Lack of association between the CALM1 core promoter polymorphism (-16C/T) and susceptibility to knee osteoarthritis in a Chinese Han population

<p>Abstract</p> <p>Background</p> <p><it>CALM1 </it>gene encodes calmodulin (CaM), an important and ubiquitous eukaryotic Ca²⁺-binding protein. Several studies have indicated that a deficient CaM function is likely to be involved in the pathogenesis of osteoarthritis (OA). Using a convincing genome-wide association study, a Japanese group has recently demonstrated a genetic association between the <it>CALM1 </it>core promoter polymorphism (-16C/T transition SNP, rs12885713) and OA susceptibility. However, the subsequent association studies failed to provide consistent results in OA patients of differently selected populations. The present study is to evaluate the association of the -16C/T polymorphism with knee OA in a Chinese Han population.</p> <p>Methods</p> <p>A case-control association study was conducted. The polymorphism was genotyped in 183 patients who had primary symptomatic knee OA with radiographic confirmation and in 210 matched controls. Allelic and genotypic frequencies were compared between patients and control subjects.</p> <p>Results</p> <p>No significant difference was detected in genotype or allele distribution between knee OA and control groups (all <it>P </it>> 0.05). The association was also negative even after stratification by sex. Furthermore, no association between the -16C/T SNP genotype and the clinical variables age, sex, BMI (body mass index) and K/L (Kellgren/Lawrence) score was observed in OA patients.</p> <p>Conclusion</p> <p>The present study suggests that the CALM1 core promoter polymorphism -16C/T is not a risk factor for knee OA susceptibility in the Chinese Han population. Further studies are needed to give a global view of this polymorphism in pathogenesis of OA.</p

Evaluation of the Effect of the Sulcus Angle and Lateral to Medial Facet Ratio of the Patellar Groove on Patella Tracking in Aging Subjects with Stable Knee Joint

Purpose. To determine whether the sulcus angle and the lateral to medial facet ratio correlate with patella lateral displacement and tilt in patients without patella instability. Methods. Computed tomography images of the lower limb of 64 patients without known arthropathy were collected. Three-dimensional models of the lower limb with a unified coordinate system were rebuilt by using Mimics software. The sulcus angle, lateral to medial facet ratio, lateral trochlear inclination of the patellar groove, tibial tuberosity-trochlear groove (TT-TG) distance, bisect offset index, and lateral tilt of the patella were measured. Pearson’s correlation test was used to determine the relationship between the aforementioned parameters. Results. Data from 51 patients were analyzed. The sulcus angle was negatively correlated with lateral tilt inclination (p<0.001, r=0.8406) and positively correlated with the bisect offset index (p=0.003, r=0.634) and patellar tilt (p=0.03, r=0.551); the lateral to medial facet ratio was positively correlated with TT-TG distance (p=0.003, r=0.643) and bisect offset index (p=0.026, r=0.559). Conclusion. The sulcus angle and lateral to medial facet ratio of the patellar groove can influence patella tracking in patients with stable knee joints

Predictive value of neutrophil to lymphocyte ratio and platelet to lymphocyte ratio for acute deep vein thrombosis after total joint arthroplasty: a retrospective study

Abstract Background Deep vein thrombosis (DVT) is a common and severe complication of total joint arthroplasty (TJA). Inflammation has been proved to play a role in DVT. The neutrophil to lymphocyte ratio (NLR) and the platelet to lymphocyte ratio (PLR) are biomarkers for systemic inflammation. The aim of the study is to investigate the predictive value of NLR and PLR for acute TJA-induced DVT. Method A total of 773 patients who underwent primary TJA in our hospital were included in this retrospective study. Venography was performed routinely after the surgery to define acute DVT. NLR and PLR before and after operation were calculated according to the blood routine test. Multiple logistic regression analyses and ROC curve analyses were performed to assess the association of NLR and PLR with TJA-induced DVT. Results One hundred twenty out of 773 patients (15.5%) were diagnosed with DVT by venography. In patients with DVT, preoperative NLR (P = 0.030) and postoperative NLR (P = 0.015) were significantly higher but postoperative PLR (P = 0.002) was significantly lower. Multiple logistic regression analyses indicated that age (OR = 1.05, P < 0.005), gender (OR = 0.47, P = 0.005), BMI (OR = 1.06, P < 0.014), preoperative NLR (OR = 1.11, P < 0.035), postoperative NLR (OR = 1.20, P < 0.001), and PLR (OR = 0.99, P < 0.001) were independently associated with DVT. However, the ROC curve analysis demonstrated the specificity and sensitivity of NLR or PLR in predicting DVT were low. Conclusion Although the present study demonstrated significant association of perioperative NLR or PLR with acute TJA-induced DVT, NLR or PLR cannot predict TJA-induced DVT accurately

