Paraoxonase activity in the serum of peripartum dairy cows with different placental lactogen concentrations
Bibliographic record
Abstract
The action of the bovine placental lactogen (bPL) hormone on maternal metabolism is still poorly known. Some markers, such as the acute phase protein paraoxonase (PON1), are used as indicators of liver function and help to determine the metabolic condition during the transition period in dairy cows. The aim of this study was to evaluate the activity of paraoxonase (PON1) in the serum of peripartum dairy cows with different levels of bPL. Based on the plasma bPL concentration, 18 cows were divided equally into three groups: LOW ( < 2,68 ng bPL mL-1), MEDIUM (2,68–2,80 ng bPL mL-1), and HIGH ( > 2,80 ng bPL mL-1). The experiment was conducted between 21 days prepartum and 28 days postpartum. Serum samples were collected during the experiment for the determination of bPL concentrations and PON1 activity. The bPL concentration was significantly different between the experimental groups (P ? 0,0001) and the days of serum collection (P ? 0,0001). In the prepartum dairy cows, the PON1 levels were different between the groups (P ? 0,05) and the days of serum collection (P ? 0,05). Cows with high bPL concentration had lower serum PON1 activity (P ? 0,05), while cows with low hormone levels had higher enzyme activity (P ? 0,05). In the postpartum period, there was a significant difference between the days of serum collection (P ? 0,0001) and the interaction between groups and collections (P ? 0,01). The group with high concentrations of bPL had lower levels of PON1 (P ? 0,01), while the group with low bPL maintained higher concentrations of PON1 (P ? 0,01). It was concluded that the cows with higher concentrations of bPL in the prepartum period present a reduction in the serum activity of the PON1 enzyme during the peripartum period.
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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 | 0.001 |
| 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".