Abstract B73: Complementary and alternative medicine and the development of self in chronic diseases: A prospective, multicenter observational survey in the Munshiganj district of Bangladesh
Bibliographic record
Abstract
Abstract Modern lifestyle has contributed to serious increases in chronic diseases like cancers and tumors, hypertension, heart diseases, and diabetes mellitus; as well as debilitating diseases like rheumatoid arthritis. Currently, most medications or therapies for treatment of the above diseases have serious side-effects, which sometimes can be more life-threatening than the disease itself. It is important, therefore, to turn to medicinal plant sources for discovery of novel yet safe compounds, which has less or no side-effects to treat the above diseases. The present survey was carried out amongst the traditional health practitioners in the Munshiganj district of Bangladesh to collect information on medicinal plants used by them to treat the above diseases. Information was collected through a series of interviews with traditional health practitioners, rural and urban people. Field notes were recorded on the medicinal plants and their uses; following the methodology of Bhat et al. (1990) and Martin (1995). The identified medicinal plant specimens were stored at the Bangladesh National Herbarium; under the first author's collector series. The following medicinal plants or plant parts were found to be used as remedy for cancers & tumors: Achyranthes aspera (L), Morinda citrifolia (L), Linum usitatissimum (L), Aegle marmelos (L.) Corr. Serr., Derris indica (Lam.) Bennet, Randia dumetorum (Retz.) Poir., Ficus racemosa (L), Ocimum tenuiflorum (L), Polygonum persicaria (L), Abrus precatorius (L), Cyrtandra cupulata Ridl., Myristica fragrans Houtt., and Nigella sativa (L). Medicinal plants used for treatment of hypertension included Bacopa monnieri(L) Pennell, Tinospora cordifolia (Thunb.) Miers, Plantago ovata Forssk., Cocos nucifera (L.), Allium sativum (L), and Manguera indica (L.). Medicinal plants used to treat heart diseases were Ocimum gratissimum (L), Terminalia arjuna (Roxb.) W. & A., Cicer arietinum (L), and Swertia chirata Buch.-Hams. ex Wall. Anti-diabetes mellitus medicinal plants included Mentha spicata (L), Lepidagathis hyalina Nees, Citrus maxima Merr., Syzygium cumini(L.) Skeels., Tamarindus indica (L), Coccinia grandis (L.) Voigt, Aloe vera (L.) Burm. f., Momordica charantia Descourt., Carica papaya (L), Withania somnifera (L.) Dunal, and Emblica officinalis Gaertn. Plants used as remedy for rheumatoid arthritis included Datura metel(L), Achyranthes aspera (L), Ricinus communis (L), Piper betle (L), Calotropis gigantea (L.) W. TAiton, Basella alba (L), Musa sapientum (L), Nigella sativa (L), Aconitum napellus (L), Santalum album (L), Brassica napus (L), Curcuma longa (L), and Boerhavia diffusa (L). A survey of the scientific literature revealed that preliminary studies conducted on some of the above medicinal plants justify their use to treat specific ailments as practiced by the traditional health practitioners. Other medicinal plants need to be scientifically studied towards obtaining new and safer medicines for treatment of diseases like cancers and tumors, hypertension, heart diseases, and diabetes mellitus; as well as debilitating diseases like rheumatoid arthritis, which affect a large portion of the world's population and have become the foremost chronic diseases in modern times. Citation Information: Cancer Epidemiol Biomarkers Prev 2010;19(10 Suppl):B73.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".