Diaqua­(2,6-dihy­droxy­benzoato-κ2 O 1 ,O 1′)bis­(2,6-dihy­droxy­benzoato-κO 1)bis­(1,10-phenanthroline-κ2 N,N′)lanthanum(III)–1,10-phenanthroline (1/1)

In the title compound, [La(C7H5O4)3(C12H8N2)3(H2O)2]·C12H8N2, the LaIII atom is coordinated by four N atoms from two chelating 1,10-phenanthroline (phen) ligands, four O atoms from three 2,6-dihy­droxy­benzoate (DHB) anions (one monodentate, the other bidentate) and two water O atoms, completing a distorted LaN4O6 bicapped square-anti­prismatic geometry. Within the mononuclear complex mol­ecule, intra­molecular π–π stacking inter­actions are observed, the first between a coordinated phen mol­ecule and a DHB ligand [centroid–centroid distance = 3.7291 (16) Å], and the second between a coordinated phen mol­ecule and an uncoordinated phen ligand [centroid–centroid distance = 3.933 (2) Å]. Inter­molecular π–π stacking is observed between adjacent complexes [inter­planar distance = 3.461 (3) Å]. Intra- and inter­molecular O—H⋯O hydrogen bonds are observed in the DHB ligands and between a water mol­ecule and DHB ligands, respectively. O—H⋯N hydrogen bonds are also observed in the DHB ligands and between uncoordinated phen mol­ecules and aqua ligands

Bio-Inspired Hydrogels via 3D Bioprinting

Many soft tissues of the human body such as cartilages, muscles, and ligaments are mainly composed of biological hydrogels possessing excellent mechanical properties and delicate structures. Nowadays, bio-inspired hydrogels have been intensively explored due to their promising potential applications in tissue engineering. However, the traditional manufacturing technology is challenging to produce the bio-inspired hydrogels, and the typical biological composite topologies of bio-inspired hydrogels are accessible completed using 3D bioprinting at micrometer resolution. In this chapter, the 3D bioprinting techniques used for the fabrication of bio-inspired hydrogels were summarized, and the materials used were outlined. This chapter also focuses on the applications of bio-inspired hydrogels fabricated using available 3D bioprinting technologies. The development of 3D bioprinting techniques in the future would bring us closer to the fabrication capabilities of living organisms, which would be widely used in biomedical applications

Alginate-Based Composite and Its Biomedical Applications

Alginate has received much attention due to its biocompatibility. However, the properties of pure alginate are limited, such as weak mechanical strength, which limits its application. Alginate-based composite effectively overcomes the defect of pure alginate. The molecular weight and microstructure can be designed. More importantly, the essential properties for clinical application are improved, including mechanical properties, biocompatibility, gelation ability, chondrogenic differentiation and cell proliferation. This chapter will describe development of alginate-based composite in biomedical application. In the fields of wound dressing, drug delivery, and tissue engineering, the impact of structural changes on performance has been stated. To provide readers with understanding of this chapter, the structure and characterization of alginate will be included

Mining breast cancer genes with a network based noise-tolerant approach

Background: Mining novel breast cancer genes is an important task in breast cancer research. Many approaches prioritize candidate genes based on their similarity to known cancer genes, usually by integrating multiple data sources. However, different types of data often contain varying degrees of noise. For effective data integration, it's important to design methods that work robustly with respect to noise

Mining breast cancer genes with a network based noise-tolerant approach

Background: Mining novel breast cancer genes is an important task in breast cancer research. Many approaches prioritize candidate genes based on their similarity to known cancer genes, usually by integrating multiple data sources. However, different types of data often contain varying degrees of noise. For effective data integration, it's important to design methods that work robustly with respect to noise

Mining breast cancer genes with a network based noise-tolerant approach

AbstractBackgroundMining novel breast cancer genes is an important task in breast cancer research. Many approaches prioritize candidate genes based on their similarity to known cancer genes, usually by integrating multiple data sources. However, different types of data often contain varying degrees of noise. For effective data integration, it’s important to design methods that work robustly with respect to noise.ResultsGene Ontology (GO) annotations were often utilized in cancer gene mining works. However, the vast majority of GO annotations were computationally derived, thus not completely accurate. A set of genes annotated with breast cancer enriched GO terms was adopted here as a set of source data with realistic noise. A novel noise tolerant approach was proposed to rank candidate breast cancer genes using noisy source data within the framework of a comprehensive human Protein-Protein Interaction (PPI) network. Performance of the proposed method was quantitatively evaluated by comparing it with the more established random walk approach. Results showed that the proposed method exhibited better performance in ranking known breast cancer genes and higher robustness against data noise than the random walk approach. When noise started to increase, the proposed method was able to maintained relatively stable performance, while the random walk approach showed drastic performance decline; when noise increased to a large extent, the proposed method was still able to achieve better performance than random walk did.ConclusionsA novel noise tolerant method was proposed to mine breast cancer genes. Compared to the well established random walk approach, it showed better performance in correctly ranking cancer genes and worked robustly with respect to noise within source data. To the best of our knowledge, it’s the first such effort to quantitatively analyze noise tolerance between different breast cancer gene mining methods. The sorted gene list can be valuable for breast cancer research. The proposed quantitative noise analysis method may also prove useful for other data integration efforts. It is hoped that the current work can lead to more discussions about influence of data noise on different computational methods for mining disease genes

An Effective Method to Identify Shared Pathways and Common Factors among Neurodegenerative Diseases

Groups of distinct but related diseases often share common symptoms, which suggest likely overlaps in underlying pathogenic mechanisms. Identifying the shared pathways and common factors among those disorders can be expected to deepen our understanding for them and help designing new treatment strategies effected on those diseases. Neurodegeneration diseases, including Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD), were taken as a case study in this research. Reported susceptibility genes for AD, PD and HD were collected and human protein-protein interaction network (hPPIN) was used to identify biological pathways related to neurodegeneration. 81 KEGG pathways were found to be correlated with neurodegenerative disorders. 36 out of the 81 are human disease pathways, and the remaining ones are involved in miscellaneous human functional pathways. Cancers and infectious diseases are two major subclasses within the disease group. Apoptosis is one of the most significant functional pathways. Most of those pathways found here are actually consistent with prior knowledge of neurodegenerative diseases except two cell communication pathways: adherens and tight junctions. Gene expression analysis showed a high probability that the two pathways were related to neurodegenerative diseases. A combination of common susceptibility genes and hPPIN is an effective method to study shared pathways involved in a group of closely related disorders. Common modules, which might play a bridging role in linking neurodegenerative disorders and the enriched pathways, were identified by clustering analysis. The identified shared pathways and common modules can be expected to yield clues for effective target discovery efforts on neurodegeneration

Fusing literature and full network data improves disease similarity computation

Background: Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature.</p

Biomimetics: Bio-Inspired Hydrogels via 3D Bioprinting

info:eu-repo/semantics/publishe