Subclinical myocardial disorder in patients restored from COVID19

Growing evidence reports that obesity might play a role in erectile dysfunction (ED), but limited knowledge is available. We conducted a meta-analysis to estimate the prevalence of ED in overweight men and men with obesity. We performed a systematic review up to 01/04/2019 to investigate the associations between obesity and ED. Applying a random-effect model, we calculated the prevalence of ED, the odds ratio (OR) for the presence of ED by Body Mass Index (BMI) categories and the mean differences between ED and controls in BMI and Waist Circumference (WC). Among 3409 studies, we included 45 articles with 42,489 men (mean age = 55 years). Taking normal weight men as reference, the prevalence of ED was significantly higher in overweight (OR = 1.31; 95%CI 1.13-1.51; I2 = 72%) and in men with obesity (OR = 1.60; 95%CI 1.29-1.98; I2 = 79%). Adjusting our analyses for potential confounders, the results were confirmed in men with obesity (OR = 1.46; 95%CI 1.24-1.72; I2 = 72%). ED was associated with significant higher values of BMI (MD = 0.769; 95%CI 0.565-0.973 Kg/m2; I2 = 78%) and WC (MD = 5.251 cm; 95%CI 1.295-9.208; I2 = 96%). Considering the high prevalence of ED among men with obesity, clinicians should screen for this clinical condition in this population. Findings from the present study suggest that reducing adiposity is a crucial approach in patients with ED who are affected by obesity.Blind predictions of octanol/water partition coefficients at 298 K for 11 kinase inhibitor fragment like compounds were made for the SAMPL6 challenge. We used the conventional, "untrained", free energy based approach wherein the octanol/water partition coefficient was computed directly as the difference in solvation free energy in water and 1-octanol. We additionally proposed and used two different forms of a "trained" approach. Physically, the goal of the trained approach is to relate the partition coefficient computed using pure 1-octanol to that using water-saturated 1-octanol. In the first case, we assumed the partition coefficient using water-saturated 1-octanol and pure 1-octanol are linearly correlated. In the second approach, we assume the solvation free energy in water-saturated 1-octanol can be written as a linear combination of the solvation free energy in pure water and 1-octanol. In all cases here, the solvation free energies were computed using electronic structure calculations in the SM12, SM8, and SMD universal solvent models. In the context of the present study, our results in general do not support the additional effort of the trained approach.Two different types of approaches (a) approaches that combine quantitative structure activity relationships, quantum mechanical electronic structure methods, and machine-learning and, (b) electronic structure vertical solvation approaches, were used to predict the logP coefficients of 11 molecules as part of the SAMPL6 logP blind prediction challenge. Using electronic structures optimized with density functional theory (DFT), several molecular descriptors were calculated for each molecule, including van der Waals areas and volumes, HOMO/LUMO energies, dipole moments, polarizabilities, and electrophilic and nucleophilic superdelocalizabilities. A multilinear regression model and a partial least squares model were used to train a set of 97 molecules. As well, descriptors were generated using the molecular operating environment and used to create additional machine learning models. Electronic structure vertical solvation approaches considered include DFT and the domain-based local pair natural orbital methods combined with the solvated variant of the correlation consistent composite approach.Water octanol partition coefficient serves as a measure for the lipophilicity of a molecule and is important in the field of drug discovery. A novel method for computational prediction of logarithm of partition coefficient (logP) has been developed using molecular fingerprints and a deep neural network. The machine learning model was trained on a dataset of 12,000 molecules and tested on 2000 molecules. In this article, we present our results for the blind prediction of logP for the SAMPL6 challenge. ARRY-382 ic50 While the best submission achieved a RMSE of 0.41 logP units, our submission had a RMSE of 0.61 logP units. Overall, we ranked in the top quarter out of the 92 submissions that were made. Our results show that the deep learning model can be used as a fast, accurate and robust method for high throughput prediction of logP of small molecules.Theoretical approaches for predicting physicochemical properties are valuable tools for accelerating the drug discovery process. In this work, quantum chemical methods are used to predict water-octanol partition coefficients as a part of the SAMPL6 blind challenge. The SMD continuum solvent model was employed with MP2 and eight DFT functionals in conjunction with correlation consistent basis sets to determine the water-octanol transfer free energy. Several tactics towards improving the predictions of the partition coefficient were examined, including increasing the quality of basis sets, considering tautomerization, and accounting for inhomogeneities in the water and n-octanol phases. Evaluation of these various schemes highlights the impact of modeling approaches across different methods. With the inclusion of tautomers and adjustments to the permittivity constants, the best predictions were obtained with smaller basis sets and the O3LYP functional, which yielded an RMSE of 0.79 logP units. The results presented correspond to the SAMPL6 logP submission IDs DYXBT, O7DJK, and AHMTF.Alzheimer's disease (AD), the most common form of dementia worldwide, is characterized by pathological hallmarks like β-amyloid peptide (Aβ) and clinical manifestations including cognitive impairment, psychiatry disorders, and behavioral changes. Salidroside (Sal) extracted from Rhodiola rosea L. showed protective effects against Aβ-induced neurotoxicity in a Drosophila AD model in our previous research. In the present study, daily doses of Sal were administered to APP/PS1 mice, a mouse model of AD, and several parameters were tested, including behavioral performance, Aβ status, levels of synapse-related proteins, and levels of PI3K/Akt targets of mTOR cell signaling pathway proteins. The behavioral testing showed an improvement in locomotor activity in the APP/PS1 mice after the administration of Sal. Treatment with Sal decreased both the soluble and insoluble Aβ levels and increased the expression of PSD95, NMDAR1, and calmodulin-dependent protein kinase II. The phosphatidylinositide PI3K/Akt/mTOR signaling was upregulated, which was in accordance with the above improvements from Sal treatment.