Serum HMGB1 and soluble urokinase plasminogen activator receptor levels aid diagnosis and prognosis prediction of sepsis with acute respiratory distress syndrome

sEH activity is associated with mortality in patients with ARDS: a retrospective cohort study

Aim: We hypothesized plasma sEH activity correlates with mortality in acute respiratory distress syndrome (ARDS) patients.Methods: Adult patients diagnosed with ARDS enrolled between 2017 and 2019 were included in this study.Results: A total of 337 adult patients met our inclusion criteria. Among them, 107 patients died within 28 days. The plasma sEH activity was higher in nonsurvivors relative to survivors. And a receiver operating characteristic curve with the area under the curve of 0.81 (95% confidence interval: 0.74-0.89) for the prediction of 28-day mortality was obtained. Its sensitivity and specificity were 72.5% and 74.6%, respectively.Conclusion: Elevated plasma sEH activity is associated with higher 28-day mortality in adult patients with ARDS.

Navigating the Modern Landscape of Sepsis: Advances in Diagnosis and Treatment

Sepsis poses a significant threat to human health due to its high morbidity and mortality rates worldwide. Traditional diagnostic methods for identifying sepsis or its causative organisms are time-consuming and contribute to a high mortality rate. Biomarkers have been developed to overcome these limitations and are currently used for sepsis diagnosis, prognosis prediction, and treatment response assessment. Over the past few decades, more than 250 biomarkers have been identified, a few of which have been used in clinical decision-making. Consistent with the limitations of diagnosing sepsis, there is currently no specific treatment for sepsis. Currently, the general treatment for sepsis is conservative and includes timely antibiotic use and hemodynamic support. When planning sepsis-specific treatment, it is important to select the most suitable patient, considering the heterogeneous nature of sepsis. This comprehensive review summarizes current and evolving biomarkers and therapeutic approaches for sepsis.

Exploring the ferroptosis-related gene lipocalin 2 as a potential biomarker for sepsis-induced acute respiratory distress syndrome based on machine learning

BACKGROUND

Sepsis is a major cause of mortality in patients, and ARDS is one of the most common outcomes. The pathophysiology of acute respiratory distress syndrome (ARDS) caused by sepsis is significantly impacted by genes related to ferroptosis.

METHODS

In this study, Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) networks, functional enrichment analysis, and machine learning were employed to identify characterized genes and to construct receiver operating characteristic (ROC) curves. Additionally, DNA methylation levels were quantified and single-cell analysis was conducted. To validate the alterations in the expression of Lipocalin-2 (LCN2) and ferroptosis-related proteins in the in vitro model, Western blotting was carried out, and the changes in intracellular ROS and Fe2+ levels were detected.

RESULTS

A combination of eight machine learning algorithms, including RFE, LASSO, RandomForest, SVM-RFE, GBDT, Bagging, XGBoost, and Boruta, were used with a machine learning model to highlight the significance of LCN2 as a key gene in sepsis-induced ARDS. Analysis of immune cell infiltration showed a positive correlation between neutrophils and LCN2. In a cell model induced by LPS, it was found that Ferrostatin-1 (Fer-1), a ferroptosis inhibitor, was able to reverse the expression of LCN2. Knocking down LCN2 in BEAS-2B cells reversed the LPS-induced lipid peroxidation, Fe2+ levels, ACSL4, and GPX4 levels, indicating that LCN2, a ferroptosis-related gene (FRG), plays a crucial role in mediating ferroptosis.

CONCLUSION

Upon establishing an FRG model for individuals with sepsis-induced ARDS, we determined that LCN2 could be a dependable marker for predicting survival in these patients. This finding provides a basis for more accurate ARDS diagnosis and the exploration of innovative treatment options.

Serum high mobility group box 1 as a potential biomarker for the progression of kidney disease in patients with type 2 diabetes

Background

As a damage-associated molecular pattern protein, high mobility group box 1 (HMGB1) is associated with kidney and systemic inflammation. The predictive and therapeutic value of HMGB1 as a biomarker has been confirmed in various diseases. However, its value in diabetic kidney disease (DKD) remains unclear. Therefore, this study aimed to investigate the correlation between serum and urine HMGB1 levels and DKD progression.

Methods

We recruited 196 patients with type 2 diabetes mellitus (T2DM), including 109 with DKD and 87 T2DM patients without DKD. Additionally, 60 healthy participants without T2DM were also recruited as controls. Serum and urine samples were collected for HMGB1 analysis. Simultaneously, tumor necrosis factor receptor superfamily member 1A (TNFR-1) in serum and kidney injury molecule (KIM-1) in urine samples were evaluated for comparison.

Results

Serum and urine HMGB1 levels were significantly higher in patients with DKD than in patients with T2DM and healthy controls. Additionally, serum HMGB1 levels significantly and positively correlated with serum TNFR-1 (R 2 = 0.567, p<0.001) and urine KIM-1 levels (R 2 = 0.440, p<0.001), and urine HMGB1 has a similar correlation. In the population with T2DM, the risk of DKD progression increased with an increase in serum HMGB1 levels. Multivariate logistic regression analysis showed that elevated serum HMGB1 level was an independent risk factor for renal function progression in patients with DKD, and regression analysis did not change in the model corrected for multiple variables. The restricted cubic spline depicted a nonlinear relationship between serum HMGB1 and renal function progression in patients with DKD (p-nonlinear=0.007, p<0.001), and this positive effect remained consistent across subgroups.

Conclusion

Serum HMGB1 was significantly correlated with DKD and disease severity. When the HMGB1 level was ≥27 ng/ml, the risk of renal progression increased sharply, indicating that serum HMGB1 can be used as a potential biomarker for the diagnosis of DKD progression.