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Big Data Analysis and Challenges in Environmental Research and Public Health

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 6503

Special Issue Editors

Dr. Styliani Geronikolou

E-Mail Website
Guest Editor
1. Clinical, Translational and Experimental Surgery Research Centre, Biomedical Research Foundation Academy of Athens, Soranou Ephessiou Street 4, 11527 Athens, Greece
2. University Research Institute of Maternal and Child Health & Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, “Aghia Sophia” Children’s Hospital, Levadias Street 8, 11527 Athens, Greece
3. Center for Adolescent Medicine, UNESCO Chair in Adolescent Health Care, First Department of Pediatrics, School of Medicine, National and Kapodistrian University of Athens, “Aghia Sophia” Children’s Hospital, Thivon Street 1, 11527 Athens, Greece
Interests: meta-analysis; systems medicine; epigenetics; foodborne infections; cancer; COVID-19; prevention; electromagnetic health; solar activity; natural radiation; electromagnetic field; public health; precision medicine; p4 medicine; environmental health; obesity
Dr. Stelios Zimeras

E-Mail Website
Guest Editor
Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, 83200 Karlovassi, Greece
Interests: big data; machine learning; neural networks; image analysis; medical imaging; Bayesian statistics; applied statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing volume of data collected by devices and new technologies (i.e., IoT, platforms such as Copernicus, WHO, etc.) cannot be processed with traditional analytical methodology or software. The analysis of such large amounts of data, which is of particular interest for environmental as well as public health researchers, authorities and organizations, confronts challenges such as complexity, capturing data (i.e., sampling), data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. The five pillars of big data are as follows: volume, value, veracity, variety and velocity. Knowing the five Vs allows data scientists to extract more value from their data while also allowing science organizations to become more person-centric. The topics of interest for this Special Issue include, but are not limited to: 

  • Big data analysis tools;
  • Surveillance and policing applications;
  • Big data hype and criticism;
  • Big data analysis in environmental research;
  • Public health;
  • Big data epidemiology;
  • Infodemiology—infoveillance;
  • Genomics;
  • Proteomics.

Dr. Styliani Geronikolou
Dr. Stelios Zimeras
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data analysis
  • public health
  • environment
  • policing
  • surveillance
  • infodemiology
  • bioinformatics
  • omics
  • epidemiology
  • climate change
  • disasters
  • prognostics

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Published Papers (2 papers)

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Research

17 pages, 5652 KiB  
Article
Soft Tissue Ewing Sarcoma Cell Drug Resistance Revisited: A Systems Biology Approach
by Seyedehsadaf Asfa, Halil Ibrahim Toy, Reza Arshinchi Bonab, George P. Chrousos, Athanasia Pavlopoulou and Styliani A. Geronikolou
Int. J. Environ. Res. Public Health 2023, 20(13), 6288; https://doi.org/10.3390/ijerph20136288 - 3 Jul 2023
Viewed by 2269
Abstract
Ewing sarcoma is a rare type of cancer that develops in the bones and soft tissues. Drug therapy represents an extensively used modality for the treatment of sarcomas. However, cancer cells tend to develop resistance to antineoplastic agents, thereby posing a major barrier [...] Read more.
Ewing sarcoma is a rare type of cancer that develops in the bones and soft tissues. Drug therapy represents an extensively used modality for the treatment of sarcomas. However, cancer cells tend to develop resistance to antineoplastic agents, thereby posing a major barrier in treatment effectiveness. Thus, there is a need to uncover the molecular mechanisms underlying chemoresistance in sarcomas and, hence, to enhance the anticancer treatment outcome. In this study, a differential gene expression analysis was conducted on high-throughput transcriptomic data of chemoresistant versus chemoresponsive Ewing sarcoma cells. By applying functional enrichment analysis and protein–protein interactions on the differentially expressed genes and their corresponding products, we uncovered genes with a hub role in drug resistance. Granted that non-coding RNA epigenetic regulators play a pivotal role in chemotherapy by targeting genes associated with drug response, we investigated the non-coding RNA molecules that potentially regulate the expression of the detected chemoresistance genes. Of particular importance, some chemoresistance-relevant genes were associated with the autonomic nervous system, suggesting the involvement of the latter in the drug response. The findings of this study could be taken into consideration in the clinical setting for the accurate assessment of drug response in sarcoma patients and the application of tailored therapeutic strategies. Full article
(This article belongs to the Special Issue Big Data Analysis and Challenges in Environmental Research and Public Health)
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13 pages, 2596 KiB  
Article
COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods
by Marios Constantinou, Themis Exarchos, Aristidis G. Vrahatis and Panagiotis Vlamos
Int. J. Environ. Res. Public Health 2023, 20(3), 2035; https://doi.org/10.3390/ijerph20032035 - 22 Jan 2023
Cited by 37 | Viewed by 3572
Abstract
Since December 2019, the coronavirus disease has significantly affected millions of people. Given the effect this disease has on the pulmonary systems of humans, there is a need for chest radiographic imaging (CXR) for monitoring the disease and preventing further deaths. Several studies [...] Read more.
Since December 2019, the coronavirus disease has significantly affected millions of people. Given the effect this disease has on the pulmonary systems of humans, there is a need for chest radiographic imaging (CXR) for monitoring the disease and preventing further deaths. Several studies have been shown that Deep Learning models can achieve promising results for COVID-19 diagnosis towards the CXR perspective. In this study, five deep learning models were analyzed and evaluated with the aim of identifying COVID-19 from chest X-ray images. The scope of this study is to highlight the significance and potential of individual deep learning models in COVID-19 CXR images. More specifically, we utilized the ResNet50, ResNet101, DenseNet121, DenseNet169 and InceptionV3 using Transfer Learning. All models were trained and validated on the largest publicly available repository for COVID-19 CXR images. Furthermore, they were evaluated on unknown data that was not used for training or validation, authenticating their performance and clarifying their usage in a medical scenario. All models achieved satisfactory performance where ResNet101 was the superior model achieving 96% in Precision, Recall and Accuracy, respectively. Our outcomes show the potential of deep learning models on COVID-19 medical offering a promising way for the deeper understanding of COVID-19. Full article
(This article belongs to the Special Issue Big Data Analysis and Challenges in Environmental Research and Public Health)
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