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Record W4313299894 · doi:10.53350/pjmhs20221611290

Decrease in Body Weight and Liver Weight of Pups in Swiss Albino Mice by Spearmint Leaves Extract

2022· article· en· W4313299894 on OpenAlexaff
Asma Zulfiqar, Sitwat Amna, Asma Siddique, Saqib Mansoor, Uzma Ali, Kanwal Sharif, Muhammad Suhail

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Biological Studies
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsBody weightAnimal scienceSignificant differenceDistilled waterTraditional medicineMedicinePhysiologyBiologyInternal medicineChemistry

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.011
GPT teacher head0.192
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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