Manipal University

About: Manipal University is a education organization based out in Manipal, Karnataka, India. It is known for research contribution in the topics: Population & Health care. The organization has 9525 authors who have published 11207 publications receiving 110687 citations.

Automated Categorization of Multi-Class Brain Abnormalities using Decomposition Techniques With MRI Images: A Comparative Study

Abstract: Medical imaging and analysis are useful to visualize anatomic structure. However, analysis of the pathologic substrate is difficult and inefficient when using simple imaging tools. The manual detection and classification of brain abnormality is particularly tedious. Moreover, the currently used methodology suffers from interobserver variability during image interpretation. Magnetic resonance imaging (MRI) is an efficient imaging technique for revealing complex anatomical architecture, and it is highly efficacious for precise brain imaging. Herein, we describe a novel computer aided diagnosis method for automated processing of brain MRI images. The performances of two decomposition techniques, namely, bidimensional empirical mode decomposition and variational mode decomposition (VMD), are compared. Thereafter, bispectral feature extraction and supervised neighborhood projection embedding are implemented to represent each feature in a new subspace, for the automated classification of various categories of disease. A support vector machine classifier is used to train and test the performance accuracy. The level of classification accuracy of 90.68%, 99.43% sensitivity and 87.95% specificity is obtained using the VMD technique. Hence, the developed system can be used as an adjunct tool by radiologists to confirm their screening.

DNA-PK: the major target for wortmannin-mediated radiosensitization by the inhibition of DSB repair via NHEJ pathway.

Abstract: The effect of wortmannin posttreatment was studied in cells derived from different species (hamster, mouse, chicken, and human) with normal and defective DNA-dependent protein kinase (DNA-PK) activity, cells with and without the ataxia telangiectasia (ATM) gene, and cells lacking other regulatory proteins involved in the DNA double-strand break (DSB) repair pathways. Clonogenic assays were used to obtain all results. Wortmannin radiosensitization was observed in Chinese hamster cells (V79-B310H , CHO-K1), mouse mammary carcinoma cells (SR-1), transformed human fibroblast (N2KYSV), chicken B lymphocyte wild-type cells (DT40), and chicken Rad54 knockout cells (Rad54-/-). However, mouse mammary carcinoma cells (SX9) with defects in the DNA-PK and chicken DNA-PK catalytic subunit (DNA-PKcs) knockout cells (DNA-PKcs-/-/-) failed to exhibit wortmannin radiosensitization. On the other hand, SCID mouse cells (SC3VA2) exposed to wortmannin exhibited significant increases in radiosensitivity, possibly because of some residual function of DNA-PKcs. Moreover, the transformed human cells derived from AT patients (AT2KYSV) and chicken ATM knockout cells (ATM-/-) showed pronounced wortmannin radiosensitization. These studies demonstrate confirm that the mechanism underlying wortmannin radiosensitization is the inhibition of DNA-PK, but not of ATM, thereby resulting in the inhibition of DSB repair via nonhomologous endjoining (NHEJ).

Multi-Omics Driven Assembly and Annotation of the Sandalwood (Santalum album) Genome.

Abstract: Indian sandalwood (Santalum album) is an important tropical evergreen tree known for its fragrant heartwood-derived essential oil and its valuable carving wood. Here, we applied an integrated genomic, transcriptomic, and proteomic approach to assemble and annotate the Indian sandalwood genome. Our genome sequencing resulted in the establishment of a draft map of the smallest genome for any woody tree species to date (221 Mb). The genome annotation predicted 38,119 protein-coding genes and 27.42% repetitive DNA elements. In-depth proteome analysis revealed the identities of 72,325 unique peptides, which confirmed 10,076 of the predicted genes. The addition of transcriptomic and proteogenomic approaches resulted in the identification of 53 novel proteins and 34 gene-correction events that were missed by genomic approaches. Proteogenomic analysis also helped in reassigning 1,348 potential noncoding RNAs as bona fide protein-coding messenger RNAs. Gene expression patterns at the RNA and protein levels indicated that peptide sequencing was useful in capturing proteins encoded by nuclear and organellar genomes alike. Mass spectrometry-based proteomic evidence provided an unbiased approach toward the identification of proteins encoded by organellar genomes. Such proteins are often missed in transcriptome data sets due to the enrichment of only messenger RNAs that contain poly(A) tails. Overall, the use of integrated omic approaches enhanced the quality of the assembly and annotation of this nonmodel plant genome. The availability of genomic, transcriptomic, and proteomic data will enhance genomics-assisted breeding, germplasm characterization, and conservation of sandalwood trees.

Role of normalization of breast thermogram images and automatic classification of breast cancer

Abstract: Breast thermography is a non-invasive imaging technique used for early detection of breast cancer based on temperatures. Temperature matrix of breast provides minute variations in temperatures, which is significant in early detection of breast cancer. The minimum, maximum temperatures and the their range may be different for each breast thermogram. Normalization of temperature matrices of breast thermograms is essential to bring the different range of temperatures to the common scale. In this article, we demonstrate the importance of temperature matrix normalization of breast thermograms. This paper also proposes a novel method for automatically classifying breast thermogram images using local energy features of wavelet sub-bands. A significant subset of features is selected by a random subset feature selection (RSFS) and genetic algorithm. Features selected by RSFS method are found to be relevant in detection of asymmetry between right and left breast. We have obtained an accuracy of 91%, sensitivity 87.23% and specificity 94.34% using SVM Gaussian classifier for normalized breast thermograms. Accuracy of classification between a set of hundred normalized and corresponding set of non-normalized breast thermograms are compared. An increase in accuracy of 16% is obtained for normalized breast thermograms in comparison with non-normalized breast thermograms.