A Review on Phytochemicals from Some Medicinal Plants of Bangladesh
Bibliographic record
Abstract
Natural products, especially those derived from higher plants, have attracted scientists from ancient time because of their potential therapeutic values. Drug development from natural sources showed that natural products or natural product-derived drugs comprised about 28% of all new chemical entities launched to the market. These are originated from terrestrial plants, microbes, marine organisms, etc. However, until recently an insignificant part of the plants has been scientifically evaluated for their medicinal properties. Bangladesh is a rich repository of medicinal plants, many of which are widely used in the Ayurvedic, Unani, herbal and other traditional systems of medicines. In our laboratory, we have extensively investigated over 48 medicinal plants and several microbial strains that have resulted in the isolation and characterization of 133 compounds, including 33 new molecules. Terpenoids, alkaloids, flavonoids and glycosides were the major classes of constituents. The crude extractives and several purified molecules demonstrated statistically significant inhibition of growth of microorganisms. On the other hand, usnic acid obtained from the lichen, Parmelia kamtschandalis, demonstrated even better of inhibition of microbial growth as compared to the standard antimicrobial agents, whereas dehydroaltenusin extracted from a Streptomyces sp. exhibited significant HIV-inhibitory activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| 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 | 0.004 |
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".