted using FMVD in cell extracts. The results obtained are discussed in view of the exploitation of SPR in the rapid and resilient fine characterization of antiviral MAbs for diagnostic or therapeutic purposes in the field of animal virology.

Real-time monitoring of cattle (Bos taurus) tissue using a novel point-of-care (POC) polymerase spiral reaction (PSR) colorimetric assay

Adulteration of cattle meat with other species raises critical public health concerns, as existing detection methods lack sensitivity, specificity and practicality. We address this gap by introducing a novel Point-of-Care (POC) polymerase spiral reaction (PSR) assay. This rapid, on-site tool enables highly specific and sensitive identification of cattle tissue, even in resource-limited settings. This innovation paves the way for improved food safety and consumer protection. The cattle-specific PSR assay employs COX3 gene-targeting primers and a chromogenic indicator triad (HNB, Malachite Green and Phenol Red) for rapid visual detection. Cross-amplification absence with other meats (buffalo, sheep, goat and pig) and concordance with SYBR Green I and agarose gel electrophoresis confirm specificity. PSR at 62 °C for 60minutes exhibits a 100 fg detection limit and identifies 0.1% meat admixing. Efficacy is demonstrated across a diverse range of cattle meat samples, blind samples and processed cattle muscle samples. Compatibility with both conventional (snap chill) and commercial DNA extraction kits enables on-site analysis within 90minutes, facilitating rapid field sample analysis.

An AmpliSeq™ HD approach for specific and sensitive identification of GMO by Next Generation Sequencing

With new genetically modified crops being constantly developed, many new targets need to be analysed for regulatory compliance. Classical PCR-based approaches currently used are however reaching their limits in terms of target throughput and new approaches are needed to adapt to the evolving situation. The Ion AmpliSeq™ HD technology was here adapted for the simultaneous detection of 108 GMO targets by Next Generation Sequencing (NGS) on the Ion GeneStudio S5 platform, using PCR primers mostly retrieved from the official European GMO methods. Tested on a wide array of reference materials, the method delivered specific detection of the expected GMO targets and reached a sensitivity down to 0.01 % (w/w) as required by GMO analytical guidelines. Numerous proficiency test samples were successfully analysed which confirmed the method performance and its applicability on real test samples. With its straightforward library preparation and ready-to-use bioinformatics analysis, the developed method is well tailored for routine GMO testing laboratories.

A Novel Application of B.EL.D™ Technology: Biosensor-Based Detection of Salmonella spp. in Food

The prevalence of foodborne diseases is continuously increasing, causing numerous hospitalizations and deaths, as well as money loss in the agri-food sector and food supply chain worldwide. The standard analyses currently used for bacteria detection have significant limitations with the most important being their long procedural time that can be crucial for foodborne outbreaks. In this study, a biosensor system able to perform robust and accurate detection of Salmonella spp. in meat products was developed. To achieve this, a portable device developed by EMBIO Diagnostics called B.EL.DTM (Bio Electric Diagnostics) and cell-based biosensor technology (BERA) were used. Results indicated that the new method could detect the pathogen within 24 h after a 3-min analysis and discriminate samples with and without Salmonella with high accuracy. Achieving an accuracy of 86.1% and a detection limit (LOD) of 1 log CFU g−1, this innovative technology enables rapid and sensitive identification of Salmonella spp. in meat and meat products, making it an excellent tool for pathogen screening.

Development and evaluation of a CRISPR/Cas12a-based diagnostic test for rapid detection and genotyping of HR-HPV in clinical specimens.

