Machine Learning-Based Quantification of (-)-trans-Δ-Tetrahydrocannabinol from Human Saliva Samples on a Smartphone-Based Paper Microfluidic Platform

Recent advances in the development of portable technologies and commercial products to detect Δ9-tetrahydrocannabinol in biofluids: a systematic review

BACKGROUND

The primary components driving the current commercial fascination with cannabis products are phytocannabinoids, a diverse group of over 100 lipophilic secondary metabolites derived from the cannabis plant. Although numerous phytocannabinoids exhibit pharmacological effects, the foremost attention has been directed towards Δ9-tetrahydrocannabinol (THC) and cannabidiol, the two most abundant phytocannabinoids, for their potential human applications. Despite their structural similarity, THC and cannabidiol diverge in terms of their psychotropic effects, with THC inducing notable psychological alterations. There is a clear need for accurate and rapid THC measurement methods that offer dependable, readily accessible, and cost-effective analytical information. This review presents a comprehensive view of the present state of alternative technologies that could potentially facilitate the creation of portable devices suitable for on-site usage or as personal monitors, enabling non-intrusive THC measurements.

METHOD

A literature survey from 2017 to 2023 on the development of portable technologies and commercial products to detect THC in biofluids was performed using electronic databases such as PubMed, Scopus, and Google Scholar. A systematic review of available literature was conducted using Preferred Reporting Items for Systematic. Reviews and Meta-analysis (PRISMA) guidelines.

RESULTS

Eighty-nine studies met the selection criteria. Fifty-seven peer-reviewed studies were related to the detection of THC by conventional separation techniques used in analytical laboratories that are still considered the gold standard. Studies using optical (n = 12) and electrochemical (n = 13) portable sensors and biosensors were also identified as well as commercially available devices (n = 7).

DISCUSSION

The landscape of THC detection technology is predominantly shaped by immunoassay tests, owing to their established reliability. However, these methods have distinct drawbacks, particularly for quantitative analysis. Electrochemical sensing technology holds great potential to overcome the challenges of quantification and present a multitude of advantages, encompassing the possibility of miniaturization and diverse modifications to amplify sensitivity and selectivity. Nevertheless, these sensors have considerable limitations, including non-specific interactions and the potential interference of compounds and substances existing in biofluids.

CONCLUSION

The foremost challenge in THC detection involves creating electrochemical sensors that are both stable and long-lasting while exhibiting exceptional selectivity, minimal non-specific interactions, and decreased susceptibility to matrix interferences. These aspects need to be resolved before these sensors can be successfully introduced to the market.

eXtreme gradient boosting-based classification of bacterial mixtures in water and milk using wireless microscopic imaging of quorum sensing peptide-conjugated particles

Numerous bacteria can cause water- and foodborne diseases and are often found in bacterial mixtures, making their detection challenging. Specific bioreceptors or selective growth media are necessary for most bacterial detection methods. In this work, we collectively used five quorum sensing-based peptides identified from bacterial biofilms to identify 10 different bacterial species (Bacillus subtilis, Campylobacter jejuni, Enterococcus faecium, Escherichia coli, Legionella pneumophila, Listeria monocytogenes, Pseudomonas aeruginosa, Salmonella Typhimurium, Staphylococcus aureus, Vibrio parahaemolyticus) and their mixtures in water and milk. Four different machine learning classification methods were used: k-nearest neighbors (k-NN), decision tree (DT), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). Peptides were crosslinked to submicron particles, and peptide-bacteria interactions on paper microfluidic chips caused the particle aggregation. A wireless, pocket fluorescence microscope (interfaced with a smartphone) counted such particle aggregations. XGBoost showed the best accuracy of 83.75% in identifying bacterial species from water samples using 320 different datasets and 91.67% from milk samples using 140 different datasets (5 peptide features per dataset). Each peptide's contribution to correct classification was evaluated. The results were concentration-dependent, allowing the identification of a dominant species from bacterial mixtures. Using XGBoost and the previous milk database, we tested 14 blind samples of various bacterial mixtures in milk samples, with an accuracy of 81.55% to predict the dominant species. The entire process could be completed within a half hour. The demonstrated system can provide a handheld, low-cost, easy-to-operate tool for potential hygiene spot-checks, public health, or personal healthcare.

Sensitive SARS-CoV-2 salivary antibody assays for clinical saline gargle samples using smartphone-based competitive particle immunoassay platforms

Antibody assay for SARS-CoV-2 has become increasingly important to track latent and asymptomatic infections, check the individual's immune status, and confirm vaccine efficacy and durability. However, current SARS-CoV-2 antibody assays require invasive blood collection, requiring a remote laboratory and a trained phlebotomist. Direct detection of SARS-CoV-2 antibodies from clinical saline gargle samples has been considered challenging due to the smaller number of antibodies in such specimens and the high limit of detection of currently available rapid tests. This work demonstrates simple and non-invasive methods for detecting SARS-CoV-2 salivary antibodies. Competitive particle immunoassays were developed on a paper microfluidic chip using the receptor-binding domain (RBD) antigens on spike proteins. Using a smartphone, they were monitored by counting the captured fluorescent particles or evaluating the capillary flow velocities. The limit of detection (LOD), cross-binding between alpha- and omicron-strains, and the effect of angiotensin-converting enzyme 2 (ACE2) presence were investigated. LODs were 1-5 ng/mL in both 10% and 1% saliva. Clinical saline gargle samples were assayed using both methods, showing a statistical difference between virus-negative and virus-positive samples, although the assays targeted antibodies. Only a small number of virus-positive samples were antibody-negative. The high assay sensitivity detected a small number of antibodies developed even during the early phase of infections. Overall, this work demonstrates the ability to detect SARS-CoV-2 salivary IgG antibodies on simple, cost-effective, portable platforms towards mitigating SARS-CoV-2 and potentially other respiratory viruses.