Less loop diuretic use in patients on sacubitril/valsartan undergoing remote pulmonary artery pressure monitoring

Aims Control of pulmonary pressures monitored remotely reduced heart failure hospitalizations mainly by lowering filling pressures through the use of loop diuretics. Sacubitril/valsartan improves heart failure outcomes and increases the kidney sensitivity for diuretics. We explored whether sacubitril/valsartan is associated with less utilization of loop diuretics in patients guided with haemodynamic monitoring in the CardioMEMS European Monitoring Study for Heart Failure (MEMS-HF). Methods and results The MEMS-HF population (n = 239) was separated by the use of sacubitril/valsartan (n = 68) or no use of it (n = 164). Utilization of diuretics and their doses was prespecified in the protocol and was monitored in both groups. Multivariable regression, ANCOVA, and a generalized linear model were used to fit baseline covariates with furosemide equivalents and changes for 12 months. MEMS-HF participants (n = 239) were grouped in sacubitril/valsartan users [n = 68, 64 ± 11 years, left ventricular ejection fraction (LVEF) 25 ± 9%, cardiac index (CI) 1.89 ± 0.4 L/min/m2] vs. non-users (n = 164, 70 ± 10 years, LVEF 36 ± 16%, CI 2.11 ± 0.58 L/min/m2, P = 0.0002, P < 0.0001, and P = 0.0015, respectively). In contrast, mean pulmonary artery pressure (PAP) values were comparable between groups (29 ± 11 vs. 31 ± 11 mmHg, P = 0.127). Utilization of loop diuretics was lower in patients taking sacubitril/valsartan compared with those without (P = 0.01). Significant predictor of loop diuretic use was a history of renal failure (P = 0.005) but not age (P = 0.091). After subjects were stratified by sacubitril/valsartan or other diuretic use, PAP was nominally, but not significantly lower in sacubitril/valsartan-treated patients (baseline: P = 0.52; 6 months: P = 0.07; 12 months: P = 0.53), while there was no difference in outcome or PAP changes. This difference was observed despite lower CI (P = 0.0015). Comparable changes were not observed for other non-loop diuretics (P = 0.21). Conclusions In patients whose treatment was guided by remote PAP monitoring, concomitant use of sacubitril/valsartan was associated with reduced utilization of loop diuretics, which could potentially be relevant for outcomes

SARS-CoV-2 detection in wastewater

COVID-19 pandemic is a global concern which has caused millions of cases and deaths. Wastewater-based epidemiology (WBE) can track SARS-CoV-2 infection dynamics through wastewater. Aiming to detect the existence of pre-symptomatic or asymptomatic patients in specific areas prior clinical test results, and to remind special attention for viral spreading, the SARS-CoV-2 viral concentration in wastewater was monitored from April to October 2021.Wastewater samples were collected in 9 locations in Urbana-Champaign and a rural area nearby. Our study developed a timesaving WBE method, which used MgCl2 addition and centrifugation to concentrate SARS-CoV-2 into sludge, based on the understanding that enveloped virus RNA in sludge was dozens of times higher than it in supernatant. We correlated wastewater viral concentration data with clinical tests and found Pearson’s relationship showed weak to moderate positive in four of seven sampling locations. Campus town, the location where had most frequent clinical tests, performed the best correlation. In addition, it was found the appearance of undetectable RNA concentration overlapped with zero clinical cases. The infection peaks of reporting cases always occurred in the periods when viral concentration kept stable.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

A Novel Parameter Estimation Method for Muskingum Model Using New Newton-Type Trust Region Algorithm