Persistent infection with high-risk human papillomavirus (HR-HPV) is the principal etiological factor of cervical cancer. Considering the gradual progression of cervical cancer, the early, rapid, sensitive, and specific identification of HPV, particularly HR-HPV types, is crucial in halting the advancement of the illness. Here, we established a rapid, highly sensitive, and specific HR-HPV detection platform, leveraging the CRISPR/Cas12a assay in conjunction with multienzyme isothermal rapid amplification. Our platform enables the detection and genotyping of 14 types of HR-HPV by using type-specific crRNAs. The outcomes of the detection can be interpreted either through a fluorescence reader or visually. Furthermore, we achieved one-tube multiplex detection of 14 HR-HPV types through the use of multiple amplifications and a crRNA pool. The detection sensitivity of this method is 2 copies/μL with no cross-reactivity, and the results can be obtained within 30 minutes. This method exhibited 100% clinical sensitivity and 100% clinical specificity when applied to 258 clinical specimens. Based on these findings, our CRISPR/Cas-based HR-HPV detection platform holds promise as a novel clinical detection tool, offering a visually intuitive and expedited alternative to existing HPV infection diagnostics and providing fresh perspectives for clinical cervical cancer screening.IMPORTANCEThis study developed a novel high-risk human papillomavirus (HR-HPV) detection platform based on CRISPR/Cas12a technology. This platform not only enables the rapid, highly sensitive, and specific detection and genotyping of 14 types of HR-HPV but also achieves single-tube multiplex detection of 14 HR-HPV types through ingenious design. The outcomes of the detection can be interpreted either through a fluorescence reader or visually. To the best of our knowledge, this is the first paper to utilize CRISPR/Cas diagnostic technology for the simultaneous detection of 14 types of HPV and to evaluate its feasibility in clinical sample detection using a large number of clinical samples. We hope that this work will facilitate the rapid and accurate detection of HPV and promote the broader application of CRISPR/Cas diagnostic technology.

One-Pot Time-Induced Proteome Integral Solubility Alteration Assay for Automated and Sensitive Drug-Target Identification.

The proteome integral solubility alteration (PISA) assay is widely used for identifying drug targets, but it is labor-intensive and time-consuming and requires a substantial amount of biological sample. Aiming at enabling automation and greatly reducing the sample amount, we developed one-pot time-induced (OPTI)-PISA. Here, we demonstrate OPTI-PISA performance on identifying targets of multiple drugs in cell lysate and scaling down the sample amount to sub-microgram levels, making the PISA method suitable for NanoProteomics. OPTI-PISA can be implemented using only the standard equipment of a proteomics lab.

Construction and evaluation of a model for efficient identification of photothermal sensitivity of tobacco cultivars based on agronomic traits

The photothermal sensitivity of tobacco refers to the degree to which tobacco responds to changes in light and temperature conditions in its growth environment, which is crucial for determining the planting area of cultivars and improving tobacco yield and quality. In order to accurately and effectively evaluate the photothermal sensitivity of tobacco cultivars, this study selected five cultivars and their hybrid combinations with significant differences planted under different ecological conditions from 2021 to 2022 as materials. The experiment was conducted in two locations with significant differences in temperature and light. We measured the agronomic traits and biomass of the experimental materials, and constructed an effective tobacco photothermal sensitivity evaluation model using principal component analysis, membership function, and regression analysis. The reliability of the model was evaluated by utilizing the photosynthetic characteristics, chlorophyll content, and antioxidant enzyme system activity of the experimental materials. The results showed that tobacco biomass is the most important principal component in agricultural traits, and the comprehensive evaluation model for tobacco photothermal sensitivity is: y = 0.4571y1 + 0.2406y2 + 0.1725y3, where the fitting coefficients R2 of y1, y2, and y3 are 0.945, 0.851, and 0.977, respectively; The photothermal sensitivity of the experimental materials was calculated using this model, and the comprehensive ranking of the 11 experimental materials is: G3 < G5 < G10 < G9 < G11 < G6 < G7 < G2 < G4 < G8 < G1. Conventional identification methods have found that G2, G4, G6, G7, G8, and G11 are sensitive materials, G3, G5, and G10 are insensitive materials, and G1 and G9 are intermediate materials. The consistency rate of the evaluation results of the two methods reached 90.91%. And there is a significant correlation between the agronomic traits selected in the model and the physiological indicators selected by conventional evaluation methods, providing a scientific basis for evaluating the light temperature sensitivity of tobacco cultivars using agronomic traits in this study. The results indicate that the photothermal sensitivity evaluation model established in this study provides an efficient, convenient, and reliable method for evaluating the photothermal sensitivity of tobacco.

