Systematic optimization of UCNPs-LFA for Helicobacter pylori nucleic acid detection at point-of-care.

Helicobacter pylori (Hp) prevail globally as the primary cause of gastritis, gastric ulcer, and potential gastric cancer, highlighting the need for rapid and precise point-of-care (POC) detection of Hp nucleic acid. Upconversion nanoparticle-based lateral flow assay (UCNPs-LFA) exhibit great potential in POC detection, due to their high optical stability and absence of background fluorescence. However, insufficient sensitivity for nucleic acid detection remains a key challenge. This study systematically optimizes UCNPs-LFA by focusing on target capture, signal transduction, signal separation, and signal analysis, to enhance its detection capabilities for Hp nucleic acid. The optimized UCNPs-LFA platform features a significantly decreased detection limit, a broadened detection range, and high reliability. Results demonstrate that the limit of detection (LOD) is 25 fM, a 105-fold improvement over the initial platform. This systematic optimization strategy is versatile and can be applied to optimize other nanoparticle-based LFAs.

Lateral flow assays: Progress and evolution of recent trends in point-of-care applications.

Paper based point-of-care (PoC) detection platforms applying lateral flow assays (LFAs) have gained paramount approval in the diagnostic domain as well as in environmental applications owing to their ease of utility, low cost, and rapid signal readout. It has centralized the aspect of self-evaluation exhibiting promising potential in the last global pandemic era of Covid-19 implementing rapid management of public health in remote areas. In this perspective, the present review is focused towards landscaping the current framework of LFAs along with integration of components and characteristics for improving the assay by pushing the detection limits. The review highlights the synergistic aspects of assay designing, sample enrichment strategies, novel nanomaterials-based signal transducers, and high-end analytical techniques that contribute significantly towards sensitivity and specificity enhancement. Various recent studies are discussed supporting the innovations in LFA systems that focus upon the accuracy and reliability of rapid PoC testing. The review also provides a comprehensive overview of all the possible difficulties in commercialization of LFAs subjecting its applicability to pathogen surveillance, water and food testing, disease diagnostics, as well as to agriculture and environmental issues.

Integrating loop-mediated isothermal amplification with lateral flow assay to achieve a highly sensitive method for detecting Streptococcus suis Genome in raw pork.

Streptococcus suis (S.suis), a zoonotic foodborne pathogen prevalent in Southeast Asia, poses a substantial threat to human and animal health because of its ability to cause severe and life-threatening illnesses. To address this challenge, a rapid and highly sensitive detection platform for S. suis in raw pork was developed by integrating loop-mediated isothermal amplification (LAMP) and a lateral flow assay (LFA), S. suis LAMP-LFA. LAMP reactions targeting the S. suis glutamate dehydrogenase (gdh) gene were optimized for specific detection of S. suis within 45 min at an isothermal temperature of 65 °C. The assay exhibited marked sensitivity, with a detection limit of 100 fg for genomic DNA extracted from S. suis cultures. Notably, this method showed no cross-reactivity with other bacterial contaminants commonly found in raw pork. The resulting LAMP amplicons were effectively detected using LFA, with a test limit of 101 CFU per 25 g of raw pork. S. suis LAMP-LFA proved to be highly specific and reliable, with no false-positives detected in spiked pork samples or pork samples containing other bacterial contaminants. Due to its high sensitivity, specificity, and rapid turnaround time, the proposed technique has immense potential as a field-deployable screening test for S. suis detection in raw pork, contributing to enhanced food safety and public health protection.

Current status of diagnostic assays for emerging zoonotic viruses: Nipah and Hendra.

INTRODUCTION: Nipah and Hendra viruses belong to the Paramyxoviridae family, which pose a significant threat to human health, with sporadic outbreaks causing severe morbidity and mortality. Early symptoms include fever, cough, sore throat, and headache, which offer little in terms of differential diagnosis. There are no specific therapeutics and vaccines for these viruses. AREAS COVERED: This review comprehensively covers a spectrum of diagnostic techniques for Nipah and Hendra virus infections, discussed in conjunction with appropriate type of samples during the progression of infection. Serological assays, reverse transcriptase Real-Time PCR assays, and isothermal amplification assays are discussed in detail, along with a listing of few commercially available detection kits. Patents protecting inventions in Nipah and Hendra virus detection are also covered. EXPERT OPINION: Despite several outbreaks of Nipah and Hendra infections in the past decade, in-depth research into their pathogenesis, Point-of-Care diagnostics, specific therapies, and human vaccines is lacking. A prompt and accurate diagnosis is pivotal for efficient outbreak management, patient treatment, and the adoption of preventative measures. The emergence of rapid point-of-care tests holds promise in enhancing diagnostic capabilities in real-world settings. The patent landscape emphasizes the importance of innovation and collaboration within the legal and business realms.

