Algorithms for immunochromatographic assay: review and impact on future application

Advancing diagnostic efficacy using a computer vision-assisted lateral flow assay for influenza and SARS-CoV-2 detection

Lateral flow assays (LFAs) have emerged as indispensable tools for point-of-care testing during the pandemic era. However, the interpretation of results through unassisted visual inspection by untrained individuals poses inherent limitations. In our study, we propose a novel approach that combines computer vision (CV) and lightweight machine learning (ML) to overcome these limitations and significantly enhance the performance of LFAs. By incorporating CV-assisted analysis into the LFA assay, we achieved a remarkable three-fold improvement in analytical sensitivity for detecting Influenza A and for SARS-CoV-2 detection. The obtained R2 values reached approximately 0.95, respectively, demonstrating the effectiveness of our approach. Moreover, the integration of CV techniques with LFAs resulted in a substantial amplification of the colorimetric signal specifically for COVID-19 positive patient samples. Our proposed approach, which incorporates a simple machine learning algorithm, provides substantial enhancements in assay sensitivity, improving diagnostic efficacy and accessibility of point-of-care testing without requiring significant additional resources. Moreover, the simplicity of the machine learning algorithm enables its standalone use on a mobile phone, further enhancing its practicality for point-of-care testing.

Immunochromatographic enhancement strategy for SARS-CoV-2 detection based on nanotechnology

The global outbreak of coronavirus disease 2019 (COVID-19) has been catastrophic to both human health and social development. Therefore, developing highly reliable and sensitive point-of-care testing (POCT) for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a priority. Among all available POCTs, the lateral flow immunoassay (LFIA, also known as immunochromatography) has proved to be effective due to its accuracy, portability, convenience, and speed. In areas with a scarcity of laboratory resources and medical personnel, the LFIA provides an affordable option for the diagnosis of COVID-19. This review offers a comprehensive overview of methods for improving the sensitivity of SARS-CoV-2 detection using immunochromatography based on nanotechnology, sorted according to the different detection targets (antigens, antibodies, and nucleic acids). It also looks into the performance and properties of the various sensitivity enhancement strategies, before delving into the remaining challenges in COVID-19 diagnosis through LFIA. Ultimately, it seeks to provide helpful guidance in selecting an appropriate strategy for SARS-CoV-2 immunochromatographic detection based on nanotechnology.

A Quantitative Detection Algorithm for Multi-Test Line Lateral Flow Immunoassay Applied in Smartphones

The traditional lateral flow immunoassay (LFIA) detection method suffers from issues such as unstable detection results and low quantitative accuracy. In this study, we propose a novel multi-test line lateral flow immunoassay quantitative detection method using smartphone-based SAA immunoassay strips. Following the utilization of image processing techniques to extract and analyze the pigments on the immunoassay strips, quantitative analysis of the detection results was conducted. Experimental setups with controlled lighting conditions in a dark box were designed to capture samples using smartphones with different specifications for analysis. The algorithm's sensitivity and robustness were validated by introducing noise to the samples, and the detection performance on immunoassay strips using different algorithms was determined. The experimental results demonstrate that the proposed lateral flow immunoassay quantitative detection method based on image processing techniques achieves an accuracy rate of 94.23% on 260 samples, which is comparable to the traditional methods but with higher stability and lower algorithm complexity.

Engineering Innovative Interfaces for Point-of-Care Diagnostics

The ongoing Coronavirus disease 2019 (COVID-19) pandemic illustrates the need for sensitive and reliable tools to diagnose and monitor diseases. Traditional diagnostic approaches rely on centralized laboratory tests that result in long wait times to results and reduce the number of tests that can be given. Point-of-care tests (POCTs) are a group of technologies that miniaturize clinical assays into portable form factors that can be run both in clinical areas --in place of traditional tests-- and outside of traditional clinical settings --to enable new testing paradigms. Hallmark examples of POCTs are the pregnancy test lateral flow assay and the blood glucose meter. Other uses for POCTs include diagnostic assays for diseases like COVID-19, HIV, and malaria but despite some successes, there are still unsolved challenges for fully translating these lower cost and more versatile solutions. To overcome these challenges, researchers have exploited innovations in colloid and interface science to develop various designs of POCTs for clinical applications. Herein, we provide a review of recent advancements in lateral flow assays, other paper based POCTs, protein microarray assays, microbead flow assays, and nucleic acid amplification assays. Features that are desirable to integrate into future POCTs, including simplified sample collection, end-to-end connectivity, and machine learning, are also discussed in this review.

