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Record W1990227322 · doi:10.1515/1553-3840.1587

Contractile Effect of Radish and Betel Nut Extracts on Rabbit Gallbladder

2012· article· en· W1990227322 on OpenAlexaff
Muhammad Nabeel Ghayur, Anwarul Hassan Gilani

Bibliographic record

VenueJournal of Complementary and Integrative Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicDrug Transport and Resistance Mechanisms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCarbacholGallbladderMuscarinic acetylcholine receptorBetelArecolineAtropineTraditional medicineMedicinePharmacologyChemistryInternal medicineNutReceptor

Abstract

fetched live from OpenAlex

Raphanus sativus (abbreviated in this paper as Rs.Cr) and Areca catechu (Ac.Cr), commonly known as radish and betel nut respectively, are traditionally used in South Asia for different gastrointestinal, gallbladder, and hepatic diseases. There has not been any study to see how they modulate gallbladder contractility. We selected isolated rabbit gallbladder tissue preparations, mounted them in tissue baths containing Krebs-Henseleit solution at 37°C, and then recorded the changes in baseline tone of the tissues upon administration of Rs.Cr and Ac.Cr. Both the extracts exhibited concentration-dependent stimulant effect on the baseline tone of gallbladder tissues, similar to carbachol, a muscarinic receptor agonist. The stimulant effect of the extract, as well as that of carbachol, was completely blocked in the presence of atropine, a muscarinic antagonist, indicating similarity in the mechanism of action of the extracts with carbachol. The result shows potential of these extracts to contract the gallbladder and to subsequently increase bile secretion, but this remains to be investigated in detail. This study justifies the traditional use of radish and betel nut in different gastrointestinal disorders.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.018
GPT teacher head0.307
Teacher spread0.290 · 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 designBench or experimental
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

Citations6
Published2012
Admission routes1
Has abstractyes

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