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Record W4293739071 · doi:10.3168/jds.2022-21838

Effect of supplementation with algae β-glucans on performance, health, and blood metabolites of Holstein dairy calves

2022· article· en· W4293739071 on OpenAlexaff
Maria Eduarda Reis, Ariany Faria de Toledo, A.P. da Silva, Milaine Poczynek, M.C. Cantor, Gercino Ferreira Virgínio Júnior, L.F. Greco, Carla Maris Machado Bittar

Bibliographic record

VenueJournal of Dairy Science · 2022
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of Guelph
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsFecesAnimal scienceStarterDairy cattleBiologyFeedlotFeed conversion ratioBody weightFood scienceEndocrinologyMicrobiology

Abstract

fetched live from OpenAlex

Studies have shown that β-glucans extracted from the cell wall of cereals, algae, and yeasts have been associated with improved immune function. However, it is unknown whether algae β-glucan supplementation affects the performance, blood metabolites, or cell counts of immune cells in dairy calves. The objective of this randomized clinical trial was to evaluate whether supplementation of β-glucans to milk replacer in dairy calves fed 6 L/d improved growth performance and fecal status and altered the blood metabolite profile. In this trial, we enrolled Holstein calves (n = 34) at birth (body weight 36.38 ± 1.33 kg; mean ± standard deviation) to receive, from 1 d of age, either 2 g/d algae β-glucans mixed into 6 L/d of milk replacer (22.4% crude protein and 16.2% fat) or an unsupplemented milk replacer (control). The calves were blocked in pairs according to birth weight, sex, and date of birth (up to 5 d difference). Calves were housed individually, and calf starter (24.7% crude protein and 13.9% neutral detergent fiber) was offered ad libitum based on orts of the previous day until 56 d of age (end of the trial). Body weight was measured weekly, and health checks and daily fecal consistency were evaluated daily in every calf by the same observer. Calves with 2 consecutive days of loose feces that sifted through bedding were considered diarrhea positive. We used a linear mixed effects model to evaluate the effects of β-glucan supplementation fed during the preweaning period on performance (average daily gain), final weight, feed efficiency (FE), white blood cell count, and selected blood metabolites, repeated by time. A generalized linear mixed effects model was also run to evaluate the likelihood of a diarrhea bout in the first 28 d of life, controlling for the calf as the subject with a logistic distribution. We included age, serum total protein at 48 h, and birth weight as covariates. At 56 d, β-glucan-supplemented calves weighed more than control calves (56.3 vs. 51.5 kg). Treatment had no effect on total starter intake, but there was a treatment by age interaction for FE, with greater FE for β-glucan-supplemented calves in wk 3 and 5 of age. There was only a tendency for average daily gain to be greater in supplemented calves than in control calves for the duration of the study. Furthermore, control calves had 14.66 [95% confidence interval (95% CI): 9.87-21.77] times greater odds of having a diarrheal bout than β-glucan-supplemented calves. Control calves had 12.70 (95% CI: 8.82-18.28) times greater odds of having an additional day with an abnormal fecal score compared with β-glucan-supplemented calves, suggesting that supplementation ameliorated diarrhea severity. We found no association of treatment with concentrations of serum total protein, albumin, creatinine, or glucose during the preweaning period. Our findings suggest that dietary supplementation of 2 g/d of algae β-glucans to milk replacer improved fecal status and may affect growth, as evidenced by a higher weaning weight, compared with control calves. Future studies should explore the effect of algae β-glucans on lower-gut physiology and digestibility in dairy calves.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.023
GPT teacher head0.334
Teacher spread0.311 · 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 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

Citations19
Published2022
Admission routes1
Has abstractyes

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