A comprehensive review on exploring the potentials of medicinal coleus (Coleus forskohlii Briq.) in the pharmaceutical industry
Bibliographic record
Abstract
Medicinal coleus contains fasciculated tubers, which are used to extract a diterpenoid active compound known as forskolin.This plant is reported in Nepal, Burma, India and Thailand.The products of coleus and related products are exported to the USA, Poland, South Korea, Australia, Japan, Italy, Spain, South Africa, and Canada.The leaves of coleus contain rich quantities of flavones, flavonols and polyphenols besides their antioxidant properties.From Coleus forskohlii Briq., A and B froms forskoditerpenoside and supplementary forms of diterpenoids were also recorded besides traceable quantities of citronellal, aamyrin, -sitosterol, betulinic acid, a-cedrol, and a-cedren.For centuries, it has been used in Ayurvedic medicine to address conditions such as underactive thyroid, heart disease, and respiratory disorders.In modern clinical studies, it has been demonstrated that it may benefit the treatment of asthma, angina, psoriasis, as well as the prevention of cancer metastases.Forskohlin significantly decreases body fat and promotes the leanness of the body and is believed to function as an antidepressant by increasing cAMP levels.Forskolin has the ability to prevent the synthesis of histamine, eotaxin, and interleukins and possesses a specific actions that hamper pathways involved in growth and survival of colon cancer cell.Forskolin is generally considered safe when used appropriately, but like any supplement, it can have side effects and potential toxicity, especially when taken in excessive amounts or combined with certain medications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".