Electrochemical Sensing of Cannabinoids in Biofluids: A Noninvasive Tool for Drug Detection
Bibliographic record
Abstract
Cannabinoid sensing in biofluids provides great insight into the effects of medicinal cannabis on the body. The prevalence of cannabis for pain management and illicit drug use necessitates knowledge translation in cannabinoids. In this Review, we provide an overview of the current detection methods of cannabinoids in bodily fluids emphasizing electrochemical sensing. First, we introduce cannabinoids and discuss the structure and metabolism of Δ 9 -THC and its metabolites in relation to blood, urine, saliva, sweat, and breath. Next, we briefly discuss lab based techniques for cannabinoids in biofluids. While these techniques are highly sensitive and specific, roadside safety requires a quick, portable, and cost-effective sensing method. These needs motivated a comprehensive review of advantages, disadvantages, and future directions for electrochemical sensing of cannabinoids. The literature shows the lowest limit of detection to be 3.3 pg of Δ 9 -THC/mL using electrochemical immunosensors, while electrodes fabricated with low cost methods such as screen-printing and carbon paste can detect as little as 25 and 1.26 ng of Δ 9 -THC/mL, respectively. Future research will include nanomaterial modified working electrodes, for simultaneous sensing of multiple cannabinoids. Additionally, there should be an emphasis on selectivity for cannabinoids in the presence of interfering compounds. Sensors should be fully integrated on biocompatible substrates with control electronics and intelligent components for wearable diagnostics. We hope this Review will prove to be the seminal work in the electrochemical sensing of cannabinoids.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".