Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and multiple machine learning in severe COVID-19.

The death of coronavirus disease 2019 (COVID-19) is primarily due to from critically ill patients, especially from ARDS complications caused by SARS-CoV-2. Therefore, it is essential to contribute an in-depth understanding of the pathogenesis of the disease and to identify biomarkers for predicting critically ill patients at the molecular level. Immunogenic cell death (ICD), as a specific variant of regulatory cell death driven by stress, can induce adaptive immune responses against cell death antigens in the host. Studies have confirmed that both innate and adaptive immune pathways are involved in the pathogenesis of SARS-CoV-2 infection. However, the role of ICD in the pathogenesis of severe COVID-19 has rarely been explored. In this study, we systematically evaluated the role of ICD-related genes in COVID-19. We conducted consensus clustering, immune infiltration analysis, and functional enrichment analysis based on ICD differentially expressed genes. The results showed that immune infiltration characteristics were altered in severe and non-severe COVID-19. In addition, we used multiple machine learning methods to screen for five risk genes (KLF5, NSUN7, APH1B, GRB10 and CD4), which are used to predict COVID-19 severity. Finally, we constructed a nomogram to predict the risk of severe COVID-19 based on the classification and recognition model, and validated the model with external data sets. This study provides a valuable direction for the exploration of the pathogenesis and progress of COVID-19, and helps in the early identification of severe cases of COVID-19 to reduce mortality.

MiR-9a-5p alleviates ventilator-induced lung injury in rats by inhibiting the activation of the MAPK signaling pathway via CXCR4 expression downregulation.

BACKGROUND: Globally, Mechanical ventilation is the most commonly used short-term life support technology. Ventilator-induced lung injury (VILI) is an inflammatory injury caused by mechanical ventilation. MicroRNAs (miRNAs) are considered as new gene regulators that play an important role in lung injury and inflammation. However, the role and mechanism of action of miR-9a-5p in VILI remain unclear. METHODS: Herein, a rat model of VILI was established. To determine the expression levels of miR-9a-5p and CXCR4 mRNA, real-time quantitative polymerase chain reactions (qRT-PCR) were conducted. As well as western blot (WB) and immunofluorescence analyses, we determined the expression of CXCR4, SDF-1 and MAPK signaling pathway-related kinases. Hematoxylin and eosin (H&E) staining and the wet-dry ratio of the lung tissue were used to evaluate organ injury. An enzyme-linked immunosorbent assay (Elisa) and myeloperoxidase (MPO) activity measurements were performed to evaluate the inflammatory response. In addition, double luciferase reporter assays were used to verify the association between miR-9a-5p and CXCR4. RESULTS: The expression of miR-9a-5p was low, whereas that of CXCR4 was high in the lung tissues of VILI rats. The overexpression of miR-9a-5p alleviated the degree of pathological injury in the lung tissues of rats with VILI, downregulating inflammatory cytokine expression and MPO activity. In the VILI rat model, miR-9a-5p targeted the negative regulation of CXCR4, and CXCR4 overexpression to reverse the lung-protective and anti-inflammatory effects of miR-9a-5p overexpression in VILI rats. miR-9a-5p also inhibited the phosphorylation of extracellular signal receptor-activated kinase (ERK), a protein related to the MAPK signaling pathway, by downregulating CXCR4 expression. CONCLUSION: miR-9a-5p can hinder the activation of the MAPK/ERK signaling pathway and reduce inflammatory reactions and lung injury in VILI rats through the targeted regulation of CXCR4 expression. Therefore, miR-9a-5p could serve as an intervention target to supply a new strategy for the care of VILI.

[Effect of varying lingual traction forces on the space-closing speed in a typodont model].

OBJECTIVE: To investigate the influences of varying lingual traction forces on the space-closing speed in a typodont model. METHODS: Forty-two Angle Class I standard typodont models of bimaxillary teeth protrusion were divided into 7 equal groups. Four regions of the model were paired to groups, and in the odd-numbered models, the top left and bottom left regions served as the experimental group and the top right and bottom right regions as the control group; in the even-numbered models, the regions in the model were grouped oppositely. In the experimental group, the space was closed by niti wire extension spring in the buccal ridge combined with lingual elastic traction of 0, 5, 10, 15, 20, 25 and 30 g. In the control group, the space was closed by exclusive niti wire extension spring in the buccal ridge. The space-closing speed were analyzed in all the groups. RESULTS: The space-closing speed was significantly lower in the control group than in the experimental groups with lingual traction forces of 5, 10, 15, 20 and 25 g (P<0.05), but a traction force of 30 g resulted in a significantly lower speed than that in the control group (P<0.05). The space closing speed was the greatest in the experimental group with a traction force of 15 g (P<0.05). CONCLUSION: Niti wire extension spring in the buccal ridge combined with lingual elastic traction results in faster space-closing speed than traditional exclusive niti wire extension spring. The speed is the fastest by applying 15 g lingual traction, which is also associated with the lowest slip resistance.