Circulating exosomes may identify biomarkers for cows at risk for metabolic dysfunction
Bibliographic record
Abstract
Abstract Disease susceptibility of dairy cows is greatest during the transition from pregnancy to lactation. Circulating exosomes may provide biomarkers to detect at-risk cows to enhance health and productivity. From 490 cows, animals at high- (n = 20) or low-risk (n = 20) of transition-related diseases were identified using plasma non-esterified fatty acid and β-hydroxybutyrate concentrations and liver triacylglyceride concentrations during the two weeks post-calving. We isolated circulating exosomes from plasma of dairy cows at low-risk (LR-EXO) and high-risk (HR-EXO), and analyzed their proteome profiles to determine markers for metabolic dysfunction. We evaluated the effects of these exosomes on eicosanoid pathway expression by bovine endometrial stromal (bCSC) and epithelial (bEEL) cells. HR-EXO had significantly lower yield of circulating exosomes compared with LR-EXO, and unique proteins were identified in HR-EXO and LR-EXO. Exposure to LR-EXO or HR-EXO differentially regulated eicosanoid gene expression and production in bCSC and bEEL cells. In bCSC, LR-EXO exposure increased PGE2 and PGD2 production, whereas HR-EXO exposure increased PTGS2 gene expression. In bEEL, HR-EXO exposure caused a decrease in PGE2, PGF2α, PGD2, PGFM and TXB2 production. The unique presence of serpin A3-7, coiled-coil domain containing 88A and inhibin/activin β A chain in HR-EXO, indicates potential biomarkers for cows at-risk for metabolic diseases. Our results are in line with the health status of the cow indicating a potential diagnostic role for exosomes in enhancing cows’ health and fertility.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".