scholarly journals Advances in Medical Wearable Biosensors: Design, Fabrication and Materials Strategies in Healthcare Monitoring

Molecules ◽  
2021 ◽  
Vol 27 (1) ◽  
pp. 165
Author(s):  
Sangeeth Pillai ◽  
Akshaya Upadhyay ◽  
Darren Sayson ◽  
Bich Hong Nguyen ◽  
Simon D. Tran
Keyword(s):  
Large Scale ◽  
Healthcare Delivery ◽  
Peptide Arrays ◽  
Widespread Application ◽  
Sensing Platforms ◽  
Optical Modes ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
New Biomarkers ◽  
Medical Healthcare

In the past decade, wearable biosensors have radically changed our outlook on contemporary medical healthcare monitoring systems. These smart, multiplexed devices allow us to quantify dynamic biological signals in real time through highly sensitive, miniaturized sensing platforms, thereby decentralizing the concept of regular clinical check-ups and diagnosis towards more versatile, remote, and personalized healthcare monitoring. This paradigm shift in healthcare delivery can be attributed to the development of nanomaterials and improvements made to non-invasive biosignal detection systems alongside integrated approaches for multifaceted data acquisition and interpretation. The discovery of new biomarkers and the use of bioaffinity recognition elements like aptamers and peptide arrays combined with the use of newly developed, flexible, and conductive materials that interact with skin surfaces has led to the widespread application of biosensors in the biomedical field. This review focuses on the recent advances made in wearable technology for remote healthcare monitoring. It classifies their development and application in terms of electrochemical, mechanical, and optical modes of transduction and type of material used and discusses the shortcomings accompanying their large-scale fabrication and commercialization. A brief note on the most widely used materials and their improvements in wearable sensor development is outlined along with instructions for the future of medical wearables.

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

scholarly journals EPCO-29. EPIGENOMICS OF THE GLIOMA LONGITUDINAL ANALYSIS (GLASS) CONSORTIUM

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii75-ii75
Author(s):  
Thais Sabedot ◽  
Michael Wells ◽  
Indrani Datta ◽  
Tathiane Malta ◽  
Ana Valeria Castro ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Longitudinal Analysis ◽  
Copy Number ◽  
Large Scale ◽  
Molecular Classification ◽  
Tumor Burden ◽  
Copy Number Alterations ◽  
Unsupervised Analysis ◽  
Diffuse Gliomas ◽  
New Biomarkers

Abstract Adult diffuse gliomas are central nervous system (CNS) tumors that arise from the malignant transformation of glial cells. Nearly all gliomas will recur despite standard treatment however, current histopathological grading fails to predict which of them will relapse and/or progress. The Glioma Longitudinal AnalySiS (GLASS) consortium is a large-scale collaboration that aims to investigate the molecular profiling of matched primary and recurrent glioma samples from multiple institutions in order to better understand the dynamic evolution of these tumors. At this time, the cohort comprises 946 samples across 11 institutions and among those, 864 have DNA methylation data available. The current molecular classification based on 7 subtypes published by TCGA in 2016 was applied to the dataset. Among the IDH wildtype tumors, 33% (16/49) of the patients showed a change of subtype upon recurrence, whereas most of them (9/16) were Classic-like at the primary stage but changed to either Mesenchymal-like or PA-like at the recurrent level. Among the IDH mutant tumors, 15% (22/142) showed a change of subtype at recurrent stage, in which 16 out of 22 progressed from G-CIMP-high to G-CIMP-low. Although some tumors progressed to a different subtype upon recurrence, an unsupervised analysis showed that the samples tend to cluster by patient instead of by subtype. By estimating the copy number alterations of these tumors using DNA methylation, the overall copy number profile of the recurrent samples remains similar to their primary counterpart. From this initial analysis using epigenomic data, we were able to characterize some aspects of glioma evolution and how the DNA methylation is associated with the progression of these tumors to different subtypes. These findings corroborate the importance of epigenetics in gliomas and can potentially lead to the identification of new biomarkers that can reflect tumor burden and predict its development.

scholarly journals Remote monitoring in heart failure: current and emerging technologies in the context of the pandemic

Heart ◽  
2021 ◽  
Vol 107 (5) ◽  
pp. 366-372
Author(s):  
Donya Mohebali ◽  
Michelle M Kittleson
Keyword(s):  
Heart Failure ◽  
At Risk ◽  
Remote Monitoring ◽  
Reliable Method ◽  
Healthcare Delivery ◽  
Rapid Expansion ◽  
Monitoring Technology ◽  
Remote Healthcare ◽  
Monitoring Technologies

The incidence of heart failure (HF) remains high and patients with HF are at risk for frequent hospitalisations. Remote monitoring technologies may provide early indications of HF decompensation and potentially allow for optimisation of therapy to prevent HF hospitalisations. The need for reliable remote monitoring technology has never been greater as the COVID-19 pandemic has led to the rapid expansion of a new mode of healthcare delivery: the virtual visit. With the convergence of remote monitoring technologies and reliable method of remote healthcare delivery, an understanding of the role of both in the management of patients with HF is critical. In this review, we outline the evidence on current remote monitoring technologies in patients with HF and highlight how these advances may benefit patients in the context of the current pandemic.

scholarly journals Remote healthcare monitoring and assessment

2011 ◽  
Vol 19 (4) ◽  
pp. 295-306
Author(s):  
Chris D. Nugent ◽  
Dewar Finlay ◽  
Richard Davies ◽  
Mark Donnelly ◽  
Josef Hallberg ◽  
...  
Keyword(s):  
Remote Healthcare ◽  
Healthcare Monitoring

scholarly journals Wired and Wireless Distributed e-Home Healthcare System

2016 ◽  
pp. 207-250
Author(s):  
Booma Devi Sekar ◽  
JiaLi Ma ◽  
MingChui Dong
Keyword(s):  
Healthcare System ◽  
Point Of Care ◽  
Access Point ◽  
Data Access ◽  
Medical Personnel ◽  
Home Healthcare ◽  
Information Communication ◽  
Healthcare Data ◽  
Remote Healthcare ◽  
Healthcare Monitoring

The proactive development in electronic health (e-health) has introduced seemingly endless number of applications such as telemedicine, electronic records, healthcare score cards, healthcare monitoring etc. Yet, these applications confront the key challenges of network dependence and medical personnel necessity, which hinders the development of universality of e-health services. To mitigate such key challenges, this chapter presents a versatile wired and wireless distributed e-home healthcare system. By exploiting the benefit of body sensor network and information communication technology, the dedicated system model methodically integrates some of the comprehensive functions such as pervasive health monitoring, remote healthcare data access, point-of-care signal interpretation and diagnosis, disease-driven uplink update and synchronization (UUS) scheme and emergency management to design a complete and independent e-home healthcare system.

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