To establish a prediction model and assess the risk factors for severe diabetic ketoacidosis (DKA) in adult patients during the ICU. With DKA hospitalization rates consistently increasing, in-hospital mortality has become a growing concern. DKA patients aged >18 years old in the US-based critical care database (Medical Information Mart for Intensive Care (MIMIC-IV)) were considered. Independent risk factors for in-hospital mortality were screened using extreme gradient boosting (XGBoost) and the Bayesian information criterion (BIC) optimal subset regression. One predictive model was developed using machine learning extreme gradient boosting (XGBoost), and the other one was a nomogram based on logistic regression to estimate risks of in-hospital mortality with severe DKA. Established models were assessed by using internal validation and external validation. The MIMIC-IV was split into training and testing samples in a 7:3 ratio. The eICU Collaborative Research Database and admissions data from the department of critical care medicine of the first affiliated hospital of Harbin medical university were used for independent validation. The discriminatory ability of the model was determined by illustrating a receiver operating curve (ROC) and calculating the C-index. Meanwhile, the calibration plot and Hosmer-Lemeshow goodness-of-fit test (HL test) was conducted to evaluate the performance of our new build model. Decision curve analysis (DCA) was performed to assess the clinical net benefit. Net Reclassification Improvement (NRI) was used to compare the predictive power of the two models. A multivariable model that included acute physiology score III (APS III), the highest levels of blood plasma osmolality (osmolarity_max), minimum osmolarity (osmolarity_min)/osmolarity _max, vasopressor, and the highest levels of blood lactate was represented as the nomogram. The C- index of the nomogram model was 0.915 (95% CI: 0.966-0.864) in the training dataset and 0.971 (95% CI: 0.992-0.950) in the internal validation. The nomogram's sensitivity was well according to all data's HL test (P > 0.05). DCA showed that our model was clinically valuable. The XGB (extreme gradient boosting) model achieved an AUC (area under the curve) of 0.950 (95% CI, 0.920-0.980); however, the nomogram model made was more effective than XGB based on NRI. The predictive XGB and nomogram models for predicting in-hospital patient deaths with DKA were effective. The forecast models can help clinical physicians promptly identify patients at high risk of DKA, prevent in-hospital deaths, and promptly intervene.

Macrophages could adhere to extracellular matrix molecules(ECM) to induce the expression of pro-inflammatory mediators and phagocytosis that contribute to the pathogenesis of pulmonary infection diseases. Fibronectin (FN) is a large glycoprotein capable of interacting with various ECM molecules produced by a variety of cell types and involved in cell attachment and chemotaxis. However, it is unknown whether FN regulates the expression of pro-inflammatory mediators and phagocytosis of macrophages in the injured lung tissue. Here, we investigated the interaction between FN and integrin β1 in macrophages, which promotes toll-like receptor 2/4 (TLR2/TLR4) signaling pathways to enhance expression of pro-inflammatory mediators and phagocytosis by macrophages. Our results show that lipopolysaccharide (LPS), lipoteichoic acid (LTA) and peptidoglycan (PGN) significantly increase FN expression of macrophages; FN substantially enhances interleukin 6 (IL-6), tumor necrosis factor-α (TNFα), ras-related C3 botulinum toxin substrate 1/2 (Rac1/2), and cell division control protein 42 homolog (Cdc42) expression and phagocytosis of macrophages. However, FN could not enhance pro-inflammatory cytokines and phagocytosis of macrophages induced by LPS and PGN in integrin β1-/- macrophages. Furthermore, applied integrin β1 blocking peptide abrogated the effects that FN promotes innate immune responses of macrophages to LPS and PGN. Those data indicated that the enhanced pro-inflammatory mediators and phagocytosis of macrophages by FN-integrin β1 signal was through co-operating with TLR2/TLR4 signaling. This study suggests that FN play an essential role in the pathogenesis of pulmonary infection disease.