Electroactivated SERS Nanoplatform for Rapid and Sensitive Detection and Identification of Tumor-Derived Exosome miRNA.

Exosomal miRNA expression derived from tumor cells provides a valuable and promising noninvasive modality for the early diagnosis and assessment of the efficacy of cancer treatment. However, accurate detection and identification of miRNA within exosomes have been challenging due to its low abundance and the complexity and tedious extraction with large sample volumes in the separation process. Here, we developed an electrically activated nanoplatform for rapid and sensitive detection and identification of exosome miRNA, through triggering miRNA release by opening exosomes that were captured on the electrode surface using a slightly applied electric field (50 mV), and simultaneously detected them with surface-enhanced Raman spectroscopy (SERS) in situ. The method possessed superior specificity and sensitivity for exosomal miRNA detection, with a low detection concentration of 0.5 nM. The SERS sensor chips also showed a superior sensing performance of exosomal miRNA in complex body fluids such as urine and blood. We found that exosomal miRNA contents derived from tumor cells were significantly higher than those in normal cells, and importantly, the concentrations of exosomes secreted from three different cell lines were distinctly augmented after mild electrical stimulation (ES) treatment. Furthermore, the miRNA expression within exosomes was upregulated after the ES treatment of cells. The developed approach and SERS detection platform for exosomal miRNA are promising for noninvasive and precise screening, classification, and monitoring of cancer.

An IoT-Based Framework for Automated Assessing and Reporting of Light Sensitivities in Children with Autism Spectrum Disorder.

Identification of light sensitivities, manifesting either as hyper-sensitive (over-stimulating) or hypo-sensitive (under-stimulating) in children with autism spectrum disorder (ASD), is crucial for the development of personalized sensory environments and therapeutic strategies. Traditional methods for identifying light sensitivities often depend on subjective assessments and manual video coding methods, which are time-consuming, and very keen observations are required to capture the diverse sensory responses of children with ASD. This can lead to challenges for clinical practitioners in addressing individual sensory needs effectively. The primary objective of this work is to develop an automated system using Internet of Things (IoT), computer vision, and data mining techniques for assessing visual sensitivities specifically associated with light (color and illumination). For this purpose, an Internet of Things (IoT)-based light sensitivities assessing system (IoT-LSAS) was designed and developed using a visual stimulating device, a bubble tube (BT). The IoT-LSAS integrates various electronic modules for (i) generating colored visual stimuli with different illumination levels and (ii) capturing images to identify children's emotional responses during sensory stimulation sessions. The system is designed to operate in two different modes: a child control mode (CCM) and a system control mode (SCM). Each mode uses a distinct approach for assessing light sensitivities, where CCM uses a preference-based approach, and SCM uses an emotional response tracking approach. The system was tested on a sample of 20 children with ASD, and the results showed that the IoT-LSAS effectively identified light sensitivities, with a 95% agreement rate in the CCM and a 90% agreement rate in the SCM when compared to the practitioner's assessment report. These findings suggest that the IoT-LSAS can be used as an alternative to traditional assessment methods for diagnosing light sensitivities in children with ASD, significantly reducing the practitioner's time required for diagnosis.

