AKTIVITAS ANTIHIPERTENSI EKSTRAK ETANOL DAUN MATOA (Pometia pinnata J.R.Forst. & G.Forst.) PADA MODEL HEWAN HIPERTENSI YANG DIINDUKSI PREDNISON DAN NaCl
Bibliographic record
Abstract
Hypertension is a disease without symptoms that is very dangerous (silent killer) because it is often not realized. Conditions that are not controlled and occur continuously will cause a disease factor related to cardiovascular disease. Empirically, matoa leaves are commonly used as antihypertensive drugs. This study aims to determine the activity of the ethanolic extract of matoa leaves as a diuretic and antihypertensive. Diuretic testing was carried out for 6 hours with urine volume measurement every hour. Each test animal was given warm water as a loading dose, using a comparison (Furosemide 3.6 mg/KgBW) and the matoa leaf ethanol extract test group with doses of 150, 300, and 500 mg/KgBW. Antihypertensive testing was carried out in a preventive manner using Prednisone 1.5 mg/KgBW and NaCl 2%, comparison (Hydrochlorothiazide 2.25 mg/KgBW) and the test group ethanol extract of matoa leaves with doses of 150, 300, and 500 mg/KgBW. given orally for 14 days. The test parameters were urine volume, systolic blood pressure and diastolic pressure. The data obtained were analyzed using One Way ANOVA and Kruskall Wallis. The results showed that the ethanol extract of matoa leaves could increase urine output, and decrease systolic and diastolic blood pressure because it had a comparable activity with the comparison group (p<0.05). A dose of 150 mg/kgBW has the most effective activity as an antihypertensive.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".