Studying trabecular bone samples demonstrates a power law relation between deteriorated structure and mechanical properties - a study combining 3D printing with the finite element method

IntroductionThe bone volume fraction (BV/TV) significantly contributes to the mechanical properties of trabecular bone. However, when studies compare normal trabeculae against osteoporotic trabeculae (in terms of BV/TV decrease), only an “average” mechanical result has been determined because of the limitation that no two trabecular structures are the same and that each unique trabecular structure can be mechanically tested only once. The mathematic relation between individual structural deterioration and mechanical properties during aging or the osteoporosis process has yet to be further clarified. Three-dimensional (3D) printing and micro-CT-based finite element method (μFEM) can assist in overcoming this issue.MethodsIn this study, we 3D printed structural-identical but BV/TV value-attenuated trabecular bones (scaled up ×20) from the distal femur of healthy and ovariectomized rats and performed compression mechanical tests. Corresponding μFEM models were also established for simulations. The tissue modulus and strength of 3D printed trabecular bones as well as the effective tissue modulus (denoted as Ez) derived from μFEM models were finally corrected by the side-artifact correction factor.ResultsThe results showed that the tissue modulus corrected, strength corrected and Ez corrected exhibited a significant power law function of BV/TV in structural-identical but BV/TV value-attenuated trabecular samples. DiscussionUsing 3D printed bones, this study confirms the long-known relationship measured in trabecular tissue with varying volume fractions. In the future, 3D printing may help us attain better bone strength evaluations and even personal fracture risk assessments for patients who suffer from osteoporosis

Analysis of Microarray-Identified Genes and MicroRNAs Associated with Idiopathic Pulmonary Fibrosis

The aim of this study was to identify potential microRNAs and genes associated with idiopathic pulmonary fibrosis (IPF) through web-available microarrays. The microRNA microarray dataset GSE32538 and the mRNA datasets GSE32537, GSE53845, and GSE10667 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DE-miRNAs)/genes (DEGs) were screened with GEO2R, and their associations with IPF were analyzed by comprehensive bioinformatic analyses. A total of 45 DE-microRNAs were identified between IPF and control tissues, whereas 67 common DEGs were determined to exhibit the same expression trends in all three microarrays. Furthermore, functional analysis indicated that microRNAs in cancer and ECM-receptor interaction were the most significant pathways and were enriched by the 45 DE-miRNAs and 67 common DEGs. Finally, we predicted potential microRNA-target interactions between 17 DE-miRNAs and 17 DEGs by using at least three online programs. A microRNA-mediated regulatory network among the DE-miRNAs and DEGs was constructed that might shed new light on potential biomarkers for the prediction of IPF progression

Differentially expressed serum proteins associated with calcium regulation and hypocalcemia in dairy cows

Objective Hypocalcemia is an important metabolic disease of dairy cows during the transition period, although the effect of hypocalcemia on biological function in dairy cows remains unknown. Methods In this study, proteomic, mass spectrum, bioinformatics and western blotting were employed to identify differentially expressed proteins related to serum Ca concentration. Serum samples from dairy cows were collected at three time points: 3rd days before calving (day −3), the day of calving (day 0), and 3rd days after calving (day +3). According to the Ca concentration on day 0, a total of 27 dairy cows were assigned to one of three groups (clinical, subclinical, and healthy). Samples collected on day −3 were used for discovery of differentially expressed proteins, which were separated and identified via proteomic analysis and mass spectrometry. Bioinformatics analysis was performed to determine the function of the identified proteins (gene ontology and pathway analysis). The differentially expressed proteins were verified by western blot analysis. Results There were 57 differential spots separated and eight different proteins were identified. Vitamin D-binding protein precursor (group-specific component, GC), alpha-2-macroglobulin (A2M) protein, and apolipoprotein A-IV were related to hypocalcemia by bioinformatics analysis. Due to its specific expression (up-regulated in clinical hypocalcemia and down-regulated in subclinical hypocalcemia), A2M was selected for validation. The results were consistent with those of proteomic analysis. Conclusion A2M was as an early detection index for distinguishing clinical and subclinical hypocalcemia. The possible pathogenesis of clinical hypocalcemia caused by GC and apolipoprotein A-IV was speculated. The down-regulated expression of GC was a probable cause of the decrease in calcium concentration

Protein profiling of plasma proteins in dairy cows with subclinical hypocalcaemia

Abstract Subclinical hypocalcaemia (SH) is an important metabolic disease in dairy cows that has a serious impact on production performance. The objective of this study was to investigate novel aspects of pathogenesis using proteomics technology to identify proteins that are differentially expressed in diseased and healthy animals. Dairy cows were divided into an SH group (T, n \u2009=\u200910) and a control group (C, n \u2009=\u200910) based on plasma calcium concentration. A total of 398 differentially expressed proteins were identified, of which 265 proteins were overlapped in the two parallel experiments. Of these, 24 differentially expressed proteins were statistically significant. Gene Ontology analysis yielded 74 annotations, including 7 cellular component, 55 biological process and 12 molecular function categories. Bioinformatics analysis indicated that calcium regulation, immune and inflammatory response, blood coagulation and complement pathway were all related to SH. Our iTRAQ/LC-MS/MS (isobaric tags for relative and absolute quantification/liquid chromatography-mass spectrometry/mass spectrometry) approach proved highly effective for plasma protein profiling of dairy cows with SH, and the results pave the way for further studies in this area

