CRISPR System: A High-throughput Toolbox for Research and Treatment of Parkinson's Disease.

In recent years, the innovation of gene-editing tools such as the CRISPR/Cas9 system improves the translational gap of treatments mediated by gene therapy. The privileges of CRISPR/Cas9 such as working in living cells and organs candidate this technology for using in research and treatment of the central nervous system (CNS) disorders. Parkinson's disease (PD) is a common, debilitating, neurodegenerative disorder which occurs due to loss of dopaminergic neurons and is associated with progressive motor dysfunction. Knowledge about the pathophysiological basis of PD has altered the classification system of PD, which manifests in familial and sporadic forms. The first genetic linkage studies in PD demonstrated the involvement of Synuclein alpha (SNCA) mutations and SNCA genomic duplications in the pathogenesis of PD familial forms. Subsequent studies have also insinuated mutations in leucine repeat kinase-2 (LRRK2), Parkin, PTEN-induced putative kinase 1 (PINK1), as well as DJ-1 causing familial forms of PD. This review will attempt to discuss the structure, function, and development in genome editing mediated by CRISP/Cas9 system. Further, it describes the genes involved in the pathogenesis of PD and the pertinent alterations to them. We will pursue this line by delineating the PD linkage studies in which CRISPR system was employed. Finally, we will discuss the pros and cons of CRISPR employment vis-à-vis the process of genome editing in PD patients' iPSCs.

Design and manufacture of 3D-cylindrical scaffolds based on PLA/TPU/n-HA with the help of dual salt leaching technique suggested for use in cancellous bone tissue engineering.

A series of 3-D scaffolds based on polylactic acid (PLA) and thermoplastic polyurethane (TPU) as major phase and hydroxyapatite nanoparticles (n-HA) were prepared by using the dual leaching technique. Fourier-transform infrared spectroscopy analysis showed that almost the interactions between the constituent materials can be identified based on their functional groups. The results of thermogravimetric analysis were used to obtain the best time to prepare the samples without residual of any progen additives. The scanning electron macroscopy images clearly proved that the dual leaching technique is an effective method to prepare the appropriate morphology and also a very good dispersion and distribution for n-HA can be obtained. Dynamic contact angles showed that the presence of TPU in the PLA matrix has a positive effect on the hydrophilicity of the scaffolds. The bulk modulus (κ) values of S-PLA70TPU30H5 in dry and wet conditions were 321 and 212 Pa, respectively and the compressibility coefficient (ß) of pure samples was higher than that of other scaffolds, while among the nanocomposite samples, the compressibility coefficient of S-PLA70TPU30H5 and S-PLA50TPU50H5 samples in dry and wet conditions was higher than that of other samples. Biological tests such as 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) tetrazolium reduction assay, cell adhesion, 4',6-diamidino-2-phenylindole (DAPI) analysis and alizarin red were also performed, and the results obtained for 3D scaffolds were good. In the DAPI analysis test, sample 3D-S-PLA70TPU30H5 showed good behavior, and also in the alizarin red test, the amount of minerals created in 3D-S-PLA50TPU50H5 was significant.

Dual leaching technique was used to prepare cylindrical scaffolds with high porosity.3D cylindrical scaffolds based on PLA/TPU/n-HA were proposed for use in cancellous bone.Bulk modulus and compressibility coefficient of 3D cylindrical scaffolds were obtained in wet and dry conditions.DAPI analysis showed the role of n-HA nanoparticles on cell growth in 3D cylindrical scaffolds.Alizarin red studies showed that sample PLA50TPU50H5 has more calcium content.

Preparation and characterization of a new bio nanocomposites based poly(glycerol sebacic-urethane) containing nano-clay (Cloisite Na+ ) and its potential application for tissue engineering.

Nanocomposites containing clay nanoparticles often present favorable properties such as good mechanical and thermal properties. They frequently have been studied for tissue engineering (TE) and regenerative medicine applications. On the other hand, poly(glycerol sebacate) (PGS), a revolutionary bioelastomer, has exhibited substantial potential as a promising candidate for biomedical application. Here, we present a facile approach to synthesizing stiff, elastomeric nanocomposites from sodium-montmorillonite nano-clay (MMT) in the commercial name of Cloisite Na+ and poly(glycerol sebacate urethane) (PGSU). The strong physical interaction between the intercalated Cloisite Na+ platelets and PGSU chains resulted in desirable property combinations for TE application to follow. The addition of 5% MMT nano-clay resulted in an over two-fold increase in the tensile modulus, increased the onset thermal decomposition temperature of PGSU matrix by 18°C, and noticeably improved storage modulus of the prepared scaffolds, compared with pure PGSU. As well, Cloisite Na+ enhanced the hydrophilicity and water uptake ability of the samples and accelerated the in-vitro biodegradation rate. Finally, in-vitro cell viability assay using L929 mouse fibroblast cells indicated that incorporating Cloisite Na+ nanoparticles into the PGSU network could improve the cell attachment and proliferation, rendering the synthesized bioelastomers potentially suitable for TE and regenerative medicine applications.

