Fluorescent double network ionogels with fast self-healability and high resilience for reliable human motion detection.

Fascinating properties are displayed by high-performance ionogel-based flexible strain sensors, thereby gaining increasing attention in various applications ranging from human motion monitoring to soft robotics. However, the integration of excellent properties such as optical and mechanical properties and satisfactory sensing performance for one ionogel sensor is still a challenge. In particular, fatigue-resistant and self-healing properties are essential to continuous sensing. Herein, we design a flexible ion-conductive sensor based on a multifunctional ionogel with a double network using polyacrylamide, amino-modified agarose, 1,3,5-benzenetricarboxaldehyde and 1-ethyl-3-methylimidazolium chloride. The ionogel exhibits comprehensive properties including high transparency (>95%), nonflammability, strong adhesion and good temperature tolerance (about -96 to 260 °C), especially adaptive for extreme conditions. The dynamic imine bonds and abundant hydrogen bonds endow the ionogel with excellent self-healing capability, to realize rapid self-repair within minutes, as well as good mechanical properties and ductility to dissipate input energy and realize high resilience. Notably, unexpected fluorescence has been observed for the ionogel because of the gelation-induced emission phenomenon. Flexible strain sensors prepared directly from ionogels can sensitively monitor and differentiate various human motions, exhibiting a fast response time (38 ms), high sensitivity (gauge factor = 3.13 at 800% strain), good durability (>1000 cycles) and excellent stability over a wide temperature range (-30 to 80 °C). Therefore, the prepared ionogel as a high-performance flexible strain sensor in this study shows tremendous potential in wearable devices and soft ionotronics.

A Novel Probability Model for LncRNA⁻Disease Association Prediction Based on the Naïve Bayesian Classifier.

An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play crucial roles in biological processes, complex disease diagnoses, prognoses, and treatments. However, experimentally validated associations between lncRNAs and diseases are still very limited. Recently, computational models have been developed to discover potential associations between lncRNAs and diseases by integrating multiple heterogeneous biological data; this has become a hot topic in biological research. In this article, we constructed a global tripartite network by integrating a variety of biological information including miRNA⁻disease, miRNA⁻lncRNA, and lncRNA⁻disease associations and interactions. Then, we constructed a global quadruple network by appending gene⁻lncRNA interaction, gene⁻disease association, and gene⁻miRNA interaction networks to the global tripartite network. Subsequently, based on these two global networks, a novel approach was proposed based on the naïve Bayesian classifier to predict potential lncRNA⁻disease associations (NBCLDA). Comparing with the state-of-the-art methods, our new method does not entirely rely on known lncRNA⁻disease associations, and can achieve a reliable performance with effective area under ROC curve (AUCs)in leave-one-out cross validation. Moreover, in order to further estimate the performance of NBCLDA, case studies of colorectal cancer, prostate cancer, and glioma were implemented in this paper, and the simulation results demonstrated that NBCLDA can be an excellent tool for biomedical research in the future.

Prediction of microRNA-disease associations based on distance correlation set.

BACKGROUND: Recently, numerous laboratory studies have indicated that many microRNAs (miRNAs) are involved in and associated with human diseases and can serve as potential biomarkers and drug targets. Therefore, developing effective computational models for the prediction of novel associations between diseases and miRNAs could be beneficial for achieving an understanding of disease mechanisms at the miRNA level and the interactions between diseases and miRNAs at the disease level. Thus far, only a few miRNA-disease association pairs are known, and models analyzing miRNA-disease associations based on lncRNA are limited. RESULTS: In this study, a new computational method based on a distance correlation set is developed to predict miRNA-disease associations (DCSMDA) by integrating known lncRNA-disease associations, known miRNA-lncRNA associations, disease semantic similarity, and various lncRNA and disease similarity measures. The novelty of DCSMDA is due to the construction of a miRNA-lncRNA-disease network, which reveals that DCSMDA can be applied to predict potential lncRNA-disease associations without requiring any known miRNA-disease associations. Although the implementation of DCSMDA does not require known disease-miRNA associations, the area under curve is 0.8155 in the leave-one-out cross validation. Furthermore, DCSMDA was implemented in case studies of prostatic neoplasms, lung neoplasms and leukaemia, and of the top 10 predicted associations, 10, 9 and 9 associations, respectively, were separately verified in other independent studies and biological experimental studies. In addition, 10 of the 10 (100%) associations predicted by DCSMDA were supported by recent bioinformatical studies. CONCLUSIONS: According to the simulation results, DCSMDA can be a great addition to the biomedical research field.