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Electrocardiogram abnormalities and higher body mass index as clinically applicable factors for predicting poor outcome in patients with coronavirus disease 2019

Zhidan Sun, Yan Hou, Zheng Zhang, Benzhi Cai, Jinliang Li

2022, 2(4): 251-256. doi: 10.2478/fzm-2022-0032

Keywords: electrocardiogram abnormalities, overweight, coronavirus disease 2019

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Background Patients with coronavirus disease 2019 (COVID-19) have high resource utilization. Identifying the causes of severe COVID-19 is helpful for early intervention to reduce the consumption of medical resources. Methods We included 103 patients with COVID-19 in this single-center observational study. To evaluate the incidence, predictors, and effects of COVID-19, we analyzed demographic information, laboratory results, comorbidities, and vital signs as factors for association with severe COVID-19. Results The incidence of severe COVID-19 was 16.5% and the percent poor outcome (including mortality, entering in ICU or transferred to a superior hospital) was 6.8%. The majority of severe COVID-19 patients had abnormal electrocardiogram (ECG) (82.35%), hypertension (76.47%) and other cardiac diseases (58.82%). Multivariate logistic regression was used to determine the predictors of severe illness. Abnormal body mass index (BMI) and ECG (P < 0.05) were independent predictors of severe COVID-19. ECG abnormality was associated with increased odds of poor outcome (area under the receiver operating characteristic curves [AUC], 0.793; P = 0.010) and severe COVID-19 (AUC, 0.807; P < 0.0001). Overweight was also associated with increased odds of poor outcome (AUC, 0.728; P = 0.045) and severe illness COVID-19 (AUC, 0.816; P < 0.0001). Conclusion Overweight and electrophysiological disorders on admission are important predictors of prognosis of patients with COVID-19.

Altered expression profile of long non-coding RNAs during heart aging in mice

Xiuxiu Wang, Bingjie Hua, Meixi Yu, Shenzhen Liu, Wenya Ma, Fengzhi Ding, Qi Huang, Lai Zhang, Chongwei Bi, Ye Yuan, Mengyu Jin, Tianyi Liu, Ying Yu, Benzhi Cai, Baofeng Yang

2022, 2(2): 109-118. doi: 10.2478/fzm-2022-0015

Keywords: heart aging, long noncoding RNAs, gene microarray, expression profile, cold stress, cardiovascular diseases

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Objective Long noncoding RNAs (lncRNAs) play an important role in regulating the occurrence and development of cardiovascular diseases. However, the role of lncRNAs in heart aging remains poorly understood. The objective of this study was to identify differentially expressed lncRNAs in the heart of aging mice and elucidate the relevant regulatory pathways of cardiac aging. Materials and methods Echocardiography was used to detect the cardiac function of 18-months (aged) and 3-months (young) old C57BL/6 mice. Microarray analysis was performed to unravel the expression profiles of lncRNAs and mRNAs, and qRT-PCR to verify the highly dysregulated lncRNAs. Results Our results demonstrated that the heart function in aged mice was impaired relative to young ones. Microarray results showed that 155 lncRNAs were upregulated and 37 were downregulated, and 170 mRNAs were significantly upregulated and 44 were remarkably downregulated in aging hearts. Gene ontology analysis indicated that differentially expressed genes are mainly related to immune function, cell proliferation, copper ion response, and cellular cation homeostasis. KEGG pathway analysis showed that the differentially expressed mRNAs are related to cytokine-cytokine receptor interaction, inflammatory mediator regulation of TRP channels, and the NF-kappa B signaling pathway. Conclusion These results imply that the differentially expressed lncRNAs may regulate the development of heart aging. This study provides a new perspective on the potential effects and mechanisms of lncRNAs in heart aging.

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