Tunable conjugated polymers for bacterial differentiation

A novel rapid method for bacterial differentiation is explored based on the specific adhesion pattern of bacterial strains to tunable polymer surfaces. Different types of counter ions were used to electrochemically fabricate dissimilar polypyrrole (PPy) films with diverse physicochemical properties such as hydrophobicity, thickness and roughness. These were then modulated into three different oxidation states in each case. The dissimilar sets of conducting polymers were exposed to five different bacterial strains, Deinococcus proteolyticus, Serratia marcescens, Pseudomonas fluorescens, Alcaligenes faecalis and Staphylococcus epidermidis. By analysis of the fluorescent microscope images, the number of bacterial cells adhered to each surface were evaluated. Generally, the number of cells of a particular bacterial strain that adhered varied when exposed to dissimilar polymer surfaces, due to the effects of the surface properties of the polymer on bacterial attachment. Similarly, the number of cells that adhered varied with different bacterial strains exposed to the same surface, reflecting the different surface properties of the bacteria. Principal component analysis showed that each strain of bacteria had its own specific adhesion pattern. Hence, they could be discriminated by this simple, label-free method based on tunable polymer arrays combined with pattern recognition

Machine Learning Classification of Females Susceptibility to Visceral Fat Associated Diseases

The problem of classifying subjects into risk categories is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of these algorithms is to predict dichotomous responses (e.g. healthy/at risk) based on several features. Similarly to statistical inference models, also ML models are subject to the common problem of class imbalance. Therefore, they are affected by the majority class increasing the false-negative rate. In this paper, we built and evaluated eighteen ML models classifying approximately 4300 female participants from the UK Biobank into three categorical risk statuses based on responses for the discretised visceral adipose tissue values from magnetic resonance imaging. We also examined the effect of sampling techniques on classification modelling when dealing with class imbalance. Results showed that the use of sampling techniques had a significant impact. They not only drove an improvement in predicting patients risk status but also facilitated an increase in the information contained within each variable. Based on domain experts criteria, the three best models for classification were finally identified. These encouraging results will guide further developments of classification models for predicting visceral adipose tissue without the need for a costly scan

The genetic basis of DOORS syndrome: an exome-sequencing study.

Deafness, onychodystrophy, osteodystrophy, mental retardation, and seizures (DOORS) syndrome is a rare autosomal recessive disorder of unknown cause. We aimed to identify the genetic basis of this syndrome by sequencing most coding exons in affected individuals