Parameters estimation of Muskingum model is very significative in both exploitation and utilization of water resources and hydrological forecasting. The optimal results of parameters directly affect the accuracy of flood forecasting. This paper considers the parameters estimation problem of Muskingum model from the following two aspects. Firstly, based on the general trapezoid formulas, a class of new discretization methods including a parameter θ to approximate Muskingum model is presented. The accuracy of these methods is second-order, when θ≠1/3. Particularly, if we choose θ=1/3, the accuracy of the presented method can be improved to third-order. Secondly, according to the Newton-type trust region algorithm, a new Newton-type trust region algorithm is given to obtain the parameters of Muskingum model. This method can avoid high dependence on the initial parameters. The average absolute errors (AAE) and the average relative errors (ARE) of the proposed algorithm of parameters estimation for Muskingum model are 8.208122 and 2.462438%, respectively, where θ=1/3. It is shown from these results that the presented algorithm has higher forecasting accuracy and wider practicability than other methods

A Class of Parameter Estimation Methods for Nonlinear Muskingum Model Using Hybrid Invasive Weed Optimization Algorithm

Nonlinear Muskingum models are important tools in hydrological forecasting. In this paper, we have come up with a class of new discretization schemes including a parameter θ to approximate the nonlinear Muskingum model based on general trapezoid formulas. The accuracy of these schemes is second order, if θ≠1/3, but interestingly when θ=1/3, the accuracy of the presented scheme gets improved to third order. Then, the present schemes are transformed into an unconstrained optimization problem which can be solved by a hybrid invasive weed optimization (HIWO) algorithm. Finally, a numerical example is provided to illustrate the effectiveness of the present methods. The numerical results substantiate the fact that the presented methods have better precision in estimating the parameters of nonlinear Muskingum models

m6A-Mediated PPARA Translational Suppression Contributes to Corticosterone-Induced Visceral Fat Deposition in Chickens

Excess fat deposition in broilers leads to great economic losses and is harmful to consumers&rsquo; health. Chronic stress in the life cycle of chickens could be an important trigger. However, the underlying mechanisms are still unclear. In this study, 30-day-old chickens were subcutaneously injected with 2 mg/kg corticosterone (CORT) twice a day for 14 days to simulate long-term stress. It was shown that chronic CORT exposure significantly increased plasma triglyceride concentrations and enlarged the adipocyte sizes in chickens. Meanwhile, chronic CORT administration significantly enlarged the adipocyte sizes, increased the protein contents of FASN and decreased HSL, ATGL, Beclin1 and PPARA protein levels. Moreover, global m6A methylations were significantly reduced and accompanied by downregulated METTL3 and YTHDF2 protein expression by CORT treatment. Interestingly, the significant differences of site-specific m6A demethylation were observed in exon7 of PPARA mRNA. Additionally, a mutation of the m6A site in the PPARA gene fused GFP and revealed that demethylated RRACH in PPARA CDS impaired protein translation in vitro. In conclusion, these results indicated that m6A-mediated PPARA translational suppression contributes to CORT-induced visceral fat deposition in chickens, which may provide a new target for the treatment of Cushing&rsquo;s syndrome

Metaproteomic analysis of ratoon sugarcane rhizospheric soil

National Natural Science Foundation of China [30772729, 30671220, 31070403]; National Key Basic Research Program of China [2012CB126309, U1205021]; earmarked fund for Modern Agro-industry Technology Research SystemBackground: The current study was undertaken to elucidate the mechanism of yield decline in ratoon sugarcane using soil metaproteomics combined with community level physiological profiles (CLPP) analysis. Results: The available stalk number, stalk diameter, single stalk weight and theoretical yield of ratoon cane (RS) were found to be significantly lower than those of plant cane (NS). The activities of several carbon, nitrogen and phosphorus processing enzymes, including invertase, peroxidase, urease and phosphomonoesterase were found to be significantly lower in RS soil than in NS soil. BIOLOG analysis indicated a significant decline in average well-color development (AWCD), Shannon's diversity and evenness indices in RS soil as compared to NS soil. To profile the rhizospheric metaproteome, 109 soil protein spots with high resolution and repeatability were successfully identified. These proteins were found to be involved in carbohydrate/energy, amino acid, protein, nucleotide, auxin and secondary metabolisms, membrane transport, signal transduction and resistance, etc. Comparative metaproteomics analysis revealed that 38 proteins were differentially expressed in the RS soil as compared to the control soil or NS soil. Among these, most of the plant proteins related to carbohydrate and amino acid metabolism and stress response were up-regulated in RS soil. Furthermore, several microbial proteins related to membrane transport and signal transduction were up-regulated in RS soil. These proteins were speculated to function in root colonization by microbes. Conclusions: Our experiments revealed that sugarcane ratooning practice induced significant changes in the soil enzyme activities, the catabolic diversity of microbial community, and the expression level of soil proteins. They influenced the biochemical processes in the rhizosphere ecosystem and mediated the interactions between plants and soil microbes

