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Record W2892964350 · doi:10.5539/ijb.v10n4p23

The Histological Effect of Aqueous Ginger Extract on Kidneys and Lungs of Diabetic Rats

2018· article· en· W2892964350 on OpenAlexvenueno aff
Maisa M. Al-Qudah, Ezz Al-Dein Al-Ramamneh, Moawiya A. Haddad, Amal A. Al-Abbadi

Bibliographic record

VenueInternational Journal of Biology · 2018
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicGinger and Zingiberaceae research
Canadian institutionsnot available
Fundersnot available
KeywordsZingiber officinaleDiabetes mellitusMedicineKidneyAqueous extractLungTraditional medicineHistologyInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Diabetes is a disorder affecting various-aged humans and which can with time cause serious problems for the patient. Medicinal plants are known for their hypoglycemic effects; and among which is ginger (Zingiber officinale) known also for its culinary uses. This study, therefore, was undertaken to evaluate the histological effect of 21-day treatment of aqueous ginger extract used at 500 mg / kg BW on female diabetic rats. Fifteen female albino rats were divided into three groups; Group I: control, Group II: non-treated diabetic, and Group III: ginger extract-treated diabetic rats. The ginger extract-treated diabetic group received the daily dose orally for three weeks. Results show that organ weight was not significantly changed. Light microscopic examination of 5µm sections of extract-treated Kidney and Lung of the diabetic rats revealed approximately normal histological structure compared with the untreated ones. The normal appearance of glomeruli and alveoli as well as the normal alveolar wall assumed the ameliorative effect ginger aqueous extract could have on kidney and lung of diabetic rats. These results indicated that this dose of ginger extract may be effective in the treatment of diabetic rats.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.069
GPT teacher head0.477
Teacher spread0.408 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2018
Admission routes1
Has abstractyes

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