Glucocorticoids (GCs) are involved in a large number of the physiological changes associated with metabolic syndrome and certain psychiatric illness. Although significance is often given to the concentration of GC, its biological action is determined by the activation of intracellular GC receptors (GR). Genetic polymorphisms of the GR and the large array of GR related cofactors can directly or indirectly affect the pathophysiology and evolution of these conditions. This review will discuss the effects of GR mutations on metabolic syndrome and psychotic depression.

A mobile-phone-based diagnostic tool, which most of the population can easily access, could be a game changer in increasing the number of examinations of people with dental caries. This study aimed to apply a deep learning algorithm in diagnosing the stages of smooth surface caries via smartphone images. Materials and methods: A training dataset consisting of 1902 photos of the smooth surface of teeth taken with an iPhone 7 from 695 people was used. Four deep learning models, consisting of Faster Region-Based Convolutional Neural Networks (Faster R-CNNs), You Only Look Once version 3 (YOLOv3), RetinaNet, and Single-Shot Multi-Box Detector (SSD), were tested to detect initial caries lesions and cavities. The reference standard was the diagnosis of a dentist based on image examination according to the International Caries Classification and Management System (ICCMS) classification. Results: For cavitated caries, YOLOv3 and Faster R-CNN showed the highest sensitivity among the four tested models, at 87.4% and 71.4%, respectively. The sensitivity levels of these two models were only 36.9 % and 26% for visually non-cavitated (VNC). The specificity of the four models reached above 86% for cavitated caries and above 71% for VNC. Conclusion: The clinical application of YOLOv3 and Faster R-CNN models for diagnosing dental caries via smartphone images was promising. The current study provides a preliminary insight into the potential translation of AI from the laboratory to clinical practice.

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Although abiotic catalysts are capable of promoting numerous new-to-nature reactions, only a small subset has so far been successfully integrated into living systems. Research in intracellular catalysis requires an interdisciplinary approach that takes advantage of both chemical and biological tools as well as state of the art instruments. In this Perspective, we will focus on the techniques that have made studying metal-catalyzed reactions in cells possible using representative examples from the literature. Although the lack of quantitative data in vitro and in vivo has somewhat limited progress in the catalyst development process, recent advances in characterization methods should help overcome some of these deficiencies. Given its tremendous potential, we believe that intracellular catalysis will play a more prominent role in the development of future biotechnologies and therapeutics.