The cerebral mechanism of acupuncture for treating knee osteoarthritis: study protocol for a randomized controlled trial

Abstract Background Acupuncture is safe and effective for reducing the symptoms of knee osteoarthritis (KOA), but the underlying mechanisms of acupuncture for treating KOA are not fully understood. Methods/design In total, 108 participants diagnosed with KOA will be recruited. They will be blinded to group assignment and randomized to either verum acupuncture, sham acupuncture or waiting-list groups with 36 patients in each group. Each patient in the acupuncture group will receive five treatments per week for 2 weeks. This study will focus on detecting the cerebral functional connectivity changes elicited by acupuncture treatment. The Visual Analog Scale and the short form of the McGill Pain Questionnaire, the Western Ontario and McMaster Universities Osteoarthritis Index, the Attention Test Scale, the Pain Assessment of Sphygmomanometer and the 12-Item Short Form Health Survey will be used to evaluate the symptoms and quality of life improvement at the baseline and the end of treatment. The Self-rating Anxiety Scale and the Self-rating Depression Scale will be used at the baseline and the end of treatment to investigate the influence of emotional state on brain activity and clinical variable. To ensure the consistency of acupuncture manipulation, the deqi scale will be performed after each acupuncture treatment. During the procedure of outcome evaluation and data analysis, the evaluators and statisticians will be blinded to the group allocation. The repeated measures analysis of variance (3 groups × 2 time points ANOVA) will be employed to analyze numerical variables of the clinical and neuroimaging data generated in the study, then the t test will be used in the post-hoc analysis. Discussion The results of this randomized, sham- and waiting-list-controlled functional magnetic resonance imaging (fMRI) study will help to investigate the influence of verum acupuncture treatment on the brain activities of patients with KOA, which might provide evidence for the clinical application of verum acupuncture for KOA management. Trial registration Chinese Clinical Trial Registry, ID: ChiCT-IOR-17012364. Registered on 14 August 2017

Building Environmental and Sociological Predictive Intelligence to Understand the Seasonal Threat of SARS-CoV-2 in Human Populations

Current modeling practices for environmental and sociological modulated infectious diseases remain inadequate to forecast the risk of outbreak(s) in human populations, partly due to a lack of integration of disciplinary knowledge, limited availability of disease surveillance datasets, and overreliance on compartmental epidemiological modeling methods. Harvesting data knowledge from virus transmission (aerosols) and detection (wastewater) of SARS-CoV-2, a heuristic score-based environmental predictive intelligence system was developed that calculates the risk of COVID-19 in the human population. Seasonal validation of the algorithm was uniquely associated with wastewater surveillance of the virus, providing a lead time of 7–14 days before a county-level outbreak. Using county-scale disease prevalence data from the United States, the algorithm could predict COVID-19 risk with an overall accuracy ranging between 81% and 98%. Similarly, using wastewater surveillance data from Illinois and Maryland, the SARS-CoV-2 detection rate was greater than 80% for 75% of the locations during the same time the risk was predicted to be high. Results suggest the importance of a holistic approach across disciplinary boundaries that can potentially allow anticipatory decision-making policies of saving lives and maximizing the use of available capacity and resources