Objective:To explore the prognostic value of the serum concentration of human soluble stromelysin-2(sST2)combined with ultrasonic left ventricular diastolic function parameters for elderly patients with septic shock.Methods:This prospective study involved 150 elderly patients with septic shock admitted to the intensive care unit(ICU)of the First Hospital of Jiaxing between May 2019 and May 2022.Data on the following parameters were recorded on days 1, 3, 5, and 7 in the ICU: sST2 concentration, mitral early-diastolic inflow peak velocity(E), mitral late-diastolic inflow peak velocity(A), E/A ratio, early diastolic mitral annular velocity(e'), and E/e' ratio.According to the 28-day prognostic outcome obtained during follow-up, patients were divided into a survival group and a death group to compare differences in values of the above parameters between the two groups and at different time points.Logistic regression was used to analyze independent risk factors for 28-day mortality.The receiver operating characteristic(ROC)curve was used to analyze the predictive value for 28-day mortality, and further risk stratification was performed according to optimal cut-off values to compare differences in 28-day mortality under different risk stratification methods.The Kaplan-Meier survival curve was used to compare 28-day cumulative survival under different risk stratification methods and analyze the predictive value of the combination of the parameters for 28-day mortality.Results:On day 5 following ICU admission, e' was lower and E/e' and sST2 were higher in the death group than in the survival group.Univariate and multivariate Logistic regression analysis suggested that sST2(odds ratio: 1.010, P<0.001)was an independent risk factor for 28-day mortality in elderly patients with septic shock.sST2 had a sensitivity of 50.2%, a specificity of 79.1%, and an area under the curve of 0.660 for predicting 28-day mortality in patients with septic shock.The sST2 concentration was 89.3 μg/L on day 5 after ICU admission, which was the clinical cutoff point for predicting 28-day mortality.Based on the risk stratification of sST2 levels, the 28-day mortality rate was higher in the sST2>89.3 μg/L group than in the sST2≤89.3 μg/L group.Kaplan-Meier survival analysis showed that the 28-day cumulative survival rate was significantly lower in the sST2>89.3 μg/L group than in the sST2≤89.3 μg/L group(44.0% vs.66.7%, log-rank test: χ2=9.101, P=0.003).The receiver operating characteristic curve showed that the combination of sST2, e', and E/e' significantly improved the prediction efficiency of 28-day mortality in elderly patients with septic shock, with an area under the curve of 0.844, a sensitivity of 89.7%, and a specificity of 66.5%. Conclusions:sST2 is an independent risk factor for 28-day mortality in elderly patients with septic shock.When combined with e' and E/e', sST2 can more accurately evaluate the survival prognosis of these patients.

The drug-target protein interaction prediction can be used for the discovery of new drug effects. Recent studies often focus on the prediction of an independent matrix filling algorithm, which apply a single algorithm to predict the drug-target protein interaction. The single-model matrix-filling algorithms have low accuracy, so it is difficult to obtain satisfactory results in the prediction of drug-target protein interaction. AdaBoost algorithm is a strong multiple classifier combination framework, which is proved by the past researches in classification applications. The drug-target interaction prediction is a matrix filling problem. Therefore, we need to adjust the matrix filling problem to a classification problem before predicting the interaction among drug-target protein. We make full use of the AdaBoost algorithm framework to integrate several weak classifiers to improve performance and make accurate prediction of drug-target protein interaction. Experimental results based on the metric datasets show that our algorithm outperforms the other state-of-the-art approaches and classical methods in accuracy. Our algorithm can overcome the limitations of the single algorithm based on machine learning method, exploit the hidden factors better and improve the accuracy of prediction effectively.

The nucleocapsid (N) protein of SARS-CoV-2 has been reported to have a high ability of liquid-liquid phase separation, which enables its incorporation into stress granules (SGs) of host cells. However, whether SG invasion by N protein occurs in the scenario of SARS-CoV-2 infection is unknow, neither do we know its consequence. Here, we used SARS-CoV-2 to infect mammalian cells and observed the incorporation of N protein into SGs, which resulted in markedly impaired self-disassembly but stimulated cell cellular clearance of SGs. NMR experiments further showed that N protein binds to the SG-related amyloid proteins via non-specific transient interactions, which not only expedites the phase transition of these proteins to aberrant amyloid aggregation in vitro, but also promotes the aggregation of FUS with ALS-associated P525L mutation in cells. In addition, we found that ACE2 is not necessary for the infection of SARS-CoV-2 to mammalian cells. Our work indicates that SARS-CoV-2 infection can impair the disassembly of host SGs and promote the aggregation of SG-related amyloid proteins, which may lead to an increased risk of neurodegeneration.
Amyloidogenic Proteins/metabolism* ; Amyotrophic Lateral Sclerosis/genetics* ; Animals ; COVID-19 ; Cytoplasmic Granules/metabolism* ; Mammals ; SARS-CoV-2 ; Stress Granules