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The morphological changes that occur in myxomatous mitral valve disease (MMVD) involve various components, ultimately leading to the impairment of mitral valve (MV) function. In this context, intrinsic mitral annular abnormalities are increasingly recognized, such as a mitral annular disjunction (MAD), a specific anatomical abnormality whereby there is a distinct separation between the mitral annulus and the left atrial wall and the basal portion of the posterolateral left ventricular myocardium. In recent years, several studies have suggested that MAD contributes to myxomatous degeneration of the mitral leaflets, and there is growing evidence that MAD is associated with ventricular arrhythmias and sudden cardiac death. In this review, the morphological characteristics of MAD and imaging tools for diagnosis will be described, and the clinical and functional aspects of the coincidence of MAD and myxomatous MVP will be discussed.The non-stationarity, nonlinearity and complexity of the PM2.5 series have caused difficulties in PM2.5 prediction. To improve prediction accuracy, many forecasting methods have been developed. However, these methods usually do not consider the importance of data preprocessing and have limitations only using a single forecasting model. Therefore, this paper proposed a new hybrid decomposition-ensemble learning paradigm based on variation mode decomposition (VMD) and improved whale-optimization algorithm (IWOA) to address complex nonlinear environmental data. First, the VMD is employed to decompose the PM2.5 sequences into a set of variational modes (VMs) with different frequencies. Then, an ensemble method based on four individual forecasting approaches is applied to forecast all the VMs. With regard to ensemble weight coefficients, the IWOA is applied to optimize the weight coefficients, and the final forecasting results were obtained by reconstructing the refined sequences. To verify and validate the proposed learning paradigm, four daily PM2.5 datasets collected from the Jing-Jin-Ji area of China are chosen as the test cases to conduct the empirical research. The experimental results indicated that the proposed learning paradigm has the best results in all cases and metrics.As catabolites of nicotinamide possess physiological relevance, pyridones are often included in metabolomics measurements and associated with pathological outcomes in acute kidney injury (AKI). Pyridones are oxidation products of nicotinamide, its methylated form, and its ribosylated form. While they are viewed as markers of over-oxidation, they are often wrongly reported or mislabeled. To address this, we provide a comprehensive characterization of these catabolites of vitamin B3, justify their nomenclature, and differentiate between the biochemical pathways that lead to their generation. Furthermore, we identify an enzymatic and a chemical process that accounts for the formation of the ribosylated form of these pyridones, known to be cytotoxic. Finally, we demonstrate that the ribosylated form of one of the pyridones, the 4-pyridone-3-carboxamide riboside (4PYR), causes HepG3 cells to die by autophagy; a process that occurs at concentrations that are comparable to physiological concentrations of this species in the plasma in AKI patients.Nanoparticles based on biocompatible methoxy poly(ethylene glycol)-b-poly(D,L-lactide) (mPEG113-b-P(D,L)LA n ) copolymers as potential vehicles for the anticancer agent oxaliplatin were prepared by a nanoprecipitation technique. It was demonstrated that an increase in the hydrophobic PLA block length from 62 to 173 monomer units leads to an increase of the size of nanoparticles from 32 to 56 nm. Small-angle X-ray scattering studies confirmed the "core-corona" structure of mPEG113-b-P(D,L)LA n nanoparticles and oxaliplatin loading. It was suggested that hydrophilic oxaliplatin is adsorbed on the core-corona interface of the nanoparticles during the nanoprecipitation process. The oxaliplatin loading content decreased from 3.8 to 1.5% wt./wt. (with initial loading of 5% wt./wt.) with increasing PLA block length. Thus, the highest loading content of the anticancer drug oxaliplatin with its encapsulation efficiency of 76% in mPEG113-b-P(D,L)LA n nanoparticles can be achieved for block copolymer with short hydrophobic block.To minimize the damage from contaminant accidents in rivers, early identification of the contaminant source is crucial. Thus, in this study, a framework combining Machine Learning (ML) and the Transient Storage zone Model (TSM) was developed to predict the spill location and mass of a contaminant source. The TSM model was employed to simulate non-Fickian Breakthrough Curves (BTCs), which entails relevant information of the contaminant source. Then, the ML models were used to identify the BTC features, characterized by 21 variables, to predict the spill location and mass. The proposed framework was applied to the Gam Creek, South Korea, in which two tracer tests were conducted. In this study, six ML methods were applied for the prediction of spill location and mass, while the most relevant BTC features were selected by Recursive Feature Elimination Cross-Validation (RFECV). Model applications to field data showed that the ensemble Decision tree models, Random Forest (RF) and Xgboost (XGB), were the most efficient and feasible in predicting the contaminant source.

There exist several prediction equations for the estimation of resting energy expenditure (REE). However, none of these equations have been validated in the Chilean female population yet. The aims of this study are (1) to determine the accuracy of existing equations for prediction of REE and (2) to develop new equations in a sample of healthy Chilean women.

A cross-sectional descriptive study was carried out on 620 Chilean women. The sample showed an age range between 18 and 73 years, a body mass index average of 28.5 ± 5.2 kg/m

, and a prevalence of overweight and obesity of 41% and 33.2%, respectively. CDK2-IN-4 inhibitor REE was measured by indirect calorimetry (REE

), which was used as the gold standard to determine the accuracy of twelve available REE prediction equations and to calculate alternative formulas for estimation of REE. Paired t-tests and Bland-Altman plots were used to know the accuracy of the estimation equations with REE

. At the same time, multiple linear regressions were performed to propose possible alternative equations.