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Bioengineering
Special Issues
Bioengineering Techniques and Applications Against COVID-19
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Bioengineering Techniques and Applications Against COVID-19

A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 21853

Special Issue Editors

Dr. Dimitrios Glotsos

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Guest Editor
Biomedical Engineering Department, University of West Attica, Athens, Greece
Interests: image processing; machine learning
Dr. Luis Coelho

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Guest Editor
ISEP—School of Engineering, Polytechnic of Porto, Porto, Portugal
Interests: physiological signal processing; image processing; machine learning; neurodegenerative diseases; virtual reality
Special Issues, Collections and Topics in MDPI journals
Dr. Sara Reis

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Guest Editor
CIETI, Polytechnic Institute of Porto, Portugal
Interests: health innovation; bioethics

Special Issue Information

Dear Colleagues,

Coronavirus disease (COVID-19) is a disease caused by a newly discovered coronavirus. Most people infected with the virus will experience no symptoms or mild to moderate respiratory illness and recover without special treatment. However, the disease can be severe, especially on older persons and those with pre-existing medical conditions (such as high blood pressure, heart problems or diabetes). The disease is highly contagious and has evolved into a pandemic, bringing several social, economic, and health challenges.

Considering the current context and the situation of uncertainty regarding the future, the scientific community has contributed with technological advances in several areas in order to combat the evolution of the disease and its consequences.

In this Special Edition, we would like to receive innovative contributions on bioengineering techniques and applications against COVID-19. Authors are invited to submit original, unpublished papers on topics including but not limited to:

  • Disease prevention techniques;
  • Simulations that make it possible to predict the behavior of the virus;
  • Development, optimization, and validation of early diagnosis methods, within a framework of training and national autonomy for the COVID diagnosis;
  • Methods for early detection of the infection and for disease prognosis;
  • Characterization of the immune response, immunopathology, and immunogenetic factors;
  • Development of new therapies or new therapeutic approaches or protocols;
  • Clinical studies for management and monitoring of infected individuals and risk groups using computational approaches;
  • Equipment or devices to improve the response of health systems;
  • Support tools to help clinic decision making processes in scarce resource environments.

Prof. Dr. Dimitrios Glotsos
Dr. Luis Coelho
Dr. Sara Reis
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. Bioengineering 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 2700 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

  • COVID-19 
  • Simulation models 
  • Computational approaches 
  • Support Tools

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Published Papers (7 papers)

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Editorial

Jump to: Research, Review

10 pages, 267 KiB  
Editorial
Exploring the Immunomodulatory Properties of Stem Cells in Combating COVID-19: Can We Expect More?
by Panagiotis Mallis
Bioengineering 2023, 10(7), 803; https://doi.org/10.3390/bioengineering10070803 - 5 Jul 2023
Cited by 1 | Viewed by 1270
Abstract
Since the first appearance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in December 2019, the disease has displayed a remarkable interindividual variability in the global population, resulting in different mortality and morbidity rates. Still, an effective cure against SARS-CoV-2 has not been developed, [...] Read more.
Since the first appearance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in December 2019, the disease has displayed a remarkable interindividual variability in the global population, resulting in different mortality and morbidity rates. Still, an effective cure against SARS-CoV-2 has not been developed, and therefore, alternative therapeutic protocols must also be evaluated. Considering that stem cells, especially Mesenchymal Stromal Cells (MSCs), are characterized by both regenerative and immunomodulatory properties and that their safety and tolerability have been investigated previously, these cells could potentially be applied against coronavirus disease 19 (COVID-19). In addition, an individual’s genetic background is further related to disease pathogenesis, especially rare Inborn Errors of Immunity (IEIs), autoantibodies against Interferon type I, and the presence of different Human Leukocyte Antigens (HLA) alleles, which are actively associated with protection or susceptibility in relation to SARS-CoV-2. Herein, the use of MSCs as a potential stem cell therapy will require a deep understanding of their immunomodulatory properties associated with their HLA alleles. In such a way, HLA-restricted MSC lines can be developed and applied precisely, offering more solutions to clinicians in attenuating the mortality of SARS-CoV-2. Full article
(This article belongs to the Special Issue Bioengineering Techniques and Applications Against COVID-19)
5 pages, 216 KiB  
Editorial
The COVID-19 Pandemic: How Technology Is Reshaping Public Health and Medicine
by Luís Coelho, Dimitrios Glotsos and Sara Reis
Bioengineering 2023, 10(5), 611; https://doi.org/10.3390/bioengineering10050611 - 19 May 2023
Viewed by 1734
Abstract
The outbreak of the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been a watershed moment in human history, causing a profound shift in the global landscape that has affected every aspect of our lives [...] Full article
(This article belongs to the Special Issue Bioengineering Techniques and Applications Against COVID-19)

