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Record W7117721818 · doi:10.3168/jds.2025-27602

Effects of an intramammary LPS challenge in lactating Holstein cows fed a probiotic-postbiotic blend on performance, inflammation, and paracellular permeability of the gastrointestinal tract

2025· article· en· W7117721818 on OpenAlexafffund
C.A. Bertens, D.M. Paulus Compart, C.M. Stoffel, Natacha Hogan, Antonio Facciuolo, G.B. Penner

Bibliographic record

VenueJournal of Dairy Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsParacellular transportGastrointestinal tractDry matterLactationIntestinal permeabilityBovine milk

Abstract

fetched live from OpenAlex

This study evaluated the effects of feeding a probiotic-postbiotic blend on DMI, milk and milk component yields, systemic inflammation, and regional paracellular permeability of the gastrointestinal tract (GIT) before and after exposure to an intramammary (IMM) challenge of LPS or no infusion. Lactating Holstein cows (n = 34 at 57 ± 4 DIM) with a SCC <250,000 cells/mL were used, including 14 that were ruminally cannulated. Cows were fed either 28 g/d of a probiotic-postbiotic blend (PB; Dairyman's Edge PRO, Papillon Agricultural Company) or no PB (NP) for 21 d before obtaining 5 d of baseline measurements. On d 27, cows received a mammary treatment (MTrt) of either 200 µg of Escherichia coli O111:B4 LPS (IML) using sterile PBS as a carrier into both rear quarters or no infusion (CON; PB-IML, n = 8; PB-CON, n = 9; NP-IML, n = 8; NP-CON, n = 9). Milk and milk component yields and DMI were not affected by PB before the MTrt. The IML increased rectal temperature by 2.8°C 6 h after the MTrt application and tended to be 0.3°C lower for PB than NP at 12 h. Milk SCS was 11 units greater at 12 h for IML versus CON and remained 1 unit greater on d 12. Relative to CON, IML reduced DMI by 28%, 11%, and 10%, and milk yield by 44%, 22%, and 10% on d 1 to 3 after the MTrt application, respectively. Dry matter intake recovered after d 4, whereas milk yield was not different on d 5 and 6 but was 6% lower for IML than CON on d 7 and 8. Milk fat yield was reduced for IML from d 1 to 13 when compared with CON. The PB reduced ruminal pH by 0.11 units, increased total short-chain fatty acid concentrations by 6% compared with NP, and stabilized the proportions of propionate and acetate following MTrt application. Plasma haptoglobin (Hp) and serum amyloid A (SAA) were greatest on d 2 for IML (562- and 16-fold greater than CON, respectively). On d 7 and 12, Hp was 37- and 6-fold greater for IML versus CON, respectively. Serum amyloid A was reduced by 42% for PB versus NP. On d 1 after the MTrt application, plasma Cr and Co area under the curve (AUC) were 24% and 28% lower for IML than CON, respectively. On d 6, Co AUC was 33% lower for IML than CON but the Cr AUC did not differ on d 6 or 11, and Co AUC did not differ on d 11. In conclusion, the IML infusion induced local and systemic inflammation resulting in reduced milk and milk fat yields that persisted beyond the decline in DMI. The PB did not improve recovery of DMI or milk yield but altered ruminal fermentation, reduced SAA, and tended to accelerate recovery of normothermia. Total GIT and postruminal paracellular permeability may transiently decrease in response to mammary and systemic inflammation, at least based on the Cr and Co AUC in plasma. These findings highlight the limited understanding whereby inflammation in the mammary gland, and potentially other non-GIT organs, influence paracellular permeability of the GIT in ruminants.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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