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Total and pathogen-specific serum Immunoglobulin G concentrations in neonatal beef calves, Part 2: Associations with health and growth

2023· article· en· W4385729185 on OpenAlexaff
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
KeywordsAntibodyAnimal scienceImmunoglobulin GPathogenBiologyFood scienceVeterinary medicineImmunologyMedicine

Abstract

fetched live from OpenAlex

The association of poor transfer of passive immunity (TPI) with negative health outcomes is extensively researched in dairy calves. However, few field studies have examined the effect of total and particularly pathogen-specific Immunoglobulin G (IgG) concentrations on pre-weaning health and growth of beef calves. Hence, the objective of this study was to determine the association of total and pathogen-specific IgG concentrations against selected pathogens associated with neonatal calf diarrhea (NCD) and bovine respiratory disease (BRD) and the odds of pre-weaning treatments, mortality, and the growth of newborn beef calves. A total of 420 serum samples from 1- to 7-day old beef calves born on 6 farms in Alberta, Canada, were available for this observational study. Serum samples were analyzed by radial immunodiffusion for total IgG concentration and by enzyme-linked immunosorbent assays for pathogen-specific IgG concentrations against Escherichia coli (E. coli), bovine Rotavirus (BRoV), Cryptosporidium parvum (C. parvum), Bovine Viral Diarrhea Virus (BVDV), Parainfluenza Virus Type 3 (PI-3), Bovine Respiratory Syncytial Virus (BRSV), and Bovine Herpesvirus Type 1 (BHV-1). Data about the individual dam- and calf-level risk factors, calf treatments, mortality, and birth and weaning weights were collected. Multivariable multilevel logistic and linear regression models were built to evaluate the association of total and pathogen-specific IgG concentrations with the odds of mortality and average daily gain (ADG), respectively, while their association with the odds of pre-weaning treatment was established by univariable logistic regression analysis. The odds of calves with IgG concentrations < 10 g/L of getting treated (OR 7.9, 95 % CI 2.7-23.7) and dying (OR: 18.5, 95 % CI: 3.7-93.4) were higher than for their counterparts (P < 0.0001). Calves with IgG concentrations < 24 g/L also had higher odds of dying (OR: 10.1, 95 % CI: 2.6-40.2) and had lower ADG (-0.09 kg, SE: 0.03, P < 0.002) than calves with IgG concentrations ≥ 24 g/L. Higher BVDV-specific IgG concentration was protective against mortality (OR: 0.97, 95 % CI: 0.96-0.99, P < 0.001). This study highlights the negative association of inadequate TPI with health and growth of beef calves and thus, the need to refine protocols for dam vaccination and colostrum intervention in cow-calf operations to meet these higher IgG targets.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.066
GPT teacher head0.341
Teacher spread0.276 · 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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Citations12
Published2023
Admission routes1
Has abstractno

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