MicroRNA detection in the pathogenesis of BAV-associated aortopathy-mediated vascular remodelling through EndMT/EMT

Prediction of PM2.5 concentration based on a CNN-LSTM neural network algorithm

Fine particulate matter (PM2.5) is a major air pollutant affecting human survival, development and health. By predicting the spatial distribution concentration of PM2.5, pollutant sources can be better traced, allowing measures to protect human health to be implemented. Thus, the purpose of this study is to predict and analyze the PM2.5 concentration of stations based on the integrated deep learning of a convolutional neural network long short-term memory (CNN-LSTM) model. To solve the complexity and nonlinear characteristics of PM2.5 time series data problems, we adopted the CNN-LSTM deep learning model. We collected the PM2.5data of Qingdao in 2020 as well as meteorological factors such as temperature, wind speed and air pressure for pre-processing and characteristic analysis. Then, the CNN-LSTM deep learning model was integrated to capture the temporal and spatial features and trends in the data. The CNN layer was used to extract spatial features, while the LSTM layer was used to learn time dependencies. Through comparative experiments and model evaluation, we found that the CNN-LSTM model can achieve excellent PM2.5 prediction performance. The results show that the coefficient of determination (R2) is 0.91, and the root mean square error (RMSE) is 8.216 µg/m3. The CNN-LSTM model achieves better prediction accuracy and generalizability compared with those of the CNN and LSTM models (R2 values of 0.85 and 0.83, respectively, and RMSE values of 11.356 and 14.367, respectively). Finally, we analyzed and explained the predicted results. We also found that some meteorological factors (such as air temperature, pressure, and wind speed) have significant effects on the PM2.5 concentration at ground stations in Qingdao. In summary, by using deep learning methods, we obtained better prediction performance and revealed the association between PM2.5 concentration and meteorological factors. These findings are of great significance for improving the quality of the atmospheric environment and protecting public health.

A Segmental Approach from Molecular Profiling to Medical Imaging to Study Bicuspid Aortic Valve Aortopathy

Bicuspid aortic valve (BAV) patients develop ascending aortic (AAo) dilation. The pathogenesis of BAV aortopathy (genetic vs. haemodynamic) remains unclear. This study aims to identify regional changes around the AAo wall in BAV patients with aortopathy, integrating molecular data and clinical imaging. BAV patients with aortopathy (n = 15) were prospectively recruited to surgically collect aortic tissue and measure molecular markers across the AAo circumference. Dilated (anterior/right) vs. non-dilated (posterior/left) circumferential segments were profiled for whole-genomic microRNAs (next-generation RNA sequencing, miRCURY LNA PCR), protein content (tandem mass spectrometry), and elastin fragmentation and degeneration (histomorphometric analysis). Integrated bioinformatic analyses of RNA sequencing and proteomic datasets identified five microRNAs (miR-128-3p, miR-210-3p, miR-150-5p, miR-199b-5p, and miR-21-5p) differentially expressed across the AAo circumference. Among them, three miRNAs (miR-128-3p, miR-150-5p, and miR-199b-5p) were predicted to have an effect on eight common target genes, whose expression was dysregulated, according to proteomic analyses, and involved in the vascular-endothelial growth-factor signalling, Hippo signalling, and arachidonic acid pathways. Decreased elastic fibre levels and elastic layer thickness were observed in the dilated segments. Additionally, in a subset of patients n = 6/15, a four-dimensional cardiac magnetic resonance (CMR) scan was performed. Interestingly, an increase in wall shear stress (WSS) was observed at the anterior/right wall segments, concomitantly with the differentially expressed miRNAs and decreased elastic fibres. This study identified new miRNAs involved in the BAV aortic wall and revealed the concomitant expressional dysregulation of miRNAs, proteins, and elastic fibres on the anterior/right wall in dilated BAV patients, corresponding to regions of elevated WSS.

Aortic Dilatation in Patients With Bicuspid Aortic Valve

Bicuspid aortic valve (BAV) is the most common congenital cardiac abnormality. BAV aortic dilatation is associated with an increased risk of adverse aortic events and represents a potentially lethal disease and hence a considerable medical burden. BAV with aortic dilatation warrants frequent monitoring, and elective surgical intervention is the only effective method to prevent dissection or rupture. The predictive value of the aortic diameter is known to be limited. The aortic diameter is presently still the main reference standard for surgical intervention owing to the lack of a comprehensive understanding of BAV aortopathy progression. This article provides a brief comprehensive review of the current knowledge on BAV aortopathy regarding clinical definitions, epidemiology, natural course, and pathophysiology, as well as hemodynamic and clinically significant aspects on the basis of the limited data available.