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Record W2774898992 · doi:10.5539/jas.v10n1p217

Blood Biochemical and Immunological Responses to Garlic Oil Administration in Growing Rabbits Diet

2017· article· en· W2774898992 on OpenAlexvenueno aff
M. R. EL-Gogary, Amira Mansour, Eman El-Said

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGarlic and Onion Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAntioxidantGarlic OilTestosterone (patch)CholesterolBiologyFood scienceAnimal scienceBody weightHormoneBlood lipidsAntibodyEndocrinologyInternal medicineChemistryMedicineBiochemistryImmunology

Abstract

fetched live from OpenAlex

The effect of dietary supplementation garlic oil on performance and blood parameters of New Zealand White rabbits has been studied. The garlic oil was added at graded levels of 0.0, 0.25, 0.5 and 0.75 g/kg for T1 (control), T2, T3 and T4, respectively. A total of thirty six male rabbits, 7 weeks of age and with an initial weigh of 950 g were used. There were four treatments, each with three replicates (n = 3) in randomly divided design. The parameters investigated were growth performance, carcass yield, glucose, plasma lipids profile, immunoglobulin’s G (IgG, IgA and IgM), antioxidant status and testosterone hormone. The results showed that feeding diet supplemented with garlic oil had insignificant effect on body weight, body weight gain, feed intake, feed conversion ratio, triglycerides, cholesterol, LDL and HDL. Rabbits fed the 0.5 g/kg garlic oil diet had significantly increased IgG level, hence improved immune responses and Testosterone hormone of rabbits. The colony forming units of coliform bacteria showed a significantly lower number compared with control. The present results indicate that supplemented of garlic oil at 0.5 g/kg of diet has a positive effect on HDL, immunoglobulin’s G, antioxidant status and testosterone hormone in addition to its antibacterial effect.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.516

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.270
Teacher spread0.241 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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