Chest radiography deep radiomics-enabled aortic arch calcification interpretation across different populations.

Earlier detection of aortic calcification can facilitate subsequent cardiovascular care planning. Opportunistic screening based on plain chest radiography is potentially feasible in various population. We used multiple deep convolutional neural network (CNN) transfer learning by fine-tuning pre-trained models followed by ensemble technique for aortic arch calcification on chest radiographs from a derivation and two external databases with distinct features.

The context-dependent role of transforming growth factor-β/miR-378a-3p/connective tissue growth factor in vascular calcification: a translational study.

Background: Vascular calcification (VC) constitutes an important vascular pathology with prognostic importance. The pathogenic role of transforming growth factor-β (TGF-β) in VC remains unclear, with heterogeneous findings that we aimed to evaluate using experimental models and clinical specimens.

Methods: Two approaches, exogenous administration and endogenous expression upon osteogenic media (OM) exposure, were adopted.

Deep Learning-Assisted Repurposing of Plant Compounds for Treating Vascular Calcification: An In Silico Study with Experimental Validation.

Background: Vascular calcification (VC) constitutes subclinical vascular burden and increases cardiovascular mortality. Effective therapeutics for VC remains to be procured. We aimed to use a deep learning-based strategy to screen and uncover plant compounds that potentially can be repurposed for managing VC.

DNA methylome and transcriptome landscapes of cancer-associated fibroblasts reveal a smoking-associated malignancy index.

Unlike the better-studied aberrant epigenome in the tumor, the clinicopathologic impact of DNA methylation in the tumor microenvironment (TME), especially the contribution from cancer-associated fibroblasts (CAFs), remains elusive. CAFs exhibit profound patient-to-patient tumorigenic heterogeneity. We asked whether such heterogeneity may be exploited to quantify the level of TME malignancy.

Superoxide Dismutase 2 (SOD2) in Vascular Calcification: A Focus on Vascular Smooth Muscle Cells, Calcification Pathogenesis, and Therapeutic Strategies.

Vascular calcification (VC) describes the pathophysiological phenotype of calcium apatite deposition within the vascular wall, leading to vascular stiffening and the loss of compliance. VC is never benign; the presence and severity of VC correlate closely with the risk of myocardial events and cardiovascular mortality in multiple at-risk populations such as patients with diabetes and chronic kidney disease. Mitochondrial dysfunction involving each of vascular wall constituents (endothelia and vascular smooth muscle cells (VSMCs)) aggravates various vascular pathologies, including atherosclerosis and VC.

Astaxanthin Counteracts Vascular Calcification In Vitro Through an Early Up-Regulation of SOD2 Based on a Transcriptomic Approach.

Vascular calcification (VC) is a critical contributor to the rising cardiovascular risk among at-risk populations such as those with diabetes or renal failure. The pathogenesis of VC involves an uprising of oxidative stress, for which antioxidants can be theoretically effective. However, astaxanthin, a potent antioxidant, has not been tested before for the purpose of managing VC.

A combined microRNA and target protein-based panel for predicting the probability and severity of uraemic vascular calcification: a translational study.

Aims: Vascular calcification (VC) increases the future risk of cardiovascular events in uraemic patients, but effective therapies are still unavailable. Accurate identification of those at risk of developing VC using pathogenesis-based biomarkers is of particular interest and may facilitate individualized risk stratification. We aimed to uncover microRNA (miRNA)-target protein-based biomarker panels for evaluating uraemic VC probability and severity.

Predicting the Associations between Meridians and Chinese Traditional Medicine Using a Cost-Sensitive Graph Convolutional Neural Network.

Natural products are the most important and commonly used in Traditional Chinese Medicine (TCM) for healthcare and disease prevention in East-Asia. Although the Meridian system of TCM was established several thousand years ago, the rationale of Meridian classification based on the ingredient compounds remains poorly understood. A core challenge for the traditional machine learning approaches for chemical activity prediction is to encode molecules into fixed length vectors but ignore the structural information of the chemical compound.