Reduced graphene oxide electrodes meet lateral flow assays: A promising path to advanced point-of-care diagnostics.

Research in electrochemical detection in lateral flow assays (LFAs) has gained significant momentum in recent years. The primary impetus for this surge in interest is the pursuit of achieving lower limits of detection, especially given that LFAs are the most widely employed point-of-care biosensors. Conventionally, the strategy for merging electrochemistry and LFAs has centered on the superposition of screen-printed electrodes onto nitrocellulose substrates during LFA fabrication. Nevertheless, this approach poses substantial limitations regarding scalability. In response, we have developed a novel method for the complete integration of reduced graphene oxide (rGO) electrodes into LFA strips. We employed a CO2 laser to concurrently reduce graphene oxide and pattern nitrocellulose, exposing its backing to create connection sites impervious to sample leakage. Subsequently, rGO and nitrocellulose were juxtaposed and introduced into a roll-to-roll system using a wax printer. The exerted pressure facilitated the transfer of rGO onto the nitrocellulose. We systematically evaluated several electrochemical strategies to harness the synergy between rGO and LFAs. While certain challenges persist, our rGO transfer technology presents compelling potential for setting a new standard in electrochemical LFA fabrication.

Optical lateral flow assays in early diagnosis of SARS-CoV-2 infection.

So far, the 2019 novel coronavirus (COVID-19) is spreading widely worldwide. The early diagnosis of infection by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is essential to provide timely treatment and prevent its further spread. Lateral flow assays (LFAs) have the advantages of rapid detection, simple operation, low cost, ease of mass production, and no need for special devices and professional operators, which make them suitable for self-testing at home. This review focuses on the early diagnosis of SARS-CoV-2 infection based on optical LFAs including colorimetric, fluorescent (FL), chemiluminescent (CL), and surface-enhanced Raman scattering (SERS) LFAs for the detection of SARS-CoV-2 antigens and nucleic acids. The types of recognition components, detection modes used for antigen detection, labels employed in different optical LFAs, and strategies to improve the detection sensitivity of LFAs were reviewed. Meanwhile, LFAs coupled with different nucleic acid amplification techniques and CRISPR-Cas systems for the detection of SARS-CoV-2 nucleic acids were summarized. We hope this review provides research mentalities for developing highly sensitive LFAs that can be used in home self-testing for the early diagnosis of SARS-CoV-2 infection.

Predicting the wicking rate of nitrocellulose membranes from recipe data: a case study using ANN at a membrane manufacturing in South Korea.

Lateral flow assays have been widely used for detecting coronavirus disease 2019 (COVID-19). A lateral flow assay consists of a Nitrocellulose (NC) membrane, which must have a specific lateral flow rate for the proteins to react. The wicking rate is conventionally used as a method to assess the lateral flow in membranes. We used multiple regression and artificial neural networks (ANN) to predict the wicking rate of NC membranes based on membrane recipe data. The developed ANN predicted the wicking rate with a mean square error of 0.059, whereas the multiple regression had a square error of 0.503. This research also highlighted the significant impact of the water content on the wicking rate through images obtained from scanning electron microscopy. The findings of this research can cut down the research and development costs of novel NC membranes with a specific wicking rate significantly, as the algorithm can predict the wicking rate based on the membrane recipe.

Development and optimization of a frequency mixing sensor for adjacent samples quantitative detection on a lateral flow assay.

Frequency-mixing technology has been widely used to precisely identify magnetic nanoparticles in applications of quantitative biomedical detection in recent years. Examples include immune adsorption, lateral flow assays (LFAs), and biomagnetic imaging. However, the signals of magnetic response generated by adjacent magnetic samples interfere with each other owing to the small spacing between them in applications involving multi-sample detection (such as the LFA and multiplexing detection). Such signal interference prevents the biosensor from obtaining characteristic peaks related to the concentration of adjacent biomarkers from the magnetic response signals. Mathematical and physical models of the structure of sensors based on frequency-mixing techniques were developed. The theoretical model was verified and its key parameters were optimized by using simulations. A new frequency-mixing magnetic sensor structure was then designed and developed based on the model, and the key technical problem of signal crosstalk between adjacent samples was structurally solved. Finally, standard cards with stable magnetic properties were used to evaluate the performance of the sensor, and strips of the gastrin-17 (G-17) LFA were used to evaluate its potential for use in clinical applications. The results show that the minimum spacing between samples required by the optimized sensor to accurately identify them was only about 4-5 mm, and the minimum detectable concentration of G-17 was 11 pg mL-1 . This is a significant reduction in the required spacing between samples for multiplexing detection. The optimized sensor also has the potential for use in multi-channel synchronous signal acquisition, and can be used to detect synchronous magnetic signals in vivo.

