A mini review focused on the proangiogenic role of silicate ions released from silicon-containing biomaterials

Angiogenesis is considered an important issue in the development of biomaterials for the successful regeneration of tissues including bone. While growth factors are commonly used with biomaterials to promote angiogenesis, some ions released from biomaterials can also contribute to angiogenic events. Many silica-based biomaterials have been widely used for the repair and regeneration of tissues, mainly hard tissues such as bone and tooth structure. They have shown excellent performance in bone formation by stimulating angiogenesis. The release of silicate and others (Co and Cu ions) has therefore been implicated to play critical roles in the angiogenesis process. In this short review, we highlight the in vitro and in vivo findings of angiogenesis (and the related bone formation) stimulated by the various types of silicon-containing biomaterials where silicate ions released might play essential roles. We discuss further the possible molecular mechanisms underlying in the ion-induced angiogenic events

SIS/aligned fibre scaffold designed to meet layered oesophageal tissue complexity and properties

With donor organs not readily available, the need for a tissue-engineered oesophagus remains high, particularly for congenital childhood conditions such as atresia. Previous attempts have not been successful, and challenges remain. Small intestine submucosa (SIS) is an acellular matrix material with good biological properties; however, as is common with these types of materials, they demonstrate poor mechanical properties. In this work, electrospinning was performed to mechanically reinforce tubular SIS with polylactic-co-glycolic acid (PLGA) nanofibres. It was hypothesised that if attachment could be achieved between the two materials, then this would (i) improve the SIS mechanical properties, (ii) facilitate smooth muscle cell alignment to support directional growth of muscle cells and (iii) allow for the delivery of bioactive molecules (VEGF in this instance). Through a relatively simple multistage process, adhesion between the layers was achieved without chemically altering the SIS. It was also found that altering mandrel rotation speed affected the alignment of the PLGA nanofibres. SIS-PLGA scaffolds performed mechanically better than SIS alone; yield stress improvement was 200% and 400% along the longitudinal and circumferential directions, respectively. Smooth muscle cells cultured on the aligned fibres showed resultant unidirectional alignment. In vivo the SIS-PLGA scaffolds demonstrated limited foreign body reaction judged by the type and proportion of immune cells present and lack of fibrous encapsulation. The scaffolds remained intact at 4 weeks in vivo, and good cellular infiltration was observed. The incorporation of VEGF within SIS-PLGA scaffolds increased the blood vessel density of the surrounding tissues, highlighting the possible stimulation of endothelialisation by angiogenic factor delivery. Overall, the designed SIS-PLGA-VEGF hybrid scaffolds might be used as a potential matrix platform for oesophageal tissue engineering. In addition to this, achieving improved attachment between layers of acellular matrix materials and electrospun fibre layers offers the potential utility in other applications. STATEMENT OF SIGNIFICANCE: Because of its multi-layered nature and complex structure, the oesophagus tissue poses several challenges for successful clinical grafting. Therefore, it is promising to utilise tissue engineering strategies to mimic and form structural compartments for its recovery. In this context, we investigated the use of tubular small intestine submucosa (SIS) reinforced with polylactic-co-glycolic acid (PLGA) nanofibres by using electrospinning and also, amongst other parameters, the integrity of the bilayered structure created. This was carried out to facilitate smooth muscle cell alignment, support directional growth of muscle cells and allow the delivery of bioactive molecules (VEGF in this study). We evaluated this approach by using in vitro and in vivo models to determine the efficacy of this new system

Nano-graphene oxide/polyurethane nanofibers: mechanically flexible and myogenic stimulating matrix for skeletal tissue engineering

For skeletal muscle engineering, scaffolds that can stimulate myogenic differentiation of cells while possessing suitable mechanical properties (e.g. flexibility) are required. In particular, the elastic property of scaffolds is of importance which helps to resist and support the dynamic conditions of muscle tissue environment. Here, we developed highly flexible nanocomposite nanofibrous scaffolds made of polycarbonate diol and isosorbide-based polyurethane and hydrophilic nano-graphene oxide added at concentrations up to 8%. The nano-graphene oxide incorporation increased the hydrophilicity, elasticity, and stress relaxation capacity of the polyurethane-derived nanofibrous scaffolds. When cultured with C2C12 cells, the polyurethane–nano-graphene oxide nanofibers enhanced the initial adhesion and spreading of cells and further the proliferation. Furthermore, the polyurethane–nano-graphene oxide scaffolds significantly up-regulated the myogenic mRNA levels and myosin heavy chain expression. Of note, the cells on the flexible polyurethane–nano-graphene oxide nanofibrous scaffolds could be mechanically stretched to experience dynamic tensional force. Under the dynamic force condition, the cells expressed significantly higher myogenic differentiation markers at both gene and protein levels and exhibited more aligned myotubular formation. The currently developed polyurethane–nano-graphene oxide nanofibrous scaffolds, due to their nanofibrous morphology and high mechanical flexibility, along with the stimulating capacity for myogenic differentiation, are considered to be a potential matrix for future skeletal muscle engineering

