Combination of Sterile Injury and Microbial Contamination to Model Post-surgical Peritoneal Adhesions in Mice
Bibliographic record
Abstract
Abdominal surgeries are frequently associated with the development of post-surgical adhesions. These are irreversible fibrotic scar bands that appear between abdominal organs and the abdominal wall. Patients suffering from adhesions are at risk of severe complications, such as small bowel obstruction, chronic pelvic pain, or infertility. To date, no cure exists, and the understanding of underlying molecular mechanisms of adhesion formation is incomplete. The current paradigm largely relies on sterile injury mouse models. However, abdominal surgeries in human patients are rarely completely sterile procedures. Here, we describe a modular surgical procedure for simultaneous or separate induction of sterile injury and microbial contamination. Combined, these insults synergistically lead to adhesion formation in the mouse peritoneal cavity. Surgical trauma is confined to a localized sterile injury of the peritoneum. Microbial contamination of the peritoneal cavity is induced by a limited perforation of the microbe-rich large intestine or by injection of fecal content. The presented protocol extends previous injury-based adhesion models by an additional insult through microbial contamination, which may more adequately model the clinical context of abdominal surgery. Graphical abstract.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".