Toward the Development of Simplified Lateral Flow Assays Using Hydrogels as the Universal Control Line.

Lateral flow assays (LFA) have been widely utilized as point-of-care testing devices in diverse fields. However, it is imperative to preprint costly bioreceptors onto the lateral flow nitrocellulose membrane at the control line. The complex manufacturing process and relatively limited detection capabilities of LFA have impeded their utilization in more challenging fields. Here, we propose a novel and simple strategy to simplify the manufacture of LFA while simultaneously improving the sensitivity by modifying the hydrogel line (HL). In our study, it was observed that the sensitivity of commercial LFA strips could be enhanced by 2-5-fold by incorporating an extra HL. Particularly, a universal control line was developed to accommodate multiple LFA detection modes by substituting the conventional antibody control line with a hydrogel control line (HCL). As a proof of concept, the HCL performance could be associated with the slowdown and interception effect toward fluid, which are dependent on the permeation and hydrophilicity of the hydrogel with varying concentrations in the nitrocellulose membrane. This new design builds the foundation to enhance the sensitivity and develop the simplified LFA sensing platform without additional complicated processes.

Lateral-flow assays for bovine paratuberculosis diagnosis.

Mycobacterium avium subsp. paratuberculosis (MAP) causes bovine paratuberculosis (PTB). PTB is responsible for significant economic losses in dairy herds around the word. PTB control programs that rely on testing and culling of test-positive cows have been developed. Current diagnostics, such as ELISA for detecting MAP antibodies in serum samples and PCR detecting MAP DNA in feces, have inadequate sensitivity for detecting subclinical animals. Innovative "omics" technologies such as next-generation sequencing (NGS) technology-based RNA-sequencing (RNA-Seq), proteomics and metabolomics can be used to find host biomarkers. The discovered biomarkers (RNA, microRNAs, proteins, metabolites) can then be used to develop new and more sensitive approaches for PTB diagnosis. Traditional approaches for measuring host antibodies and biomarkers, such as ELISAs, northern blotting, quantitative reverse-transcriptase polymerase chain reaction (RT-qPCR), cDNA microarrays, and mass spectrometry are time-consuming, expensive, and sometimes exhibit poor sensitivity. With the rapid development of nanotechnology, low-cost monitoring devices for measuring antibodies against MAP proteins in point-of-care (POC) settings have been developed. Lateral flow assays (LFAs), in particular, are thought to be appropriate for the on-site detection of antibodies to MAP antigens and/or host biomarkers. This review aims to summarize LFAs that have recently been developed to accurately detect antibodies against MAP antigens, as well as the benefits that host biomarkers linked with MAP infection give to PTB diagnosis. The identification of these novel biomarkers could be the basis for the development of new LFAs. The dairy industry and producers are likely to benefit from reliable and rapid technologies capable of detecting MAP infection in situ to establish a quick and sensitive PTB diagnosis.

Decorated DNA-Based Scaffolds as Lateral Flow Biosensors.

Here we develop Lateral Flow Assays (LFAs) that employ as functional elements DNA-based structures decorated with reporter tags and recognition elements. We have rationally re-engineered tile-based DNA tubular structures that can act as scaffolds and can be decorated with recognition elements of different nature (i.e. antigens, aptamers or proteins) and with orthogonal fluorescent dyes. As a proof-of-principle we have developed sandwich and competitive multiplex lateral flow platforms for the detection of several targets, ranging from small molecules (digoxigenin, Dig and dinitrophenol, DNP), to antibodies (Anti-Dig, Anti-DNP and Anti-MUC1/EGFR bispecific antibodies) and proteins (thrombin). Coupling the advantages of functional DNA-based scaffolds together with the simplicity of LFAs, our approach offers the opportunity to detect a wide range of targets with nanomolar sensitivity and high specificity.

Flow-Through Carbon Nanofiber-Based Transducer for Inline Electrochemical Detection in Paper-Based Analytical Devices.

