Facilitating the Bloom of Intestinal Flora: Observing the Influence of Tetrahydrocannabinol (THC) on Beneficial Bacteria Inhabiting the Intestinal Microbiota
Bibliographic record
Abstract
Healthy human bodies are inhabited by over 100 trillion bacterial cells. The study of the symbiotic relationship between humans and these unicellular organisms has become of great importance in the medical field. In fact, the pathology of many diseases involves perturbation of the intestinal flora. Tetrahydrocannabinol (THC), the psychotropic ingredient of cannabis, has caught the attention of healthcare researchers given its ability to increase appetite, and reduce nausea and inflammation. THC has also been associated with modulating the effects arising in the endocannabinoid system (ECS), however, its ability to affect bacteria growth has not been studied. This proposed experiment seeks to explore the growth and vitality of beneficial gut microbiota (Lactobacillus acidophilus (LA), Akkermansia muciniphila (AM), Bifidobacterium bifidum (BB), and Streptococcus thermophilus (ST)) upon exposure to THC. Through utilizing an experimental approach, beneficial bacterial cultures will be collected to study the growth of the microorganisms incubated with THC, relative to a positive and negative control. The results of the proposed study could inform future experiments investigating the treatment of a range of diseases with cannabis. The benefits of these treatments could also be extended to improving the daily health of the general population.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".