Non-targeted metabolomics of saliva to explore potential biomarkers for gastric ulceration in pigs fed hemp
Bibliographic record
Abstract
Gastric ulceration is a common disease in pig production worldwide and is associated with economic losses as well as animal health and welfare issues. The aim of this study was to explore potential salivary biomarkers for gastric ulceration in pigs. In addition, the aim was to study the effect of hemp on the incidence of gastric ulcers. Approximately 440 growing-finishing pigs in the period from 30 to 110 kg BW were allocated to four different diets: meal feed (Meal); pelleted feed (Pellets); pelleted feed added 4% hempseed cake (Hemp Cake); pelleted feed added 4% hempseed hulls (Hemp Hulls). The day before slaughter, saliva samples from each pig were collected. After slaughter, the stomachs were emptied to assess the consistency of the stomach content and examined for gastric ulceration using an index scale (0-10). Noticeable changes of the gastric mucosa (total index score ≥ 6) were observed in 291 pigs. The odds of having index scores 0-5 relative to index scores 6-8 and 9-10, respectively, were higher (P < 0.001) for pigs fed Meal compared to pigs fed Pellets. The odds of suffering from severe gastric ulcers tended (P = 0.08) to be lower in pigs fed Hemp Hulls compared to pigs fed Pellets. A non-targeted liquid chromatography mass spectrometry based metabolomics analysis was performed on saliva samples to determine any separation between pigs with healthy stomachs and those with gastric ulcers and to examine a possible correlation between gastric ulcer index and potential biomarkers. Partial least-squares discriminant analysis showed a separation between pigs with ulcers and those with healthy stomachs/hyperkeratosis (HK). Metabolites contributing to the separation between groups were identified. Levels of oxylipins deriving from linoleic acid were lower (P < 0.001) in pigs with ulcers compared to healthy/HK pigs. This may indicate a shift in the metabolic pathways towards more pro-inflammatory arachidonic acid-derived eicosanoids, which might reflect an increased inflammatory response. Thus, reduced levels of oxylipins derived from linoleic acid seemed to be associated with active gastric ulcers, and thereby they might function as biomarkers for gastric ulceration in pigs. In addition, supplementation of hempseed hulls had a beneficial effect on severe gastric ulcers, as hempseed hulls changed the consistency of the gastric content by conferring more solidness. However, it was not possible to observe any reliable separation between pigs fed pellets supplemented with hemp products and pigs fed non-supplemented pellets according to the identified salivary metabolites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".