Billions of math operations per second may be performed by computers anymore. Obviously, a human life-time would be needed to do the same number of computations. When used in medication, the... Show More

Billions of math operations per second may be performed by computers anymore. Obviously, a human life-time would be needed to do the same number of computations. When used in medication, the groundbreaking potential of the mathematical modeling approach is obvious. In Medicine, mathematical modeling is able to vastly improve both drug creation and clinic technology. Progress in technology and the development of new experimental methods has had a noteworthy effect on the study of disease. This has raised new researching opportunities, such as: gathering in-depth ‘molecular fingerprints’ from patients carrying information, for example, on genotype, gene or protein expression, or metabolism levels; studying intracellular processes in living and diseased tissue through the control of gene activity inside the cells; and creating understandable illness-specific databases that include both patients’ medical history with laboratory and clinical data in addition to storing useful tissue samples. In this article, the authors attempt to provide the readers with a view of current and future use of mathematical modeling in medicine.

Cathelicidins constitute important antimicrobial peptides of innate immunity. In order to elucidate the evolutionary history of cathelicidins, the author performed comprehensive phylogenetic... Show More

Cathelicidins constitute important antimicrobial peptides of innate immunity. In order to elucidate the evolutionary history of cathelicidins, the author performed comprehensive phylogenetic analyses of cathelicidin homologs in all available genomes including those completed recently. The organization of cathelicidin genes, as well as the secondary and tertiary structures of the inferred proteins are conserved. Based on integrated genomic, structural, and functional data, the author identified the last common ancestor of the cathelicidin family in lampreys, thus tracing the evolutionary origin of cathelicidins 550 million years ago. The author’s data suggest that cathelicidins arose concordantly with the adaptive immune system, a new organismal function first acquired in lampreys. The appearance of cathelicidins at the junction of innate and adaptive immunity may explain their dual roles as signal transducing molecules and as antimicrobial peptide precursors. Conserved regulatory elements associated with functions of the immune system were identified in cathelicidin gene promoter sequences invariably from fishes to humans.

The classification of diseases appears as one of the fundamental problems for a medical practitioner, which might be substantially improved by intelligent systems. The present work is aimed at... Show More

The classification of diseases appears as one of the fundamental problems for a medical practitioner, which might be substantially improved by intelligent systems. The present work is aimed at designing in what way an intelligent system supporting medical decision can be developed by hybridizing radial basis function neural networks (RBFNs) and differential evolution (DE). To this extent, a two phases learning algorithm with a modified kernel for radial basis function neural networks is proposed for classification. In phase one, differential evolution is used to reveal the parameters of the modified kernel. The second phase focus on optimization of weights for learning the networks. The proposed method is validated using five medical datasets such as bupa liver disorders, pima Indians diabetes, new thyroid, stalog (heart), and hepatitis. In addition, a predefined set of basis functions are considered to gain insight into, which basis function is better for what kind of domain through an empirical analysis. The experiment results indicate that the proposed method classification accuracy with 95% and 98% confidence interval is better than the base line classifier (i.e., simple RBFNs) in all aforementioned datasets. In the case of imbalanced dataset like new thyroid, the authors have noted that with 98% confidence level the classification accuracy of the proposed method based on the multi-quadratic kernel is better than other kernels; however, in the case of hepatitis, the proposed method based on cubic kernel is promising.

3D printing is about being able to print any object layer by layer. But if one questions this proposition, can one find any three-dimensional objects that can't be printed layer by layer? To banish... Show More

3D printing is about being able to print any object layer by layer. But if one questions this proposition, can one find any three-dimensional objects that can't be printed layer by layer? To banish any disbeliefs the authors walked together through the mathematics that prove 3d printing is feasible for any real life object. 3d printers create three-dimensional objects by building them up layer by layer. The current generation of 3d printers typically requires input from a CAD program in the form of an STL file, which defines a shape by a list of triangle vertices. The vast majority of 3d printers use two techniques, FDM (Fused Deposition Modelling) and PBP (Powder Binder Printing). One advanced form of 3d printing that has been an area of increasing scientific interest the recent years is bioprinting. Cell printers utilizing techniques similar to FDM were developed for bioprinting. These printers give us the ability to place cells in positions that mimic their respective positions in organs. Finally, through a series of case studies the authors show that 3d printers have made a massive breakthrough in medicine lately.

Personalized medicine (PM) is a rapidly growing field of healthcare and medicine. The advantage of a personalized medicine is the availability of each person’s unique genetic and genomic print. The... Show More

Personalized medicine (PM) is a rapidly growing field of healthcare and medicine. The advantage of a personalized medicine is the availability of each person’s unique genetic and genomic print. The healthcare that incorporates personalized medicine provides coordinated, continuous patient-specific data. The goal of personalized medicine is to promote health wellness, satisfaction, and to increase the likelihood of a successful disease prevention, detection and treatment. This form of medicine, apart from patient’s personal data and medicine-biological measurements, uses genomic information data to understand the molecular structure of the disease and to optimize health care strategies and drug therapies. Clinical trials that investigate personalized approaches are subject to special rules, for example, pertain the selection of participating patients. In personalized medicine, a certain genetic profile must be identified so that the treatment can work. This is why potential participants are first screened and selected accordingly for clinical trials. The special design of such clinical trials has an impact on the evaluation of data collected during the given study.