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Phosphodiesterase 5 (PDE5), a cyclic guanosine monophosphate (cGMP)-specific hydrolase, is targets cGMP produced by soluble guanosine cyclase (sGC) that is activated by nitric oxide (NO). It catalyzes the hydrolysis of the phosphodiester bond in cGMP and converts cGMP to the inactive 5′-GMP form. The cGMP-PKG axis dysfunction is one of the main causes of heart failure (HF) and causes cardiac remodeling. However, the clinical efficacy of PDE5 inhibitors in the treatment of heart failure is controversial. This paper summarizes the mechanism and research progress of PDE5 in heart failure in recent years, which has important significance for the clinical application of PDE5 in heart failure.

Background Facial pressure injury is a common complication in patients with prone position ventilation. Local exposure of the trauma can increase the risk of systemic infection, and affect the therapeutic effect of prone position ventilation, and even cause permanent functional damage to local tissues. Exploring the risk factors and constructing a prediction model are of great clinical significance for the prevention of prone position ventilation related facial pressure injuries. Objective To investigate the risk factors for prone position ventilation-related facial pressure injuries and its optimal modeling methods. Methods A total of 159 patients who were admitted to the Department of Critical Care Medicine of the First Affiliated Hospital of Xinjiang Medical University from June 2020 to March 2023 and received prone position ventilation were selected and divided into the pressure injury group (n=22) and non-pressure injury group (n=137) according to whether facial pressure injuries occurred or not. General information, disease diagnosis, therapeutic measures, and laboratory test results were collected. Stepwise Logistic regression, multivariate Logistic regression, and Lasso-Logistic regression were used to screen risk factors for facial pressure injuries and develop predictive models, respectively. The area under receiver operating characteristic curve (AUC) was plotted to evaluate the model discrimination. The Akaike Information Criterion (AIC) , Bayesian Information Criterion (BIC) , and calibration curve were applied to evaluate the calibration of the model. Decision curves were applied to evaluate the clinical application value of the models. The optimal modeling method was selected by comparing the predictive efficacy and clinical application differences of the three logistic regression models. Results The results of stepwise Logistic regression model showed that the influencing factors of facial pressure injuries were age (OR=39.041) , diabetes mellitus (OR=7.256) , and duration of a single-prone ventilation session (OR=6.705) . The results of the multivariate Logistic regression model showed that the factors influencing facial pressure injuries were age (OR=26.882) , diabetes mellitus (OR=1.770) , length of stay in the ICU (OR=2.610) , and duration of a single-prone ventilation session (OR=5.340) . The results of Lasso-Logistic regression showed that the factors influencing facial pressure injuries were age (OR=38.256) , diabetes mellitus (OR=1.094) , duration of single prone ventilation (OR=5.738) , and RASS score (OR=1.179) . The AUC, sensitivity and specificity of the Lasso-Logistic regression model for predicting prone position ventilation-related facial pressure injuries were 0.855, 0.959 and 0.750, respectively, which were better than those of the stepwise and multivariate Logistic regression models. The AIC and BIC were 44.634 and 55.745, respectively, which were lower than the stepwise and multivariate Logistic regression models. The calibration curves showed that the Lasso-Logistic regression model predicted probabilities fitted the actual probabilities best. The decision curve showed that the Lasso-Logistic regression model obtained clinical benefits corresponding to risk thresholds of 0.01 to 0.98, which was better than the stepwise and multivariate Logistic regression models. Conclusion Age, diabetes mellitus, length of a single prone ventilation session, and Richmond Agitation Sedation Score are risk factors for ventilation-related facial pressure injuries. The Lasso-Logistic regression model has better predictive efficacy and clinical application value than stepwise and multivariate Logistic regression models, making it the best modeling method.

