Identifying the risk factors of ICU-acquired fungal infections: clinical evidence from using machine learning.

Background: Fungal infections are associated with high morbidity and mortality in the intensive care unit (ICU), but their diagnosis is difficult. In this study, machine learning was applied to design and define the predictive model of ICU-acquired fungi (ICU-AF) in the early stage of fungal infections using Random Forest. Objectives: This study aimed to provide evidence for the early warning and management of fungal infections. Methods: We analyzed the data of patients with culture-positive fungi during their admission to seven ICUs of the First Affiliated Hospital of Chongqing Medical University from January 1, 2015, to December 31, 2019. Patients whose first culture was positive for fungi longer than 48 h after ICU admission were included in the ICU-AF cohort. A predictive model of ICU-AF was obtained using the Least Absolute Shrinkage and Selection Operator and machine learning, and the relationship between the features within the model and the disease severity and mortality of patients was analyzed. Finally, the relationships between the ICU-AF model, antifungal therapy and empirical antifungal therapy were analyzed. Results: A total of 1,434 cases were included finally. We used lasso dimensionality reduction for all features and selected six features with importance ≥0.05 in the optimal model, namely, times of arterial catheter, enteral nutrition, corticosteroids, broadspectrum antibiotics, urinary catheter, and invasive mechanical ventilation. The area under the curve of the model for predicting ICU-AF was 0.981 in the test set, with a sensitivity of 0.960 and specificity of 0.990. The times of arterial catheter (p = 0.011, OR = 1.057, 95% CI = 1.053-1.104) and invasive mechanical ventilation (p = 0.007, OR = 1.056, 95%CI = 1.015-1.098) were independent risk factors for antifungal therapy in ICU-AF. The times of arterial catheter (p = 0.004, OR = 1.098, 95%CI = 0.855-0.970) were an independent risk factor for empirical antifungal therapy. Conclusion: The most important risk factors for ICU-AF are the six time-related features of clinical parameters (arterial catheter, enteral nutrition, corticosteroids, broadspectrum antibiotics, urinary catheter, and invasive mechanical ventilation), which provide early warning for the occurrence of fungal infection. Furthermore, this model can help ICU physicians to assess whether empiric antifungal therapy should be administered to ICU patients who are susceptible to fungal infections.

Aspergillus fumigatus Influences Gasdermin-D-Dependent Pyroptosis of the Lung via Regulating Toll-Like Receptor 2-Mediated Regulatory T Cell Differentiation.

PURPOSE: Aspergillus fumigatus, as an opportunistic fungus, has developed a series of escape mechanisms under the host's immune response to obtain nutrients and promote fungal growth in the hostile environment. The immune escape of pathogens may be through suppressing the inflammatory response mediated by regulatory T cells (Tregs). The aim of this study was to explore whether A. fumigatus influences Gasdermin-D-dependent pyroptosis of the lung by regulating Toll-like receptor 2-mediated regulatory T cell differentiation. METHODS: Collect peripheral blood from patients with A. fumigatus. ELISA kits we used to detect the expression levels of IL-1ß, IL-6, IL-2R, and IL-10 in the serum and flow cytometry to detect the percentage of CD4+CD25+Foxp3+ Tregs in the patients' peripheral blood mononuclear cells (PBMCs). The mouse model of A. fumigatus infection was constructed by tracheal instillation. The pathological changes in the lungs of the mice were observed under a microscope. The fungal load in the lung tissue was determined by the plate colony count. ELISA kit was used to detect the lung tissue homogenate proinflammatory cytokines TNF-α, IL-6, CCL2, and VEGF. Q-PCR was used for the detection of the expression of Foxp3 and TLR2 genes in the lung. Western blot was used for the detection of the expression of TLR2, Gasdermin-D (GSDMD), IL-1α, and IL-1ß in the lung. Flow cytometry was used to detect splenic CD4+CD25+FOXP3+ Tregs. Using magnetic beads to extract CD4+ T cells from mice spleen, the effects of A. fumigatus conidia or TLR2 inhibitor (C29) to differentiate CD4+ T cells in vitro were tested. RESULTS: The expression of Foxp3 and TLR2 in the lung tissue of mice infected with A. fumigatus increased, and we observed that the proportion of Tregs in both A. fumigatus infection patients and mice was upregulated. After using the CD25 neutralizing antibody, the number of Tregs in the mice spleen was significantly reduced. However, lung damage was reduced and the ability to clear lung fungi was enhanced. We found that the Tregs in TLR2-/- mice were significantly reduced and the nonlethal dose of A. fumigatus conidia did not cause severe lung damage in TLR2-/- mice. Compared with that of wild-type mice, the fungal burden in the lung of TLR2-deficient mice was reduced and the knockout of TLR2 changed the expression of GSDMD, IL-1α, and IL-1ß in A. fumigatus. In in vitro experiments, we found that the inhibition of TLR2 can reduce Treg differentiation. CONCLUSIONS: A. fumigatus triggers CD4+CD25+FOXP3+ Treg proliferation and differentiation by activating the TLR2 pathway, which may be a potential mechanism for evading host defenses in A. fumigatus. This effect can modulate GSDMD-dependent pyroptosis and may partly involve TRL2 signaling.

