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Fast, real‐time detection of gases and volatile organic compounds (VOCs) is an emerging research field relevant to most aspects of modern society, from households to health facilities, industrial units, and military environments. Sensor features such as high sensitivity, selectivity, fast response, and low energy consumption are essential. Liquid crystal (LC)‐based sensors fulfill these requirements due to their chemical diversity, inherent self‐assembly potential, and reversible molecular order, resulting in tunable stimuli‐responsive soft materials. Sensing platforms utilizing thermotropic uniaxial systems—nematic and smectic—that exploit not only interfacial phenomena, but also changes in the LC bulk, are demonstrated. Special focus is given to the different interaction mechanisms and tuned selectivity toward gas and VOC analytes. Furthermore, the different experimental methods used to transduce the presence of chemical analytes into macroscopic signals are discussed and detailed examples are provided. Future perspectives and trends in the field, in particular the opportunities for LC‐based advanced materials in artificial olfaction, are also discussed.

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.

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.

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