Endocrine Imbalance Associated With Proteome Changes in Diabetes.

The dynamics of cellular metabolism involves rapid interactions between proteins and nucleic acids, proteins and proteins, and signaling. These involve the interactions with respect to the sulfur bond, noncovalent electrostatic interactions, protein structure stabilization and protein-ligand binding, weak electrostatic interactions in proteins, oxygen radicals that initiate a change in conformation and a chain of events. We review a development in molecular medicine that is a very promising work in progress. We also review the current and future research methods involving mitochondria. Long-term effects of diabetes include glycation of proteins, for example, glycohemoglobin (HbA1c), increased risk of cardiovascular diseases, atherosclerosis, retinopathy, nephropathy, and neurological dysfunctions. Tissues are exposed to significant quantities of highly reactive chemical species including nitric oxide • NO and reactive oxygen species ROS over months to years, to an extent generated by mitochondrial activities. The reactions of • NO can be broadly discussed with reference to three main processes which control their fate in biological systems: (1) diffusion and intra-cellular consumption; (2) autooxidation to form nitrous anhydride N2 O3 ; and (3) reaction with superoxide O2• - to form peroxynitrite ONOO-. Reactive nitrogen species produced by macrophages and neutrophils in the interstitial space, with emphasis on • NO, N2 O3 , ONOO-, and nitrogen dioxide radicals • NO2 generate protein and DNA damage. Serum thiol (-SH) groups act as an important extracellular scavenger of peroxides and are therefore helpful in protecting the surrounding tissues. The events described here are a homeostatic endocrine imbalance that is associated with proteostasis. The advances we have seen in untangling this web of interactions are sure to continue at a breathtaking pace. J. Cell. Biochem. 118: 3569-3576, 2017. © 2017 Wiley Periodicals, Inc.

NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer.

BACKGROUND: Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists. RESULTS: This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei. We used this approach to produce the NuCLS dataset, containing >220,000 annotations of cell nuclei in breast cancers. This builds on prior work labeling tissue regions to produce an integrated tissue region- and cell-level annotation dataset for training that is the largest such resource for multi-scale analysis of breast cancer histology. This article presents data and analysis results for single and multi-rater annotations from both non-experts and pathologists. We present a novel workflow that uses algorithmic suggestions to collect accurate segmentation data without the need for laborious manual tracing of nuclei. Our results indicate that even noisy algorithmic suggestions do not adversely affect pathologist accuracy and can help non-experts improve annotation quality. We also present a new approach for inferring truth from multiple raters and show that non-experts can produce accurate annotations for visually distinctive classes. CONCLUSIONS: This study is the most extensive systematic exploration of the large-scale use of wisdom-of-the-crowd approaches to generate data for computational pathology applications.