Visual Observation of Abdominal Adhesion Progression Based on an Optimized Mouse Model of Postoperative Abdominal Adhesions.
J Invest Surg
; 36(1): 2225104, 2023 Dec.
Article
en En
| MEDLINE
| ID: mdl-37357336
Background: There is no clear description of the evolution of the progression of abdominal adhesions over time.Method: The optimized model was selected using different adhesion scoring systems. Then, this model was used to observe the progression of abdominal adhesions. Visualized observation of abdominal adhesion evolution was performed by laparoscopy and computed tomography. The inflammatory cell infiltration and collagen fibers in adhesion tissues at different times were evaluated by hematoxylin-eosin and picrosirius red staining. RNA sequencing was used to predict potential key targets of abdominal adhesions at different times.Results: The abdominal adhesion model showed the highest reproducibility when it was established using a circular tool and an electric brush. Based on this model, we found that the inflammatory response was activated early in the process of adhesion formation, peaking on day 3 and then gradually decreasing until stabilization on day 7. Collagen and fibronectin formed on day 1 and gradually increased until remaining stable on day 7. In addition, the characteristic changes in the adhesion zone from initial congestion, edema and fragile tissue to later dense and stable tissue could be vividly observed in live mice by laparoscopy and artificial pneumoperitoneum CT. The RNA sequencing results revealed that Hck on day 1, Ndufs3 and Ndufs8 on day 3 and Aif1 on day 7 might play key roles in abdominal adhesion formation.Conclusion: The construction of a standard process for describing the evolution of abdominal adhesions based on an optimized mouse model will help to facilitate subsequent adhesion-related studies.
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Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Laparoscopía
Tipo de estudio:
Etiology_studies
/
Prognostic_studies
Límite:
Animals
Idioma:
En
Revista:
J Invest Surg
Año:
2023
Tipo del documento:
Article