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Record W2603333258 · doi:10.2527/asasmw.2017.12.144

144 Pre-programming of the immune system to enhance immunological capacity of offspring

2017· article· en· W2603333258 on OpenAlexaff
Niel A. Karrow, Sarah Oh, A. Lee, Z. Li, Richard Fisher, C. F. M. de Lange

Bibliographic record

VenueJournal of Animal Science · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsOffspringImmune systemImmunologyBiologyPregnancyGenetics

Abstract

fetched live from OpenAlex

Livestock experience different forms of immune system stimulation (ISS) during their production cycle including microbial infection, vaccination, and tissue trauma. The release of pro-inflammatory cytokines during ISS activates the hypothalamic-pituitary-adrenal (HPA) axis or “stress axis”, which regulates metabolism and the immune response during ISS to restore physiological homeostasis. When ISS occurs during pregnancy, it can later cause programming of the immature fetal neuroendocrine-immune system. It is hypothesized that this plasticity in neuroendocrine-immune programming allows the fetus to adapt to its post-natal environment; however, risk of adulthood disease may also increase if the newborn is not appropriately matched to its novel environment. Bacterial lipopolysaccharide (LPS) endotoxin is an ISS that is commonly used by immunologists to simulate and study the host response to bacterial infection. When LPS was administered to sheep and pigs during late pregnancy, and the dams were consuming diets containing different n3 and n6 polyunsaturated fatty acid (PUFA) profiles, we observed long-term changes to various offspring health-related phenotypes. This presentation will highlight the immunological changes that were observed, and the health implications of these changes will be discussed.

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)0.000
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 score0.227
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.016
GPT teacher head0.286
Teacher spread0.270 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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