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Total and pathogen-specific serum Immunoglobulin G concentrations in neonatal beef calves, Part 1: Risk factors

2023· article· en· W4386954771 on OpenAlexaffabout
Lisa Gamsjäger, Deborah M. Haines, Michel Lévy, Edmond A. Pajor, John Campbell, M. Claire Windeyer

Bibliographic record

VenuePreventive Veterinary Medicine · 2023
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of SaskatchewanUniversity of Calgary
Fundersnot available
KeywordsColostrumBovine respiratory diseaseAntibodyPassive immunityBiologyImmunoglobulin GRadial immunodiffusionImmunologyMicrobiologyDiarrheaPathogenPorcine circovirusRotavirusImmunityHerdVirusImmune systemVirologyAnimal scienceMedicineInternal medicine

Abstract

fetched live from OpenAlex

Maternal antibodies, delivered to the calf via colostrum, are crucial to prevent calfhood diseases and death. However, knowledge regarding the factors influencing this transfer of total and specific Immunoglobulin G (IgG) against common enteric and respiratory disease pathogens under current production conditions is sparse. The objectives of this study were to determine risk factors influencing total and pathogen-specific immunoglobulin G (IgG) concentrations against Escherichia coli (E. coli), bovine Rotavirus (BRoV), Cryptosporidium parvum (C. parvum), Bovine Viral Diarrhea Virus type 1 and 2 (BVDV), Parainfluenza Virus Type 3 (PI-3), Bovine Respiratory Syncytial Virus (BRSV), and Bovine Herpesvirus type 1 (BHV-1) in the serum of newborn beef calves. A total of 420 serum samples were collected from 1- to 7-day-old beef calves born on 6 farms in Alberta, Canada. Samples were analyzed by radial immunodiffusion for total IgG concentration and by enzyme-linked immunosorbent assays for pathogen-specific IgG concentrations against E. coli, BRoV, C. parvum, BVDV, PI-3, BRSV, and BHV-1. Multivariable multilevel linear and logistic regression models were built to evaluate dam- and calf-level risk factors associated with total and pathogen-specific IgG concentrations, failed transfer of passive immunity (FTPI; serum IgG < 10 g/L), and inadequate transfer of passive immunity (ITPI; serum IgG < 24 g/L). Farm was included as a random effect in all models to account for clustering at the herd level. Of the 420 calves included in this study, 5% (n = 20) and 18% (n = 75) of calves had FTPI and ITPI, respectively. Receiving colostrum intervention (i.e., being fed colostrum or colostrum product by either bottle or tube) was the most consistent risk factor for low total IgG concentration and significantly increased the odds of FTPI (Odds ratio (OR): 6.1, 95% CI: 2.0-18.9) and ITPI (OR: 4.8, 95% CI: 2.1-10.8). Calves born to cows consistently had higher pathogen-specific IgG concentrations (P < 0.0001), compared to calves born from heifers, and calves born to vaccinated dams had significantly higher BRoV, BVDV, and BHV-1-specific IgG concentrations. Interestingly, E.coli-specific IgG concentrations were associated with dam vaccination only in cows but not in heifers, which was likely due to differing vaccination strategies used. This study highlights the need to review and refine protocols with respect to dam vaccination and colostrum intervention on cow-calf operations.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.079
GPT teacher head0.344
Teacher spread0.266 · 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.

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

Citations10
Published2023
Admission routes2
Has abstractyes

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