Clinical significance of serum miR-101-3p expression in patients with neonatal sepsis

MiRNAs as biomarkers for diagnosis of neonatal sepsis: a systematic review and meta-analysis

BACKGROUND

The relationship between circulating miRNAs and neonatal sepsis and the mechanism of action are still unclear at this time. Therefore, the potential diagnostic role of miRNAs in neonatal sepsis (NS) was studied through meta-analysis.

METHOD

Web of Science, Cochrane Library, PubMed, and Embase are retrieved, supplemented by manual search, and the search was conducted to find related studies without time limit until May 2022.The quality of the literature was assessed via QUADAS criteria and meta-analyzed via Stata 11.0 software, including the assessment of specificity, sensitivity, likelihood ratio and diagnostic odds ratio. Then, sensitivity analysis and heterogeneity testing were conducted, and finally, the summary receiver operating characteristics (SROC) curve was drawn.

RESULT

This study included 14 articles, including 20 miRNAs and 1597 newborns(control group: 727 and case group: 870). Among them, one article was of low quality, three articles were of high quality, and the rest were of medium quality. According to the results of random effects model analysis, the pooled specificity and sensitivity of miRNA for the diagnosis of NS were 0.83 (95%CI: 0.79-0.87) and 0.76 (95%CI: 0.72-0.80), respectively. And negative likelihood ratio, positive likelihood ratio, and diagnostic odds ratio were 0.29 (95%CI: 0.24-0.34), 4.51 (95%CI: 3.52-5.78), and 15.81 (95%CI: 10.71-23.35), respectively. The area under the SROC curve was 0.86, and there was no evidence publication bias detected in the funnel plot.

CONCLUSION

Circulating miRNAs may be very useful in the development of early diagnostic strategies for neonatal sepsis.

DUSP1 interacts with and dephosphorylates VCP to improve mitochondrial quality control against endotoxemia-induced myocardial dysfunction

Dual specificity phosphatase 1 (DUSP1) and valosin-containing protein (VCP) have both been reported to regulate mitochondrial homeostasis. However, their impact on mitochondrial quality control (MQC) and myocardial function during LPS-induced endotoxemia remains unclear. We addressed this issue by modeling LPS-induced endotoxemia in DUSP1 transgenic (DUSP1TG) mice and in cultured DUSP1-overexpressing HL-1 cardiomyocytes. Accompanying characteristic structural and functional deficits, cardiac DUSP1 expression was significantly downregulated following endotoxemia induction in wild type mice. In contrast, markedly reduced myocardial inflammation, cardiomyocyte apoptosis, cardiac structural disorder, cardiac injury marker levels, and normalized systolic/diastolic function were observed in DUSP1TG mice. Furthermore, DUSP1 overexpression in HL-1 cells significantly attenuated LPS-mediated mitochondrial dysfunction by preserving MQC, as indicated by normalized mitochondrial dynamics, improved mitophagy, enhanced biogenesis, and attenuated mitochondrial unfolded protein response. Molecular assays showed that VCP was a substrate of DUSP1 and the interaction between DUSP1 and VCP primarily occurred on the mitochondria. Mechanistically, DUSP1 phosphatase domain promoted the physiological DUSP1/VCP interaction which prevented LPS-mediated VCP Ser784 phosphorylation. Accordingly, transfection with a phosphomimetic VCP mutant abolished the protective actions of DUSP1 on MQC and aggravated inflammation, apoptosis, and contractility/relaxation capacity in HL-1 cardiomyocytes. These findings support the involvement of the novel DUSP1/VCP/MQC pathway in the pathogenesis of endotoxemia-caused myocardial dysfunction.

Circulating microRNA signatures associated with disease severity and outcome in COVID-19 patients

Background

SARS-CoV-2 induces a spectrum of clinical conditions ranging from asymptomatic infection to life threatening severe disease. Host microRNAs have been involved in the cytokine storm driven by SARS-CoV-2 infection and proposed as candidate biomarkers for COVID-19.