Point-of-care (POC) devices are rapid, simple, portable, inexpensive, and convenient, but typically they only deliver qualitative results when used in the form of a lateral flow assay (LFA). Electrochemical detection could improve their sensitivity and ensure quantitative detection; however, a breakthrough in material-based technology is needed. We demonstrate a new concept in which electrodes are directly embedded within the lateral flow, enabling flow-through and hence interaction with the entire sample. This is accomplished through laser-induced carbon nanofibers (LCNFs) made by electrospinning Matrimid into nanofiber mats with subsequent pyrolyzing of electrode structures through a CO2 laser. Their highly porous 3D structure and superior graphene-like electrochemical properties are ideally suited for flow-through electrochemical LFA (EC-LFA), where the LCNFs are simply added in line with the other membranes. After optimization of the setup, biological binding assays typical for LFA diagnostics were successfully implemented, enabling the highly sensitive and quantitative detection of 137 pM DNA target sequences of a pathogenic organism that rivals the performance of pump-controlled microfluidic bioassays. This demonstrates that LCNF-based transducers can transform paper-based diagnostic tests to enable precise, quantitative analysis without reliance on cost-intensive read-out systems.

SALAD: Syringe-based Arduino-operated Low-cost Antibody Dispenser.

Lateral Flow Assays (LFA) have been one of the most widely adopted technologies in clinical diagnosis over recent years, especially during the COVID-19 pandemic, due to their feasibility, compactness, and rapid readout. However, the precise dispensing of antibodies-a key part of the fabrication process-requires costly line dispenser equipment, which poses a challenge to researchers with limited budgets. This study aims to resolve this key issue by introducing a Syringe-based Arduino-operated Low-cost Antibody Dispenser (SALAD). By utilizing a microneedle, stepper motor-driven syringe pump, and conveyor belt, SALAD can form micro-droplets to create an even band of antibodies. Our evaluation results showed comparable performance between SALAD and a commercialized model - Claremont ALFRD, with SALAD exceeding in affordability and feasibility. SALAD yielded an even signal, uniform bandwidth, and low background noise, yet optimization in the conveyor belt should be considered to enhance stability. With a low manufacturing cost ($200.61) compared to the commercialized models, our model is expected to provide an affordable approach for LFA researchers.

Use of Lateral Flow Assays in Forensics.

Already for some decades lateral flow assays (LFAs) are 'common use' devices in our daily life. Also, for forensic use LFAs are developed, such as for the analysis of illicit drugs and DNA, but also for the detection of explosives and body fluid identification. Despite their advantages, including ease-of-use, LFAs are not yet frequently applied at a crime scene. This review describes (academic) developments of LFAs for forensic applications, focusing on biological and chemical applications, whereby the main advantages and disadvantages of LFAs for the different forensic applications are summarized. Additionally, a critical review is provided, discussing why LFAs are not frequently applied within the forensic field and highlighting the steps that are needed to bring LFAs to the forensic market.

Developments in Fungal Serology.

Purpose of Review: The true incidence of fungal disease is hampered by conventionally poor diagnostic tests, limited access to advanced diagnostics, and limited surveillance. The availability of serological testing has been available for over two decades and generally underpins the modern diagnosis of the most common forms of fungal disease. This review will focus on technical developments of serological tests for the diagnosis of fungal disease, describing advances in clinical performance when available. Recent Findings: Despite their longevity, technical, clinical, and performance limitations remain, and tests specific for fungal pathogens outside the main pathogens are lacking. The availability of LFA and automated systems, capable of running multiple different tests, represents significant developments, but clinical performance data is variable and limited. Summary: Fungal serology has significantly advanced the diagnosis of the main fungal infections, with LFA availability increasing accessibility to testing. Combination testing has the potential to overcome performance limitations.

A nitrocellulose/cotton fiber hybrid composite membrane for paper-based biosensor.

Nitrocellulose (NC) membrane was fabricated and tested for its potential use in various paper-based biosensors for use in point-of-care testing. However, contemporary technologies are complex, expensive, non-scalable, limited by conditions, and beset with potentially adverse effects on the environment. Herein, we proposed a simple, cost-effective, scalable technology to prepare nitrocellulose/cotton fiber (NC/CF) composite membranes. The NC/CF composite membranes with a diameter of 20 cm were fabricated in 15 min using papermaking technology, which contributes to scalability in the large-scale production of these composites. Compared with existing commercial NC membranes, the NC/CF composite membrane is characterized by small pore size (3.59 ± 0.19 µm), low flow rate (156 ± 55 s/40 mm), high dry strength (up to 4.04 MPa), and wet strength (up to 0.13 MPa), adjustable hydrophilic-hydrophobic (contact angles ranged from 29 ± 4.6 to 82.8 ± 2.4°), the good adsorption capacity of protein (up to 91.92 ± 0.07 µg). After lateral flow assays (LFAs) detection, the limit of detection is 1 nM, which is similar to commercial NC membrane (Sartorius CN 140). We envision the NC/CF composite membrane as a promising material for paper-based biosensors of point-of-care testing applications.

