Quantification of Carpaine and Antioxidant Properties of Extracts from Carica Papaya Plant Leaves and Stalks
Bibliographic record
Abstract
Significance increase in dengue cases have been recorded worldwide every year and South East Asian countries have been badly affected. Currently, antiviral drug to treat dengue is still not available but papaya leaves extract (PLE) have been successfully used in treating dengue patients. Carpaine in PLE is the major active compounds that contributes to the anti-thrombocytopenic activity (raising platelet count in patient's blood). The PLE also contains polyphenols that contribute to antioxidant properties. Studies were carried out to extract and quantify carpaine from young leaves, old leaves and stalks of papaya plant. Carpaine crystalline powders were successfully purified and confirmed by 1H and 13C NMR (Nuclear Magnetic Resonance). Young papaya leaves recorded significantly higher amount of carpaine (P < 0.05) as compared with old leaves and stalks. Blending treatment showed significant effect (P < 0.05) on the amount of carpaine extracted from the young and old leaves and blended young leaves showed significantly much higher amount of carpaine as compared with the unblended samples (P < 0.05). In terms of total polyphenols content (TPC), young leaves (both unblended and blended samples) contained significantly higher amount of TPC (P < 0.05) followed by the old leaves and stalks. Results also showed relationship between the TPC and the 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay scavenging activities namely IC50 (R2 = 0.9743) and acid equivalent antioxidant capacity (AEAC) (R2 = 0.9581). Both young and old leaves also showed significantly higher (P < 0.05) DPPH scavenging activities as compared with the stalks in both unblended and blended samples. Young papaya leaves were recommended as source of material to extract carpaine for future development of drug in dengue treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".