PENGARUH PEMBERIAN EKSTRAK CURCUMA LONGA DENGAN TINGKAT TOKSISITAS PARASETAMOL PADA GASTER, HEPAR DAN RENAL MENCIT JANTAN GALUR SWISS
Bibliographic record
Abstract
Latar Belakang: Curcuma longa dikatakan memiliki aktifitas hepatoprotektor, renoprotektor dan antiinflamasi terhadap dosis toksik parasetamol. Mengingat pemakaian jangka pendek dan jangka panjang, dan prevalensi toksisitas overdosis parasetamol semakin meningkat, perlu diteliti apakah Curcuma longa memproteksi kerusakan lambung, hepar dan renal sehingga dapat bersinergi dengan pengobatan overdosis parasetamol. Metode Penelitian: Penelitian ini adalah eksperimental murni dengan rancangan acak lengkap (completely randomized design), menggunakan 36 ekor mencit jantan galur swiss. Dibagi 6 kelompok yaitu kontrol negatif, kontrol sham, kontrol positif, perlakuan satu, dua dan tiga. Tiga dosis ekstrak Curcuma longa terpurifikasi etil asetat 65 mg/kgBB, 487mg/kgBB dan 1040mg/kgBB diberikan selama 14 hari, dilanjutkan dosis tosik parasetamol 520 mg/kgBB selama 7 hari. Pemeriksaan serum SGOT, SGPT, ureum, kreatinin dan histopatologi lambung, hepar, renal untuk menilai apakah ekstrak Curcuma longa dapat memberikan proteksi kerusakan lambung, hepar dan renal dari akibat pemberian parasetamol dosis toksik. Hasil dan Diskusi: Hasil pemeriksaan SGOT (p = 0,233), SGPT (p = 0,004), ureum (p = 0,19), kreatinin (p = 0,009) dan histopatologi lambung (p = 0,00), hepar (p = 0,00), dan renal (p = 0,00) menunjukkan ekstrak Curcuma longa terpurifikasi etil asetat memberikan efek toksik yang simultan dengan dosis toksik parasetamol. Efek toksik ini dapat dijelaskan karena bioaviabilitas curcumin dalam ekstrak Curcuma longa rendah sehingga pengaruh terhadap dosis toksik parasetamol dalam hepatosit diragukan, meningkatkan efek sitotoksisitas, dan menurunkan ekskresi metabolit toksik parasetamol. Kesimpulan: Ekstrak Curcuma longa tidak memproteksi toksisitas terhadap gaster, hepar dan renal dari pemberian parasetamol dosis toksis pada mencit galur Swiss.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".