DNA barcoding of endangered medicinal plant Cayratia pedata
Bibliographic record
Abstract
Acknowledging the effectiveness of plants and their products in the treatment of diseases, the WHO recognizes that medicinal plants play an important role in the low-cost primary healthcare of about 80% of world's population in developing countries including India. Plant and other natural products are gaining popularity as an alternative and system of medicine all over the world. Cayratia pedata is an indigenous endangered medicinal herb of south India belonging to the family Vitaceae. Traditionally, the leaves of this plant have been used as a dietary ingredient in the treatment of ulcers and diarrhoea. In Ayurveda the extract from Cayratia pedata is used to prepare formulations prescribed to treat microbial infections, ulcers, inflammations and arthritis. We have identified this plant to be a good source of phytochemicals like alkaloids, tannins, phenolic compounds, flavonoids and terpenoids. Correct identification of any medicinal plant is an absolute requirement in order to avoid errors in collection of the plants used for the formulations whose effectiveness depends on the natural products contained in them. DNA barcoding is a reliable tool in scientifically identifying medicinal plants. The current study explains how DNA barcode analysis of the plant Cayratia pedata helps in the proper identification based on nucleotide diversity of short DNA segments. DNA from the leaves of the plant was extracted and the chloroplast gene rbcL was amplified by PCR and sequenced. The sequence was subjected to a BLAST analysis to compare it with that of other species and a phylogenetic tree was constructed. The results confirmed that the plant belonged to the family Vitaceae. DNA bar-code analysis is a powerful technique for the identification, vouching and registration of medicinal plants especially when there is high species diversity. This helps in collecting the precise species that has the maximum yield of the active principles needed by the unskilled user as well as the pharmaceutical industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".