Gelatin-apatite bone mimetic co-precipitates incorporated within biopolymer matrix to improve mechanical and biological properties useful for hard tissue repair

Synthetic biopolymers are commonly used for the repair and regeneration of damaged tissues. Specifically targeting bone, the composite approach of utilizing inorganic components is considered promising in terms of improving mechanical and biological properties. We developed gelatin-apatite co-precipitates which mimic the native bone matrix composition within poly(lactide-co-caprolactone) (PLCL). Ionic reaction of calcium and phosphate with gelatin molecules enabled the co-precipitate formation of gelatin-apatite nanocrystals at varying ratios. The gelatin-apatite precipitates formed were carbonated apatite in nature, and were homogeneously distributed within the gelatin matrix. The incorporation of gelatin-apatite significantly improved the mechanical properties, including tensile strength, elastic modulus and elongation at break, and the improvement was more pronounced as the apatite content increased. Of note, the tensile strength increased to as high as 45 MPa (a four-fold increase vs. PLCL), the elastic modulus was increased up to 1500 MPa (a five-fold increase vs. PLCL), and the elongation rate was ∼240% (twice vs. PLCL). These results support the strengthening role of the gelatin-apatite precipitates within PLCL. The gelatin-apatite addition considerably enhanced the water affinity and the acellular mineral-forming ability in vitro in simulated body fluid; moreover, it stimulated cell proliferation and osteogenic differentiation. Taken together, the GAp-PLCL nanocomposite composition is considered to have excellent mechanical and biological properties, which hold great potential for use as bone regenerative matrices

Glass-forming compositions and physicochemical properties of degradable phosphate and silver-doped phosphate glasses in the P2O5–CaO–Na2O–Ag2O system

Phosphate and silver-doped phosphate glasses are potential candidates for use as degradable biomaterials and as antibacterial materials as well. The present investigation explores the glass-forming compositions (GFC), physical properties and degradation rates of both phosphate glasses in the P2O5–CaO–Na2O ternary system and silver-phosphate glasses derived from it by introducing Ag2O in replacement of Na2O. The glasses were prepared using the traditional melting–annealing technique applied in glass making industry. Bulk glasses were prepared without using any special precautions or specific conditions (contrary to previous studies) which can prevent crystallization or segregation of silver particles from the melt. A wide glass formation domain with ≥40 mol% P2O5 was determined in the ternary P2O5–CaO–Na2O system. However, up on Ag2O addition, the amount of Ag2O that can exist in the glass and remains amorphous was limited to 2 mol% as ensured from X-ray diffraction (XRD). The compositions with ≥60 mol% P2O5 and 0.5, 1 or 2 mol% Ag2O formed transparent and colorless silver phosphate glasses. Whereas, the compositions with ≤55 mol% P2O5 did not form glasses and showed immediate partial crystallization and separation of silver particles. Thereafter, the structure of representative glasses was studied by FT-IR and UV–vis absorption spectroscopy. Finally, as silver ions function as antibacterial metal ions, the amounts of silver ions released from silver phosphate glasses were measured by atomic absorption spectrometry (AAS). Keywords: Glass-forming compositions, Degradable phosphate glasses, Silver-doped phosphate glasses, Physicochemical properties, Degradation rate, Silver ions releas

