New technologies and reagents in lateral flow assay (LFA) designs for enhancing accuracy and sensitivity

Review of Detection Limits for Various Techniques for Bacterial Detection in Food Samples

Foodborne illnesses can be infectious and dangerous, and most of them are caused by bacteria. Some common food-related bacteria species exist widely in nature and pose a serious threat to both humans and animals; they can cause poisoning, diseases, disabilities and even death. Rapid, reliable and cost-effective methods for bacterial detection are of paramount importance in food safety and environmental monitoring. Polymerase chain reaction (PCR), lateral flow immunochromatographic assay (LFIA) and electrochemical methods have been widely used in food safety and environmental monitoring. In this paper, the recent developments (2013-2023) covering PCR, LFIA and electrochemical methods for various bacterial species (Salmonella, Listeria, Campylobacter, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli)), considering different food sample types, analytical performances and the reported limit of detection (LOD), are discussed. It was found that the bacteria species and food sample type contributed significantly to the analytical performance and LOD. Detection via LFIA has a higher average LOD (24 CFU/mL) than detection via electrochemical methods (12 CFU/mL) and PCR (6 CFU/mL). Salmonella and E. coli in the Pseudomonadota domain usually have low LODs. LODs are usually lower for detection in fish and eggs. Gold and iron nanoparticles were the most studied in the reported articles for LFIA, and average LODs were 26 CFU/mL and 12 CFU/mL, respectively. The electrochemical method revealed that the average LOD was highest for cyclic voltammetry (CV) at 18 CFU/mL, followed by electrochemical impedance spectroscopy (EIS) at 12 CFU/mL and differential pulse voltammetry (DPV) at 8 CFU/mL. LOD usually decreases when the sample number increases until it remains unchanged. Exponential relations (R2 > 0.95) between LODs of Listeria in milk via LFIA and via the electrochemical method with sample numbers have been obtained. Finally, the review discusses challenges and future perspectives (including the role of nanomaterials/advanced materials) to improve analytical performance for bacterial detection.

A simplified lateral flow immunosensor for the assay of carcinoembryonic antigen in low-resource settings

Carcinoembryonic antigen (CEA) is a glycoprotein widely used as a tumor marker. In this work, a colorimetric lateral flow immunosensor is developed for rapid and low-cost quantification of CEA in human blood serum. The immunosensor consists of a glass fiber sample/conjugation pad, a nitrocellulose detection pad and a cellulose absorption pad. The detection is based on a sandwich immunoreaction: the sample/conjugation pad is modified with gold nanoparticles (GNPs)-labeled anti-CEA conjugate probes which bind to the CEA target molecules in the sample and the complexes are captured at capture anti-CEA immobilized at the test line. The color intensity of the test line, measured from a scanned image of the strip, is related to the CEA concentration in the sample. The different assay parameters are studied in detail. The linearity holds from 1.25 to 640 ng mL-1 of CEA, the instrumental and visual limits of detection are 0.45 and 0.63 ng mL-1, respectively, and the total assay time is 15 min. The specificity of the immunoassay versus other cancer biomarkers is satisfactory. The recovery in samples of human serum spiked with CEA is in the range of 81-118% and the coefficient of variation of the method is ≤10%. Results obtained with the lateral flow immunosensor correlated well with a reference radioimmunoassay method (R2 = 0.99). This immunosensor can be readily applied to CEA monitoring at the point-of-care (POC) or in resource-limited settings thanks to its low-cost and simplicity.

Loop-Mediated Isothermal Amplification and Lateral Flow Immunochromatography Technology for Rapid Diagnosis of Influenza A/B

Influenza viruses cause highly contagious respiratory diseases that cause millions of deaths worldwide. Rapid detection of influenza viruses is essential for accurate diagnosis and the initiation of appropriate treatment. We developed a loop-mediated isothermal amplification and lateral flow assay (LAMP-LFA) capable of simultaneously detecting influenza A and influenza B. Primer sets for influenza A and influenza B were designed to target conserved regions of segment 7 and the nucleoprotein gene, respectively. Optimized through various primer set ratios, the assay operated at 62 °C for 30 min. For a total of 243 (85 influenza A positive, 58 influenza B positive and 100 negative) nasopharyngeal swab samples, the performance of the influenza A/B multiplex LAMP-LFA was compared with that of the commercial AllplexTM Respiratory Panel 1 assay (Seegene, Seoul, Korea). The influenza A/B multiplex LAMP-LFA demonstrated a specificity of 98% for the non-infected clinical samples, along with sensitivities of 94.1% for the influenza A clinical samples and 96.6% for the influenza B clinical samples, respectively. The influenza A/B multiplex LAMP-LFA showed high sensitivity and specificity, indicating that it is reliable for use in a low-resource environment.

NFC-enabled potentiostat and nitrocellulose-based metal electrodes for electrochemical lateral flow assay

Rapid detection of pathogens at the point-of-need is crucial for preventing the spread of human, animal and plant diseases which can have devastating consequences both on the lives and livelihood of billions of people. Colorimetric, lateral flow assays consisting of a nitrocellulose membrane, are the preferred format today for low-cost on-site detection of pathogens. This assay format has, however, historically suffered from poor analytical performance and is not compatible with digital technologies. In this work, we report the development of a new class of digital diagnostics platform for precision point-of-need testing. This new versatile platform consists of two important innovations: i) A wireless and batteryless, microcontroller-based, low-cost Near Field Communication (NFC)-enabled potentiostat that brings high performance electroanalytical techniques (cyclic voltammetry, chronoamperometry, square wave voltammetry) to the field. The NFC-potentiostat can be operated with a mobile app by minimally trained users; ii) A new approach for producing nitrocellulose membranes with integrated electrodes that facilitate high performance electrochemical detection at the point-of-need. We produced an integrated system housed in a 3D-printed phone case and demonstrated its use for the detection of Maize Mosaic Virus (MMV), a plant pathogen, as a proof-of-concept application.

AI-Enhanced Visual-Spectral Synergy for Fast and Ultrasensitive Biodetection of Breast Cancer-Related miRNAs

In biomedical testing, artificial intelligence (AI)-enhanced analysis has gradually been applied to the diagnosis of certain diseases. This research employs AI algorithms to refine the precision of integrative detection, encompassing both visual results and fluorescence spectra from lateral flow assays (LFAs), which signal the presence of cancer-linked miRNAs. Specifically, the color shift of gold nanoparticles (GNPs) is paired with the red fluorescence from nitrogen vacancy color centers (NV-centers) in fluorescent nanodiamonds (FNDs) and is integrated into LFA strips. While GNPs amplify the fluorescence of FNDs, in turn, FNDs enhance the color intensity of GNPs. This reciprocal intensification of fluorescence and color can be synergistically augmented with AI algorithms, thereby improving the detection sensitivity for early diagnosis. Supported by the detection platform based on this strategy, the fastest detection results with a limit of detection (LOD) at the fM level and the R2 value of ∼0.9916 for miRNA can be obtained within 5 min. Meanwhile, by labeling the capture probes for miRNA-21 and miRNA-96 (both of which are early indicators of breast cancer) on separate T-lines, simultaneous detection of them can be achieved. The miRNA detection methods employed in this study may potentially be applied in the future for the early detection of breast cancer.