Current progress in organic–inorganic hetero-nano-interfaces based electrochemical biosensors for healthcare monitoring

2022 ◽  
Vol 452 ◽  
pp. 214282
Author(s):  
Anees A. Ansari ◽  
Bansi D. Malhotra
2020 ◽  
Vol 8 (33) ◽  
pp. 7303-7318 ◽  
Author(s):  
Jinho Yoon ◽  
Hyeon-Yeol Cho ◽  
Minkyu Shin ◽  
Hye Kyu Choi ◽  
Taek Lee ◽  
...  

Flexible electrochemical biosensors are studied hugely because of tremendous advantages including the fast response, high sensitivity, inherent miniaturization, convenient operation, and portability which are suitable for wearable biosensors.


Author(s):  
P. Jeyadurga ◽  
S. Ebenezer Juliet ◽  
I. Joshua Selwyn ◽  
P. Sivanisha

The Internet of things (IoT) is one of the emerging technologies that brought revolution in many application domains such as smart cities, smart retails, healthcare monitoring and so on. As the physical objects are connected via internet, security risk may arise. This paper analyses the existing technologies and protocols that are designed by different authors to ensure the secure communication over internet. It additionally focuses on the advancement in healthcare systems while deploying IoT services.


2005 ◽  
Vol 1 (3) ◽  
pp. 217-224 ◽  
Author(s):  
Graziana Bagni ◽  
Mauro Ravera ◽  
Domenico Osella ◽  
Marco Mascini

Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti

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.


Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 352
Author(s):  
Ruonan Li ◽  
Xuelian Wei ◽  
Jiahui Xu ◽  
Junhuan Chen ◽  
Bin Li ◽  
...  

Accurate monitoring of motion and sleep states is critical for human health assessment, especially for a healthy life, early diagnosis of diseases, and medical care. In this work, a smart wearable sensor (SWS) based on a dual-channel triboelectric nanogenerator was presented for a real-time health monitoring system. The SWS can be worn on wrists, ankles, shoes, or other parts of the body and cloth, converting mechanical triggers into electrical output. By analyzing these signals, the SWS can precisely and constantly monitor and distinguish various motion states, including stepping, walking, running, and jumping. Based on the SWS, a fall-down alarm system and a sleep quality assessment system were constructed to provide personal healthcare monitoring and alert family members or doctors via communication devices. It is important for the healthy growth of the young and special patient groups, as well as for the health monitoring and medical care of the elderly and recovered patients. This work aimed to broaden the paths for remote biological movement status analysis and provide diversified perspectives for true-time and long-term health monitoring, simultaneously.


Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 246
Author(s):  
Patrick Severin Sfragano ◽  
Giulia Moro ◽  
Federico Polo ◽  
Ilaria Palchetti

Peptides represent a promising class of biorecognition elements that can be coupled to electrochemical transducers. The benefits lie mainly in their stability and selectivity toward a target analyte. Furthermore, they can be synthesized rather easily and modified with specific functional groups, thus making them suitable for the development of novel architectures for biosensing platforms, as well as alternative labelling tools. Peptides have also been proposed as antibiofouling agents. Indeed, biofouling caused by the accumulation of biomolecules on electrode surfaces is one of the major issues and challenges to be addressed in the practical application of electrochemical biosensors. In this review, we summarise trends from the last three years in the design and development of electrochemical biosensors using synthetic peptides. The different roles of peptides in the design of electrochemical biosensors are described. The main procedures of selection and synthesis are discussed. Selected applications in clinical diagnostics are also described.


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