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Record W2900052859 · doi:10.3168/jds.2018-15070

Using serum and plasma samples to assess failure of transfer of passive immunity in dairy calves

2018· article· en· W2900052859 on OpenAlexafffundabout
Ibrahim Elsohaby, J. Trenton McClure, L.A. Waite, M. Cameron, Luke C. Heider, G.P. Keefe

Bibliographic record

VenueJournal of Dairy Science · 2018
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of Prince Edward Island
FundersAtlantic Canada Opportunities AgencyMitacsZoetis
KeywordsRadial immunodiffusionRefractometerBrixAnimal scienceChemistryRefractometryBlood plasmaDairy cattleAndrologyAntibodyChromatographyVeterinary medicineImmunologyBiologyFood scienceMedicineBiochemistrySugarMaterials science

Abstract

fetched live from OpenAlex

The objectives of this study were (1) to determine the differences in IgG and total protein (TP) content of serum and plasma samples collected from the same calves; (2) to evaluate the correlation between calf serum and plasma IgG levels, Brix scores, and TP concentrations; (3) to determine whether different cut-off values should be used for plasma and serum to assess failure of transfer of passive immunity (FTPI) in dairy calves; and (4) to evaluate the level of agreement between results obtained from using serum and plasma samples of the same calves to assess FTPI using optimal cut-off values. Blood samples (n = 217) were collected from Holstein calves at 3 to 10 d of age on 30 commercial dairy farms in Nova Scotia and Newfoundland, Canada. Paired serum and plasma samples were analyzed for IgG concentration by the reference radial immunodiffusion assay, transmission infrared (TIR) spectroscopy, digital and optical Brix refractometers, and optical TP refractometer. The IgG concentrations measured by RID and TIR spectroscopy in serum were similar to those in plasma. However, the Brix and TP refractometer readings were significantly higher in plasma than in serum. The prevalence of FTPI in serum and plasma samples based on a RID-IgG concentration <10 g/L was 43.3 and 46.5%, respectively. The RID-IgG concentration was correlated with TIR-IgG (r = 0.92 and 0.89), digital Brix (r = 0.80 and 0.80), optical Brix (r = 0.77 and 0.77), and optical TP (r = 0.75 and 0.77) refractometers in serum and plasma, respectively. The correlations between paired serum and plasma IgG content were 0.85 by TIR spectroscopy, 0.80 by digital Brix, 0.77 by optical Brix, and 0.79 by optical TP refractometer. The optimal cut-off values for TIR spectroscopy, digital Brix, optical Brix, and TP refractometers to assess FTPI using serum were 13.1 g/L, 8.7% Brix, 8.4% Brix and 5.1 g/dL, respectively; and the optimal cut-off values with plasma were 13.4 g/L, 9.4% Brix, 9.3% Brix and 5.8 g/dL, respectively. When using these optimal cut-off values, the level of agreement (88.1%) between results derived from testing serum and plasma by TIR spectroscopy was substantial, with a kappa (κ) value of 0.76. The results derived from testing serum and plasma by digital Brix refractometer showed substantial agreement (83.4%), with a κ value of 0.65, which is higher than the agreement and κ value (74.7% and 0.51) reported for the optical Brix refractometer. Substantial agreement (81.6%) between serum and plasma TP was also obtained when using the optical TP refractometer, with a κ value of 0.63. In conclusion, serum or plasma samples can be used interchangeably for measuring IgG concentrations and assessing FTPI in dairy calves. However, different cut-offs must be used to assess FTPI depending on the sample matrix. Furthermore, results obtained from serum samples showed higher agreement with the reference RID assay than those obtained from plasma samples.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.312

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.152
GPT teacher head0.392
Teacher spread0.240 · 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

Citations58
Published2018
Admission routes3
Has abstractyes

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