Identification of Transcription Factor Networks during Mouse Hindlimb Development

Mammalian hindlimb development involves a variety of cells and the regulation of spatiotemporal molecular events, but regulatory networks of transcription factors contributing to hindlimb morphogenesis are not well understood. Here, we identified transcription factor networks during mouse hindlimb morphology establishment through transcriptome analysis. We used four stages of embryonic hindlimb transcription profiles acquired from the Gene Expression Omnibus database (GSE30138), including E10.5, E11.5, E12.5 and E13.5, to construct a gene network using Weighted Gene Co-expression Network Analysis (WGCNA), and defined seven stage-associated modules. After filtering 7625 hub genes, we further prioritized 555 transcription factors with AnimalTFDB3.0. Gene ontology enrichment showed that transcription factors of different modules were enriched in muscle tissue development, connective tissue development, embryonic organ development, skeletal system morphogenesis, pattern specification process and urogenital system development separately. Six regulatory networks were constructed with key transcription factors, which contribute to the development of different tissues. Knockdown of four transcription factors from regulatory networks, including Sox9, Twist1, Snai2 and Klf4, showed that the expression of limb-development-related genes was also inhibited, which indicated the crucial role of transcription factor networks in hindlimb development

Identification of Transcription Factor Networks during Mouse Hindlimb Development

Mammalian hindlimb development involves a variety of cells and the regulation of spatiotemporal molecular events, but regulatory networks of transcription factors contributing to hindlimb morphogenesis are not well understood. Here, we identified transcription factor networks during mouse hindlimb morphology establishment through transcriptome analysis. We used four stages of embryonic hindlimb transcription profiles acquired from the Gene Expression Omnibus database (GSE30138), including E10.5, E11.5, E12.5 and E13.5, to construct a gene network using Weighted Gene Co-expression Network Analysis (WGCNA), and defined seven stage-associated modules. After filtering 7625 hub genes, we further prioritized 555 transcription factors with AnimalTFDB3.0. Gene ontology enrichment showed that transcription factors of different modules were enriched in muscle tissue development, connective tissue development, embryonic organ development, skeletal system morphogenesis, pattern specification process and urogenital system development separately. Six regulatory networks were constructed with key transcription factors, which contribute to the development of different tissues. Knockdown of four transcription factors from regulatory networks, including Sox9, Twist1, Snai2 and Klf4, showed that the expression of limb-development-related genes was also inhibited, which indicated the crucial role of transcription factor networks in hindlimb development

Impacts of Urban Transportation Mode Split on CO2 Emissions in Jinan, China

As the world’s largest developing country, China currently is undergoing rapid urbanization and motorization, which will result in far-reaching impacts on energy and the environment. According to estimates, energy use and carbon emissions in the transportation sector will comprise roughly 30% of total emissions by 2030. Since the late 1990s, transportation-related issues such as energy, consumption, and carbon emissions have become a policy focus in China. To date, most research and policies have centered on vehicle technologies that promote vehicle efficiency and reduced emissions. Limited research exists on the control of greenhouse gases through mode shifts in urban transportation—in particular, through the promotion of public transit. The purpose of this study is to establish a methodology to analyze carbon emissions from the urban transportation sector at the Chinese city level. By using Jinan, the capital of China’s Shandong Province, as an example, we have developed an analytical model to simulate energy consumption and carbon emissions based on the number of trips, the transportation mode split, and the trip distance. This model has enabled us to assess the impacts of the transportation mode split on energy consumption and carbon emissions. Furthermore, this paper reviews a set of methods for data collection, estimation, and processing for situations where statistical data are scarce in China. This paper also describes the simulation of three transportation system development scenarios. The results of this study illustrate that if no policy intervention is implemented for the transportation mode split (the business-as-usual (BAU) case), then emissions from Chinese urban transportation systems will quadruple by 2030. However, a dense, mixed land-use pattern, as well as transportation policies that encourage public transportation, would result in the elimination of 1.93 million tons of carbon emissions—approximately 50% of the BAU scenario emissions