Nanoparticulate systems for polynucleotide delivery

Nanotechnology has tremendously influenced gene therapy research in recent years. Nanometer-size systems have been extensively investigated for delivering genes at both local and systemic levels. These systems offer several advantages in terms of tissue penetrability, cellular uptake, systemic circulation, and cell targeting as compared to larger systems. They can protect the polynucleotide from a variety of degradative and destabilizing factors and enhance delivery efficiency to the cells. A variety of polymeric and non-polymeric nanoparticles have been investigated in an effort to maximize the delivery efficiency while minimizing the toxic effects. This article provides a review on the most commonly used nanoparticulate systems for gene delivery. We have discussed frequently used polymers, such as, polyethyleneimine, poly (lactide-co-glycolide), chitosan, as well as non-polymeric materials such as cationic lipids and metallic nanoparticles. The advantages and limitations of each system have been elaborated.     

Text mining biomedical literature for discovering gene-to-gene relationships: a comparative study of algorithms

Partitioning closely related genes into clusters has become an important element of practically all statistical analyses of microarray data. A number of computer algorithms have been developed for this task. Although these algorithms have demonstrated their usefulness for gene clustering, some basic problems remain. This paper describes our work on extracting functional keywords from MEDLINE for a set of genes that are isolated for further study from microarray experiments based on their differential expression patterns. The sharing of functional keywords among genes is used as a basis for clustering in a new approach called BEA-PARTITION in this paper. Functional keywords associated with genes were extracted from MEDLINE abstracts. We modified the Bond Energy Algorithm (BEA), which is widely accepted in psychology and database design but is virtually unknown in bioinformatics, to cluster genes by functional keyword associations. The results showed that BEA-PARTITION and hierarchical clustering algorithm outperformed k-means clustering and self-organizing map by correctly assigning 25 of 26 genes in a test set of four known gene groups. To evaluate the effectiveness of BEA-PARTITION for clustering genes identified by microarray profiles, 44 yeast genes that are differentially expressed during the cell cycle and have been widely studied in the literature were used as a second test set. Using established measures of cluster quality, the results produced by BEA-PARTITION had higher purity, lower entropy, and higher mutual information than those produced by k-means and self-organizing map. Whereas BEA-PARTITION and the hierarchical clustering produced similar quality of clusters, BEA-PARTITION provides clear cluster boundaries compared to the hierarchical clustering. BEA-PARTITION is simple to implement and provides a powerful approach to clustering genes or to any clustering problem where starting matrices are available from experimental observations.     

Gum arabic modified Fe<Subscript>3</Subscript>O<Subscript>4</Subscript> nanoparticles cross linked with collagen for isolation of bacteria

Background  

Multifunctional magnetic nanoparticles are important class of materials in the field of nanobiotechnology, as it is an emerging area of research for material science and molecular biology researchers. One of the various methods to obtain multifunctional nanomaterials, molecular functionalization by attaching organic functional groups to nanomagnetic materials is an important technique. Recently, functionalized magnetic nanoparticles have been demonstrated to be useful in isolation/detection of dangerous pathogens (bacteria/viruses) for human life. Iron (Fe) based material especially FePt is used in the isolation of ultralow concentrations (< 10² cfu/ml) of bacteria in less time and it has been demonstrated that van-FePt may be used as an alternative fast detection technique with respect to conventional polymerase chain reaction (PCR) method. However, still further improved demonstrations are necessary with interest to biocompatibility and green chemistry. Herein, we report the synthesis of Fe₃O₄ nanoparticles by template medication and its application for the detection/isolation of S. aureus bacteria.     

Visual Recognition Software for Binary Classification and Its Application to Spruce Pollen Identification

Discriminating between black and white spruce (Picea mariana and Picea glauca) is a difficult palynological classification problem that, if solved, would provide valuable data for paleoclimate reconstructions. We developed an open-source visual recognition software (ARLO, Automated Recognition with Layered Optimization) capable of differentiating between these two species at an accuracy on par with human experts. The system applies pattern recognition and machine learning to the analysis of pollen images and discovers general-purpose image features, defined by simple features of lines and grids of pixels taken at different dimensions, size, spacing, and resolution. It adapts to a given problem by searching for the most effective combination of both feature representation and learning strategy. This results in a powerful and flexible framework for image classification. We worked with images acquired using an automated slide scanner. We first applied a hash-based “pollen spotting” model to segment pollen grains from the slide background. We next tested ARLO’s ability to reconstruct black to white spruce pollen ratios using artificially constructed slides of known ratios. We then developed a more scalable hash-based method of image analysis that was able to distinguish between the pollen of black and white spruce with an estimated accuracy of 83.61%, comparable to human expert performance. Our results demonstrate the capability of machine learning systems to automate challenging taxonomic classifications in pollen analysis, and our success with simple image representations suggests that our approach is generalizable to many other object recognition problems.     

