Numerical operations in living cells by programmable RNA devices

細胞内の複数のマイクロRNAを同時に検知して細胞を生きたまま精密に分けることに成功. 京都大学プレスリリース. 2019-08-30.Integrated bioengineering systems can make executable decisions according to the cell state. To sense the state, multiple biomarkers are detected and processed via logic gates with synthetic biological devices. However, numerical operations have not been achieved. Here, we show a design principle for messenger RNA (mRNA) devices that recapitulates intracellular information by multivariate calculations in single living cells. On the basis of this principle and the collected profiles of multiple microRNA activities, we demonstrate that rationally programmed mRNA sets classify living human cells and track their change during differentiation. Our mRNA devices automatically perform multivariate calculation and function as a decision-maker in response to dynamic intracellular changes in living cells

Effects of glucosamine on tooth pulpal nociceptive responses in the rat

AbstractBackground/purposed-Glucosamine hydrochloride (DGL) has a variety of biological activities and is noted as a nutritional supplement that is effective for improvement and care of various disorders, such as osteoarthritis and atherosclerosis. Although, it has been reported that DGL has a significant pain relief effect in treating osteoarthritis, little is known about its effect on dental pain. The applicability of DGL as a medicament to control pain in pulpalgia has not been reported. In this study, using an in vitro rat mandible-inferior alveolar nerve preparation (jaw-nerve preparation), the effect of DGL on nociceptive responses in the tooth pulpal nerve was examined.Materials and methodsThe effect of DGL on nociceptive responses for 20 male Wistar albino rats was evaluated using an in vitro jaw-nerve preparation. Bradykinin (BK), used as a chemical nociceptive stimulant, was applied near the exposed tooth pulp. Sixty seconds after BK application, the surface of the exposed pulp was treated with DGL solution or physiological saline (control).ResultsThe nerve firing rate was 2.06±0.21Hz (n=10) after 5 minutes of saline application, and 0.76±0.16Hz (n=10) after 5 minutes of DGL application. The DGL group showed significantly lower nerve firing rate than the control group.ConclusionBK-induced nociceptive responses were significantly suppressed by direct application of DGL. Our results suggest that DGL might have a pain relief effect in dental pain

Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning

Convolutional Neural Networks (CNNs) are image analysis techniques that have been applied to image classification in various fields. In this study, we applied a CNN to classify scanning electron microscopy (SEM) images of pharmaceutical raw material powders to determine if a CNN can evaluate particle morphology. We tested 10 pharmaceutical excipients with widely different particle morphologies. SEM images for each excipient were acquired and divided into training, validation, and test sets. Classification models were constructed by applying transfer learning to pretrained CNN models such as VGG16 and ResNet50. The results of a 5-fold cross-validation showed that the classification accuracy of the CNN model was sufficiently high using either pretrained model and that the type of excipient could be classified with high accuracy. The results suggest that the CNN model can detect differences in particle morphology, such as particle size, shape, and surface condition. By applying Grad-CAM to the constructed CNN model, we succeeded in finding particularly important regions in the particle image of the excipients. CNNs have been found to have the potential to be applied to the identification and characterization of raw material powders for pharmaceutical development