Methods

To discover signatures of circulating miRNAs associated with COVID-19, disease severity and mortality, small RNA-sequencing was performed on serum samples collected from 89 COVID-19 patients (34 severe, 29 moderate, 26 mild) at hospital admission and from 45 healthy controls (HC). To search for possible sources of miRNAs, investigation of differentially expressed (DE) miRNAs in relevant human cell types in vitro.

Results

COVID-19 patients showed upregulation of miRNAs associated with lung disease, vascular damage and inflammation and downregulation of miRNAs that inhibit pro-inflammatory cytokines and chemokines, angiogenesis, and stress response. Compared with mild/moderate disease, patients with severe COVID-19 had a miRNA signature indicating a profound impairment of innate and adaptive immune responses, inflammation, lung fibrosis and heart failure. A subset of the DE miRNAs predicted mortality. In particular, a combination of high serum miR-22-3p and miR-21-5p, which target antiviral response genes, and low miR-224-5p and miR-155-5p, targeting pro-inflammatory factors, discriminated severe from mild/moderate COVID-19 (AUROC 0.88, 95% CI 0.80-0.95, p<0.0001), while high leukocyte count and low levels of miR-1-3p, miR-23b-3p, miR-141-3p, miR-155-5p and miR-4433b-5p predicted mortality with high sensitivity and specificity (AUROC 0.95, 95% CI 0.89-1.00, p<0.0001). In vitro experiments showed that some of the DE miRNAs were modulated directly by SARS-CoV-2 infection in permissive lung epithelial cells.

Conclusions

We discovered circulating miRNAs associated with COVID-19 severity and mortality. The identified DE miRNAs provided clues on COVID-19 pathogenesis, highlighting signatures of impaired interferon and antiviral responses, inflammation, organ damage and cardiovascular failure as associated with severe disease and death.

Modes of action and diagnostic value of miRNAs in sepsis

Sepsis is a clinical syndrome defined as a dysregulated host response to infection resulting in life-threatening organ dysfunction. Sepsis is a major public health concern associated with one in five deaths worldwide. Sepsis is characterized by unbalanced inflammation and profound and sustained immunosuppression, increasing patient susceptibility to secondary infections and mortality. microRNAs (miRNAs) play a central role in the control of many biological processes, and deregulation of their expression has been linked to the development of oncological, cardiovascular, neurodegenerative and metabolic diseases. In this review, we discuss the role of miRNAs in sepsis pathophysiology. Overall, miRNAs are seen as promising biomarkers, and it has been proposed to develop miRNA-based therapies for sepsis. Yet, the picture is not so straightforward because of the versatile and dynamic features of miRNAs. Clearly, more research is needed to clarify the expression and role of miRNAs in sepsis, and to promote the use of miRNAs for sepsis management.

Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury

This study was aimed at analyzing the diagnostic value of convolutional neural network models on account of deep learning for severe sepsis complicated with acute kidney injury and providing an effective theoretical reference for the clinical use of ultrasonic image diagnoses. 50 patients with severe sepsis complicated with acute kidney injury and 50 healthy volunteers were selected in this study. They all underwent ultrasound scans. Different deep learning convolutional neural network models dense convolutional network (DenseNet121), Google inception net (GoogLeNet), and Microsoft's residual network (ResNet) were used for training and diagnoses. Then, the diagnostic results were compared with professional image physicians' artificial diagnoses. The results showed that accuracy and sensitivity of the three deep learning algorithms were significantly higher than professional image physicians' artificial diagnoses. Besides, the error rates of the three algorithm models for severe sepsis complicated with acute kidney injury were significantly lower than professional physicians' artificial diagnoses. The areas under curves (AUCs) of the three algorithms were significantly higher than AUCs of doctors' diagnosis results. The loss function parameters of DenseNet121 and GoogLeNet were significantly lower than that of ResNet, with the statistically significant difference (P < 0.05). There was no significant difference in training time of ResNet, GoogLeNet, and DenseNet121 algorithms under deep learning, as the convergence was reached after 700 times, 700 times, and 650 times, respectively (P > 0.05). In conclusion, the value of the three algorithms on account of deep learning in the diagnoses of severe sepsis complicated with acute kidney injury was higher than professional physicians' artificial judgments and had great clinical value for the diagnoses and treatments of the disease.