A Protocol for Roux-en-Y Gastric Bypass in Rats using Linear Staplers
Bibliographic record
Abstract
Roux-en-Y gastric bypass (RYGB) is commonly performed for the treatment of severe obesity and type 2 diabetes. However, the mechanism of weight loss and metabolic changes are not well understood. Multiple factors are thought to play a role, including reduced caloric intake, decreased nutrient absorption, increased satiety, the release of satiety-promoting hormones, shifts in bile acid metabolism, and alterations in the gut microbiota. The rat RYGB model presents an ideal framework to study these mechanisms. Prior work on mouse models have had high mortality rates, ranging from 17 to 52%, limiting their adoption. Rat models demonstrate more physiologic reserve to surgical stimulus and are technically easier to adopt as they allow for the use of surgical staplers. One challenge with surgical staplers, however, is that they often leave a large gastric pouch which is not representative of RYGB in humans. In this protocol, we present a RYGB protocol in rats that result in a small gastric pouch using surgical staplers. Utilizing two stapler fires which remove the forestomach of the rat, we obtain a smaller gastric pouch similar to that following a typical human RYGB. Surgical stapling also results in better hemostasis than sharp division. Additionally, the forestomach of the rat does not contain any glands and its removal should not alter the physiology of RYGB. Weight loss and metabolic changes in the RYGB cohort were significant compared to the sham cohort, with significantly lower glucose tolerance at 14 weeks. Furthermore, this protocol has an excellent survival of 88.9% after RYGB. The skills described in this protocol can be acquired without previous microsurgical experience. Once mastered, this procedure will provide a reproducible tool for studying the mechanisms and effects of RYGB.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".