Decrease in Body Weight and Liver Weight of Pups in Swiss Albino Mice by Spearmint Leaves Extract
Bibliographic record
Abstract
Objective: To evaluate the effects of spearmint leaves extract on body weight, liver weight and RTWI of pups in Swiss albino mice. Study Design: Experimental study Place and Duration of Study: Anatomy Department, Shaikh Zayed PGMI, Lahore from 1st March 2014 to 30th June 2014. Methodology: Seven male and 21 female mice were used. After conception female mice were divided into control (A), low dose (B) and high dose (C) experimental groups. There were7 mice in each group. Group A was given distilled water where as group B was given 3g/kg/day and group C were given 6g/kg/day spearmint leaves extract. After 21 days (duration of pregnancy in mice) hysterotomy was done after euthanasia. Three groups of pups were made. These pups were selected randomly and marked as control, low dose and high dose experimental groups (A1, B1 & C1 respectively). Their body weights and liver weights were taken and recorded. Results: Body weight, weight of liver and RTWI of pups in both the experimental groups were decreased significantly with p<0.001 and difference between the experimental groups was also highly significant with p<0.001. Conclusion: The spearmint leaves extract decrease the body weight, weight of liver and RTWI of pups of Swiss Albino mice. Keywords: Spearmint, Garden mint, Herbal tea, Swiss Albino mice, Conception, Body weight, Weight of liver, RTWI
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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".