One-step, rapid, nanoparticle-based biosensor platform for the simultaneous identification of hepatitis B virus and hepatitis C virus in clinical applications

ObjectivesViral hepatitis caused by hepatitis B virus (HBV) and hepatitis C virus (HCV) infections remain a major global public health challenge, particularly in low- and middle-income countries. It is crucial to utilize a pointof-care (POC) testing platform that is sensitive, specific, rapid, and user-friendly for screening and diagnosis of the two infections. Here, a novel molecular diagnostic assay, integrating multiplex loop-mediated isothermal amplification with a gold nanoparticle-based lateral flow biosensor (mLAMP-AuNPs-LFB) was developed and applied for one-step, visual, rapid, sensitive, and specific identification of HBV and HCV.MethodsThe AuNPs-based LFB was devised and constructed for the simultaneous detection of HBV and HCV. The HBV-LAMP and HCV-LAMP primers were designed against the S and 5′-untranslated region (5′-UTR) genes from the major HBV genotypes (B, C, D, B/C recombinant, and C/D recombinant) and HCV subtypes (1b, 2a, 3a, 3b, and 6a) in China, respectively. Our assay conditions, both multiplex-LAMP amplification temperature and time were optimized. The sensitivity and specificity of our assay were tested, and the feasibility of our assay was verified through clinical samples.ResultsThe AuNPs-based LFB used here was successfully manufactured according to our devise manual. The two unique independent primer pairs were successfully designed based on the S and 5′-UTR genes, respectively. The optimal mLAMP-AuNPs-LFB detection process, involving rapid nucleic acid isolation (10 min), mLAMP (63 °C for 35 min), and visual AuNPs-LFB interpretation (less than 2 min), could be completed within 50 min. The HBV&HCV-mLAMP-AuNPs-LFB assay can detect the target genes (HBV-S and HCV-5′-UTR) with as low as 20 copies of plasmid template per test, and the specificity was 100% for the experimental pathogens.ConclusionsThe preliminary results manifested that our mLAMP-AuNPs-LFB assay is a valuable tool and has tremendous potential as a POC testing approach for HBV and HCV identification, especially in undeveloped regions.

An Application Programming Interface (API) Sensitive Data Identification Method Based on the Federated Large Language Model

The traditional methods for identifying sensitive data in APIs mainly encompass rule-based and machine learning-based approaches. However, these methods suffer from inadequacies in terms of security and robustness, exhibit high false positive rates, and struggle to cope with evolving threat landscapes. This paper proposes a method for detecting sensitive data in APIs based on the Federated Large Language Model (FedAPILLM). This method applies the large language model Qwen2.5 and the LoRA instruction tuning technique within the framework of federated learning (FL) to the field of data security. Under the premise of protecting data privacy, a domain-specific corpus and knowledge base are constructed for pre-training and fine-tuning, resulting in a large language model specifically designed for identifying sensitive data in APIs. This paper conducts comparative experiments involving Llama3 8B, Llama3.1 8B, and Qwen2.5 14B. The results demonstrate that Qwen2.5 14B can achieve similar or better performance levels compared to the Llama3.1 8B model with fewer training iterations.

A simple and enzyme-free method for sensitive p53 analysis based on DNAzyme-mediated signal amplification

There is an urgent demand for a simple yet extremely accurate biosensor to analyze tumorigenesis. Herein, we present a novel fluorescent and enzyme-free approach for detecting p53 gene cascading proximity ligation-mediated catalytic hairpin assembly and DNAzyme-assisted signal reaction. When the target p53 gene is present, the interaction between p53 and L1 and L2 chains initiates catalytic hairpin assembly and subsequently exposes DNAzyme in the P3 probe. The exposed DNAzyme binds with the loop region of the P4 probe and generates a nicking site, resulting in the release of a significant amount of ATMND that is conjugated in the stem section of P4. This leads to an amplified fluorescence response, which serves as a fluorescence signal for the detection of the p53 gene. This method allows for the accurate and sensitive identification of the p53 gene, exhibiting a linear reaction range of 1 fM to 1 nM, with a limit of detection as low as 0.23 fM. Furthermore, this fluorescent method has been utilized for the examination of clinical samples with a favorable recovery rate. Crucially, this versatile platform may be expanded to analyze different targets by changing the corresponding recognition unit, showing great potential for point-of-care testing in tumorigenesis analysis.