Research

Jump to: Editorial, Review

22 pages, 6274 KiB  
Article
A Decision Support System for Diagnosis of COVID-19 from Non-COVID-19 Influenza-like Illness Using Explainable Artificial Intelligence
by Krishnaraj Chadaga, Srikanth Prabhu, Vivekananda Bhat, Niranjana Sampathila, Shashikiran Umakanth and Rajagopala Chadaga
Bioengineering 2023, 10(4), 439; https://doi.org/10.3390/bioengineering10040439 - 31 Mar 2023
Cited by 27 | Viewed by 3460
Abstract
The coronavirus pandemic emerged in early 2020 and turned out to be deadly, killing a vast number of people all around the world. Fortunately, vaccines have been discovered, and they seem effectual in controlling the severe prognosis induced by the virus. The reverse [...] Read more.
The coronavirus pandemic emerged in early 2020 and turned out to be deadly, killing a vast number of people all around the world. Fortunately, vaccines have been discovered, and they seem effectual in controlling the severe prognosis induced by the virus. The reverse transcription-polymerase chain reaction (RT-PCR) test is the current golden standard for diagnosing different infectious diseases, including COVID-19; however, it is not always accurate. Therefore, it is extremely crucial to find an alternative diagnosis method which can support the results of the standard RT-PCR test. Hence, a decision support system has been proposed in this study that uses machine learning and deep learning techniques to predict the COVID-19 diagnosis of a patient using clinical, demographic and blood markers. The patient data used in this research were collected from two Manipal hospitals in India and a custom-made, stacked, multi-level ensemble classifier has been used to predict the COVID-19 diagnosis. Deep learning techniques such as deep neural networks (DNN) and one-dimensional convolutional networks (1D-CNN) have also been utilized. Further, explainable artificial techniques (XAI) such as Shapley additive values (SHAP), ELI5, local interpretable model explainer (LIME), and QLattice have been used to make the models more precise and understandable. Among all of the algorithms, the multi-level stacked model obtained an excellent accuracy of 96%. The precision, recall, f1-score and AUC obtained were 94%, 95%, 94% and 98% respectively. The models can be used as a decision support system for the initial screening of coronavirus patients and can also help ease the existing burden on medical infrastructure. Full article
(This article belongs to the Special Issue Bioengineering Techniques and Applications Against COVID-19)
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Graphical abstract

17 pages, 2693 KiB  
Article
Perceptive SARS-CoV-2 End-To-End Ultrasound Video Classification through X3D and Key-Frames Selection
by Marco Gazzoni, Marco La Salvia, Emanuele Torti, Gianmarco Secco, Stefano Perlini and Francesco Leporati
Bioengineering 2023, 10(3), 282; https://doi.org/10.3390/bioengineering10030282 - 21 Feb 2023
Cited by 2 | Viewed by 2226
Abstract
The SARS-CoV-2 pandemic challenged health systems worldwide, thus advocating for practical, quick and highly trustworthy diagnostic instruments to help medical personnel. It features a long incubation period and a high contagion rate, causing bilateral multi-focal interstitial pneumonia, generally growing into acute respiratory distress [...] Read more.
The SARS-CoV-2 pandemic challenged health systems worldwide, thus advocating for practical, quick and highly trustworthy diagnostic instruments to help medical personnel. It features a long incubation period and a high contagion rate, causing bilateral multi-focal interstitial pneumonia, generally growing into acute respiratory distress syndrome (ARDS), causing hundreds of thousands of casualties worldwide. Guidelines for first-line diagnosis of pneumonia suggest Chest X-rays (CXR) for patients exhibiting symptoms. Potential alternatives include Computed Tomography (CT) scans and Lung UltraSound (LUS). Deep learning (DL) has been helpful in diagnosis using CT scans, LUS, and CXR, whereby the former commonly yields more precise results. CXR and CT scans present several drawbacks, including high costs. Radiation-free LUS imaging requires high expertise, and physicians thus underutilise it. LUS demonstrated a strong correlation with CT scans and reliability in pneumonia detection, even in the early stages. Here, we present an LUS video-classification approach based on contemporary DL strategies in close collaboration with Fondazione IRCCS Policlinico San Matteo’s Emergency Department (ED) of Pavia. This research addressed SARS-CoV-2 patterns detection, ranked according to three severity scales by operating a trustworthy dataset comprising ultrasounds from linear and convex probes in 5400 clips from 450 hospitalised subjects. The main contributions of this study are related to the adoption of a standardised severity ranking scale to evaluate pneumonia. This evaluation relies on video summarisation through key-frame selection algorithms. Then, we designed and developed a video-classification architecture which emerged as the most promising. In contrast, the literature primarily concentrates on frame-pattern recognition. By using advanced techniques such as transfer learning and data augmentation, we were able to achieve an F1-Score of over 89% across all classes. Full article
(This article belongs to the Special Issue Bioengineering Techniques and Applications Against COVID-19)
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Graphical abstract

