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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».