An innovative electrochemical immuno-platform towards ultra-sensitive monitoring of 2-arachidonoyl glycerol in samples from rats with sleep deprivation: bioanalysis of endogenous cannabinoids using biosensor technology

Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe condition with an uncertain origin and a dismal prognosis. There is presently no precise diagnostic test for ME/CFS, and the diagnosis is determined primarily by the presence of certain symptoms. The current study presents an explainable artificial intelligence (XAI) integrated machine learning (ML) framework that identifies and classifies potential metabolic biomarkers of ME/CFS. Metabolomic data from blood samples from 19 controls and 32 ME/CFS patients, all female, who were between age and body mass index (BMI) frequency-matched groups, were used to develop the XAI-based model. The dataset contained 832 metabolites, and after feature selection, the model was developed using only 50 metabolites, meaning less medical knowledge is required, thus reducing diagnostic costs and improving prognostic time. The computational method was developed using six different ML algorithms before and after feature selection. The final classification model was explained using the XAI approach, SHAP. The best-performing classification model (XGBoost) achieved an area under the receiver operating characteristic curve (AUCROC) value of 98.85%. SHAP results showed that decreased levels of alpha-CEHC sulfate, hypoxanthine, and phenylacetylglutamine, as well as increased levels of N-delta-acetylornithine and oleoyl-linoloyl-glycerol (18:1/18:2)[2], increased the risk of ME/CFS. Besides the robustness of the methodology used, the results showed that the combination of ML and XAI could explain the biomarker prediction of ME/CFS and provided a first step toward establishing prognostic models for ME/CFS.

An Innovative Sandwich Type Biosensor towards Sensitive and Selective Monitoring of 2-Arachidonoylglycerol in Human Plasma Samples Using P(β-CD)-AuNPs-DDT as Amplificant Agent: A New Immuno-Platform for the Recognition of Endocannabinoids in Real Samples

In this work, 2-AG was successfully detected in human plasma samples using a new sandwich-type electrochemical immune device based on poly-β-cyclodextrin P(β-CD) functionalized with AuNPs-DDT and toluidine blue. The P(β-CD) ensured the bioactivity and stability of the immobilized 2-AG antibody by providing a broad surface for the efficient immobilization of the biotinylated antibody. To complete the top section of the immunosensor (reporter), an HRP-conjugated antibody of 2-AG (secondary antibody (Ab2)) was attached to the surface of a glassy carbon electrode (GCE) modified by P(β-CD), as well as a primarily biotinylated antibody (Ab1). The biosensor fabrication process was monitored using field-emission scanning electron microscope (FE-SEM) and EDS methods. Using the differential pulse voltammetry technique, the immunosensor was utilized for detection of 2-AG in real samples. The suggested interface increased the surface area, which allowed for the immobilization of a large quantity of anti-2-AG antibody while also improving biocompatibility, stability, and electrical conductivity. Finally, the suggested immunosensor’s limit of quantitation was determined to be 0.0078 ng/L, with a linear range of 0.0078 to 1.0 ng/L. The results showed that the suggested bioassay can be utilized for diagnosis of 2-AG in clinical samples as a unique and ultrasensitive electrochemical biodevice.

Affinity Assays for Cannabinoids Detection: Are They Amenable to On-Site Screening?

Roadside testing of illicit drugs such as tetrahydrocannabinol (THC) requires simple, rapid, and cost-effective methods. The need for non-invasive detection tools has led to the development of selective and sensitive platforms, able to detect phyto- and synthetic cannabinoids by means of their main metabolites in breath, saliva, and urine samples. One may estimate the time passed from drug exposure and the frequency of use by corroborating the detection results with pharmacokinetic data. In this review, we report on the current detection methods of cannabinoids in biofluids. Fluorescent, electrochemical, colorimetric, and magnetoresistive biosensors will be briefly overviewed, putting emphasis on the affinity formats amenable to on-site screening, with possible applications in roadside testing and anti-doping control.