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1.
Journal of Central South University(Medical Sciences) ; (12): 213-220, 2023.
Artículo en Inglés | WPRIM | ID: wpr-971388

RESUMEN

OBJECTIVES@#Abdominal aortic aneurysm is a pathological condition in which the abdominal aorta is dilated beyond 3.0 cm. The surgical options include open surgical repair (OSR) and endovascular aneurysm repair (EVAR). Prediction of acute kidney injury (AKI) after OSR is helpful for decision-making during the postoperative phase. To find a more efficient method for making a prediction, this study aims to perform tests on the efficacy of different machine learning models.@*METHODS@#Perioperative data of 80 OSR patients were retrospectively collected from January 2009 to December 2021 at Xiangya Hospital, Central South University. The vascular surgeon performed the surgical operation. Four commonly used machine learning classification models (logistic regression, linear kernel support vector machine, Gaussian kernel support vector machine, and random forest) were chosen to predict AKI. The efficacy of the models was validated by five-fold cross-validation.@*RESULTS@#AKI was identified in 33 patients. Five-fold cross-validation showed that among the 4 classification models, random forest was the most precise model for predicting AKI, with an area under the curve of 0.90±0.12.@*CONCLUSIONS@#Machine learning models can precisely predict AKI during early stages after surgery, which allows vascular surgeons to address complications earlier and may help improve the clinical outcomes of OSR.


Asunto(s)
Humanos , Aneurisma de la Aorta Abdominal/complicaciones , Procedimientos Endovasculares/métodos , Estudios Retrospectivos , Implantación de Prótesis Vascular/efectos adversos , Lesión Renal Aguda/etiología , Aprendizaje Automático , Resultado del Tratamiento , Complicaciones Posoperatorias/etiología , Factores de Riesgo
2.
Chinese Journal of Tissue Engineering Research ; (53): 258-261, 2010.
Artículo en Chino | WPRIM | ID: wpr-403474

RESUMEN

BACKGROUND: Previous studies demonstrated that proliferation of cancer cells can be inhibited via RNA interference on the expression of vascular endothelial growth factor (VEGF). However, few studies report RNA interference on the expression of VEGF in gallbladder carcinoma, OBJECTIVE: To design and screen shRNA targeting VEGF, and to observe the effect of small interfering RNA targeting on proliferation of gallbladder cancer cells. METHODS: The VEGF-shRNA fragment was synthetized and connected with pCYU6/GFP/Neo-shRNA plasmid vector, shRNA was transfected into gallbladder cancer cells. The gallbladder carcinoma models of nude mice were prepared and randomly divided into blank control, negative control and experimental groups, With 6 animals in each group. ShRNA was injected into tumor. Cell growth was detected by fluorescence microscope MTT. The RNA interference efficiency was examined by fluorescent quantitative RT-PCR. Changes of tumor volume were also observed. RESULTS AND CONCLUSION: Gallbladder cancer cells ware shrunk with round shapes and a part of cells were dead after RNA interference on VEGF. shRNA-VEGF1 and shRNA-VEGF2 could signiticently inhibit mRNA gene expression of VEGF, the inhibition ratio was 86% and 82%, respectively. The tumor volume of the experimental group was smaller than the other groups, with slowly growth (P < 0.05). No obvious changes were found in the blank control and negative control groups. The constructed hVEGF-shRNA vector markedly decreases VEGF gene expression and inhibits cellular proliferation, eventually, to treat gallbladder cancer.

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