This study examined the relationship between obesity (OB) and osteoporosis (OP), aiming to identify shared genetic markers and molecular mechanisms to facilitate the development of therapies that target both conditions simultaneously. Using weighted gene co-expression network analysis (WGCNA), we analyzed datasets from the Gene Expression Omnibus (GEO) database to identify co-expressed gene modules in OB and OP. These modules underwent Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction analysis to discover Hub genes. Machine learning refined the gene selection, with further validation using additional datasets. Single-cell analysis emphasized specific cell subpopulations, and enzyme-linked immunosorbent assay (ELISA), protein blotting, and cellular staining were used to investigate key genes.Aims
Methods
We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism. Five datasets were obtained from the Gene Expression Omnibus database. Unsupervised consensus cluster analysis was used to determine new PMOP subtypes. To determine the central genes and the core modules related to PMOP, the weighted gene co-expression network analysis (WCGNA) was applied. Gene Ontology enrichment analysis was used to explore the biological processes underlying key genes. Logistic regression univariate analysis was used to screen for statistically significant variables. Two algorithms were used to select important PMOP-related genes. A logistic regression model was used to construct the PMOP-related gene profile. The receiver operating characteristic area under the curve, Harrell’s concordance index, a calibration chart, and decision curve analysis were used to characterize PMOP-related genes. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression of the PMOP-related genes in the gene signature.Aims
Methods
The decrease in the number of satellite cells (SCs), contributing to myofibre formation and reconstitution, and their proliferative capacity, leads to muscle loss, a condition known as sarcopenia. Resistance training can prevent muscle loss; however, the underlying mechanisms of resistance training effects on SCs are not well understood. We therefore conducted a comprehensive transcriptome analysis of SCs in a mouse model. We compared the differentially expressed genes of SCs in young mice (eight weeks old), middle-aged (48-week-old) mice with resistance training intervention (MID+ T), and mice without exercise (MID) using next-generation sequencing and bioinformatics.Aims
Methods
Platelet concentrates, like platelet-rich plasma (PRP) and platelet lysate (PL), are widely used in regenerative medicine, especially in bone regeneration. However, the lack of standard procedures and controls leads to high variability in the obtained results, limiting their regular clinical use. Here, we propose the use of platelet-derived extracellular vesicles (EVs) as an off-the-shelf alternative for PRP and PL for bone regeneration. In this article, we evaluate the effect of PL-derived EVs on the biocompatibility and differentiation of mesenchymal stromal cells (MSCs). EVs were obtained first by ultracentrifugation (UC) and then by size exclusion chromatography (SEC) from non-activated PL. EVs were characterized by transmission electron microscopy, nanoparticle tracking analysis, and the expression of CD9 and CD63 markers by western blot. The effect of the obtained EVs on osteoinduction was evaluated in vitro on human umbilical cord MSCs by messenger RNA (mRNA) expression analysis of bone markers, alkaline phosphatase activity (ALP), and calcium (Ca2+) content.Aims
Methods
Aims. Tibial plateau fractures (TPFs) are complex injuries around the knee caused by high- or low-energy trauma. In the present study, we aimed to define the distribution and frequency of TPF lines using a 3D mapping technique and analyze the rationalization of divisions employed by frequently used