In this study, we aimed to explore whether lymphocyte–C-reactive protein ratio (LCR) can differentiate disease severity of coronavirus disease 2019 (COVID-19) patients and its value as an assistant screening tool for admission to hospital and intensive care unit (ICU). A total of 184 adult COVID-19 patients from the COVID-19 Treatment Center in Heilongjiang Province at the First Affiliated Hospital of Harbin Medical University between January 2020 and March 2021 were included in this study. Patients were divided into asymptomatic infection group, mild group, moderate group, severe group, and critical group according to the Diagnosis and Treatment of New Coronavirus Pneumonia (ninth edition). Demographic and clinical data including gender, age, comorbidities, severity of COVID-19, white blood cell count (WBC), neutrophil proportion (NEUT%), lymphocyte count (LYMPH), lymphocyte percentage (LYM%), red blood cell distribution width (RDW), platelet (PLT), C-reactive protein (CRP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine (SCr), albumin (ALB), total bilirubin (TB), direct bilirubin (DBIL), indirect bilirubin (IBIL), and D-dimer were obtained and collated from medical records at admission, from which sequential organ failure assessment (SOFA) score and LCR were calculated, and all the above indicators were compared among the groups. Multiple clinical parameters, including LYMPH, CRP, and LCR, showed significant differences among the groups. The related factors to classify COVID-19 patients into moderate, severe, and critical groups included age, number of comorbidities, WBC, LCR, and AST. Among these factors, the number of comorbidities showed the greatest effect, and only WBC and LCR were protective factors. The area under the receiver operating characteristic (ROC) curve of LCR to classify COVID-19 patients into moderate, severe, and critical groups was 0.176. The cutoff value of LCR and the sensitivity and specificity of the ROC curve were 1,780.7050 and 84.6% and 66.2%, respectively. The related factors to classify COVID-19 patients into severe and critical groups included the number of comorbidities, PLT, LCR, and SOFA score. Among these factors, SOFA score showed the greatest effect, and LCR was the only protective factor. The area under the ROC curve of LCR to classify COVID-19 patients into severe and critical groups was 0.106. The cutoff value of LCR and the sensitivity and specificity of the ROC curve were 571.2200 and 81.3% and 90.0%, respectively. In summary, LCR can differentiate disease severity of COVID-19 patients and serve as a simple and objective assistant screening tool for hospital and ICU admission.

Background. Sepsis is a systemic inflammatory response that can elicit organ dysfunction as well as circulatory diseases in serious cases. When inflammatory responses are especially dysregulated, severe complications can arise, including sepsis-induced liver injury. Various microRNAs along with circular (circ) RNAs are involved in inflammatory responses; nevertheless, their functions in regulating sepsis-induced liver injury remain unknown. The cecal ligation and puncture (CLP) procedure can induce liver injury as well as polymicrobial sepsis. Methods. In this study, CLP was used to induce liver injury as well as polymicrobial sepsis. Then, liver function, inflammatory cytokine expression, and hepatic histopathology were evaluated. High-throughput sequencing was employed to investigate the abnormal hepatic circRNA expression after CLP. Raw264.7 cells were utilized to simulation an in vitro sepsis inflammation model with LPS induce. The relative mRNA as well as protein levels of TNF-α, IL-1β, and IL-6 was explored by quantitative polymerase chain reaction (PCR) and enzyme-linked immunosorbent assays. We explored functional connections among circRNAs, miR-31-5p, and gasdermin D (GSDMD) using dual-luciferase reporter assays. Western blot was employed to test GSDMD, caspase-1, and NLRP3 expression in mice and cell models. Results. Our results showed that CLP-induced sepsis promoted liver injury via increasing inflammatory pyroptosis. The abnormal expression of circ-Katnal1 played an important role in CLP-induced sepsis. Downregulating circ-Katnal1 suppressed LPS-induced inflammatory pyroptosis in Raw264.7 cells. Bioinformatics and luciferase reporter results confirmed that miR-31-5p and GSDMD were downstream targets of circ-Katnal1. Inhibiting miR-31-5p or upregulating GSDMD reversed the protective effects of silencing circ-Katnal1. Conclusion. Taken together, circ-Katnal1 enhanced inflammatory pyroptosis in sepsis-induced liver injury through the miR-31-5p/GSDMD axis.

Acute lung injury/acute respiratory distress syndrome (ALI/ARDS) is an acute, diffuse, inflammatory lung injury caused by various endogenous or exogenous factors. It is currently widely recognized that an excessive inflammatory response resulting from immune imbalance constitutes a crucial pathogenic mechanism in ALI/ARDS. SS31 is a novel mitochondria-targeted antioxidant peptide. This article validates the role of SS31 in lipopolysaccharide (LPS)-induced ALI. The study applied transcriptome sequencing, immunofluorescence, PCR, immunofluorescence and other methods to explore the mechanism of SS31 in LPS induced ALI. Transcriptome sequencing results indicate that LPS-induced ALI is closely associated with immune regulatory processes, the Toll-like receptor pathway, and the NF-κB signaling pathway. The role of SS31 in acute lung injury is closely related to biological processes, such as immune regulation and cell death. This study demonstrated that SS31 can inhibit the expression of inflammatory factors IL-6, IL-1β, IL-18, and TNF-α, and reduce the expression of pyroptosis-related proteins NLRP3, and GSDMD-N. Further analysis revealed that S100A8 may be a key gene in the effect of SS31. LPS stimulation leads to increased expression of S100A8, while SS31 decreases its expression. Recombinant protein S100A8 can attenuate the inhibitory effect of SS31 on IL-1β, IL-18, NLRP3, and GSDMD-N. The research results indicate that SS31 may inhibit the activation of the NLRP3 inflammasome and suppress inflammatory responses by regulating S100A8, thereby alleviating LPS-induced ALI in mice; this process may be related to pyroptosis.