4.7 Editorial Material

Special Issue: Bioinformatics and Omics Tools

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Article Biochemistry & Molecular Biology

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Summary: The aim of this study was to investigate the relationship between P. gingivalis and AD, and explore the role of genes and molecular targets. Through gene expression analysis, 10 common genes associated with both diseases were identified, and molecular simulations were conducted. The findings of this study revealed novel genes related to P. gingivalis-associated AD that require further validation.

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PARM1 Drives Smooth Muscle Cell Proliferation in Pulmonary Arterial Hypertension via AKT/FOXO3A Axis

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Coronary Artery Disease and Aortic Valve Stenosis: A Urine Proteomics Study

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Summary: Coronary artery disease (CAD) and aortic valve stenosis (AVS) are common heart diseases with shared risk factors and pathogenesis. Urine, as a noninvasive collection method, is promising for biomarker assessment. This study identified dysregulated proteins in urine that could potentially differentiate these diseases from healthy subjects, and showed strong correlations between these proteins and a common cardiovascular risk factor.

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Quasispecies Fitness Partition to Characterize the Molecular Status of a Viral Population. Negative Effect of Early Ribavirin Discontinuation in a Chronically Infected HEV Patient

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Summary: This study presents a method to partition quasispecies haplotypes into four fractions based on their fitness and uses diversity indices to analyze the molecular changes in the composition of a quasispecies over time. The results suggest that mutagenic antiviral treatment can lead to the selection of a diverse set of synonymous haplotypes, while a prevalent master phenotype is observed at the protein level. This observation may have implications for mutagenic antiviral treatment.

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Review Medicine, Research & Experimental

High-throughput proteomics: a methodological mini-review

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Summary: Proteomics plays a vital role in biomedical research by providing new insights into complex biomedical problems. This mini-review summarizes existing and emerging high-throughput proteomics methodologies and discusses the importance of computational methods and statistical algorithms in maximizing the value of proteomic data. The advances in high-throughput proteomics not only enhance our understanding of disease mechanisms, but also offer potential applications in various fields such as basic research, oncology, precision medicine, and drug discovery.

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