Effect of film thickness in gelatin hybrid gels for artificial olfaction.

Artificial olfaction is a fast-growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition in which volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. This results in electrical signals transduced to the brain where pattern recognition is performed. An efficient approach in artificial olfaction combines gas-sensitive materials with dedicated signal processing and classification tools. In this work, films of gelatin hybrid gels with a single composition that change their optical properties upon binding to VOCs were studied as gas-sensing materials in a custom-built electronic nose. The effect of films thickness was studied by acquiring signals from gelatin hybrid gel films with thicknesses between 15 and 90 µm when exposed to 11 distinct VOCs. Several features were extracted from the signals obtained and then used to implement a dedicated automatic classifier based on support vector machines for data processing. As an optical signature could be associated to each VOC, the developed algorithms classified 11 distinct VOCs with high accuracy and precision (higher than 98%), in particular when using optical signals from a single film composition with 30 µm thickness. This shows an unprecedented example of soft matter in artificial olfaction, in which a single gelatin hybrid gel, and not an array of sensing materials, can provide enough information to accurately classify VOCs with small structural and functional differences.

Author Correction: Machine learning for the meta-analyses of microbial pathogens' volatile signatures.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Machine learning for the meta-analyses of microbial pathogens' volatile signatures.

Non-invasive and fast diagnostic tools based on volatolomics hold great promise in the control of infectious diseases. However, the tools to identify microbial volatile organic compounds (VOCs) discriminating between human pathogens are still missing. Artificial intelligence is increasingly recognised as an essential tool in health sciences. Machine learning algorithms based in support vector machines and features selection tools were here applied to find sets of microbial VOCs with pathogen-discrimination power. Studies reporting VOCs emitted by human microbial pathogens published between 1977 and 2016 were used as source data. A set of 18 VOCs is sufficient to predict the identity of 11 microbial pathogens with high accuracy (77%), and precision (62-100%). There is one set of VOCs associated with each of the 11 pathogens which can predict the presence of that pathogen in a sample with high accuracy and precision (86-90%). The implemented pathogen classification methodology supports future database updates to include new pathogen-VOC data, which will enrich the classifiers. The sets of VOCs identified potentiate the improvement of the selectivity of non-invasive infection diagnostics using artificial olfaction devices.

Neural commitment of human pluripotent stem cells under defined conditions recapitulates neural development and generates patient-specific neural cells.

Standardization of culture methods for human pluripotent stem cell (PSC) neural differentiation can greatly contribute to the development of novel clinical advancements through the comprehension of neurodevelopmental diseases. Here, we report an approach that reproduces neural commitment from human induced pluripotent stem cells using dual-SMAD inhibition under defined conditions in a vitronectin-based monolayer system. By employing this method it was possible to obtain neurons derived from both control and Rett syndrome patients' pluripotent cells. During differentiation mutated cells displayed alterations in the number of neuronal projections, and production of Tuj1 and MAP2-positive neurons. Although investigation of a broader number of patients would be required, these observations are in accordance with previous studies showing impaired differentiation of these cells. Consequently, our experimental methodology was proved useful not only for the generation of neural cells, but also made possible to compare neural differentiation behavior of different cell lines under defined culture conditions. This study thus expects to contribute with an optimized approach to study the neural commitment of human PSCs, and to produce patient-specific neural cells that can be used to gain a better understanding of disease mechanisms.