Hemp ( <i>Cannabis Sativa</i> L.) Extract: Anti-Microbial Properties, Methods of Extraction, and Potential Oral Delivery
Bibliographic record
Abstract
The evolving public and regulatory outlook concerning the health and nutritional properties of industrial hemp (Cannabis sativa L.) products has prompted recent research to focus on developing new methods for isolation and oral delivery of bioactive constituents present in hemp extracts. While cannabinoid extracts derived from hemp are renowned for the psychoactive and medicinal properties of the cannabinoids; however, other functional properties attributed to the nutritional value of the hemp seed oil, and, the anti-microbial properties of hemp extract are often overlooked. Isolation of the bioactive compounds from hemp and conversion into products that can be useful for a variety of applications, ranging from nutritional supplements to antimicrobials, as well as new developments in the delivery of medicinal bioactives, are areas of considerable interest for both the cannabis and hemp industries. This review examines these topics and moreover, critiques methods used for the extraction of cannabinoids and hempseed oil bioactives. Finally, novel advances in technologies designed to use nano-carriers for oral delivery of cannabinoids are introduced with the goal to highlight the latest developments in hemp extract processing and delivery.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".