Identification of serum metabolites associated with the risk of metritis in transition dairy cows
Bibliographic record
Abstract
In this study, we aimed to identify metabolite signatures that characterize metritis prior to, during, and after the disease incidence. Blood samples were collected from 100 Holstein cows at five time points before and after parturition. Six cows that developed metritis and 20 controls were selected for metabolomics analysis in a nested case-control study. Twenty nine serum metabolites were quantified using gas chromatography–mass spectroscopy. Results showed that similar panels of metabolites differentiated pre-metritic and control cows at 8 and 4 wk prepartum. The top most important metabolites that differentiated the two groups of cows at 8 wk prepartum were oxalate, ornithine, pyroglutamic acid, d-mannose, and glutamic acid, and at 4 wk prepartum were ornithine, pyroglutamic acid, d-mannose, glutamic acid, and phosphoric acid, suggesting their potential use as risk biomarkers for metritis. Area under the curve with values of 1.0 and 0.969 at 8 and 4 wk, respectively, indicated that those panels of metabolites have a very high sensitivity and specificity to be used as risk biomarkers for metritis. Overall, results showed that specific serum metabolite signatures can be used to screen cows for susceptibility to metritis during the dry off period, and to better understand the etiopathobiology of the disease.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".