Impact of Co-enzyme Q-10 on Liver Functions and Histology in INH Induced Rat Model
Bibliographic record
Abstract
Background: The Tuberculosis is the national disease of Pakistan as it affected the father of the nation morbidly resulting into his death and it is a common disease of the sub-continent as well. Liver is the site of metabolism for most of the anti-tuberculosis drugs as well as these agent harm the liver resulting into elevated liver enzymes following inflammation (hepatitis). Isoniazid (INH) and rifampin are well known for their side effects on liver so often being used in experimental studies in animal models for induction of hepatotoxicity or liver injury. Objective: We aimed this research work for exploring the hepatoprotective effects of the Co-enzyme Q-10 in rat model with INH induced hepatotoxicity. Methodology: Rats (n=50) of Albino Westar category were bought from Karachi and divided in 5 equal groups randomly followed by 2 weeks acclimatization. Group A (control) was kept on normal diet without any intervention whereas group B (experimental negative control) was given INH 100 mg/day for induction of hepatitis. Groups C was administered an oral dose of INH as100mg+CoQ 100mg/day. The group D rats were given INH 150+ Q-10 100mg/day similarly rats in group E were administered INH 200mg+CoQ 100mg/day .Samples of blood were obtained by scarifying rats at the end of study (1month) LFTs(liver function test)for each group were done, comparing different groups on ANOVA using SPSS 22nd version. Results: There was significant difference in serum AST ALT, LDH, ALP, GGT and bilirubin levels between various animal groups P-values were 0.0013, 0.00002, 0.00001, 0.00003, 0. 000001 and 0. 000037 for respective parameter. Conclusion: Co-enzyme Q-10 improved INH induced changes in liver functions and histology.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".