Aims. The use of 3D printing has become increasingly popular and has been widely used in orthopaedic surgery. There has been a trend towards an increasing number of publications in this field, but existing literature incorporates limited high-quality studies, and there is a lack of reports on outcomes. The aim of this study was to perform a scoping review with Level I evidence on the application and effectiveness of 3D printing. Methods. A literature search was performed in PubMed, Embase, and Web of Science databases. The keywords used for the search criteria were ((3d print*) OR (rapid prototyp*) OR (additive manufactur*)) AND (orthopaedic). The inclusion criteria were: 1) use of 3D printing in orthopaedics, 2) randomized controlled trials, and 3) studies with participants/patients. Risk of bias was assessed with Cochrane Collaboration Tool and PEDro Score. Pooled analysis was performed. Results. Overall, 21 studies were included in our study with a pooled total of 932 participants. Pooled analysis showed that operating time (p < 0.001), blood loss (p < 0.001), fluoroscopy times (p < 0.001), bone union time (p < 0.001), pain (p = 0.040),
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
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Objectives. The objective of this study was to investigate the association of four single-nucleotide polymorphisms (SNPs) of the cannabinoid receptor 2 (CNR2) gene, gene-obesity interaction, and haplotype combination with osteoporosis (OP) susceptibility. Methods. Chinese patients with OP were recruited between March 2011 and December 2015 from our hospital. In this study, a total of 1267 post-menopausal female patients (631 OP patients and 636 control patients) were selected. The mean age of all subjects was 69.2 years (sd 15.8). A generalized multifactor dimensionality reduction (GMDR) model and logistic regression model were used to examine the interaction between SNP and obesity on OP. For OP patient-control haplotype analyses, the SHEsis online haplotype analysis software (. http://analysis.bio-x.cn/. ) was employed. Results. The logistic regression model revealed that the C allele of rs2501431 and the G allele of rs3003336 were associated with increased OP risk, compared with those with wild genotype. However, no significant correlations were found when analyzing the association of rs4237 and rs2229579 with OP risk. The GMDR analysis suggested that the interaction model composed of two factors, rs3003336 and abdominal obesity (AO), was the best model with statistical significance (p-value from sign test (P. sign. ) = 0.012), indicating a potential gene-environment interaction between rs3003336 and AO. Overall, the two-locus models had a cross-validation consistency of 10/10 and had a testing
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
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Experimental studies indicate that non-steroidal anti-inflammatory drugs (NSAIDs) may have negative effects on fracture healing. This study aimed to assess the effect of immediate and delayed short-term administration of clinically relevant parecoxib doses and timing on fracture healing using an established animal fracture model. A standardized closed tibia shaft fracture was induced and stabilized by reamed intramedullary nailing in 66 Wistar rats. A ‘parecoxib immediate’ (Pi) group received parecoxib (3.2 mg/kg bodyweight twice per day) on days 0, 1, and 2. A ‘parecoxib delayed’ (Pd) group received the same dose of parecoxib on days 3, 4, and 5. A control group received saline only. Fracture healing was evaluated by biomechanical tests, histomorphometry, and dual-energy x-ray absorptiometry (DXA) at four weeks.Objectives
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Enhanced micromotions between the implant and surrounding bone can impair osseointegration, resulting in fibrous encapsulation and aseptic loosening of the implant. Since the effect of micromotions on human bone cells is sparsely investigated, an Micromotions ranging from 25 µm to 100 µm were applied as sine or triangle signal with 1 Hz frequency to human osteoblasts seeded on collagen scaffolds. Micromotions were applied for six hours per day over three days. During the micromotions, a static pressure of 527 Pa was exerted on the cells by Ti6Al4V cylinders. Osteoblasts loaded with Ti6Al4V cylinders and unloaded osteoblasts without micromotions served as controls. Subsequently, cell viability, expression of the osteogenic markers collagen type I, alkaline phosphatase, and osteocalcin, as well as gene expression of osteoprotegerin, receptor activator of NF-κB ligand, matrix metalloproteinase-1, and tissue inhibitor of metalloproteinase-1, were investigated.Objectives
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