Band 14, Heft 2

The topic of brain stem computational simulation still seems understudied in contemporary scientific literature. Current advances in neuroscience leave the brain stem as one of the least known parts of the human central nervous system. Brain stem lesions are particularly damaging to the most important physiological functions. Advances in brain stem modeling may influence important issues within the core of neurology, neurophysiology, neurosurgery, and neurorehabilitation. Direct results may include both development of knowledge and optimization and objectivization of clinical practice in the aforementioned medical areas. Despite these needs, progress in the area of computational brain stem models seems to be too slow. The aims of this paper are both to recognize the strongest limitations in the area of computational brain stem simulations and to assess the extent to which current opportunities may be exploited. Despite limitations, the emerging view of the brain stem provided by its computational models enables a wide repertoire of functions, including core dynamic behavior.

After the onset of the genomic era, the detection of ligand binding sites in proteins has emerged over the last few years as a powerful tool for protein function prediction. Several approaches, both sequence and structure based, have been developed, but the full potential of the corresponding tools has not been exploited yet. Here, we describe the development and classification of a large, almost exhaustive, collection of protein-ligand binding sites to be used, in conjunction with the Ligand Binding Site Recognition Application Web Application developed in our laboratory, as an alternative to virtual screening through molecular docking simulations to identify novel lead compounds for known targets. Ligand binding sites derived from the Protein Data Bank have been clustered according to ligand similarity, and given a known ligand, the binding mode of related ligands to the same target can be predicted. The collection of ligand binding sites contains more than 200,000 sites corresponding to more than 20,000 different ligands. Furthermore, the ligand binding sites of all Food and Drug Administration-approved drugs have been classified as well, allowing to investigate the possible binding of each of them (and related compounds) to a given target for drug repurposing and redesign initiatives. Sample usage cases are also described to demonstrate the effectiveness of this approach.

The analysis of amyloid structures is much easier recently due to the availability of the solid-state nuclear magnetic resonance technique, which allows the determination of the 3D structure of amyloid forms. The amyloidogenic polypeptide Aβ(1-40) (PDB ID 2M9R, 2M9S) in its soluble form is the object of analysis in this paper. The solubility of this polypeptide is reached due to the presence of a complexed ligand: polyphenol ε-viniferin glucoside. Two forms of complexes available in the PDB were taken for analysis with respect to the presence of a hydrophobic core in the 3D structure of these complexes. The idealized hydrophobic core structure assumed to be accordant with the 3D Gauss function distribution was taken as the pattern. The aim of this analysis is the possible further comparison to the structures of the hydrophobic core present in amyloids. It is shown that the discordant (versus the 3D Gauss function) fragments present in amyloids appear accordant in the discussed complexes.

Microsoft has created HoloLens glasses, a high-tech device used for holographic purposes, which is unique and superior to other available solutions. We present a new idea of a holographic assistant to doctors, using as an example a wireless patient data monitor. A dedicated application will be created to be used by doctors, allowing hands-free access to patient cards/data, reviewing of new/old examination results, and even the ability to work on real-time data. Doctors will be able to use this in the examination room, at a patient’s bedside, or in an entirely different location. Currently, analysis of patient data is done mostly by the doctor; however, huge progress in computer hardware performance and artificial intelligence (AI) algorithms has allowed the development of new methods used to analyze and classify patient examination results. In the same way that doctors learn and practice how to treat patients during their studies, algorithms can learn to spot abnormalities, allowing current technology and advanced AI algorithms to be joined in one high-tech solution that should provide initial assessment of patients’ health and give treatment guidance, if necessary.

Deep convolution neural networks (CNNs) have demonstrated their capabilities in modern-day medical image classification and analysis. The vital edge of deep CNN over other techniques is their ability to train without expert knowledge. Time bound detection is very beneficial for the early cure of disease. In this paper, a deep CNN architecture is proposed to classify nondiabetic retinopathy and diabetic retinopathy fundus eye images. Kaggle 2015 diabetic retinopathy competition dataset and messier experiment dataset are used in this study. The proposed deep CNN algorithm produces significant results with 93% area under the curve (AUC) for the Kaggle dataset and 91% AUC for the Messidor dataset. The sensitivity and specificity for the Kaggle dataset are 90.22% and 85.13%, respectively; the corresponding values of the Messidor dataset are 91.07% and 80.23%, respectively. The results outperformed many existing studies. The present architecture is a promising tool for diabetic retinopathy image classification.