CRISPR-cas technology: A key approach for SARS-CoV-2 detection

The CRISPR (Clustered Regularly Spaced Short Palindromic Repeats) system was first discovered in prokaryotes as a unique immune mechanism to clear foreign nucleic acids. It has been rapidly and extensively used in basic and applied research owing to its strong ability of gene editing, regulation and detection in eukaryotes. Hererin in this article, we reviewed the biology, mechanisms and relevance of CRISPR-Cas technology and its applications in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnosis. CRISPR-Cas nucleic acid detection tools include CRISPR-Cas9, CRISPR-Cas12, CRISPR-Cas13, CRISPR-Cas14, CRISPR nucleic acid amplification detection technology, and CRISPR colorimetric readout detection system. The above CRISPR technologies have been applied to the nucleic acid detection, including SARS-CoV-2 detection. Common nucleic acid detection based on CRISPR derivation technology include SHERLOCK, DETECTR, and STOPCovid. CRISPR-Cas biosensing technology has been widely applied to point-of-care testing (POCT) by targeting recognition of both DNA molecules and RNA Molecules.

Highly Sensitive Nanomagnetic Quantification of Extracellular Vesicles by Immunochromatographic Strips: A Tool for Liquid Biopsy

Extracellular vesicles (EVs) are promising agents for liquid biopsy—a non-invasive approach for the diagnosis of cancer and evaluation of therapy response. However, EV potential is limited by the lack of sufficiently sensitive, time-, and cost-efficient methods for their registration. This research aimed at developing a highly sensitive and easy-to-use immunochromatographic tool based on magnetic nanoparticles for EV quantification. The tool is demonstrated by detection of EVs isolated from cell culture supernatants and various body fluids using characteristic biomarkers, CD9 and CD81, and a tumor-associated marker—epithelial cell adhesion molecules. The detection limit of 3.7 × 10⁵ EV/µL is one to two orders better than the most sensitive traditional lateral flow system and commercial ELISA kits. The detection specificity is ensured by an isotype control line on the test strip. The tool’s advantages are due to the spatial quantification of EV-bound magnetic nanolabels within the strip volume by an original electronic technique. The inexpensive tool, promising for liquid biopsy in daily clinical routines, can be extended to other relevant biomarkers.

Rapid segmentation and sensitive analysis of CRP with paper-based microfluidic device using machine learning

Microfluidic paper-based analytical devices (μPADs) have been widely used in point-of-care testing owing to their simple operation, low volume of the sample required, and the lack of the need for an external force. To obtain accurate semi-quantitative or quantitative results, μPADs need to respond to the challenges posed by differences in reaction conditions. In this paper, multi-layer μPADs are fabricated by the imprinting method for the colorimetric detection of C-reactive protein (CRP). Different lighting conditions and shooting angles of scenes are simulated in image acquisition, and the detection-related performance of μPADs is improved by using a machine learning algorithm. The You Only Look Once (YOLO) model is used to identify the areas of reaction in μPADs. This model can observe an image only once to predict the objects present in it and their locations. The YOLO model trained in this study was able to identify all the reaction areas quickly without incurring any error. These reaction areas were categorized by classification algorithms to determine the risk level of CRP concentration. Multi-layer perceptron, convolutional neural network, and residual network algorithms were used for the classification tasks, where the latter yielded the highest accuracy of 96%. It has a promising application prospect in fast recognition and analysis of μPADs.

Highly sensitive time-resolved fluoroimmunoassay for the quantitative onsite detection of Alternaria longipes in tobacco

AIMS

Alternaria longipes is a causal agent of brown spot of tobacco, which remains a serious threat to tobacco production. Herein, we established a detection method for A. longipes in tobacco samples based on the principle of time-resolved fluoroimmunoassay, in order to fulfil the requirement of rapid, sensitive and accurate detection in situ.

METHODS AND RESULTS

A monoclonal antibody against A. longipes was generated, and its purity and titration were assessed using western blot and ELISA. The size of europium (III) nanospheres was measured to confirm successful antibody conjugation. The method described here can detect A. longipes protein lysates as low as 0.78 ng ml-1 , with recovery rates ranging from 85.96% to 99.67% in spiked tobacco. The specificity was also confirmed using a panel of microorganisms.

CONCLUSIONS

The fluorescent strips allow rapid and sensitive onsite detection of A. longipes in tobacco samples, with high accuracy, specificity, and repeatability.

SIGNIFICANCE AND IMPACT OF THE STUDY

This novel detection method provides convenience of using crude samples without complex procedures, and therefore allows rapid onsite detection by end users and quick responses towards A. longipes, which is critical for disease control and elimination of phytopathogens.

Nanozyme enhanced magnetic immunoassay for dual-mode detection of gastrin-17

A lateral flow detection was developed for dual-mode detection of gastrin-17, including nanozyme-enhanced chromatographic detection and magnetic quantification.

