The clinical significance of body mass index in the early evaluation of acute biliary pancreatitis

Zheng, Zhixue; Bi, Jing; Cai, Xuan; Liu, Ya Qi

doi:10.1016/j.heliyon.2022.e12003

Cited by 3 publications

(2 citation statements)

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“…As an inflammatory pancreatic disorder, AP is one of the more common acute abdominal diseases ( Zheng et al, 2022 ), characterized by acute abdominal pain and increased concentrations of serum amylase and lipase. The pathological response of AP is cell death and inflammation ( Zhan et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

Mo,

Wu,

Luo

et al. 2024

Front. Genet.

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Background: Chemokines and NETosis are significant contributors to the inflammatory response, yet there still needs to be a more comprehensive understanding regarding the specific molecular characteristics and interactions of NETosis and chemokines in the context of acute pancreatitis (AP) and severe AP (SAP).Methods: To address this gap, the mRNA expression profile dataset GSE194331 was utilized for analysis, comprising 87 AP samples (77 non-SAP and 10 SAP) and 32 healthy control samples. Enrichment analyses were conducted for differentially expressed chemokine-related genes (DECRGs) and NETosis-related genes (DENRGs). Three machine-learning algorithms were used for the identification of signature genes, which were subsequently utilized in the development and validation of nomogram diagnostic models for the prediction of AP and SAP. Furthermore, single-gene Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed. Lastly, an interaction network for the identified signature genes was constructed.Results: We identified 12 DECRGs and 7 DENRGs, and enrichment analyses indicated they were primarily enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and T cell receptor signaling pathway. Moreover, these machine learning algorithms finally recognized three signature genes (S100A8, AIF1, and IL18). Utilizing the identified signature genes, we developed nomogram models with high predictive accuracy for AP and differentiation of SAP from non-SAP, as demonstrated by area under the curve (AUC) values of 0.968 (95% CI 0.937–0.990) and 0.862 (95% CI 0.742–0.955), respectively, in receiver operating characteristic (ROC) curve analysis. Subsequent single-gene GESA and GSVA indicated a significant positive correlation between these signature genes and the proteasome complex. At the same time, a negative association was observed with the Th1 and Th2 cell differentiation signaling pathways.Conclusion: We have identified three genes (S100A8, AIF1, and IL18) related to chemokines and NETosis, and have developed accurate diagnostic models that might provide a novel method for diagnosing AP and differentiating between severe and non-severe cases.

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Section: Discussionmentioning

confidence: 99%

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

Mo,

Wu,

Luo

et al. 2024

Front. Genet.

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“…For example, O’Connell et al 41 indicated that RDW could be used to identify patients at risk of severe disease condition on the presentation to the emergency department. Zheng et al 42 concluded that white blood cell count can be employed to construct tool to predict the severity of AP. Tokoro et al 43 suggested that platelets play a pivotal role in the mechanism of disease progression in AP.…”

Section: Discussionmentioning

confidence: 99%

Risk Factors and Prediction of 28-Day-All Cause Mortality Among Critically Ill Patients with Acute Pancreatitis Using Machine Learning Techniques: A Retrospective Analysis of Multi-Institutions

Cai,

Wu,

Chen

et al. 2024

JIR

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The Research Status of TyG Index and Obesity Evaluation Index in the Evaluation of the Condition and Prognosis of Acute Pancreatitis

何

2024

ACM

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The clinical significance of body mass index in the early evaluation of acute biliary pancreatitis

Cited by 3 publications

References 50 publications

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

Risk Factors and Prediction of 28-Day-All Cause Mortality Among Critically Ill Patients with Acute Pancreatitis Using Machine Learning Techniques: A Retrospective Analysis of Multi-Institutions

The Research Status of TyG Index and Obesity Evaluation Index in the Evaluation of the Condition and Prognosis of Acute Pancreatitis

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