Serum concentrations of brain-derived neurotrophic factor as a potential biomarker of swine welfare.
Bibliographic record
Abstract
Objective: Describe concentrations of brain-derived neurotrophic factor (BDNF) detectable in piglet sera before and after road transport, and evaluate the correlation of serum BDNF with other physiological parameters used to assess swine welfare. Animals: Commercial crosses of piglets that underwent weaning and transport at approximately 3 wk of age. Procedure: Sixteen piglets were randomly selected from a larger study for complete blood counts, serum biochemistry testing, cortisol assays, and BDNF assays. Samples were collected 1 d before transport and immediately after transport (> 30 h) under commercial conditions. We assessed the change in serum BDNF concentration; and the correlations between serum BDNF and serum cortisol, neutrophil to lymphocyte ratios (N:L), glucose, and hematological indicators of muscle fatigue. Results: < 0.05) and changed inversely compared to cortisol and N:L. Consistent correlations between BDNF and other physiological parameters were not observed. High inter-pig variation in serum BDNF was present at both sample times. Conclusions: Serum BDNF may be used as an additional indicator of swine welfare. Further research characterizing piglet BDNF concentrations in response to conditions promoting positive or negative affective states would be valuable. Clinical relevance: This communication discusses common hematological parameters used to quantify changes in pig welfare and introduces BDNF, which is a parameter of interest in human cognitive functioning research that may be useful for evaluating the effect of exposure to beneficial or aversive stimuli in animals. The implications of variation in sample collection, handling, and storage procedures for BDNF detection are highlighted.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".