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Record W4388529327 · doi:10.1093/jas/skad281.031

124 Disease Resilience Indicators Based on Phagocytosis Assays on Blood from Young Healthy Pigs

2023· article· en· W4388529327 on OpenAlexaffabout
Vishesh Bhatia, Xuechun Bai, Frédéric Fortin, John C. S. Harding, Michael K. Dyck, Catherine J. Field, Claire Rogel Gaillard, Fany Blanc, Graham Plastow, Jack C. M. Dekkers

Bibliographic record

VenueJournal of Animal Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsUniversity of AlbertaUniversity of SaskatchewanCentre de Développement du Porc du QuébecHendrix Genetics (Canada)
Fundersnot available
KeywordsHeritabilityBiologyOutbreakDiseaseQuarantineImmune systemLitterVeterinary medicineImmunologyMedicineGeneticsVirologyEcologyInternal medicine

Abstract

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Abstract Infectious diseases impact the swine industry through increased morbidity and mortality, leading to large economic losses and reduced animal welfare. Disease resilience is the ability of an animal to maintain production performance under pathogen exposure and has gained traction as an approach to help reduce the impact of infectious disease. For this purpose, a wean-to-finish polymicrobial natural disease challenge model was set up, in which, using a continuous flow system, batches of 60 or 75 young healthy Landrace × Yorkshire barrows from one of seven breeding companies of PigGen Canada were entered into a quarantine nursery before their entry in a nursery/finisher that was seeded with multiple pathogens. The objective of this study was to evaluate phagocytosis assays that can be conducted on blood from young healthy pigs as potential indicator traits to select for disease resilience in high-health nucleus breeding herds. Immune cell proportions and their phagocytic activity were evaluated in blood collected at ~26 days of age in the quarantine nursery on 985 pigs. Genetic parameters, including heritabilities and genetic correlations with disease resilience phenotypes collected on these pigs and an additional 2,220 pigs from the same companies in the polymicrobial challenge were estimated using GBLUP in ASReml 4.2, with the fixed effect of batch, the covariate of entry age, and random effects of pen by batch, litter, and additive genetics, using a genomic relationship matrix derived from genotypes of 650K single nucleotide polymorphisms. Results showed moderate heritability estimates for immune cell proportions (average = 0.36; range = 0.22-0.42) and the % of phagocytizing cells for each cell type (0.32; 0.19-0.48), but low estimates for mean fluorescence intensity (MFI) of the phagocytizing cells of each type (0.2; 0.05-0.3). Estimates of genetic correlations with disease resilience traits ranged from -0.93 to 0.72, but had substantial standard errors. Granulocyte % had a high positive genetic correlation with number of health treatments across the challenge nursery and finisher (0.72 ± 0.5), while a greater % phagocytizing eosinophils was genetically correlated with decreased growth rate in the challenge nursery (-0.26 ± 0.2) but greater growth rate (0.33 ± 0.2) and reduced mortality in the finisher (-0.78 ± 0.6) and across the challenge nursery and finisher (-0.50 ± 0.4). Greater MFI of phagocytizing eosinophils was also genetically correlated with reduced mortality across the challenge nursery and finisher (-0.93 ± 0.5). These results provide insights into potential indicator traits that can be used to select for disease resilience without the need to subject animals to disease, with the overall aim to improve animal welfare and increase production performance in herds that are subject to hard-to-manage infectious pathogens. This project was supported by Genome Canada, Genome Alberta, PigGen Canada, and USDA-NIFA grant #2017-67007-26144.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.283
Teacher spread0.250 · 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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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