11 pages, 2448 KiB  
Communication
Risk Factors Associated with Mortality in Hospitalized Patients with COVID-19 during the Omicron Wave in Brazil
by Marilaine Colnago, Giovana A. Benvenuto, Wallace Casaca, Rogério G. Negri, Eder G. Fernandes and José A. Cuminato
Bioengineering 2022, 9(10), 584; https://doi.org/10.3390/bioengineering9100584 - 20 Oct 2022
Cited by 16 | Viewed by 2574
Abstract
Considering the imminence of new SARS-CoV-2 variants and COVID-19 vaccine availability, it is essential to understand the impact of the disease on the most vulnerable groups and those at risk of death from the disease. To this end, the odds ratio (OR) for [...] Read more.
Considering the imminence of new SARS-CoV-2 variants and COVID-19 vaccine availability, it is essential to understand the impact of the disease on the most vulnerable groups and those at risk of death from the disease. To this end, the odds ratio (OR) for mortality and hospitalization was calculated for different groups of patients by applying an adjusted logistic regression model based on the following variables of interest: gender, booster vaccination, age group, and comorbidity occurrence. A massive number of data were extracted and compiled from official Brazilian government resources, which include all reported cases of hospitalizations and deaths associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Brazil during the “wave” of the Omicron variant (BA.1 substrain). Males (1.242; 95% CI 1.196–1.290) aged 60–79 (3.348; 95% CI 3.050–3.674) and 80 years or older (5.453; 95% CI 4.966–5.989), and hospitalized patients with comorbidities (1.418; 95% CI 1.355–1.483), were more likely to die. There was a reduction in the risk of death (0.907; 95% CI 0.866–0.951) among patients who had received the third dose of the anti-SARS-CoV-2 vaccine (booster). Additionally, this big data investigation has found statistical evidence that vaccination can support mitigation plans concerning the current scenario of COVID-19 in Brazil since the Omicron variant and its substrains are now prevalent across the entire country. Full article
(This article belongs to the Special Issue Bioengineering Techniques and Applications Against COVID-19)
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19 pages, 9508 KiB  
Article
Analysis and Forecasting Incidence, Intensive Care Unit Admissions, and Projected Mortality Attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France: Predictions for 4 Weeks Ahead
by Kathleen Carvalho, João Paulo Vicente, Mihajlo Jakovljevic and João Paulo Ramos Teixeira
Bioengineering 2021, 8(6), 84; https://doi.org/10.3390/bioengineering8060084 - 11 Jun 2021
Cited by 12 | Viewed by 4093
Abstract
The use of artificial neural networks (ANNs) is a great contribution to medical studies since the application of forecasting concepts allows for the analysis of future diseases propagation. In this context, this paper presents a study of the new coronavirus SARS-COV-2 with a [...] Read more.
The use of artificial neural networks (ANNs) is a great contribution to medical studies since the application of forecasting concepts allows for the analysis of future diseases propagation. In this context, this paper presents a study of the new coronavirus SARS-COV-2 with a focus on verifying the virus propagation associated with mitigation procedures and massive vaccination campaigns. There were two proposed methodologies in making predictions 28 days ahead for the number of new cases, deaths, and ICU patients of five European countries: Portugal, France, Italy, the United Kingdom, and Germany. A case study of the results of massive immunization in Israel was also considered. The data input of cases, deaths, and daily ICU patients was normalized to reduce discrepant numbers due to the countries’ size and the cumulative vaccination values by the percentage of population immunized (with at least one dose of the vaccine). As a comparative criterion, the calculation of the mean absolute error (MAE) of all predictions presents the best methodology, targeting other possibilities of use for the method proposed. The best architecture achieved a general MAE for the 1-to-28-day ahead forecast, which is lower than 30 cases, 0.6 deaths, and 2.5 ICU patients per million people. Full article
(This article belongs to the Special Issue Bioengineering Techniques and Applications Against COVID-19)
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Review

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17 pages, 5566 KiB  
Review
Advanced Vaccine Design Strategies against SARS-CoV-2 and Emerging Variants
by Jianzhong Zhang, Yutian Xia, Xuan Liu and Gang Liu
Bioengineering 2023, 10(2), 148; https://doi.org/10.3390/bioengineering10020148 - 22 Jan 2023
Cited by 5 | Viewed by 4237
Abstract
Vaccination is the most cost-effective means in the fight against infectious diseases. Various kinds of vaccines have been developed since the outbreak of COVID-19, some of which have been approved for clinical application. Though vaccines available achieved partial success in protecting vaccinated subjects [...] Read more.
Vaccination is the most cost-effective means in the fight against infectious diseases. Various kinds of vaccines have been developed since the outbreak of COVID-19, some of which have been approved for clinical application. Though vaccines available achieved partial success in protecting vaccinated subjects from infection or hospitalization, numerous efforts are still needed to end the global pandemic, especially in the case of emerging new variants. Safe and efficient vaccines are the key elements to stop the pandemic from attacking the world now; novel and evolving vaccine technologies are urged in the course of fighting (re)-emerging infectious diseases. Advances in biotechnology offered the progress of vaccinology in the past few years, and lots of innovative approaches have been applied to the vaccine design during the ongoing pandemic. In this review, we summarize the state-of-the-art vaccine strategies involved in controlling the transmission of SARS-CoV-2 and its variants. In addition, challenges and future directions for rational vaccine design are discussed. Full article
(This article belongs to the Special Issue Bioengineering Techniques and Applications Against COVID-19)
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