Talent in autism: hyper-systemizing,hyper-attention to detail and sensory hypersensitivity

We argue that hyper-systemizing predisposes individuals to show talent, and review evidence that hyper-systemizing is part of the cognitive style of people with autism spectrum conditions (ASC). We then clarify the hyper-systemizing theory, contrasting it to the weak central coherence (WCC) and executive dysfunction (ED) theories. The ED theory has difficulty explaining the existence of talent in ASC. While both hyper-systemizing and WCC theories postulate excellent attention to detail, by itself excellent attention to detail will not produce talent. By contrast, the hyper-systemizing theory argues that the excellent attention to detail is directed towards detecting ‘if p, then q’ rules (or [input–operation–output] reasoning). Such law-based pattern recognition systems can produce talent in systemizable domains. Finally, we argue that the excellent attention to detail in ASC is itself a consequence of sensory hypersensitivity. We review an experiment from our laboratory demonstrating sensory hypersensitivity detection thresholds in vision. We conclude that the origins of the association between autism and talent begin at the sensory level, include excellent attention to detail and end with hyper-systemizing.     

Structure-properties relations in graphene derivatives and metamaterials obtained by atomic-scale modeling

ABSTRACT

Recent findings of atomic-scale modelling studies are reviewed on graphene derivatives and metamaterials fabricated through chemical functionalization and/or defect engineering of graphene sheets. Results of molecular-statics and molecular-dynamics simulations according to a reliable bond-order potential, as well as first-principles density functional theory calculations are reviewed that have established useful structure-properties relations in two-dimensional materials, such as graphene nanomeshes (GNMs), electron-irradiated graphene, and interlayer-bonded twisted bilayer graphene. Quantitative relationships are established for the elastic moduli, mechanical properties, and thermal conductivity of GNMs as a function of the nanomesh porosity and the mechanical response of GNMs to uniaxial tensile straining is explored over the range of nanomesh porosities. The dependence of structural, mechanical, and thermal transport properties of electron-irradiated graphene sheets on the density of irradiation-induced defects is reviewed, highlighting an amorphization transition accompanied by a brittle-to-ductile transition and a transition in thermal transport mechanism beyond a critical defect concentration. The tunability of the electronic band structure, mechanical properties, and structural response to mechanical loading of graphene-diamond nanocomposite superstructures consisting of nanodiamond superlattices in interlayer-bonded twisted bilayer graphene also is demonstrated by precise control of the density and distribution of covalent interlayer C–C bonds.     

Factors Associated with Increased Survival after Surgical Resection of Glioblastoma in Octogenarians

Elderly patients with glioblastoma represent a clinical challenge for neurosurgeons and oncologists. The data available on outcomes of patients greater than 80 undergoing resection is limited. In this study, factors linked to increased survival in patients over the age of 80 were analyzed. A retrospective chart review of all patients over the age of 80 with a new diagnosis of glioblastoma and who underwent surgical resection with intent for maximal resection were examined. Patients who had only stereotactic biopsies were excluded. Immunohistochemical expression of oncogenic drivers (p53, EGFR, IDH-1) and a marker of cell proliferation (Ki-67 index) performed upon routine neuropathological examination were recorded. Stepwise logistic regression and Kaplan Meier survival curves were plotted to determine correlations to overall survival. Fifty-eight patients fit inclusion criteria with a mean age of 83 (range 80–93 years). The overall median survival was 4.2 months. There was a statistically significant correlation between Karnofsky Performance Status (KPS) and overall survival (P < 0.05). There was a significantly longer survival among patients undergoing either radiation alone or radiation and chemotherapy compared to those who underwent no postoperative adjuvant therapy (p < 0.05). There was also an association between overall survival and lack of p53 expression (p < 0.001) and lack of EGFR expression (p <0.05). In this very elderly population, overall survival advantage was conferred to those with higher preoperative KPS, postoperative adjuvant therapy, and lack of protein expression of EGFR and p53. These findings may be useful in clinical decision analysis for management of patients with glioblastoma who are octogenarians, and also validate the critical role of EGFR and p53 expression in oncogenesis, particularly with advancing age.     

Target of rapamycin activation predicts lifespan in fruit flies

Aging and age-related diseases are one of the most important health issues that the world will confront during the 21^st century. Only by understanding the proximal causes will we be able to find treatments to reduce or delay the onset of degenerative diseases associated with aging. Currently, the prevalent paradigm in the field is the accumulation of damage. However, a new theory that proposes an alternative explanation is gaining momentum. The hyperfunction theory proposes that aging is not a consequence of a wear and tear process, but a result of the continuation of developmental programs during adulthood. Here we use Drosophila melanogaster, where evidence supporting both paradigms has been reported, to identify which parameters that have been previously related with lifespan best predict the rate of aging in wild type flies cultured at different temperatures. We find that mitochondrial function and mitochondrial reactive oxygen species (mtROS) generation correlates with metabolic rate, but not with the rate of aging. Importantly, we find that activation of nutrient sensing pathways (i.e. insulin-PI3K/Target of rapamycin (Tor) pathway) correlates with lifespan, but not with metabolic rate. Our results, dissociate metabolic rate and lifespan in wild type flies and instead link nutrient sensing signaling with longevity as predicted by the hyperfunction theory.