Threonine utilization in the small intestine of the pig
Bibliographic record
Abstract
Since the intestine is one of the most metabolically active tissues in the body, a novel technique was developed to determine if threonine incorporation into protein in the small intestine of the pig varies as threonine levels are varied. We used the gut loop model and intraluminal flooding dose technique in anesthetized pigs to observe how varying luminal availability of threonine affects the amount of threonine that is incorporated into total protein and mucin in small intestinal mucosa. Three loops per pig (n=5) were isolated and a complete amino acid mixture containing 0, 75 or 200% of the threonine requirement of the gut was continuously perfused for 1.5 hours. Immediately following, an identical amino acid mixture containing a 3H‐phenylalanine flooding dose was continuously circulated for an additional 0.5 hours. Following the perfusion, the tissue was removed and mucosa analyzed for threonine incorporation. Preliminary data from three pigs suggest that threonine incorporation rate was lower in the presence of deficient (0%: 55 ± 24 dpm/umol/min) and excess (200%: 59 ± 22 dpm/umol/min) luminal threonine compared to the adequate level (75%: 70 ± 23 dpm/umol/min). Threonine incorporation rate into mucin will also be presented. This model and technique can be used as a novel approach to measure the intestinal requirement for threonine. Future experiments will use this method to investigate the impact of gut stress on intestinal amino acid. (Supported by NSERC).
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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 | 0.000 |
| 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) | 0.001 | 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".