Immune and metabolic effects of rumen-protected methionine during a heat stress challenge in lactating Holstein cows
Bibliographic record
Abstract
Multiparous, lactating Holstein cows (n = 32) were randomly assigned to one of two dietary treatments [TMR with rumen-protected Met (RPM) or TMR without RPM (CON)], and within each dietary treatment group cows were randomly assigned to one of two environmental treatment groups in a split-plot crossover design. In phase 1 (9 d), all cows were fed ad libitum and in thermoneutral conditions (TN). In phase 2 (9 d), group 1 (n = 16) was exposed to a heat stress (HS) challenge (HSC). Group 2 cows (n = 16) were pair-fed (PFTN) to HSC counterparts and remained in TN. After a 21-d washout period, the study was repeated (period 2) and the environmental treatments were inverted relative to treatments from phase 2 of period 1, while dietary treatments remained the same for each cow. During phase 1, cows in RPM had greater plasma Met concentration compared with cows in CON (59 and 30 µM, respectively; P < 0.001). Cows in PFTN had a greater decrease (P < 0.05) in plasma insulin than cows in HSC at 4 h (-2.7 µIU/mL vs. -0.7 µIU/mL) and 8 h (-7.7 µIU/mL vs. -0.4 µIU/mL) during phase 2. Compared with cows in PFTN, cows in HSC had an increase (P < 0.05) in plasma serum amyloid A (-59 µg/mL vs. +58 µg/mL), serum haptoglobin (-3 µg/mL vs. +33 µg/mL), plasma lipopolysaccharide binding protein (-0.27 and +0.11 µg/mL), and plasma interleukin-1β (-1.9 and +3.9 pg/mL) during phase 2. In conclusion, HSC elicited immunometabolic alterations; however, there were limited effects of RPM on cows in HSC.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".