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Anti-Inflammatory Effect of Nicavet-2500 in Rodent Models of Acute Inflammation

2018· article· en· W2800907110 on OpenAlexvenueno aff
David A. Areshidze, Lyudmila Timchenko, Igor Rzhepakovsky, Maria A. Kozlova, Iaroslavna A. Kusnetsova, Lyudmila A. Makartseva

Bibliographic record

VenueJournal of Pharmacy and Nutrition Sciences · 2018
Typearticle
Languageen
FieldMedicine
TopicHuman Health and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsInflammationOrganismSpleenImmune systemRodentBiologyModel organismImmunologyCell biologyBiochemistryEcologyGenetics

Abstract

fetched live from OpenAlex

This study examines the influence of the tissue preparation "NICAVET 2500" on an organism of mammals with use of rodent models of acute inflammation. It is established that action of a preparation leads to decrease in ESR. Hematologic and biochemical parameters also testify to anti-inflammatory action of "NICAVET 2500". Results of histological and morphometric research of a spleen and a thymus show processes of proliferation and migration of immunocytes, testifying to activization of immune reactions. In a thymus of rats of experimental group in comparison with control an increase in the dimensions of thymic lobules and also an increase in quantity of Hassal's bodies, testifying to intensification of synthesis of the thymic hormones participating in process of an immunopoesis are revealed. As a result of the use of the preparation an increase of ability of an organism to resist alteration and also essential decrease in a level of development of inflammatory reaction of an organism are observed. The conducted research demonstrates the expressed immunomodulatory action of a preparation "NICAVET 2500" at an experimental model of inflammation.

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 score0.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.396
Teacher spread0.356 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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