The Effect of Omega-3 Rich Fish Oil on the Kidney Changes in Mice Induced by Azoxymethane and Dextran Sodium Sulfate
Bibliographic record
Abstract
This research is an in vivo experimental study using animal trials of Balb / c mice.The study protocol was approved by the Institutional Animal Care and Use Committee, Faculty of Medicine, Universitas Indonesia (ND 473/UN-2.F1/ETIK/ PPM.00.02/2018,April 19, 2018). AnimalsThe experimental animals were 16-week-old Balb / c mice with an average weight of 20 grams obtained from the FKUI Animal Pathology Anatomy Laboratory.Mice are acclimatized for one week before treatment.Mice were kept and treated ABSTRACTBackground: The study aimed to investigate the effect of omega-3 rich fish oil to kidney of mice induced by Azoxymethane (AOM) and DSS using histopathology parameters.Method: The experimental mice were induced using 10 mg/kg AOM and 2% DSS for 2 weeks randomly allocated randomly into four groups as follows; Control Group: mice that not received fish oil, Low Dose Group: mice that received 1.5 mg/day fish oil, Medium Dose Group: mice that received 3 mg/day fish oil, and High Dose Group: mice that received 6 mg/day fish oil.The omega-3 rich fish oil was given for 12 weeks.Result: The administration of high dose omega-3 rich fish oil was able to reduced necrosis and inflammation foci compared to the control group (p<0.05).Furthermore, the administration of low, medium, and high dose omega-3 rich fish oil was able to significantly reduced vascular edema and cell degeneration foci (p<0.05).The administration of medium and high dose of omega-3 rich fish oil were able to reduce the amount of fibrosis foci compared to the control group (p<0.05)compared to the control group.Conclusion: The result suggested anti-nephrotoxic effect of omega-3 rich fish oil in mice induced by azoxymethane and DSS.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".