Coronavirus disease 2019 (COVID-19): cytokine storms, hyper-inflammatory phenotypes, and acute respiratory distress syndrome.

Coronavirus Disease 2019 (COVID-19) was first identified in China at the end of 2019. Acute respiratory distress syndrome (ARDS) represents the most common and serious complication of COVID-19. Cytokine storms are a pathophysiological feature of COVID-19 and play an important role in distinguishing hyper-inflammatory subphenotypes of ARDS. Accordingly, in this review, we focus on hyper-inflammatory host responses in ARDS that play a critical role in the differentiated development of COVID-19. Furthermore, we discuss inflammation-related indicators that have the potential to identify hyper-inflammatory subphenotypes of COVID-19, especially for those with a high risk of ARDS. Finally, we explore the possibility of improving the quality of monitoring and treatment of COVID-19 patients and in reducing the incidence of critical illness and mortality via better distinguishing hyper- and hypo-inflammatory subphenotypes of COVID-19.

Exploring plasma metabolomic changes in sepsis: a clinical matching study based on gas chromatography-mass spectrometry.

BACKGROUND: Sepsis is a deleterious systemic inflammatory response to infection, and despite advances in treatment, the mortality rate remains high. We hypothesized that plasma metabolism could clarify sepsis in patients complicated by organ dysfunction. METHODS: Plasma samples from 31 patients with sepsis and 23 healthy individuals of comparable age, gender, and body mass index (BMI) were collected. Plasma metabolites were detected through gas chromatography-mass spectrometry (GC-MS), and relevant metabolic pathways were predicted using the Kyoto Encyclopedia of Genes and Genomics (KEGG) pathway database. Student's t-test was employed for statistical analysis. In addition, to explore sepsis organ dysfunction, plasma samples of sepsis patients were further analyzed by metabolomics subgroup analysis according to organ dysfunction. RESULTS: A total of 222 metabolites were detected, which included 124 metabolites with statistical significance between the sepsis and control groups. Among these, we found 26 were fatty acids, including 3 branched fatty acids, 10 were saturated fatty acids, and 13 were unsaturated fatty acids that were found in sepsis plasma samples but not in the controls. In addition, 158 metabolic pathways were predicted, 74 of which were significant. Further subgroup analysis identified seven metabolites in acute kidney injury (AKI), three metabolites in acute respiratory distress syndrome (ARDS), seven metabolites in sepsis-induced myocardial dysfunction (SIMD), and four metabolites in acute hepatic ischemia (AHI) that were significantly different. The results showed that the sepsis samples exhibited extensive changes in amino acids, fatty acids, and tricarboxylic acid (TCA)-cycle products. In addition, three metabolic pathways-namely, energy metabolism, amino acid metabolism, and lipid metabolism-were downregulated in sepsis patients. CONCLUSIONS: The downregulated energy, amino acid, and lipid metabolism found in our study may serve as a novel clinical marker for the dysregulated internal environment, particularly involving energy metabolism, which results in sepsis.