Label-free detection and discrimination of respiratory pathogens based on electrochemical synthesis of biomaterials-mediated plasmonic composites and machine learning analysis

Machine learning powered detection of biological toxins in association with confined lateral flow immunoassay (c-LFA)

Biological weapons, primarily dispersed as aerosols, can spread not only to the targeted area but also to adjacent regions following the movement of air driven by wind. Thus, there is a growing demand for toxin analysis because biological weapons are among the most influential and destructive. Specifically, such a technique should be hand-held, rapid, and easy to use because current methods require more time and well-trained personnel. Our study demonstrates the use of a novel lateral flow immunoassay, which has a confined structure like a double barbell in the detection area (so called c-LFA) for toxin detection such as staphylococcal enterotoxin B (SEB), ricinus communis (Ricin), and botulinum neurotoxin type A (BoNT-A). Additionally, we have explored the integration of machine learning (ML), specifically, a toxin chip boosting (TOCBoost) hybrid algorithm for improved sensitivity and specificity. Consequently, the ML powered c-LFA concurrently categorized three biological toxin types with an average accuracy as high as 95.5%. To our knowledge, the sensor proposed in this study is the first attempt to utilize ML for the assessment of toxins. The advent of the c-LFA orchestrated a paradigm shift by furnishing a versatile and robust platform for the rapid, on-site detection of various toxins, including SEB, Ricin, and BoNT-A. Our platform enables accessible and on-site toxin monitoring for non-experts and can potentially be applied to biosecurity.

Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy

The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.

Affinity Peptide-Tethered Suspension Hydrogel Sensor for Selective and Sensitive Detection of Influenza Virus

Influenza viruses are known to cause pandemic flu outbreaks through both inter-human and animal-to-human transmissions. Therefore, the rapid and accurate detection of such pathogenic viruses is crucial for effective pandemic control. Here, we introduce a novel sensor based on affinity peptide-immobilized hydrogel microspheres for the selective detection of influenza A virus (IAV) H3N2. To enhance the binding affinity performance, we identified novel affinity peptides using phage display and further optimized their design. The functional hydrogel microspheres were constructed using the drop microfluidic technique, employing a structure composed of natural (chitosan) and synthetic (poly(ethylene glycol) diacrylate and PEG 6 kDa) polymers with the activation of azadibenzocyclooctyne for the subsequent click chemistry reaction. The binding peptide-immobilized hydrogel microsphere (BP-Hyd) was characterized by field emission scanning electron microscopy, X-ray photoelectron spectroscopy, and Fourier transform infrared spectroscopy and exhibited selective detection capability for the IAV H3N2. To capture the detected IAV H3N2, a Cy3-labeled IAV hemagglutinin antibody was utilized. By incorporating the affinity peptide with hydrogel microspheres, we achieved quantitative and selective detection of IAV H3N2 with a detection limit of 1.887 PFU mL-1. Furthermore, the developed suspension sensor exhibited excellent reproducibility and showed reusability potential. Our results revealed that the BP-Hyd-based fluorescence sensor platform could be feasibly employed to detect other pathogens because the virus-binding peptides can be easily replaced with other peptides through phage display, enabling selective and sensitive binding to different targets.