Effects of hesperidin on the histological structure, oxidative stress, and apoptosis in the liver and kidney induced by NiCl2

The aim of this study was to investigate the effect of hesperidin on the liver and kidney dysfunctions induced by nickel. The mice were divided into six groups: nickel treatment with 80 mg/kg, 160 mg/kg, 320 mg/kg hesperidin groups, 0.5% CMC-Na group, nickel group, and blank control group. Histopathological techniques, biochemistry, immunohistochemistry, and the TUNEL method were used to study the changes in structure, functions, oxidative injuries, and apoptosis of the liver and kidney. The results showed that hesperidin could alleviate the weight loss and histological injuries of the liver and kidney induced by nickel, and increase the levels of lactate dehydrogenase (LDH), alanine aminotransferase (GPT), glutamic oxaloacetic transaminase (GOT) in liver and blood urea nitrogen (BUN), creatinine (Cr) and N-acetylglucosidase (NAG) in kidney. In addition, hesperidin could increase the activities of superoxide dismutase (SOD), catalase (CAT), glutathione (GSH), and glutathione peroxidase (GSH-Px) in the liver and kidney, decrease the content of malondialdehyde (MDA) and inhibit cell apoptosis. It is suggested that hesperidin could help inhibit the toxic effect of nickel on the liver and kidney

Relation Extraction for Protein-protein Interactions Affected by Mutations

M.S.Precision Medicine (PM) is a promising approach for cancer treatment in the modern medical practice. Information about protein-protein interaction and mutations affecting the interaction is essential in understanding biological processes and is one of the key aims in PM research. While previous research in text mining has achieved great progress in extracting protein-protein interactions (PPIs) in biomedical literature, few efforts have been made to explore methods to extract PPIs which are affected by mutations.In this thesis, I propose a feature-rich supervised method to extract PPIs affected by mutations from biomedical literature. First, a supervised model is trained to predict if a pair of proteins is interacting for new instances. Next, a ‘mutation refinement’ step is incorporated as a filter to determine the final answer. I compared effectiveness of two different training corpora (i. BioCreative VI PM track training; ii. AIMed+BioInfer) for model training. Experimental result shows that supervised model trained with combined corpus (AIMed+BioInfer) achieved better performance. Additionally, features selected from previous PPI extraction work and additional features were tested for model training. Evaluation of the result using BioCreative VI PM track testing dataset proves the effectiveness of the features proposed in my method. The system achieves up to 44% to improvement in F1-score over baseline method

The Design and Practice of Library Maker Service in Shenyang Normal University

Library maker services are a current focus of the industry's theoretical research and practice. With the popularization of the maker movement and the development of “mass entrepreneurship and innovation” in China, the library has been forced to re-examine itself and seek new development opportunities and spaces. Based on the experience of library maker service activities both at home and abroad, this paper explores the design of the library space transformation and the practice of the maker services in Shenyang Normal University. In the face of “mass entrepreneurship and innovation” and the background of education, library maker services have become the main melody, and the construction of makerspaces is the panacea to boost the development of library services. Every qualified library needs to be transformed, insufficient ones need to be reformed, and maker services are not only a development drive of the era, but also the essential path to the future.</jats:p

MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery

The recent advancement of omic technologies provides researchers with the possibility to search for disease-associated biomarkers at the system level. The integrative analysis of data from a large number of molecules involved at various layers of the biological system offers a great opportunity to rank disease biomarker candidates. In this paper, we propose MOTA, a network-based method that uses data acquired at multiple layers to rank candidate disease biomarkers. The networks constructed by MOTA allow users to investigate the biological significance of the top-ranked biomarker candidates. We evaluated the performance of MOTA in ranking disease-associated molecules from three sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and controls with liver cirrhosis. The results demonstrate that MOTA allows the identification of more top-ranked metabolite biomarker candidates that are shared by two different cohorts compared to traditional statistical methods. Moreover, the mRNA candidates top-ranked by MOTA comprise more cancer driver genes compared to those ranked by traditional differential expression methods

MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery

The recent advancement of omic technologies provides researchers with the possibility to search for disease-associated biomarkers at the system level. The integrative analysis of data from a large number of molecules involved at various layers of the biological system offers a great opportunity to rank disease biomarker candidates. In this paper, we propose MOTA, a network-based method that uses data acquired at multiple layers to rank candidate disease biomarkers. The networks constructed by MOTA allow users to investigate the biological significance of the top-ranked biomarker candidates. We evaluated the performance of MOTA in ranking disease-associated molecules from three sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and controls with liver cirrhosis. The results demonstrate that MOTA allows the identification of more top-ranked metabolite biomarker candidates that are shared by two different cohorts compared to traditional statistical methods. Moreover, the mRNA candidates top-ranked by MOTA comprise more cancer driver genes compared to those ranked by traditional differential expression methods.</jats:p