Electrochemical Sensing of Cannabinoids in Biofluids: A Noninvasive Tool for Drug Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 Δ9-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 Δ9-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 Δ9-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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it