Structural damage identification has been one of the key applications in the field of Structural Health Monitoring (SHM). With the development of technology and the growth of demand, the method of identifying damage anomalies in plate structures is increasingly being developed in pursuit of accuracy and high efficiency. Principal Component Analysis (PCA) has always been effective in damage identification in SHM, but because of its sensitivity to outliers and low robustness, it does not work well for complex damage or data. The effect is not satisfactory. This paper introduces the Robust Principal Component Analysis (RPCA) model framework for the characteristics of PCA that are too sensitive to the outliers or noise in the data and combines it with Lamb to achieve the damage recognition of wavefield images, which greatly improves the robustness and reliability. To further improve the real-time monitoring efficiency and reduce the error, this paper proposes a non-convex approximate RPCA (NCA-RPCA) algorithm model. The algorithm uses a non-convex rank approximation function to approximate the rank of the matrix, a non-convex penalty function to approximate the norm to ensure the uniqueness of the sparse solution, and an alternating direction multiplier method to solve the problem, which is more efficient. Comparison and analysis with various algorithms through simulation and experiments show that the algorithm in this paper improves the real-time monitoring efficiency by about ten times, the error is also greatly reduced, and it can restore the original data at a lower rank level to achieve more effective damage identification in the field of SHM. [ABSTRACT FROM AUTHOR]

To compare the differences in the soil microbial community structure in yellow soil paddy fields after the long-term application of chemical fertilizer, the role and mechanism of chemical fertilizer in maintaining soil microbial diversity were analyzed, and a theoretical basis for fertilization management in farmlands was provided. In this study, long-term experiments were conducted at the Scientific Observation and Experimental Station of the Arable Land Conservation and Agricultural Environment (Guizhou), Ministry of Agriculture and Rural Affairs. Soil samples were collected from five treatments: fertilizer N, P, and K (NPK); fertilizer P and K (PK); fertilizer N and K (NK); fertilizer N and P (NP); and no fertilizer (CK). High-throughput sequencing technology was used to analyze the microbial composition and diversity of the colonies, and the influencing factors are discussed. An analysis of the soil bacterial α diversity indices under the different fertilization treatments revealed that the long-term application of NPK fertilizers had less of an effect on the soil bacterial α diversity indices than did the CK. The long-term application of chemical fertilizers significantly reduced the soil bacterial Chao1, Shannon, and Simpson indices, while there was no significant difference in the bacterial Pielou e index among the treatments. The long-term application of chemical fertilizers significantly increased the soil fungal Chao1 index, but the effects on the other indices were not significant (p < 0.05). An analysis of the bacterial and fungal species under different fertilization treatments showed found that compared with CK, the long-term application of chemical fertilizer increased the relative abundance of Proteobacteria to varying degrees while reducing the relative abundance of Chloroflexi. The impact of other phyla was relatively small, and the difference in the relative abundance of fungi was not significant (p < 0.05). Principal component analysis revealed that, at the genus level, the bacterial and fungal community structures in the CK and NK treatments were relatively independent, while those in the NPK, PK, and NP treatments were similar. Random forest analysis revealed that OM, TP, and TK are the dominant factors that affect soil bacteria α diversity. The dominant factors affecting fungi α diversity are pH, OM, and AK. Redundancy analysis indicated that AK and TP were the main factors affecting bacterial community structure, while AP, AK, and pH were the main factors affecting fungal community structure. The conclusion drawn from this study is that the long-term application of nitrogen, phosphorus, and potassium fertilizer; phosphorus and potassium fertilizer; and nitrogen and phosphorus fertilizer can improve soil fertility, alter the soil environment, enhance microbial diversity, and improve the microbial community structure in yellow soil paddy fields, promoting soil ecosystem stability and health. [ABSTRACT FROM AUTHOR]

Jet flows are ubiquitous in nature and laboratories. For example, helium and argon jets into ambient air are commonly used to produce atmospheric plasma jets. The jets before and after discharge both are typical multicomponent flows. This study reports on the implementation of a numerical solver based on the graphic processing units to model multicomponent mixture gas flows. To this end, two- and three-dimensional Jacobian matrices for the multicomponent Euler equations were obtained to reconstruct numerical fluxes and build a total variation diminishing scheme. The two-dimensional early-stage mass transfer of gas jets was studied for three mixture gases: He/air, Ar/air, and N2/air to numerically verify the steady functioning of the numerical solver despite the high mass fraction gradients. The mass fraction distribution of helium in ambient air exhibited more complex substructures than those of argon and nitrogen in ambient air. Using the watershed method, self-generated bullets were extracted from the mass fraction distributions of the helium jet. The proposed numerical solver implemented in this study is expected to provide a better understanding of gas jet dynamics, and the Jacobian matrices can provide a mathematical foundation for constructing potential higher schemes. [ABSTRACT FROM AUTHOR]