Measurement of Nasal Mucociliary Clearance in Indian Adults: Normative Data.

Nasal mucociliary clearance (NMC) plays an important role in removal of inhaled particles. The aim of this study was to assess the normal nasal mucociliary clearance time in Indian adult population in age group 18-60 years. A cross sectional, descriptive, observational study was performed. Two hundred participants in the age group 18-60 years were included in this study. Saccharin transit test was performed in these subjects. Saccharin particle was placed 0.5 cm away from the inferior turbinate from its anterior part. The participants were asked to inform the appearance of sweet taste. Duration between placement of particle and the appearance of taste was noted in minutes. Mean saccharin transit time was 9.44?2.73 minutes. There was no statistically significant difference in saccharin transit time between males & females. Nasal mucociliary clearance time between < 40 years & ≥40 years was compared and there was no significant difference between the 2 groups. The normal mucociliary clearance value in healthy adult Indian population-based on saccharin transit time is 9.44 ± 2.73 min. The earliest change in respiratory defense mechanism is change in nasal mucociliary clearance time and saccharin test is a simple, easy test to detect this. Supplementary Information: The online version contains supplementary material available at 10.1007/s12070-023-03915-x.

A meta-learning framework using representation learning to predict drug-drug interaction.

MOTIVATION: Predicting Drug-Drug Interaction (DDI) has become a crucial step in the drug discovery and development process, owing to the rise in the number of drugs co-administered with other drugs. Consequently, the usage of computational methods for DDI prediction can greatly help in reducing the costs of in vitro experiments done during the drug development process. With lots of emergent data sources that describe the properties and relationships between drugs and drug-related entities (gene, protein, disease, and side effects), an integrated approach that uses multiple data sources would be most effective. METHOD: We propose a semi-supervised learning framework which utilizes representation learning, positive-unlabeled (PU) learning and meta-learning efficiently to predict the drug interactions. Information from multiple data sources is used to create feature networks, which is used to learn the meta-knowledge about the DDIs. Given that DDIs have only positive labeled data, a PU learning-based classifier is used to generate meta-knowledge from feature networks. Finally, a meta-classifier that combines the predicted probability of interaction from the meta-knowledge learnt is designed. RESULTS: Node2vec, a network representation learning method and bagging SVM, a PU learning algorithm, are used in this work. Both representation learning and PU learning algorithms improve the performance of the system by 22% and 12.7% respectively. The meta-classifier performs better and predicts more reliable DDIs than the base classifiers.