Septic shock is induced by an uncontrolled inflammatory immune response to pathogens and the survival rate of patients with pediatric septic shock (PSS) is particularly low, with a mortality rate of 25-50%. The present study explored the mechanisms of PSS using four microarray datasets (GSe26378, GSe26440, GSe13904 and GSe4607) that were obtained from the Gene Expression Omnibus database. Based on the MetaDE package, the consistently differentially expressed genes (DEGs) in the four datasets were screened. Using the WGCNA package, the disease-associated modules and genes were identified. Subsequently, the optimal feature genes were further selected using the caret package. Finally, a support vector machine (SVM) classifier based on the optimal feature genes was built using the e1071 package. Initially, there were 2,699 consistent DEGs across the four datasets. From the 10 significantly stable modules across the datasets, four stable modules (including the magenta, purple, turquoise and yellow modules), in which the consistent DEGs were significantly enriched (P<0.05), were further screened. Subsequently, six optimal feature genes (including cysteine rich transmembrane module containing 1, S100 calcium binding protein A9, solute carrier family 2 member 14, stomatin, uridine phosphorylase 1 and utrophin) were selected from the genes in the four stable modules. Additionally, an effective SVM classifier was constructed based on the six optimal genes. The SVM classifier based on the six optimal genes has the potential to be applied for PSS diagnosis. This may improve the accuracy of early PSS diagnosis and suggest possible molecular targets for interventions.

Objective: This study aimed is to discuss the clinical effects of nursing intervention on severe patients in a respiratory department. Methods: Eighty-four severe patients admitted in the respiratory department in our hospital from January 2016 to September 2017 were selected and categorized into the control (n=42) and intervention groups (n=42) through random number table method. The control group was provided with conventional intervention, and the intervention group was treated with nursing intervention. The nursing effect, blood indexes, hours of respirator use, observation time in the Intensive Care Unit (ICU), occurrence rate of adverse event, and nursing satisfaction of the two groups were compared. Results: The total nursing efficiencies of the intervention and control groups were 92.9% and 76.2%, which showed significant difference (P<0.05). The intervention group was superior to the control group in terms of heart rate, respiratory rate, arterial partial pressure of oxygen, arterial partial pressure of carbon dioxide, hours of respirator use, and observation time in the ICU (P<0.05). The occurrence rate of adverse events of the intervention group was 4.8%, which was significantly lower than that of the control group (19.0%) (P<0.05). The nursing satisfaction of the intervention group (95.2%) was exceedingly higher than that of the control group (81.0%) (P<0.05). Conclusions: Compared with conventional intervention, nursing intervention presented more outstanding clinical effects to severe patients in the respiratory department. Nursing intervention can not only improve the blood indexes of patients but also reduce the occurrence rate of adverse events and increase the nursing satisfaction of patients. Nursing intervention was associated with safety and reliability. Therefore, nursing intervention has promising clinical application and should be promoted.