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Record W4312411270 · doi:10.47552/ijam.v13i2.2559

Antihistaminic effects of Azadirachta indica leaves in laboratory animals

2022· article· en· W4312411270 on OpenAlexaff
Padmaja Kore, Akash Gaikwad, Auradha G More, Shivanjali Shinde, Rutuja Sawkar, Poonam R. Inamdar

Bibliographic record

VenueInternational Journal of Ayurvedic Medicine · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhytochemical compounds biological activities
Canadian institutionsCanadian Parks and Wilderness Society
Fundersnot available
KeywordsCatalepsyAzadirachtaClonidineHaloperidolTraditional medicinePharmacologyHistamineChemistryMedicineDopamineAnesthesiaEndocrinology

Abstract

fetched live from OpenAlex

Azadirachta indica (Meliaceae) leaves have been traditionally used in the management of asthma and the current study was undertaken to scientifically validate the benefits of plant as an antihistaminic agent using the suitable animal model. The agents with antihistaminic properties are known to be good antiasthmatic agents; hence, in the current research work, the antihistaminic activity of an ethanolic extract of Azadirachta indica leaves (at a dose of 250 mg/kg, i.p.) was evaluated using haloperidol-induced catalepsy and clonidine-induced catalepsy in laboratory rats. The results showed that the ethanolic extract inhibits the catalepsy induced by the clonidine but no remarkable effect was observed on the catalepsy induced by haloperidol. This strongly suggests that, the inhibition is mediated through an antihistaminic action and there is no role of dopamine. Hence, in the present study, it is concluded that, the ethanolic extract has significant antihistaminic activity. The polar constituents in the ethanolic extract of leaves of Azadirachta indica may be responsible for the antihistaminic effects and therefore, the ethanolic leaves extract can be a better remedy as an antihistaminic agent.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.113
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.273
Teacher spread0.264 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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