Case Western Reserve University

About: Case Western Reserve University is a education organization based out in Cleveland, Ohio, United States. It is known for research contribution in the topics: Population & Cancer. The organization has 54617 authors who have published 106568 publications receiving 5071613 citations. The organization is also known as: Case & Case Western.

The Photobiology of Photodynamic Therapy: Cellular Targets and Mechanisms

Abstract: Photodynamic therapy (PDT) is dependent on the uptake of a photosensitizing dye, often a porphyrin-related macrocycle, by the tumor or other abnormal tissue that is to be treated, the subsequent irradiation of the tumor with visible light of an appropriate wavelength matched to the absorption spectrum of the dye, and molecular oxygen to generate reactive oxygen intermediates. The initial oxidative reactions lead to damage to organelles in which the dye is bound, culminating in cell death and destruction of the tumor or abnormal tissue. Apoptosis is a common mechanism of cell death after PDT both in vitro and in vivo. PDT also triggers the activation of several signal transduction pathways in the treated cells; some of these are stress responses aimed at cell protection, while others are likely to contribute to the cell death process. The photosensitizers of greatest interest in PDT bind to various cytoplasmic membranes but are not found in the nucleus and do not bind to DNA. Nevertheless, some DNA damage is produced that can lead to mutagenesis, the extent of which is dependent on the photosensitizer, the cellular repair properties and the target gene. Thus, in spite of generating some responses common to ionizing radiation and other oxidative stresses, PDT is unique in the subcellular localization of damage, the combination of signaling pathways that are activated, and rapid kinetics of the induction of cell death processes.

Predicting cancer outcomes from histology and genomics using convolutional networks

Abstract: Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how these survival convolutional neural networks (SCNNs) can integrate information from both histology images and genomic biomarkers into a single unified framework to predict time-to-event outcomes and show prediction accuracy that surpasses the current clinical paradigm for predicting the overall survival of patients diagnosed with glioma. We use statistical sampling techniques to address challenges in learning survival from histology images, including tumor heterogeneity and the need for large training cohorts. We also provide insights into the prediction mechanisms of SCNNs, using heat map visualization to show that SCNNs recognize important structures, like microvascular proliferation, that are related to prognosis and that are used by pathologists in grading. These results highlight the emerging role of deep learning in precision medicine and suggest an expanding utility for computational analysis of histology in the future practice of pathology.

The Wolbachia genome of Brugia malayi: endosymbiont evolution within a human pathogenic nematode.

Abstract: Complete genome DNA sequence and analysis is presented for Wolbachia, the obligate alpha-proteobacterial endosymbiont required for fertility and survival of the human filarial parasitic nematode Brugia malayi. Although, quantitatively, the genome is even more degraded than those of closely related Rickettsia species, Wolbachia has retained more intact metabolic pathways. The ability to provide riboflavin, flavin adenine dinucleotide, heme, and nucleotides is likely to be Wolbachia's principal contribution to the mutualistic relationship, whereas the host nematode likely supplies amino acids required for Wolbachia growth. Genome comparison of the Wolbachia endosymbiont of B. malayi (wBm) with the Wolbachia endosymbiont of Drosophila melanogaster (wMel) shows that they share similar metabolic trends, although their genomes show a high degree of genome shuffling. In contrast to wMel, wBm contains no prophage and has a reduced level of repeated DNA. Both Wolbachia have lost a considerable number of membrane biogenesis genes that apparently make them unable to synthesize lipid A, the usual component of proteobacterial membranes. However, differences in their peptidoglycan structures may reflect the mutualistic lifestyle of wBm in contrast to the parasitic lifestyle of wMel. The smaller genome size of wBm, relative to wMel, may reflect the loss of genes required for infecting host cells and avoiding host defense systems. Analysis of this first sequenced endosymbiont genome from a filarial nematode provides insight into endosymbiont evolution and additionally provides new potential targets for elimination of cutaneous and lymphatic human filarial disease.