Editor's Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to authors, or important in this field. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Order results
Result details
Results per page
Select all
Export citation of selected articles as:

Article

Article
Last Aid Training Online: Participants’ and Facilitators’ Perceptions from a Mixed-Methods Study in Rural Scotland
Healthcare 2022, 10(5), 918; https://doi.org/10.3390/healthcare10050918 - 16 May 2022
Cited by 1 | Viewed by 574
Abstract
(1) Background: Palliative and end-of-life care services are increasingly gaining centre stage in health and social care contexts in the UK and globally. Death and dying need are relational processes. Building personal and community capacity along with resilience is vital to support families [...] Read more.
(1) Background: Palliative and end-of-life care services are increasingly gaining centre stage in health and social care contexts in the UK and globally. Death and dying need are relational processes. Building personal and community capacity along with resilience is vital to support families and communities to normalise death and dying. Last Aid Training (LAT) is one such innovative educational initiative which teaches the general public about the fundamentals of palliative care and promotes public discussion about death and dying. The Highland Hospice [HH] in Scotland has pioneered delivery of LAT in face-to-face settings since March 2019 and online since March 2020 to accommodate pandemic restrictions. (2) Methods: This study used a mixed-methods approach, combining an online survey with LAT participants followed by individual semi-structured qualitative interviews with both LAT participants and facilitators. The primary aim of this study was to investigate the impacts of LAT for participants at the individual, family, and community levels, as well as explore participant and facilitator experiences and perspectives of LAT in an online environment. (3) Results: Overall, this evaluation demonstrates that provision of foundational death literacy education in social contexts enhances the personal knowledge, skills, and confidence of individual community members and supports the notion that this personal growth could lead to strengthened community action. (4) Conclusions: Findings from this study concluded that there is potential to include LAT as the foundational core training to promote death literacy in communities with further exploration to integrate/align LAT with other national/global end-of-life care frameworks. Full article
(This article belongs to the Special Issue Public Health Palliative Care and Public Palliative Care Education)
Show Figures

Figure 1

Article
A Two-Stage De-Identification Process for Privacy-Preserving Medical Image Analysis
Healthcare 2022, 10(5), 755; https://doi.org/10.3390/healthcare10050755 - 19 Apr 2022
Viewed by 686
Abstract
Identification and re-identification are two major security and privacy threats to medical imaging data. De-identification in DICOM medical data is essential to preserve the privacy of patients’ Personally Identifiable Information (PII) and requires a systematic approach. However, there is a lack of sufficient [...] Read more.
Identification and re-identification are two major security and privacy threats to medical imaging data. De-identification in DICOM medical data is essential to preserve the privacy of patients’ Personally Identifiable Information (PII) and requires a systematic approach. However, there is a lack of sufficient detail regarding the de-identification process of DICOM attributes, for example, what needs to be considered before removing a DICOM attribute. In this paper, we first highlight and review the key challenges in the medical image data de-identification process. In this paper, we develop a two-stage de-identification process for CT scan images available in DICOM file format. In the first stage of the de-identification process, the patient’s PII—including name, date of birth, etc., are removed at the hospital facility using the export process available in their Picture Archiving and Communication System (PACS). The second stage employs the proposed DICOM de-identification tool for an exhaustive attribute-level investigation to further de-identify and ensure that all PII has been removed. Finally, we provide a roadmap for future considerations to build a semi-automated or automated tool for the DICOM datasets de-identification. Full article
(This article belongs to the Section Health Informatics and Big Data)
Show Figures

