P A College of Engineering

About: P A College of Engineering is a based out in . It is known for research contribution in the topics: Dihedral angle & Ring (chemistry). The organization has 298 authors who have published 594 publications receiving 4888 citations.

Toxicology of carbon nanotubes - A review

Abstract: A large number of nanoparticles are present in the environment in which some are unintentionally produced; ultra fine particles or intentionally produced engineered nanoparticle (ENPs). The carbon based ENPs include singlewalled and multi walled carbon nanotubes (SWCNTs and MWCNTs), spherical fullerenes and dendrimers. Among all ENPs, the carbon based ENPs are attracting much attention for potential biomedical applications, such as biosensors design, drug design, drug delivery, tumor therapy and tissue engineering, because of their electronic, optical and mechanical properties. The pristine CNTs are inert in nature so it needs to be functionalized to make it reactive. The functionalization appends different functional group e.g. C=O, C–O, –OH and –COOH to CNTs, which make it dispersible and suitable for different applications. The biocompatibility of these functionalized CNTs and their composite has to be tested before real time applications in the biological system. Determining the toxicity of CNT is the most persistent questions in nanotechnology. Inconsistent reports on toxicity of CNTs often appear in the literature and a mechanistic explanation of the reported toxicity remains incomprehensible. Results from various scientific tests on cells have so far proven confusing, with some results indicating it to be highly toxic and others showing no signs of toxicity. Several toxicity mechanisms have been proposed for CNTs including interruption of trans membrane electron transfer, disruption/penetration of the cell envelope, oxidation of cell components, and production of secondary products such as dissolved heavy metal ions or reactive oxygen species (ROS).Toxicity of a CNT sample is dependent on its composition along with its geometry and surface functionalization. Several studies have suggested that wellfunctionalized CNTs are safe to animal cells, while raw CNTs or CNTs without functionalization show severe toxicity to animal or human cells at even moderate dosage.

Chromosome instability and unusual features of some widely used strains of Candida albicans

Abstract: Electrophoretic karyotyping of the Candida albicans revealed a different migration pattern of ChR in three different stocks of the sequencing strain SC5314. In one stock, the high instability of ChR size prevented the migration of ChR as a compact band; ChR appeared, instead, as a smear. In some stocks, ChR and/or Ch1 ploidy diminished, suggesting mixed populations of disomic and monosomic cells. Similarly, some stocks of widely used derivatives CAI4 and BWP17 contained smearing of ChR. In addition, the most manipulated strain in the lineage of SC5314, the last derivative, BWP17, acquired an increase in the size of Ch7b and revealed an unusual property. BWP17 did not tolerate a well-established procedure of telomere-mediated fragmentation of a chromosome; the remaining intact homologue always duplicated. We suggest that some stocks of SC5314 are unstable and that BWP17 may not be appropriate for general studies. Instead of BWP17 or CAI4, we recommend using for general research CAF4-2, which is a relatively stable Ura- derivative, and which has been successfully used for more than a decade in our laboratory.

Deep Belief CNN Feature Representation Based Content Based Image Retrieval for Medical Images

Abstract: Avascular Necrosis (AN) is a cause of muscular-skeletal disability. As it is common amongst the younger people, early intervention and prompt diagnosis is requisite. This disease normally affects the femoral bones, in order that the bones’ shape gets altered due to the fracture. Other common sites encompass knees, humerus, shoulders, jaw, and ankles. The retrieval of the AN affected bone images is challenging due to its varied fracture locations. This work proposes an effectual methodology for retrieval of AN images utilizing Deep Belief CNN Feature Representation. Initially, the input dataset undergoes preprocessing. The image noise is eradicated utilizing Median Filter (MF) and is resized in the preprocessing stage. Features are represented using Deep Belief Convolutional Neural Network (DB-CNN). Now, the image feature representations are transmuted to binary codes. Then, the similarity measurement is computed utilizing Modified-Hamming Distance. Finally, the images are retrieved centered on the similarity values. The test outcomes evinced that the proposed work is better than the other existent techniques.