Glioblastoma multiforme: a glance at advanced therapies based on nanotechnology.

Glioblastoma multiforme (GBM, grade IV) is the most common malignant and invasive central nervous system tumor with poor survival outcome. Various pathogenesis signatures such as genetic mutation, hypoxia, necrosis and neo-angiogenesis are involved in GBM. Standard treatment includes surgical resection along with radiation therapy and temozolomide (TMZ) chemotherapy that do not improve the overall survival of patients. In this review, we focused on the diagnosis, risk factors and novel therapies, using advanced therapies such as nanotechnology in drug delivery, gene therapy and hyperthermia that have promising roles in the treatment of aggressive brain tumors.

Dendritic Spines Taxonomy: The Functional and Structural Classification • Time-Dependent Probabilistic Model of Neuronal Activation.

Categorizing spines into four subpopulations, stubby, mushroom, thin, or filopodia, is one of the common approaches in morphological analysis. Most cellular models describing synaptic plasticity, long-term potentiation (LTP), and long-term depression associate synaptic strength with either spine enlargement or spine shrinkage. Unfortunately, although we have a lot of available software with automatic spine segmentation and feature extraction methods, at present none of them allows for automatic and unbiased distinction between dendritic spine subpopulations, or for the detailed computational models of spine behavior. Therefore, we propose structural classification based on two different mathematical approaches: unsupervised construction of spine shape taxonomy based on arbitrary features (SpineTool) and supervised classification exploiting convolution kernels theory (2dSpAn). We compared two populations of spines in a form of static and dynamic data sets gathered at three time points. The dynamic data contain two sets of spines: the active set and the control set. The first population was stimulated with LTP, and the other population in its resting state was used as a control population. We propose one equation describing the distribution of variables that best fits all dendritic spine parameters.

Developing science gateways for drug discovery in a grid environment.

BACKGROUND: Methods for in silico screening of large databases of molecules increasingly complement and replace experimental techniques to discover novel compounds to combat diseases. As these techniques become more complex and computationally costly we are faced with an increasing problem to provide the research community of life sciences with a convenient tool for high-throughput virtual screening on distributed computing resources. RESULTS: To this end, we recently integrated the biophysics-based drug-screening program FlexScreen into a service, applicable for large-scale parallel screening and reusable in the context of scientific workflows. CONCLUSIONS: Our implementation is based on Pipeline Pilot and Simple Object Access Protocol and provides an easy-to-use graphical user interface to construct complex workflows, which can be executed on distributed computing resources, thus accelerating the throughput by several orders of magnitude.

Generalized Baum-Welch algorithm based on the similarity between sequences.

The profile hidden Markov model (PHMM) is widely used to assign the protein sequences to their respective families. A major limitation of a PHMM is the assumption that given states the observations (amino acids) are independent. To overcome this limitation, the dependency between amino acids in a multiple sequence alignment (MSA) which is the representative of a PHMM can be appended to the PHMM. Due to the fact that with a MSA, the sequences of amino acids are biologically related, the one-by-one dependency between two amino acids can be considered. In other words, based on the MSA, the dependency between an amino acid and its corresponding amino acid located above can be combined with the PHMM. For this purpose, the new emission probability matrix which considers the one-by-one dependencies between amino acids is constructed. The parameters of a PHMM are of two types; transition and emission probabilities which are usually estimated using an EM algorithm called the Baum-Welch algorithm. We have generalized the Baum-Welch algorithm using similarity emission matrix constructed by integrating the new emission probability matrix with the common emission probability matrix. Then, the performance of similarity emission is discussed by applying it to the top twenty protein families in the Pfam database. We show that using the similarity emission in the Baum-Welch algorithm significantly outperforms the common Baum-Welch algorithm in the task of assigning protein sequences to protein families.