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Effects of dietary algal supplementation on bovine immunity (39.50)

2009· article· en· W101079792 on OpenAlexaff
Julia M. Green-Johnson, Adriana Masotti, Lisa E. Wagar, Sherry Fillmore, Kathleen Glover, Leslie A. MacLaren, Cathy Enright, Alan H. Fredeen

Bibliographic record

VenueThe Journal of Immunology · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeaweed-derived Bioactive Compounds
Canadian institutionsNova Scotia Department of AgricultureAgriculture and Agri-Food CanadaOntario Tech University
Fundersnot available
KeywordsBiologyAlgaeImmunityFood scienceKeyhole limpet hemocyaninImmunizationAnimal scienceImmune systemBotanyImmunology

Abstract

fetched live from OpenAlex

Abstract Marine algae are primary producers of long chain omega-3 fatty acids and can serve as novel dairy feed supplements with the added potential to influence bovine immunity. Our objective was to assess the immunomodulatory impact of dietary supplementation with different algal types (macroalgae and microalgae). Effects of dietary algal supplementation were examined using a crossover design and a 28 day feeding period with each algal type. Primary immunization with Keyhole Limpet Hemocyanin (KLH) was carried out 8 days into the trial, with secondary immunization 14 days later. KLH-specific serum IgG responses were significantly higher in cows receiving macroalgae-supplemented feed than in cows receiving microalgae-supplemented feed. In contrast, cows receiving microalgae-supplemented feed showed significantly higher levels of macrophage oxidative burst activity in response to PMA challenge in vitro. These results suggest that dietary supplementation with algal products has immunomodulatory potential and also indicates that the effects on cellular and humoral immunity are dependent on the type of algae used. Funded by the Atlantic Innovation Fund.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.017
GPT teacher head0.246
Teacher spread0.230 · 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 designObservational
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

Citations0
Published2009
Admission routes1
Has abstractyes

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