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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]

Switching wells in high-temperature and high-pressure gas wells will affect parameters such as the temperature and pressure of the fluid in the wellbore. Dynamic monitoring of temperature and pressure is difficult, and wellbore temperature, pressure, and fluid physical parameters are coupled to each other. Obtaining them separately will lead to large calculation errors. In order to improve the prediction accuracy of temperature and pressure in high-temperature and high-pressure gas wells, Based on the temperature–pressure coupling algorithm, this study compares the advantages and disadvantages of nine classic algorithms based on the temperature–pressure coupling algorithm, considers the impact of high temperature and high pressure on the temperature and pressure of the gas wellbore fluid, and establishes an unsteady temperature–pressure coupling model for high-temperature and high-pressure gas wells under on–off well conditions. Comparing with the measured data, it is proved that the prediction accuracy of the unsteady temperature–pressure coupling model of high-temperature and high-pressure gas wells meets the construction requirements of switch wells. The established model is used to simulate the temperature and pressure distribution of two high-temperature and high-pressure gas wells under switching conditions. The analysis shows that the distribution of wellbore temperature and pressure under the switch on and off conditions is affected by the gas–water ratio, heat transfer coefficient, tube size, and gas well production. Among them, the gas–water ratio increased by 1.5 times, the wellhead temperature increased by 25%, and the wellhead pressure decreased is 7.68%; When the heat transfer coefficient is increased by 1.5 times, the wellhead temperature drops to 34.38% and the wellhead pressure drops to 2.29%. When the tube size is increased by 1.125 times, the wellhead temperature is reduced by 44.20% and the pressure is increased by 6.09%. When the production of gas well is doubled, the wellhead temperature increases by 40.79% and the wellhead pressure decreases by 2.29%. The results can be used as a basis for the construction of high-temperature and high-pressure gas wells. [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]

Nowadays, there has been a rapid expansion of tea plantations in the mountainous areas of southwest China. However, little research has focused on the pollution problems caused by the losses of nitrogen and phosphorus from tea plantations in this area. Therefore, a field experiment was conducted using the runoff plots in situ monitoring method following farmers' conventional management from 2018 to 2020 in Guizhou Province, southwest China. The characteristics of nitrogen and phosphorus losses from tea plantation in the mountainous area were clarified, and the effect of rainfall intensity on the nitrogen and phosphorus losses were explored. 298 natural rainfall events with a total rainfall of 2258 mm were observed during the 2-year observation period, and erosive rainfall accounted for 78.1% of the total rainfall. The total surface runoff amount was 72 mm, and the surface runoff coefficient was 3.19%. The total nitrogen (TN) and total phosphorus (TP) concentrations in the surface runoff ranged from 0.68 to 14.86 mg·L−1 and 0.18 to 2.34 mg·L−1, respectively. The TN and TP losses from tea plantations were 1.47 kg N ha−1 yr−1 and 0.210 kg P ha−1 yr−1. Rainfall intensity directly and significantly affected the surface runoff and nitrogen and phosphorus loss. Where 72.6% of the cumulative rainfall, 92.5% of the total surface runoff amounts, 87.4% of total nitrogen loss, and 90.5% of total phosphorus loss were observed in rainfall events above 10 mm. Taken together, the results provide scientific guidance for quantifying the characteristics of nutrient loss in subtropical mountain tea plantations. [ABSTRACT FROM AUTHOR]

SMARCA4-deficient non-small cell lung cancer (NSCLC) is a more recently recognized subset of NSCLC. We describe the 18F-fluorodeoxyglucose (FDG) PET/CT findings in a rare case of SMARCA4-deficient NSCLC and response to therapy. A 45-year-old male patient with a history of heavy smoking (10 years) underwent an 18F-fluorodeoxyglucose (FDG) PET/CT dynamic (chest) + static (whole-body) scan for diagnosis and pre-treatment staging. 18F-FDG PET/CT showed an FDG-avid mass in the upper lobe of the left lung (SUVmax of 22.4) and FDG-avid lymph nodes (LN) in the left pulmonary hilar region (SUVmax of 5.7). In addition, there were multiple metastases throughout the body, including in the distant LNs, adrenal glands, bone, left subcutaneous lumbar region, and brain. Pathological findings confirmed SMARCA4-deficient NSCLC. After four cycles of chemotherapy and immune checkpoint inhibitors (ICI), the patient underwent again an 18F-FDG PET/CT scan (including a dynamic scan) for efficacy evaluation. We report a case that deepens the understanding of the 18F-FDG PET/CT presentation of SMARCA4-deficient NSCLC as well as dynamic imaging features and parametric characteristics. [ABSTRACT FROM AUTHOR]

In this paper, we prove the uniqueness and H 2 -regularity of solution u for a semi-linear elliptical equation with a 2 m − 1 polynomial nonlinearity under the stability and uniqueness conditions. Moreover, we design the finite element method based on P 1 -element for the semi-linear elliptical equation and prove the existence and uniqueness of the finite element solution u h by the finite element Oseen iterative method under the stability and uniqueness condition. Next, we prove the existence and uniqueness of the solution u for the semi-linear elliptical equation by the finite element solution u h under the stability and uniqueness conditions. Furthermore, we provide the H 1 − L 2 optimal error estimates of the finite element solution u h with respect to the exact solution u for the semi-linear elliptical equation under the stability and uniqueness conditions. Finally, some numerical tests are presented to show the efficiency of the proposed method for the semi-linear elliptical equation with a 2 m − 1 polynomial nonlinearity. [ABSTRACT FROM AUTHOR]

Translation of mRNA to protein is tightly regulated by tRNAs, which are subject to various chemical modifications that maintain the structure, stability and function. Deficiency of tRNA N7-methylguanosine (m7G) modification in patients causes a type of primordial dwarfism, but the underlying mechanism remains unknown. Here we report the loss of m7G rewires cellular metabolism, leading to the pathogenesis of primordial dwarfism. Conditional deletion of the catalytic enzyme Mettl1 or missense mutation of the scaffold protein Wdr4 severely impaired endochondral bone formation and bone mass accrual. Mechanistically, Mettl1 knockout decreased abundance of m7G-modified tRNAs and inhibited translation of mRNAs relating to cytoskeleton and Rho GTPase signaling. Meanwhile, Mettl1 knockout enhanced cellular energy metabolism despite of incompetent proliferation and osteogenic commitment. Further exploration revealed that impaired Rho GTPase signaling upregulated branched-chain amino acid transaminase 1 (BCAT1) level that rewired cell metabolism and restricted intracellular α-ketoglutarate (αKG). Supplementation of αKG ameliorated the skeletal defect of Mettl1-deficient mice. In addition to the selective translation of metabolism-related mRNAs, we further revealed that Mettl1 knockout globally regulated translation via integrated stress response (ISR) and mammalian target of rapamycin complex 1 (mTORC1) signaling. Restoring translation either by targeting ISR or mTORC1 aggravated bone defects of Mettl1-deficient mice. Overall, our study unveils a critical role of m7G tRNA modification in bone development by regulating cellular metabolism, and indicates that suspension of translation initiation as quality control mechanism in response to tRNA dysregulation.