Health Informatics and Epidemiological Data Analysis in COVID-19 Based Internet of Medical Things (HIEDA-COVID19-IOMT)

Topic Information

Dear Colleagues,

In the presence of external environmental factors, COVID-19 has become a worldwide epidemic problem. The journal of the National Academy of Sciences of the United States calculates that the total weight of COVID-19 virus particles is about 100 grams to 10 kilograms, which is between an apple and a child. It is just like a nuclear bomb, with a small mass but capable of devastating destruction. The study stated that each coronavirus particle had a mass of 1 nano coronavirus gram, which calculated that each infected person carried about 1 to 10 micrograms of virus particles at the peak of infection, based on the estimated 10 to 100 billion coronavirus particles per infected person at the peak of infection. IoMT medical applications can also help the patients of COVID-19 keep in touch with doctors or nurses. The intelligent detection means using the Internet of Things technology sends various physical data of patients in real time through the communication module, and at the same time has the optimization of clinical basic nursing work, such as automatic recording, summary, and alarm. In order to further improve the hospitalization experience of patients and improve the quality of medical services, 5G technology and related smart hardware devices based on the Internet, artificial intelligence, and the Internet of Things are used to build mobile nursing, physical sign monitoring, wireless infusion monitoring, and mobile rounds under 5G technology. Then combined with deep learning to analyze and integrate medical data to optimize management methods and rationally allocate medical resources. This Special Issue is aimed at presenting the state-of-the-art, current challenges and future trends for the successful application of artificial intelligence in management of epidemic control of COVID-19 using the IoMT platform. Original contributions considering recent findings in theory, methodologies, and applications in the field of artificial Intelligence and Internet computing in COVID-19 are welcome.

Prof. Dr. Wenjun (Chris) Zhang
Prof. Dr. Dhanjoo N. Ghista
Prof. Dr. Kelvin K.L. Wong
Prof. Dr. Zhili Sun
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
BioMed biomed	-	-	2021	21.9 Days	CHF 1000
Biomedicines biomedicines	3.9	5.2	2013	14.6 Days	CHF 2600
BioMedInformatics biomedinformatics	-	1.7	2021	22 Days	CHF 1000
Epidemiologia epidemiologia	-	3.6	2020	21.5 Days	CHF 1200
Journal of Clinical Medicine jcm	3.0	5.7	2012	16 Days	CHF 2600

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Published Papers (3 papers)

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All BioMed Biomedicines BioMedInformatics Epidemiologia JCM

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Open AccessProtocol

Protocol of the Long-COVID Patients Causal Diagnosis and Rehabilitation Randomized Feasibility Controlled Trial in Patients with Dysautonomia: The LoCoDiRe-Dys Study

by Dimitrios Spaggoulakis, Antonios Kontaxakis, Andreas Asimakos, Stavroula Spetsioti, Archontoula Antonoglou, Pantelis Gounopoulos, Martha Katsarou, Helen Iasonidou, Stergios-Stylianos Gatzonis and Paraskevi Katsaounou

BioMed 2023, 3(3), 309-319; https://doi.org/10.3390/biomed3030026 - 26 Jun 2023

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Abstract

Dysautonomia in the post-COVID-19 condition appears to affect a significant number of patients, with reports raising the incidence up to 61%, having an overlap with myalgic encephalomyelitis/chronic fatigue syndrome. Quality of life and daily function are significantly impacted and conservative management interventions, despite [...] Read more.

Dysautonomia in the post-COVID-19 condition appears to affect a significant number of patients, with reports raising the incidence up to 61%, having an overlap with myalgic encephalomyelitis/chronic fatigue syndrome. Quality of life and daily function are significantly impacted and conservative management interventions, despite the lack of high-quality evidence to date, are needed to ameliorate disability. A total of 50 adults with a dysautonomia post-COVID-19 diagnosis based on the Ewing battery and a NASA lean test will be enrolled in a randomized single blinded controlled trial with a crossover design. Feasibility and lack of definite dysautonomia diagnosis will be the primary outcomes, while secondary outcomes will be health-related, clinical and cardiopulmonary exercise test indicators. Safety and acceptance will also be checked, primarily excluding participants with post-exertional malaise. The Long-COVID patients Causal Diagnosis and Rehabilitation study in patients with Dysautonomia (LoCoDiRE-Dys) intervention will consist of an educational module, breathing retraining and an individualized exercise intervention of biweekly sessions for two months with regular assessment of both groups. LoCoDiRe-Dys aims to be the first post-COVID-19 randomized study in people with dysautonomia offering a multimodal intervention both in diagnosis and management. The need for evidence in effectively supporting patients is eminent. Full article

(This article belongs to the Topic Health Informatics and Epidemiological Data Analysis in COVID-19 Based Internet of Medical Things (HIEDA-COVID19-IOMT))

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Open AccessArticle

Learning and Investigation of the Role of Angiotensin-Converting Enzyme in Radiotherapy for Nasopharyngeal Carcinoma

by Yanan Ding, Huanhuan Xiu, Yanling Zhang, Miaola Ke, Letao Lin, Huzheng Yan, Pan Hu, Meigui Xiao, Xu He and Tao Zhang

Biomedicines 2023, 11(6), 1581; https://doi.org/10.3390/biomedicines11061581 - 30 May 2023

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Abstract

Ionizing radiation (IR) is an important treatment for nasopharyngeal carcinoma (NPC) that mainly kills tumor cells by producing large amounts of reactive oxygen species (ROS). Intracellular ROS levels affect the sensitivity of tumor cells to IR. Recently, angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin-converting [...] Read more.

Ionizing radiation (IR) is an important treatment for nasopharyngeal carcinoma (NPC) that mainly kills tumor cells by producing large amounts of reactive oxygen species (ROS). Intracellular ROS levels affect the sensitivity of tumor cells to IR. Recently, angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin-converting enzyme (ACE) have been found to affect the intracellular levels of ROS. Therefore, we performed a health informatics assessment of ACE in the TCGA database. We explored the effect of ACE in NPC cells. We found that either knockdown of ACE or inhibition of ACE by enalaprilat could decrease ROS levels in NPC cells. Furthermore, knockdown of ACE or inhibition of ACE by enalaprilat could reduce IR-induced ROS levels. ACE knockdown or inhibition reduced IR-induced DNA damage and apoptosis. ACE overexpression increased the level of ROS in NPC cells and further increased sensitivity to IR. These findings indicate that ACE influences the effect of IR by regulating the level of ROS in NPC cells. Full article

(This article belongs to the Topic Health Informatics and Epidemiological Data Analysis in COVID-19 Based Internet of Medical Things (HIEDA-COVID19-IOMT))

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Open AccessArticle

Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers

by Salvatore Greco, Alessandro Salatiello, Nicolò Fabbri, Fabrizio Riguzzi, Emanuele Locorotondo, Riccardo Spaggiari, Alfredo De Giorgi and Angelina Passaro

Biomedicines 2023, 11(3), 831; https://doi.org/10.3390/biomedicines11030831 - 9 Mar 2023

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Abstract

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can [...] Read more.

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission. Full article

(This article belongs to the Topic Health Informatics and Epidemiological Data Analysis in COVID-19 Based Internet of Medical Things (HIEDA-COVID19-IOMT))

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