231 Urinary metabotyping around parturition indicates consistent metabolite signatures that can be used for monitoring and diagnosing of subclinical mastitis in dairy cows.
Bibliographic record
Abstract
The objective of this study was to identify urinary metabolite signatures that can be used for monitoring and diagnosing of subclinical mastitis in Holstein dairy cows. Six multiparous pregnant Holstein cows with subclinical mastitis (SCM) and 20 healthy controls (CON) were selected out of 100 cows in the study. DI/LC-MS/MS-based urinary analyses were conducted on samples collected at -8, -4, SCM diagnosis, and at +4 and +8 wks around the expected day of calving. Data indicated that 14 amino acids and derivatives were found in the urine with Gln and SDMA consistently increased (P<0.005) in the urine of SCM cows. Out of 39 acylcarnitines (ACs) quantified C12:DC, C14, and C18:2, were higher (P<0.05) in SCM at all time points (P<0.05). Seventeen ACs at -8 wks, 10 at -4 wks, 23 at disease wk, 8 at +4 wks, and 12 at +8 wks were higher (P<0.05) in the urine of SCM cows. Most of the alterations regarding lysophosphatidylcholines (LPCs) were found at -8 and -4 wks prepartum with 6 and 4 LPCs species lowered at -8 and -4 wks, respectively (P<0.05). Multiple sphingomyelin (SM) species prior to calving (3 at -8 wks and 8 at -4 wks) and 2 at disease week were lowered in SCM cows (P<0.05). A total of 74 phosphatidylcholines (PCs) were measured in the urine. A total of 12, 12, 10, 9, and 9 species of PC aa and 7, 15, 15, 3, and 3 species of PC ae were altered during weeks -8, -4, Disease wk, +4, + 8 wks around calving. Overall, multiple metabolite species related to amino acids, ACs, LPCs, and PCs metabolism were identified as altered in the urine of periparturient dairy cows. Some of the most important metabolites might be used as biomarkers to screen cows for SCM.
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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 | 0.000 | 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".