Lung or heart sound classification is challenging due to the complex nature of audio data, its dynamic properties of time, and frequency domains. It is also very difficult to detect lung or heart conditions with small amounts of data or unbalanced and high noise in data. Furthermore, the quality of data is a considerable pitfall for improving the performance of deep learning. In this paper, we propose a novel feature-based fusion network called FDC-FS for classifying heart and lung sounds. The FDC-FS framework aims to effectively transfer learning from three different deep neural network models built from audio datasets. The innovation of the proposed transfer learning relies on the transformation from audio data to image vectors and from three specific models to one fused model that would be more suitable for deep learning. We used two publicly available datasets for this study, i.e., lung sound data from ICHBI 2017 challenge and heart challenge data. We applied data augmentation techniques, such as noise distortion, pitch shift, and time stretching, dealing with some data issues in these datasets. Importantly, we extracted three unique features from the audio samples, i.e., Spectrogram, MFCC, and Chromagram. Finally, we built a fusion of three optimal convolutional neural network models by feeding the image feature vectors transformed from audio features. We confirmed the superiority of the proposed fusion model compared to the state-of-the-art works. The highest accuracy we achieved with FDC-FS is 99.1% with Spectrogram-based lung sound classification while 97% for Spectrogram and Chromagram based heart sound classification.

The objective of this study was to optimize the physicodynamic conditions of polymeric system as a coating substrate for drug eluting stents against restenosis. As Nitric Oxide (NO) has multifunctional activities, such as regulating blood flow and pressure, and influencing thrombus formation, a continuous and spatiotemporal delivery of NO loaded in the polymer based nanoparticles could be a viable option to reduce and prevent restenosis. To identify the most suitable carrier for S-Nitrosoglutathione (GSNO), a NO prodrug, stents were coated with various polymers, such as poly (lactic-co-glycolic acid) (PLGA), polyethylene glycol (PEG) and polycaprolactone (PCL), using solvent evaporation technique. Full factorial design was used to evaluate the effects of the formulation variables in polymer-based stent coatings on the GSNO release rate and weight loss rate. The least square regression model was used for data analysis in the optimization process. The polymer-coated stents were further assessed with Differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy analysis (FTIR), Scanning electron microscopy (SEM) images and platelet adhesion studies. Stents coated with PCL matrix displayed more sustained and controlled drug release profiles than those coated with PLGA and PEG. Stents coated with PCL matrix showed the least platelet adhesion rate. Subsequently, stents coated with PCL matrix were subjected to the further optimization processes for improvement of surface morphology and enhancement of the drug release duration. The results of this study demonstrated that PCL matrix containing GSNO is a promising system for stent surface coating against restenosis.

In recent years, there is an increasing demand for sharing and integration of medical data in biomedical research. In order to improve a health care system, it is required to support the integration of data by facilitating semantic interoperability systems and practices. Semantic interoperability is difficult to achieve in these systems as the conceptual models underlying datasets are not fully exploited. In this paper, we propose a semantic framework, called Medical Knowledge Discovery and Data Mining (MedKDD), that aims to build a topic hierarchy and serve the semantic interoperability between different ontologies. For the purpose, we fully focus on the discovery of semantic patterns about the association of relations in the heterogeneous information network representing different types of objects and relationships in multiple biological ontologies and the creation of a topic hierarchy through the analysis of the discovered patterns. These patterns are used to cluster heterogeneous information networks into a set of smaller topic graphs in a hierarchical manner and then to conduct cross domain knowledge discovery from the multiple biological ontologies. Thus, patterns made a greater contribution in the knowledge discovery across multiple ontologies. We have demonstrated the cross domain knowledge discovery in the MedKDD framework using a case study with 9 primary biological ontologies from Bio2RDF and compared it with the cross domain query processing approach, namely SLAP. We have confirmed the effectiveness of the MedKDD framework in knowledge discovery from multiple medical ontologies.