Network motifs for translator stylometry identification

<div><p>Despite the extensive literature investigating stylometry analysis in authorship attribution research, translator stylometry is an understudied research area. The identification of translator stylometry contributes to many fields including education, intellectual property rights and forensic linguistics. In a two stage process, this paper first evaluates the use of existing lexical measures for the translator stylometry problem. Similar to previous research we found that using vocabulary richness in its traditional form as it has been used in the literature could not identify translator stylometry. This encouraged us to design an approach with the aim of identifying the distinctive patterns of a translator by employing network-motifs. Networks motifs are small sub-graphs which aim at capturing the local structure of a complex network. The proposed approach achieved an average accuracy of 83% in three-way classification. These results demonstrate that classic tools based on lexical features can be used for identifying translator stylometry if they get augmented with appropriate non-parametric scaling. Moreover, the use of complex network analysis and network motifs mining provided made it possible to design features that can solve translator stylometry analysis problems.</p></div

Pairwise comparative classification for translator stylometric analysis

In this article, we present a new type of classification problem, which we call Comparative Classification Problem (CCP), where we use the term data record to refer to a block of instances. Given a single data record with n instances for n classes, the CCP problem is to map each instance to a unique class. This problem occurs in a wide range of applications where the independent and identically distributed assumption is broken down. The primary difference between CCP and classical classification is that in the latter, the assignment of a translator to one record is independent of the assignment of a translator to a different record. In CCP, however, the assignment of a translator to one record within a block excludes this translator from further assignments to any other record in that block. The interdependency in the data poses challenges for techniques relying on the independent and identically distributed (iid) assumption. In the Pairwise CCP (PWCCP), a pair of records is grouped together. The key difference between PWCCP and classical binary classification problems is that hidden patterns can only be unmasked by comparing the instances as pairs. In this article, we introduce a new algorithm, PWC4.5, which is based on C4.5, to manage PWCCP. We first show that a simple transformation—that we call Gradient-Based Transformation (GBT)—can fix the problem of iid in C4.5. We then evaluate PWC4.5 using two real-world corpora to distinguish between translators on Arabic-English and French-English translations. While the traditional C4.5 failed to distinguish between different translators, GBT demonstrated better performance. Meanwhile, PWC4.5 consistently provided the best results over C4.5 and GBT. </jats:p

Convolutional Neural Networks Using Dynamic Functional Connectivity for EEG-Based Person Identification in Diverse Human States

Highly secure access control requires Swiss-cheese-type multi-layer security protocols. The use of electroencephalogram (EEG) to provide cognitive indicators for human workload and fatigue has created environments where the EEG data are well-integrated into systems, making it readily available for more forms of innovative uses including biometrics. However, most of the existing studies on EEG biometrics rely on resting state signals or require specific and repetitive sensory stimulation, limiting their uses in naturalistic settings. Moreover, the limited discriminatory power of uni-variate measures denies an opportunity to use dependences information inherent in brain regions to design more robust biometric identifiers. In this paper, we proposed a novel model for ongoing EEG biometric identification using EEG collected during a diverse set of tasks. The novelty lies in representing EEG signals as a graph based on within-frequency and cross-frequency functional connectivity estimates, and the use of graph convolutional neural network (GCNN) to automatically capture deep intrinsic structural representations from the EEG graphs for person identification. An extensive investigation was carried out to assess the robustness of the method against diverse human states, including resting states under eye-open and eye-closed conditions and active states drawn during the performance of four different tasks. We compared our method with the state-of-the-art EEG features, classifiers, and models of EEG biometrics. Results show that the representation drawn from EEG functional connectivity graphs demonstrates more robust biometric traits than direct use of uni-variate features. Moreover, the GCNN can effectively and efficiently capture discriminative traits, thus generalizing better over diverse human states.</p

Proceedings of the International Joint Conference on Neural Networks

Autoencoders (AE) have been used successfully as unsupervised learners for inferring latent information, learning hidden features and reducing the dimensionality of the data. In this paper, we propose a new AE architecture: Gate-Layer AE (GLAE). The novelty of GLAE lies in its ability to encourage learning of the relationships among different input variables, which affords it with an inherent ability to recover missing variables from the available ones and to act as a concurrent multi-function approximator.GLAE uses a network architecture that associates each input with a binary gate acting as a switch that turns on or off the flow to each input unit, while synchronising its action with data flow to the network. We test GLAE with different coding sizes and compare its performance against the Classic AE, Denoising AE and Variational AE. The evaluation uses Electroencephalograph (EEG) data with an aim to reconstruct the EEG signal when some data are missing. The results demonstrate GLAE's superior performance in reconstructing EEG signals with up to 25% missing data in an input stream