MicroRNA-125b in vascular diseases: An updated systematic review of pathogenetic implications and clinical applications.

Epigenetic changes, particularly non-coding RNAs, have been implicated extensively in the pathogenesis of vascular diseases. Specific miRNAs are involved in the differentiation, phenotypic switch, proliferation, apoptosis, cytokine production and matrix deposition of endothelial cells and/or vascular smooth muscle cells. MicroRNA-125b has been studied in depth for its role in carcinogenesis with a double-edged role; that is, it can act as an oncogene in some cancer types and as a tumour suppressor gene in others.

Risk Factors Associated With Altered Circulating Micro RNA -125b and Their Influences on Uremic Vascular Calcification Among Patients With End-Stage Renal Disease.

Background Micro RNA -125b (miR-125b) has been shown to regulate vascular calcification ( VC ), and serum miR-125b levels are a potential biomarker for estimating the risk of uremic VC status. However, it is unknown whether clinical features, including chronic kidney disease-mineral bone disorder molecules, affect serum miR-125b levels. Methods and Results Patients receiving chronic dialysis for ≥3 months were recruited from different institutes.

Circulating MicroRNA-125b Predicts the Presence and Progression of Uremic Vascular Calcification.

Objective: Vascular calcification (VC) is a major cause of mortality in patients with end-stage renal diseases. Biomarkers to predict the progression of VC early are in urgent demand.

Approach And Results: We identified circulating, cell-free microRNAs as potential biomarkers using in vitro VC models in which both rat and human aortic vascular smooth muscle cells were treated with high levels of phosphate to mimic uremic hyperphosphatemia.

Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation.

Background: During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.

Methods: We combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge.

A brief survey on computational approaches to reveal drug and disease associations.

People worldwide are still threatened by various complex disease phenotypes, especially cancer which is usually caused by the accumulation of multi-factor-driven alterations. Although drugs achieve the therapeutic functions by targeting particular molecular, the therapies used nowadays against diseases are not effective enough due to the limitation of the knowledge about the drug-disease associations. The rapid increasing of the available experimental data and knowledge enable scientists to reveal drug-disease associations by the systematic integration and analysis.

Biomarker identification for prostate cancer and lymph node metastasis from microarray data and protein interaction network using gene prioritization method.

Finding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene prioritization method called gene prioritization from microarray data based on shortest paths, extended with structural and biological properties and edge flux using voting scheme (GP-MIDAS-VXEF).

Pathway detection from protein interaction networks and gene expression data using color-coding methods and A∗ search algorithms.

With the large availability of protein interaction networks and microarray data supported, to identify the linear paths that have biological significance in search of a potential pathway is a challenge issue. We proposed a color-coding method based on the characteristics of biological network topology and applied heuristic search to speed up color-coding method. In the experiments, we tested our methods by applying to two datasets: yeast and human prostate cancer networks and gene expression data set.

Identifying co-targets to fight drug resistance based on a random walk model.

Background: Drug resistance has now posed more severe and emergent threats to human health and infectious disease treatment. However, wet-lab approaches alone to counter drug resistance have so far still achieved limited success due to less knowledge about the underlying mechanisms of drug resistance. Our approach apply a heuristic search algorithm in order to extract active network under drug treatment and use a random walk model to identify potential co-targets for effective antibacterial drugs.

A network flow approach to predict drug targets from microarray data, disease genes and interactome network - case study on prostate cancer.

Background: Systematic approach for drug discovery is an emerging discipline in systems biology research area. It aims at integrating interaction data and experimental data to elucidate diseases and also raises new issues in drug discovery for cancer treatment. However, drug target discovery is still at a trial-and-error experimental stage and it is a challenging task to develop a prediction model that can systematically detect possible drug targets to deal with complex diseases.

Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency.

Background: Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear.