Integrated metabolome and immunity analysis of immune-physiological responses in dairy cows under heat stress condition
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the metabolic changes and immune responses in Jersey dairy cows under heat stress (HS). The focus was on understanding HS-induced alterations in metabolism and immunity compared to HS-free cows. METHODS: Proton nuclear magnetic resonance spectroscopy-based metabolomics was performed on rumen fluid, serum, and milk samples which were collected through single sampling from HS-free (n = 9) and HS-exposed (n = 8) Jersey cows. Immune cell proportions and functions in peripheral blood mononuclear cells (PBMC) were analyzed to identify heat-sensitive immunological changes. Correlation analysis was conducted to link immune indicators with serum metabolites. RESULTS: Metabolomics revealed potential HS biomarkers in biofluids: nicotinate and phenylacetate in rumen fluid; isopropanol in serum; and glycine and trehalose in milk. HS increased B cell and CD4+ T cell populations, as well as Th17 cells and IL-17A expression in PBMCs. Twenty-three metabolites correlated strongly with five immune indicators, with nine metabolites exhibiting a positive correlation and fourteen a negative correlation. CONCLUSION: HS alters the both metabolism and immune cell function in Jersey dairy cows. These findings provide key information for the development of diagnostic methods for HS and nutritional intervention strategies to mitigate HS for dairy cows.
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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) | 0.000 | 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 | 0.000 |
| 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".