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Background The onset and mechanisms of endothelial‐to‐mesenchymal transition (EndMT) in mitral valve (MV) leaflets following myocardial infarction (MI) are unknown, yet these events are closely linked to stiffening of leaflets and development of ischemic mitral regurgitation. We investigated whether circulating molecules present in plasma within days after MI incite EndMT in MV leaflets. Methods and Results We examined the onset of EndMT in MV leaflets from 9 sheep with inferior MI, 8 with sham surgery, and 6 naïve controls. Ovine MVs 8 to 10 days after inferior MI displayed EndMT, shown by increased vascular endothelial cadherin/α‐smooth muscle actin–positive cells. The effect of plasma on EndMT in MV endothelial cells (VECs) was assessed by quantitative polymerase chain reaction, migration assays, and immunofluorescence. In vitro, post‐MI plasma induced EndMT marker expression and enhanced migration of mitral VECs; sham plasma did not. Analysis of sham versus post‐MI plasma revealed a significant drop in the Wnt signaling antagonist sFRP3 (secreted frizzled‐related protein 3) in post‐MI plasma. Addition of recombinant sFRP3 to post‐MI plasma reversed its EndMT‐inducing effect on mitral VECs. RNA‐sequencing analysis of mitral VECs exposed to post‐MI plasma showed upregulated FOXM1 (forkhead box M1). Blocking FOXM1 reduced EndMT transcripts in mitral VECs treated with post‐MI plasma. Finally, FOXM1 induced by post‐MI plasma was downregulated by sFRP3. Conclusions Reduced sFRP3 in post‐MI plasma facilitates EndMT in mitral VECs by increasing the transcription factor FOXM1. Restoring sFRP3 levels or inhibiting FOXM1 soon after MI may provide a novel strategy to modulate EndMT in the MV to prevent ischemic mitral regurgitation and heart failure.

In many surveys, we often deal with situations where measuring the study variable is expensive; however, there are easy-to-measure characteristics which can be used as ranking information to obtain more representative samples from the population. Ranked set sampling is successfully employed in these cases as an alternative to commonly used simple random sampling. When the data is ordinal categorical, it is common to apply the ordinal logistic regression approach to ranked set sampling data for the estimation of parameters. This technique first depends on the information of training data. Besides, one is not capable of using the ranking information in the estimation process. In this paper, we propose a ranked set sampling scheme in which ranking information from multiple sources can be combined and incorporated efficiently into both data collection and estimation. The ranked set sampling data is used for non-parametric and maximum likelihood estimation of ordinal categorical population. Through extensive simulation studies, the performance of estimators is evaluated. The methods are finally applied to analyze bone disorder data and obesity data.

In surveys requiring cost efficiency, such as medical research, measuring the variable of interest (e.g., disease status) is expensive and/or time-consuming; However, we often have access to easily attainable characteristics about sampling units. These characteristics are not typically employed in data collection process. Judgment post-stratification (JPS) sampling enables us to supplement the random samples from the population of interest with these characteristics as ranking information. In this paper, we develop methods based on JPS samples for the estimation of categorical ordinal populations. We develop various estimators from JPS data even for a situation that JPS suffers from empty strata. We also propose JPS estimators using multiple ranking resources. Through extensive numerical studies, we evaluate the performance of the methods in the estimation of the population. Finally, the developed estimation methods are applied to bone mineral data to estimate the bone disorder status of patients aged 50 and older.