Probing the antioxidant activity of Δ<sup>9</sup>-tetrahydrocannabinol and cannabidiol in<i>Cannabis sativa</i>extracts
Bibliographic record
Abstract
Herein, we report the antioxidant activity of cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC) in pure and mixed solutions at different ratios, as well as of six different Cannabis sativa extracts containing various proportions of CBD and THC by using spectrophotometric (reducing power assay, 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH), hypochlorous acid (HOCl) scavenging assays) and electrochemical methods (cyclic voltammetry and differential pulse voltammetry). The isolated cannabinoids, the different stoichiometric ratios of CBD and THC, and the natural extracts proved to have remarkable antioxidant properties in all the methods employed in this work. The antioxidant activity of CBD and THC was compared against that of the well-defined antioxidants such as ascorbic acid (AA), resveratrol (Resv) and (-)-epigallocatechin-3-gallate (EGCG). Clear evidence of the synergistic and antagonistic effects between CBD and THC regarding to their antioxidant activities was observed. Moreover, a good correlation was obtained between the optical and electrochemical methods, which proved that the reported experimental procedures can easily be adapted to determine the antioxidant activity of extracts from various Cannabis sativa species and related compounds.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".