Experimental dog model for assessment of fasting and postprandial fatty acid metabolism: pitfalls and feasibility
Bibliographic record
Abstract
The dog is a widely-used model for conducting metabolic studies. This is mainly due to its large size and its physiology which is relatively similar to that of humans. Here, we attempted to optimize a postprandial metabolic study protocol used in dogs. Following acclimatization, female mongrel dogs underwent 9 h profiling for time-course baseline plasma data on triglyceride, adrenocorticotropic hormone (ACTH) and cortisol levels. One week later, carotid and jugular catheters were surgically inserted for sampling and infusions. Initial post-operative care, based on the literature (Protocol 1), consisted of analgesia (buprenorphine every 8-12 h and 2-3 doses/day of acepromazine), restriction by Pavlov harness within cages, and a two- to three-day recovery period. Throughout the experiment, dogs received a lipid tracer diluted in 5% bovine serum albumin (BSA). Compared with baseline, animals vomited (n = 6/6) and exhibited high ACTH + cortisol levels (stress biomarkers), resulting in blunted triglyceride peak levels. To avoid these undesirable effects, post-operative care was modified (Protocol 2) as follows: animals (n = 19) were given a single dose of buprenorphine and no acepromazine, were unrestrained and free to move within cages, the recovery period was extended to seven days, and the lipid tracer was diluted in 0.002% versus 5% BSA. Using this modified protocol, postprandial plasma-triglyceride and ACTH/cortisol patterns were similar to baseline values. Controlling for stressors, as well as for factors which may alter proper digestion, is critical for all postprandial metabolic studies. Our results show that an optimized postprandial metabolic protocol used in dogs reduces experimental variability, while improving animal care and comfort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".