Machine learning assisted dual-modal SERS detection for circulating tumor cells

Detecting circulating tumor cells (CTCs) from blood has become a promising approach for cancer diagnosis. Surface-enhanced Raman Spectroscopy (SERS) has rapidly developed as a significant detection technology for CTCs, offering high sensitivity and selectivity. Encoded SERS bioprobes have gained attention due to their excellent specificity and ability to identify tumor cells using Raman signals. Machine learning has also made significant contributions to biomedical applications, especially in medical diagnosis. In this study, we developed a detection strategy combining encoded SERS bioprobes and machine learning models to identify CTCs. Dual-modal SERS bioprobes were designed and co-incubated with tumor cells by the “cocktail” method. An identification model for CTCs was constructed using principal component analysis (PCA) and the Random Forest classification algorithm. This innovative strategy endows SERS bioprobes with both effective magnetic separation and highly sensitive identification of CTCs, even at low concentrations of 2 cells/mL. It achieved a high detection rate of 98% for CTCs and effectively eliminated interference from peripheral WBCs. This simple and efficient strategy provides a new approach for CTCs detection and holds important significance for cancer diagnosis.

Retraction Note: Automatic identification of drug sensitivity of cancer cell with novel regression-based ensemble convolution neural network model

Retraction Note: Automatic identification of drug sensitivity of cancer cell with novel regression-based ensemble convolution neural network model

Rapid screening and identification strategy of lactic acid bacteria and yeasts based on Ramanomes technology and its application in fermented food

There is an urgent need for quick, sensitive, thorough, and low-cost preliminary screening and rapid identification method for probiotic resource development. Here, we constructed a culture-free, accurate and sensitive “separation-enrichment-detection” rapid evaluation and identification method of probiotics in fermented food based on Ramanomes technology, and established a Ramanome reference of lactic acid bacteria and yeasts by AgNPs nanostructure array (AgNPs NSA) chips with uniform “hot spots” and high signal enhancement ability as SERS substrates. Systematic cluster analysis could clearly distinguish among different genera of lactic acid bacteria and yeast, and could indicate the affinity and difference between lactic acid bacteria of the same genus and yeast of the same genus. The recognition accuracy of convolutive neural networks was 100 %, and the recognition sensitivity was less than 10 CFU/mL. We constructed a probiotic isolation and enrichment method by velocity gradient centrifugation and density gradient differential centrifugation, and the average recoveries of lactobacilli and yeasts were greater than 98 % in the practical application, and the accuracy of the verified classification was 100 %. In conclusion, this study has established a preliminary screening strategy of “screening before cultivating” for lactic acid bacteria and yeasts, which breaks through the principle limitation of the current traditional paradigm of “cultivating before screening”. It can greatly improve the preliminary screening rate of probiotics in fermented foods, and provide a good technical support for the mining of probiotic resources from environmental or complex matrix samples.

Surface-functionalized PAN fiber membranes for the sensitive detection of airborne specific markers.

PAN fibers are characterized by having a large surface-to-volume ratio and small pores, which are beneficial for applications in filtration and specific molecular detection systems. Naturally, larger items are filtered, and a lower ratio between specific and nonspecific binding is expected since small pores do not allow larger elements to penetrate through membranes; thus, nonspecific binding is enhanced. We prepared and tested fiber membranes (diameter cca 700 nm) functionalized with a specific antibody to prove that even microscopic systems such as bacteria could be specifically identified. In addition, we established a methodology that enabled the effective binding of bacteria in not only an aqueous environment but also air. Our data clearly prove that even large systems such as bacteria could be specifically identified by fiber membranes surface-functionalized with a specific antibody. This research opens the door to the construction of biosensors for the fast, inexpensive, and sensitive identification of airborne bacterial contaminants and other airborne pollutants.