Islam Asem Salah Abusohyon , Giuseppe Aiello , Cinzia Muriana , Maria Giuseppina Bruno , Bernardo Patella , Maria Ferraro , Serena Di Vincenzo , Chiara Cipollina , Elisabetta Pace , Rosalinda Inguanta , Mo’men Abu Sahyoun
{"title":"A novel healthcare 4.0 system for testing respiratory diseases based on nanostructured biosensors and fog networking","authors":"Islam Asem Salah Abusohyon , Giuseppe Aiello , Cinzia Muriana , Maria Giuseppina Bruno , Bernardo Patella , Maria Ferraro , Serena Di Vincenzo , Chiara Cipollina , Elisabetta Pace , Rosalinda Inguanta , Mo’men Abu Sahyoun","doi":"10.1016/j.cie.2024.110698","DOIUrl":null,"url":null,"abstract":"<div><div>New digital healthcare models based on advanced bio-sensing technologies are regarded as a possible solution to improve the screening and prevention processes and the overall performance of healthcare supply chains. This is particularly relevant for respiratory diseases, which are currently among the first causes of death and medical expenditures in industrialized countries. This research proposes a new e-health model based on a fog architecture and a smart device, enabling remote diagnostics of respiratory diseases and allowing for decentralized patient testing and self-testing. According to such a model, the patients’ testing is executed through the analysis of the exhaled breath collected using a smart device based on a novel nanostructured sensor and transferring relevant information to the medical staff involved in the diagnosis process. This research proposes an original testing method and system, validated in the lab through a comparative analysis of culture media samples collected from healthy patients, and subsequently exposed to cigarette smoke extract (CSE, an inducer of oxidative stress). The preliminary results obtained demonstrate the validity of the approach proposed, encouraging further experimental analyses on human patients for the implementation into clinical practice.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"198 ","pages":"Article 110698"},"PeriodicalIF":6.7000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Industrial Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360835224008209","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
New digital healthcare models based on advanced bio-sensing technologies are regarded as a possible solution to improve the screening and prevention processes and the overall performance of healthcare supply chains. This is particularly relevant for respiratory diseases, which are currently among the first causes of death and medical expenditures in industrialized countries. This research proposes a new e-health model based on a fog architecture and a smart device, enabling remote diagnostics of respiratory diseases and allowing for decentralized patient testing and self-testing. According to such a model, the patients’ testing is executed through the analysis of the exhaled breath collected using a smart device based on a novel nanostructured sensor and transferring relevant information to the medical staff involved in the diagnosis process. This research proposes an original testing method and system, validated in the lab through a comparative analysis of culture media samples collected from healthy patients, and subsequently exposed to cigarette smoke extract (CSE, an inducer of oxidative stress). The preliminary results obtained demonstrate the validity of the approach proposed, encouraging further experimental analyses on human patients for the implementation into clinical practice.
期刊介绍:
Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.