MétaCan
Menu
Back to cohort
Record W2233918017 · doi:10.5539/jfr.v5n1p26

Characterization of Algerian Honey from Tiaret Region and Immunoassay Study of Its Immunomodulatory Effect in BALB/c Mice

2015· article· en· W2233918017 on OpenAlexaffvenue
Yamina Mehdi, Saad Mebrek, Soraya Djebara, Yamina Aissaoui, Benahmed Khadidja, Mohammed Bénali, Slimane Belbraouet

Bibliographic record

VenueJournal of Food Research · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsOvalbuminImmune systemRoyal jellyIsotypeAllergic responseImmunologyAllergenAntigenBiologyAntibodyChemistryAllergyImmunoglobulin EFood science

Abstract

fetched live from OpenAlex

Honey is a food that possesses several antiseptic, antibacterial, anti-inflammatory and immunomodulatory properties. In this study, the immunomodulatory effect of honey was evaluated by Enzyme Linked Immunosorbent Assay (ELISA) using ovalbumin as an allergen model. To compare the honey quality, we conducted a range of physicochemical analyses on four different samples from the Tiaret region (Algeria) using the immunosuppressive or immunomodulatory effect in Balb/c mice. Our results show that the injection of 100 μL of honey before 6 hours, 6 hours after and at the same time as the injection of antigen (ovalbumin) causes a significant suppressive activity on production of the IgG isotype by Balb/c mice. This result corroborates this therapeutic virtue ascribed to honey which has resulted in a suppressive demonstration of honey on the humoral immune response. This opens an interesting perspective in the clinical area, as immunosuppressive agents play an important role in the transfer of various organs and immune system diseases.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.077
GPT teacher head0.296
Teacher spread0.219 · 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

Citations1
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Food ResearchSame topicBee Products Chemical AnalysisFrench-language works237,207