Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis

This thesis proposes a method to analyse the dynamic behaviour of process systems using sets of textural features extracted from distance matrices obtained from time series data. Algorithms based on the use of grey level co-occurrence matrices, wavelet transforms, local binary patterns, textons, and the pretrained convolutional neural networks (AlexNet and VGG16) were used to extract features. The method was demonstrated to effectively capture the dynamics of mineral process systems and could outperform competing approaches

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Allergic Diseases

The present Edition "Allergic diseases - highlights in the clinic, mechanisms and treatment" aims to present some recent aspects related to one of the most prevalent daily clinical expression disease. The effort of a group of outstanding experts from many countries reflects a set of scientific studies very promising for a better clinical care and also to the treatment and control of the allergy. This book provides a valuable reference text in several topics of the clinical allergy and basic issues related to the immune system response. The inflammatory reaction understanding in allergic disease is clearly evidenced, as well as new strategies for further researches

Characterising the pleiotropic activity and spatial dynamics of Prostaglandin EP2 receptor signalling

EP2 is a member of the G protein coupled receptor superfamily and is implicated in both physiological and pathophysiological signalling in reproductive tissues. Classically reported as Gαs-mediated in reproductive tissues EP2 can also signal via Gαq/11, although the mechanism underlying this promiscuity is unknown. Tight regulation of internalisation and trafficking to endosomal compartments such as the early, or very early endosomes, is fundamental to GPCR signalling capacity, thus discovery of ligands that take advantage of EP2 pleiotropy by preferentially activating specific EP2 pathways could be valuable therapeutically. Therefore, I first characterised EP2 trafficking and signalling using three highly specific ligands – butaprost, AH13205, and PGN9856i. In HEK 293 cells, EP2 undergoes dynamin-dependent constitutive, but limited ligand-directed, internalisation, which was required for full activation of Gαs-cAMP and Gαq/11-Ca2+ pathways. In pregnant myometrium, EP2 signals via Gαs/Gαq/11, activating contrasting pro- and anti- labour pathways until labour, when EP2 Gαs signalling is downregulated. Prostaglandin signalling is a key part of physiological labour, thus understanding the mechanics of EP2 signalling during labour could lead to novel therapeutics to combat preterm birth, which represents a major challenge to public health. I found that EP2 signalling is altered with the onset of labour, favouring pro-inflammatory pathways in early and late labour. OTR activation ‘switched’ EP2 signalling pathways to Gαi/o when activated with butaprost, enhancing pro-labour pathways and dampening Gαs-cAMP, which may be mediated by formation and rearrangement of EP2-OTR heteromers. Whilst AH13205 exhibited an extreme inflammatory profile in both HEK 293 and myometrial cells, and engaged EP2 in crosstalk with OTR, enhancing pro-labour pathways, PGN9856i did not activate Gαq/11-mediated pro-labour/inflammatory pathways in HEK cells or primary myometrial cells, and may actively antagonise OT-mediated pathways. Together, these findings uncover novel aspects of EP2 pleiotropy and suggest that biased EP2 ligands may be valuable tools for targeting reproductive tissues.Open Acces