Nanobiosensing based on optically selected antibodies and superparamagnetic labels for rapid and highly sensitive quantification of polyvalent hepatitis B surface antigen

Hepatitis B surface antigen (HBsAg) is the most clinically relevant serological marker of hepatitis B virus (HBV) infection. Its detection in blood is extremely important for identification of asymptomatic individuals or chronic HBV carriers, screening blood donors, and early seroconversion. Rapid point-of-care HBsAg tests are predominantly qualitative, and their analytical sensitivity does not meet the requirements of regulatory agencies. We present a highly sensitive lateral flow assay based on superparamagnetic nanoparticles for rapid quantification (within 30 min) of polyvalent HBsAg in serum. The demonstrated limit of detection (LOD) of 80 pg mL^-1 in human serum is better than both the FDA recommendations for HBsAg assays (which is 0.5 ng mL^-1) and the sensitivity of traditional laboratory-based methods such as enzyme linked immunosorbent assays. Along with the attractive LOD at lower concentrations and the wide linear dynamic range of more than 2.5 orders, the assay features rapidity, user-friendliness, on-site operation and effective performance in the complex biological medium. These are due to the combination of the immunochromatographic approach with a highly sensitive electronic registration of superparamagnetic nanolabels over the entire volume of a 3D test structure by their non-linear magnetization and selection of optimal antibodies by original optical label-free methods. The developed cost-efficient bioanalytical technology can be used in many socially important fields such as out-of-lab screening and diagnosis of HBV infection at a point-of-demand, especially in hard-to-reach or sparsely populated areas, as well as highly endemic regions.

Diagnostic Tests for COVID-19

The diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has ramifications on both an individual level and a public health level. The use of appropriate testing mechanisms is paramount to preventing transmission, along with offering treatment to those who are infected and show appropriate symptomatology. The choice of employing a specific test often relies on laboratory capabilities, including the abilities of the medical technologists, the cost of testing platforms, and the individual quirks of each test. This chapter intends to discuss the relevant issues relating to diagnostic testing for SARS-CoV-2, including specimen types and collection methods, viral detection methods, and serological testing.

Deep Learning Image Feature Recognition Algorithm for Judgment on the Rationality of Landscape Planning and Design

This paper uses an improved deep learning algorithm to judge the rationality of the design of landscape image feature recognition. The preprocessing of the image is proposed to enhance the data. The deficiencies in landscape feature extraction are further addressed based on the new model. Then, the two-stage training method of the model is used to solve the problems of long training time and convergence difficulties in deep learning. Innovative methods for zoning and segmentation training of landscape pattern features are proposed, which makes model training faster and generates more creative landscape patterns. Because of the impact of too many types of landscape elements in landscape images, traditional convolutional neural networks can no longer effectively solve this problem. On this basis, a fully convolutional neural network model is designed to perform semantic segmentation of landscape elements in landscape images. Through the method of deconvolution, the pixel-level semantic segmentation is realized. Compared with the 65% accuracy rate of the convolutional neural network, the fully convolutional neural network has an accuracy rate of 90.3% for the recognition of landscape elements. The method is effective, accurate, and intelligent for the classification of landscape element design, which better improves the accuracy of classification, greatly reduces the cost of landscape element design classification, and ensures that the technical method is feasible. This paper classifies landscape behavior based on this model for full convolutional neural network landscape images and demonstrates the effectiveness of using the model. In terms of landscape image processing, the image evaluation provides a certain basis.

Rapid and Easy-to-Use Method for Accurate Characterization of Target Binding and Kinetics of Magnetic Particle Bioconjugates for Biosensing

The ever-increasing use of magnetic particle bioconjugates (MPB) in biosensors calls for methods of comprehensive characterization of their interaction with targets. Label-free optical sensors commonly used for studying inter-molecular interactions have limited potential for MPB because of their large size and multi-component non-transparent structure. We present an easy-to-use method that requires only three 20-min express measurements to determine the key parameters for selection of optimal MPB for a biosensor: kinetic and equilibrium characteristics, and a fraction of biomolecules on the MPB surface that are capable of active targeting. The method also provides a prognostic dependence of MPB targeting efficiency upon interaction duration and sample volume. These features are possible due to joining a magnetic lateral flow assay, a highly sensitive sensor for MPB detection by the magnetic particle quantification technique, and a novel mathematical model that explicitly describes the MPB-target interactions and does not comprise parameters to be fitted additionally. The method was demonstrated by experiments on MPB targeting of cardiac troponin I and staphylococcal enterotoxin B. The validation by an independent label-free technique of spectral-correlation interferometry showed good correlation between the results obtained by both methods. The presented method can be applied to other targets for faster development and selection of MPB for affinity sensors, analytical technologies, and realization of novel concepts of MPB-based biosensing in vivo.

Current Status of Clinical Application of Point-of-Care Testing

CONTEXT.—

The clinical applications of point-of-care testing (POCT) are gradually increasing in many health care systems. Recently, POCT devices using molecular genetic method techniques have been developed. We need to examine clinical pathways to see where POCT can be applied to improve them.

