Expression and influence of pentraxin-3, HbAlc and ApoA1/ApoB in serum of patients with acute myocardial infarction combined with diabetes mellitus type 2

Zhu, Yi; Wang, Xiaofang; Wang, Hongguang; Zhang, Fenglei

doi:10.3892/etm.2018.5930

Cited by 3 publications

(1 citation statement)

References 22 publications

(22 reference statements)

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“…In contrast, apolipoprotein-B is the primary apolipoprotein in low-density lipoprotein. Low-density lipoprotein cholesterol is a significant constituent in atherosclerotic plaque, thus making it the main risk factor of atherosclerosis [ 7 ].…”

Section: Discussionmentioning

confidence: 99%

The Significance of Apolipoprotein-A in the Long-Term Death of Patients with STEMI

Lin

Chen

Dai

et al. 2022

Journal of Healthcare Engineering

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Objective. To analyze apolipoprotein-A for its predictive value for long-term death in individuals suffering from acute ST-segment elevation myocardial infarction following percutaneous coronary intervention. Methods. We selected patients suffering from acute ST-segment elevation myocardial infarction who underwent emergency PCI at the Affiliated Hospital of Putian University from January 2017 to August 2019. The patients were divided into a high-Apo-A group and low-Apo-A group, and we observed all-cause deaths of patients in the 2 groups within 2 years. Results. The ROC curve analysis indicated the best critical value for predicting 2-year mortality as 0.8150 (area under the curve was 0.626, sensitivity 75.1%, and specificity 51.9%). There was no statistical difference among the two groups in gender, age, lesion vessel, and comorbidities. The two groups had statistically significant differences in apolipoprotein-B/A, high-density lipoprotein, apolipoprotein-A, and hypersensitivity C-reactive protein. Correlation analysis showed a significant negative correlation between apolipoprotein-A and hypersensitive C-reactive protein. The results of the 24-month analysis indicated the incidence of all-cause mortality as higher in the low-Apo-A group, and Kaplan–Meier survival analysis showed the same trend. Conclusion. Apolipoprotein-A can predict the potential for long-term mortality among individuals having acute ST-segment elevation myocardial infarction.

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Section: Discussionmentioning

confidence: 99%

The Significance of Apolipoprotein-A in the Long-Term Death of Patients with STEMI

Lin

Chen

Dai

et al. 2022

Journal of Healthcare Engineering

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Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

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Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analysis of DEGs were performed. Then, a protein protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst database was used to construct the targeted microRNAs (miRNAs) and transcription factors (TFs) of the hub genes. Finally receiver operating characteristic (ROC) curve analysis was used to validates the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8 and EFHC2 were selected. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

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Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus

Cai,

Zheng,

Wang

et al. 2024

World J Diabetes

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BACKGROUND Among older adults, type 2 diabetes mellitus (T2DM) is widely recognized as one of the most prevalent diseases. Diabetic nephropathy (DN) is a frequent complication of DM, mainly characterized by renal microvascular damage. Early detection, aggressive prevention, and cure of DN are key to improving prognosis. Establishing a diagnostic and predictive model for DN is crucial in auxiliary diagnosis. AIM To investigate the factors that impact T2DM complicated with DN and utilize this information to develop a predictive model. METHODS The clinical data of 210 patients diagnosed with T2DM and admitted to the First People’s Hospital of Wenling between August 2019 and August 2022 were retrospectively analyzed. According to whether the patients had DN, they were divided into the DN group (complicated with DN) and the non-DN group (without DN). Multivariate logistic regression analysis was used to explore factors affecting DN in patients with T2DM. The data were randomly split into a training set (n = 147) and a test set (n = 63) in a 7:3 ratio using a random function. The training set was used to construct the nomogram, decision tree, and random forest models, and the test set was used to evaluate the prediction performance of the model by comparing the sensitivity, specificity, accuracy, recall, precision, and area under the receiver operating characteristic curve. RESULTS Among the 210 patients with T2DM, 74 (35.34%) had DN. The validation dataset showed that the accuracies of the nomogram, decision tree, and random forest models in predicting DN in patients with T2DM were 0.746, 0.714, and 0.730, respectively. The sensitivities were 0.710, 0.710, and 0.806, respectively; the specificities were 0.844, 0.875, and 0.844, respectively; the area under the receiver operating characteristic curve (AUC) of the patients were 0.811, 0.735, and 0.850, respectively. The Delong test results revealed that the AUC values of the decision tree model were lower than those of the random forest and nomogram models (P < 0.05), whereas the difference in AUC values of the random forest and column-line graph models was not statistically significant (P > 0.05). CONCLUSION Among the three prediction models, random forest performs best and can help identify patients with T2DM at high risk of DN.

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Expression and influence of pentraxin-3, HbAlc and ApoA1/ApoB in serum of patients with acute myocardial infarction combined with diabetes mellitus type 2

Cited by 3 publications

References 22 publications

The Significance of Apolipoprotein-A in the Long-Term Death of Patients with STEMI

The Significance of Apolipoprotein-A in the Long-Term Death of Patients with STEMI

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus

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