Activities of gastric, pancreatic, and intestinal brush-border membrane enzymes during postnatal development of dogs
Bibliographic record
Abstract
OBJECTIVE: To measure activities of digestive enzymes during postnatal development in dogs. SAMPLE POPULATION: Gastrointestinal tract tissues obtained from 110 Beagles ranging from neonatal to adult dogs. PROCEDURE: Pepsin and lipase activities were measured in gastric contents, and amylase, lipase, trypsin, and chymotrypsin activities were measured in small intestinal contents and pancreatic tissue. Activities of lactase, sucrase, 4 peptidases, and enteropeptidase were assayed in samples of mucosa obtained from 3 regions of the small intestine. RESULTS: Gastric pH was low at all ages. Pepsin was not detected until day 21, and activity increased between day 63 and adulthood. Activities of amylase and lipase in contents of the small intestine and pancreatic tissue were lower during suckling than after weaning. Activities of trypsin and chymotrypsin did not vary among ages for luminal contents, whereas activities associated with pancreatic tissue decreased between birth and adulthood for trypsin but increased for chymotrypsin. Lactase and gamma-glutamyltranspeptidase activities were highest at birth, whereas the activities of sucrase and the 4 peptidases increased after birth. Enteropeptidase was detected only in the proximal region of the small intestine at all ages. CONCLUSIONS AND CLINICAL RELEVANCE: Secretions in the gastrointestinal tract proximal to the duodenum, enzymes in milk, and other digestive mechanisms compensate for low luminal activities of pancreatic enzymes during the perinatal period. Postnatal changes in digestive secretions influence nutrient availability, concentrations of signaling molecules, and activity of antimicrobial compounds that inhibit pathogens. Matching sources of nutrients to digestive abilities will improve the health of dogs during development.
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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 | 0.001 | 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".