Levels of Apigenin and Immunostimulatory Activity of Leaf Extracts of Bangunbangun (Plectranthus Amboinicus Lour)
Bibliographic record
Abstract
Bangunbangun (Plectranthus amboinicus Lour) consumed by the mother, who just gave birth in North Sumatra, Indonesia in particular the Batak tribe, to increase the production of breast milk. This plant is known to have a high content of nutrients, especially iron and carotene. Also known to have many benefits, among others, as an antipyretic, analgesic, wound medicine, cough medicine, and thrush, antioxidant, antitumor, anticancer, and hypotensive. The study was conducted to determine levels of apigenin of Bangunbangun’s leaves and evaluate its immunostimulatory activity in rats (Rattus norvegicus). Analysis of apigenin using High Performance Liquid Chramtography method (HPLC). Evaluation of immunostimulatory activity carried out by measuring the levels of imonoglobulin G (IgG), imonoglobulin M (IgM), Lysozyme and Monocytes. Analysis of IgG and IgM are using Elisa method (Sigma). Serum lysozyme activity was measured by the spectrophotometric method. Monocytes were analyzed by using ABX Micros 60. Organ histology preparations made by hematoxylin-eosin staining. Data were analyzed by ANOVA and showed that by giving the Ethanol Extract of Propolis (EEP) of 500 mg / kg bw in rats, with a significant increase of IgM and lysozyme activity with very significant. EEP give a very significant effect on levels of IgG. Monocytes were higher in mice given EEP, but did not differ significantly compared with mice not given EEP. Lymphoid organ weights are all under normal circumstances. Giving EEP 500 mg / kg bw mice, significantly increased the weight of the liver and spleen, but does not affect kidney weight.
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 | 0.000 | 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".