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Record W4292510257 · doi:10.21769/bioprotoc.4491

Combination of Sterile Injury and Microbial Contamination to Model Post-surgical Peritoneal Adhesions in Mice

2022· article· en· W4292510257 on OpenAlexaff
J. M. Bayer, Deborah Stroka, Paul Kubes, Daniel Candinas, Joël Zindel

Bibliographic record

VenueBIO-PROTOCOL · 2022
Typearticle
Languageen
FieldMedicine
TopicIntestinal and Peritoneal Adhesions
Canadian institutionsUniversity of Calgary
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsMedicinePeritoneal cavityAdhesionAbdominal cavitySurgeryPerforationPeritoneumAbdominal wallContext (archaeology)Biology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.327
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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