Advances in paper based isothermal nucleic acid amplification tests for water-related infectious diseases.

Water-associated or water-related infectious disease outbreaks are caused by pathogens such as bacteria, viruses, and protozoa, which can be transmitted through contaminated water sources, poor sanitation practices, or insect vectors. Low- and middle-income countries bear the major burden of these infections due to inadequate hygiene and subpar laboratory facilities, making it challenging to monitor and detect infections in a timely manner. However, even developed countries are not immune to these diseases, as inadequate wastewater management and contaminated drinking water supplies can also contribute to disease outbreaks. Nucleic acid amplification tests have proven to be effective for early disease intervention and surveillance of both new and existing diseases. In recent years, paper-based diagnostic devices have made significant progress and become an essential tool in detecting and managing water-associated infectious diseases. In this review, we have highlighted the importance of paper and its variants as a diagnostic tool and discussed the properties, designs, modifications, and various paper-based device formats developed and used for detecting water-associated pathogens.

Low-Cost Microfluidic Systems for Detection of Neglected Tropical Diseases.

Neglected tropical diseases (NTDs) affect tropical and subtropical countries and are caused by viruses, bacteria, protozoa, and helminths. These kinds of diseases spread quickly due to the tropical climate and limited access to clean water, sanitation, and health care, which make exposed people more vulnerable. NTDs are reported to be difficult and inefficient to diagnose. As mentioned, most NTDs occur in countries that are socially vulnerable, and the lack of resources and access to modern laboratories and equipment intensify the difficulty of diagnosis and treatment, leading to an increase in the mortality rate. Portable and low-cost microfluidic systems have been widely applied for clinical diagnosis, offering a promising alternative that can meet the needs for fast, affordable, and reliable diagnostic tests in developing countries. This review provides a critical overview of microfluidic devices that have been reported in the literature for the detection of the most common NTDs over the past 5 years.

Chemically Amplified Multiplex Detection of SARS-CoV-2 and Influenza A and B Viruses via Paint-Programmed Lateral Flow Assays.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) continues to threaten lives by evolving into new variants with greater transmissibility. Although lateral flow assays (LFAs) are widely used to self-test for coronavirus disease 2019 (COVID-19), these tests suffer from low sensitivity leading to a high rate of false negative results. In this work, a multiplexed lateral flow assay is reported for the detection of SARS-CoV-2 and influenza A and B viruses in human saliva with a built-in chemical amplification of the colorimetric signal for enhanced sensitivity. To automate the amplification process, the paper-based device is integrated with an imprinted flow controller, which coordinates the routing of different reagents and ensures their sequential and timely delivery to run an optimal amplification reaction. Using the assay, SARS-CoV-2 and influenza A and B viruses can be detected with ≈25x higher sensitivity than commercial LFAs, and the device can detect SARS-CoV-2-positive patient saliva samples missed by commercial LFAs. The technology provides an effective and practical solution to enhance the performance of conventional LFAs and will enable sensitive self-testing to prevent virus transmission and future outbreaks of new variants.

A novel patches-selection method for the classification of point-of-care biosensing lateral flow assays with cardiac biomarkers.

Cardiovascular Disease (CVD) is amongst the leading cause of death globally, which calls for rapid detection and treatment. Biosensing devices are used for the diagnosis of cardiovascular disease at the point-of-care (POC), with lateral flow assays (LFAs) being particularly useful. However, due to their low sensitivity, most LFAs have been shown to have difficulties detecting low analytic concentrations. Breakthroughs in artificial intelligence (AI) and image processing reduced this detection constraint and improved disease diagnosis. This paper presents a novel patches-selection approach for generating LFA images from the test line and control line of LFA images, analyzing the image features, and utilizing them to reliably predict and classify LFA images by deploying classification algorithms, specifically Convolutional Neural Networks (CNNs). The generated images were supplied as input data to the CNN model, a strong model for extracting crucial information from images, to classify the target images and provide risk stratification levels to medical professionals. With this approach, the classification model produced about 98% accuracy, and as per the literature review, this approach has not been investigated previously. These promising results show the proposed method may be useful for identifying a wide variety of diseases and conditions, including cardiovascular problems.