The use of modern metabolomics and proteomics to address the health challenges facing the Canadian cattle industry
Bibliographic record
Abstract
Naturally cattle only consumed grass, hay and other forage crops but modern cattle industry has started shifting them from a natural grazing diet to a more balanced grain-rich diet. However, feeding dairy cows grain rich diet is associated with a rapid release of large amounts of SCFA that have been linked to acute and sub-acute rumen acidosis and related metabolic diseases. However, one disease in particular had more profound impact than all of other disaeses-- bovine spongiform encephalopathy (BSE). When BSE was discovered in Alberta in 2003 it nearly wiped out Canada’s beef export industry. The central objective of my thesis is to address: 1) Ruminal acidosis and acidosis-related metabolic disorders, 2) BSE, commonly known as mad mcow disease. More specifically, I tested the hypothesis that modern cattle feeding practices (i.e. grain rich diets) significantly changed the rumen environment, its chemical composition and is responsible for all of these conditions. Metabolomics is such a powerful approach for studying the chemical changes in biological systems. To test these hypotheses, I chose to use modern metabolomics techniques including NMR, GC-MS and DFI-MS to characterize the ruminal fluid of dairy cattle fed with different diets. From these experiments I determined that grain-rich diets led to ruminal acidosis along with unusually high levels of ruminal LPS. Based on the association of high-grain diets with various metabolic diseases, this suggests that feeding practices lower the ruminal pH and alter the chemical content of the ruminal fluid, thereby leading to elevated levels of LPS which, in turn, lead to greater risk for developing these diseases. LPS induces the conversion of helical native prion proteins into protease-resistant, beta-sheet rich proteins similar to that of infectious prions. This suggests that elevated levels of LPS in the rumen from grain-rich diets may also play a role in the induction of BSE.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".