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Record W4286637532 · doi:10.3168/jds.2022-21960

Invited review: Effect of subacute ruminal acidosis on gut health of dairy cows

2022· review· en· W4286637532 on OpenAlexaff
J.C. Plaizier, F.J. Mulligan, Enda Neville, Le Luo Guan, M.A. Steele, G.B. Penner

Bibliographic record

VenueJournal of Dairy Science · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of SaskatchewanUniversity of GuelphUniversity of AlbertaUniversity of Manitoba
FundersEuropean Regional Development Fund
KeywordsRumenBiologyAcidosisFecesAnimal scienceDysbiosisPhysiologyGut floraFood scienceEndocrinologyMicrobiologyImmunologyFermentation

Abstract

fetched live from OpenAlex

Subacute ruminal acidosis (SARA) is assumed to be a common disease in high-yielding dairy cows. Despite this, the epidemiological evidence is limited by the lack of survey data. The prevalence of SARA has mainly been determined by measuring the pH of ruminal fluid collected using rumenocentesis. This may not be sufficiently accurate, because the symptoms of SARA are not solely due to ruminal pH depression, and ruminal pH varies among sites in the rumen, throughout a 24-h period, and among days. The impact of SARA has mainly been studied by conducting SARA challenges in cows, sheep, and goats based on a combination of feed restriction and high-grain feeding. The methodologies of these challenges vary considerably among studies. Variations include differences in the duration and amount of grain feeding, type of grain, amount and duration of feed restriction, number of experimental cows, and sensitivity of cows to SARA challenges. Grain-based SARA challenges affect gut health. These effects include depressing the pH in, and increasing the toxin content of, digesta. They also include altering the taxonomic composition of microbiota, reducing the functionality of the epithelia throughout the gastrointestinal tract (GIT), and a moderate inflammatory response. The effects on the epithelia include a reduction in its barrier function. Effects on microbiota include reductions in their richness and diversity, which may reduce their functionality and reflect dysbiosis. Changes in the taxonomic composition of gut microbiota throughout the GIT are evident at the phylum level, but less evident and more variable at the genus level. Effects at the phylum level include an increase in the Firmicutes to Bacteroidetes ratio. More studies on the effects of a SARA challenge on the functionality of gut microbiota are needed. The inflammatory response resulting from grain-based SARA challenges is innate and moderate and mainly consists of an acute phase response. This response is likely a combination of systemic inflammation and inflammation of the epithelia of the GIT. The systemic inflammation is assumed to be caused by translocation of immunogenic compounds, including bacterial endotoxins and bioamines, through the epithelia into the interior circulation. This translocation is increased by the increase in concentrations of toxins in digesta and a reduction of the barrier function of epithelia. Severe SARA can cause rumenitis, but moderate SARA may activate an immune response in the epithelia of the GIT. Cows grazing highly fermentable pastures with high sugar contents can also have a low ruminal pH indicative of SARA. This is not accompanied by an inflammatory response but may affect milk production and gut microbiota. Grain-based SARA affects several aspects of gut health, but SARA resulting from grazing high-digestible pastures and insufficient coarse fiber less so. We need to determine which method for inducing SARA is the most representative of on-farm conditions.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0140.004

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 head0.058
GPT teacher head0.339
Teacher spread0.282 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations77
Published2022
Admission routes1
Has abstractyes

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