Extracellular vesicles in bone and tooth: A state-of-art paradigm in skeletal regeneration

Bone and tooth, fundamental parts of the craniofacial skeleton, are anatomically and developmentally interconnected structures. Notably, pathological processes in these tissues underwent together and progressed in multilevels. Extracellular vesicles (EVs) are cell-released small organelles and transfer proteins and genetic information into cells and tissues. Although EVs have been identified in bone and tooth, particularly EVs have been identified in the bone formation and resorption, the concrete roles of EVs in bone and tooth development and diseases remain elusive. As such, we review the recent progress of EVs in bone and tooth to highlight the novel findings of EVs in cellular communication, tissue homeostasis, and interventions. This will enhance our comprehension on the skeletal biology and shed new light on the modulation of skeletal disorders and the potential of genetic treatment.     

Characteristics of interhemispheric relationships in the absence or presence of a skill

A comparative analysis of the time and amplitude characteristics of the negative N200 and positive P300 components of visual evoked potentials recorded at symmetric points of the frontal, parietal, temporal, and occipital areas of the right and left hemispheres of the cerebral cortex has been performed in subjects with or without the skill of operating a computer. Subjects inexperienced in an operator’s work exhibited an interhemispheric difference in the time and amplitude characteristics of the studied components. In subjects that had the skill of operating a computer, the interhemispheric difference was little, which suggests that the cortex plays only a small role in the cerebral control of this activity.     

A Mathematical Framework for Combining Decisions of Multiple Experts toward Accurate and Remote Diagnosis of Malaria Using Tele-Microscopy

We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing ‘slide-level’ diagnosis by using individual ‘cell-level’ diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform.     

Effects of two-vessel forebrain ischemia and of administration of indomethacin and quinacrine on Na<Superscript>+</Superscript>, K<Superscript>+</Superscript>-ATPase activity in various rat brain areas

The effect of a two-vessel forebrain ischemia (induced by occlusion of carotid arteries and hypotension), subsequent reperfusion, and administration of indomethacin and quinacrine on the Na⁺,K⁺-ATPase activity and diene conjugate content was studied in various rat forebrain fields. The most pronounced metabolic alterations were observed during ischemia and reperfusion. Under these effects, there was a statistically significant reduction of the Na⁺,K⁺-ATPase activity in the brain cortex and striatum and an increase of the diene conjugate content in the rat brain cortex in comparison with sham-operated animals. Injection of indomethacin, a cyclooxygenase inhibitor, to rats subjected to ischemia and reperfusion, resulted to a statistically significant increase of the Na⁺,K⁺-ATPase activity in the brain cortex, hippocampus, and striatum (p < 0.02) as compared with control animals. The diene conjugate content in the rat brain cortex during brain ischemia and reperfusion was statistically significantly lower in the rats injected with indomethacin. The effect of quinacrine (a blocker of phospholipase A₂) was similar to that of indomethacin in the rat cortex, whereas in the rat striatum and hippocampus, the quinacrine effect during ischemia and reperfusion was less marked than that of indomethacin. The obtained data indicate the ability of inhibitors of the arachidonic pathway of free radical formation to normalize the Na⁺, K⁺-ATPase activity during brain ischemia. There also revealed local peculiarities of metabolic disturbances in different regions of the rat forebrain during ischemia and reperfusion.Translated from Zhurnal Evolyutsionnoi Biokhimii i Fiziologii, Vol. 41, No. 1, 2005, pp. 33–38.Original Russian Text Copyright © 2005 by Molchanova, Moskvin, Zakharova, Yurlova, Nosova, Avrova.     

Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.