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Record W4401077945 · doi:10.53902/sioaj.2024.02.000506

Novel Therapeutic Approach to Post-Operative Adhesions: Enhancing Resident Repair Cells in the Abdomen

2024· article· en· W4401077945 on OpenAlexfundno aff
Adebola Obayan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicIntestinal and Peritoneal Adhesions
Canadian institutionsnot available
FundersRoyal University Hospital Foundation
KeywordsAbdomenMedicineSurgery

Abstract

fetched live from OpenAlex

Background: This study proposed to identify the possible mechanism of action of novel treatment, alanyl-glutamine (AG) in adhesion prevention. The aim was to outline the natural history of adhesion formation in a randomized animal model and to confirm the effect of peritoneal infiltration of AG on adhesion formation in rats post-laparotomy. The study also challenged the role of macrophage chemotactic protein 1 (MCP-1) on adhesion formation. Method: This study involved open abdominal surgery on 53 Wistar rats. They were assessed for AG's efficacy in preventing adhesion. Rats were randomly assigned to three groups: 1) Open laparotomy (no treatment), 2) Open laparotomy with saline, and 3) Open laparotomy with AG. Tissue explants were analyzed and assessed for fibrosis. Macrophage activity was also evaluated using ED1 and CD68 markers. Results: 6 days after surgery, severe adhesion was evident in the saline and the non-treatment group. This persisted up to day 42, while the treatment group showed minimal evidence of adhesion, with 95%-100% adhesion prevention. MCP-1 did not have a clear role in controlling macrophage infiltration. Conclusion: Our adhesion model showed a better outcome for adhesion prevention in all the rats instilled with AG after laparotomy. While MCP-1 is a marker of inflammation, it does not appear to play a role in preventing intraperitoneal adhesion, nor does it have a clear controlling role in macrophage infiltration.

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)0.001
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 score0.436
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.038
GPT teacher head0.322
Teacher spread0.284 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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