OBJECTIVE.—

To introduce up-to-date POCT items and equipment and to provide the content that should be prepared for clinical application of POCT.

DATA SOURCES.—

Literature review based on PubMed searches containing the terms point-of-care testing, clinical chemistry, diagnostic hematology, and clinical microbiology.

CONCLUSIONS.—

If medical resources are limited, POCT can help clinicians make quick medical decisions. As POCT technology improves and menus expand, areas where POCT can be applied will also increase. We need to understand the limitations of POCT so that it can be optimally used to improve patient management.

Current and Future Trends in the Laboratory Diagnosis of Sexually Transmitted Infections

Sexually transmitted infections (STIs) continue to exert a considerable public health and social burden globally, particularly for developing countries. Due to the high prevalence of asymptomatic infections and the limitations of symptom-based (syndromic) diagnosis, confirmation of infection using laboratory tools is essential to choose the most appropriate course of treatment and to screen at-risk groups. Numerous laboratory tests and platforms have been developed for gonorrhea, chlamydia, syphilis, trichomoniasis, genital mycoplasmas, herpesviruses, and human papillomavirus. Point-of-care testing is now a possibility, and microfluidic and high-throughput omics technologies promise to revolutionize the diagnosis of STIs. The scope of this paper is to provide an updated overview of the current laboratory diagnostic tools for these infections, highlighting their advantages, limitations, and point-of-care adaptability. The diagnostic applicability of the latest molecular and biochemical approaches is also discussed.

Tutorial: design and fabrication of nanoparticle-based lateral-flow immunoassays

Lateral-flow assays (LFAs) are quick, simple and cheap assays to analyze various samples at the point of care or in the field, making them one of the most widespread biosensors currently available. They have been successfully employed for the detection of a myriad of different targets (ranging from atoms up to whole cells) in all type of samples (including water, blood, foodstuff and environmental samples). Their operation relies on the capillary flow of the sample throughout a series of sequential pads, each with different functionalities aiming to generate a signal to indicate the absence/presence (and, in some cases, the concentration) of the analyte of interest. To have a user-friendly operation, their development requires the optimization of multiple, interconnected parameters that may overwhelm new developers. In this tutorial, we provide the readers with: (i) the basic knowledge to understand the principles governing an LFA and to take informed decisions during lateral flow strip design and fabrication, (ii) a roadmap for optimal LFA development independent of the specific application, (iii) a step-by-step example procedure for the assembly and operation of an LF strip for the detection of human IgG and (iv) an extensive troubleshooting section addressing the most frequent issues in designing, assembling and using LFAs. By changing only the receptors, the provided example procedure can easily be adapted for cost-efficient detection of a broad variety of targets.

Development of a Prototype Lateral Flow Immunoassay for Rapid Detection of Staphylococcal Protein A in Positive Blood Culture Samples

Bloodstream infection (BSI) is a major cause of mortality in hospitalized patients worldwide. Staphylococcus aureus is one of the most common pathogens found in BSI. The conventional workflow is time consuming. Therefore, we developed a lateral flow immunoassay (LFIA) for rapid detection of S. aureus-protein A in positive blood culture samples. A total of 90 clinical isolates including 58 S. aureus and 32 non-S. aureus were spiked in simulated blood samples. The antigens were extracted by a simple boiling method and diluted before being tested using the developed LFIA strips. The results were readable by naked eye within 15 min. The sensitivity of the developed LFIA was 87.9% (51/58) and the specificity was 93.8% (30/32). When bacterial colonies were used in the test, the LFIA provided higher sensitivity and specificity (94.8% and 100%, respectively). The detection limit of the LFIA was 10⁷ CFU/mL. Initial evaluation of the LFIA in 20 positive blood culture bottles from hospitals showed 95% agreement with the routine methods. The LFIA is a rapid, simple and highly sensitive method. No sophisticated equipment is required. It has potential for routine detection particularly in low resource settings, contributing an early diagnosis that facilitates effective treatment and reduces disease progression.

Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors

We present a deep learning-based framework to design and quantify point-of-care sensors. As a use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, commonly used for assessing risk of cardio-vascular disease (CVD). A machine learning-based framework was developed to (1) determine an optimal configuration of immunoreaction spots and conditions, spatially-multiplexed on a sensing membrane, and (2) to accurately infer target analyte concentration. Using a custom-designed handheld VFA reader, a clinical study with 85 human samples showed a competitive coefficient-of-variation of 11.2% and linearity of R 2 = 0.95 among blindly-tested VFAs in the hsCRP range (i.e., 0-10 mg/L). We also demonstrated a mitigation of the hook-effect due to the multiplexed immunoreactions on the sensing membrane. This paper-based computational VFA could expand access to CVD testing, and the presented framework can be broadly used to design cost-effective and mobile point-of-care sensors.