Figure 1

Article
Establishing a New ECMO Referral Center Using an ICU-Based Approach: A Feasibility and Safety Study
Healthcare 2022, 10(3), 414; https://doi.org/10.3390/healthcare10030414 - 22 Feb 2022
Viewed by 445
Abstract
Background: A high-volume center with a multidisciplinary team is regarded as the optimal place for providing extracorporeal membrane oxygenation (ECMO). We hypothesize that an ECMO center can also be successfully created and subsequently developed entirely by intensivists in a mid-size mixed intensive care [...] Read more.
Background: A high-volume center with a multidisciplinary team is regarded as the optimal place for providing extracorporeal membrane oxygenation (ECMO). We hypothesize that an ECMO center can also be successfully created and subsequently developed entirely by intensivists in a mid-size mixed intensive care unit (ICU). Methods: A model was created for setting up a new ECMO referral center within the structure of an existing mixed ICU in a tertiary hospital. A retrospective analysis was carried out of the first 33 patients treated in the initial period of the center’s activity, from mid 2018 to the end of 2020. Results: An ECMO center was established and developed entirely based on the resources of an existing mixed ICU. Thirty-three patients were treated. They had an overall survival rate at 90 days of 60.6%. In veno-venous (VV) mode ECMO duration, ICU length of stay, and SOFA score were significantly higher than in veno-arterial mode. No significant differences in clinical characteristics were observed between survivors and non-survivors on VV-ECMO. Conclusions: A regional ECMO center can be set up as an integral part of a mixed ICU in a tertiary hospital. Extracorporeal therapy, such as continuous renal replacement therapy and mechanical ventilation can be managed entirely by intensivists. Further studies are needed to show that the ICU-based approach to setting up a new ECMO center is no less effective than the multidisciplinary approach. Full article
(This article belongs to the Special Issue Pulmonary and Critical Care Medicine)
Show Figures

Figure 1

Article
Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues
Healthcare 2022, 10(2), 324; https://doi.org/10.3390/healthcare10020324 - 08 Feb 2022
Cited by 2 | Viewed by 1032
Abstract
COVID-19, or SARS-CoV-2, is considered as one of the greatest pandemics in our modern time. It affected people’s health, education, employment, the economy, tourism, and transportation systems. It will take a long time to recover from these effects and return people’s lives back [...] Read more.
COVID-19, or SARS-CoV-2, is considered as one of the greatest pandemics in our modern time. It affected people’s health, education, employment, the economy, tourism, and transportation systems. It will take a long time to recover from these effects and return people’s lives back to normal. The main objective of this study is to investigate the various factors in health and food access, and their spatial correlation and statistical association with COVID-19 spread. The minor aim is to explore regression models on examining COVID-19 spread with these variables. To address these objectives, we are studying the interrelation of various socio-economic factors that would help all humans to better prepare for the next pandemic. One of these critical factors is food access and food distribution as it could be high-risk population density places that are spreading the virus infections. More variables, such as income and people density, would influence the pandemic spread. In this study, we produced the spatial extent of COVID-19 cases with food outlets by using the spatial analysis method of geographic information systems. The methodology consisted of clustering techniques and overlaying the spatial extent mapping of the clusters of food outlets and the infected cases. Post-mapping, we analyzed these clusters’ proximity for any spatial variability, correlations between them, and their causal relationships. The quantitative analyses of the health issues and food access areas against COVID-19 infections and deaths were performed using machine learning regression techniques to understand the multi-variate factors. The results indicate a correlation between the dependent variables and independent variables with a Pearson correlation R2-score = 0.44% for COVID-19 cases and R2 = 60% for COVID-19 deaths. The regression model with an R2-score of 0.60 would be useful to show the goodness of fit for COVID-19 deaths and the health issues and food access factors. Full article
(This article belongs to the Special Issue Socio-Economic Burden of Disease: The COVID-19 Case)
Show Figures

Figure 1

Article
Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series
Healthcare 2022, 10(1), 155; https://doi.org/10.3390/healthcare10010155 - 14 Jan 2022
Cited by 4 | Viewed by 1193
Abstract
Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and [...] Read more.
Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients’ overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series. Full article
Show Figures

Figure 1

Review

Review
Current and Future Applications of Artificial Intelligence in Coronary Artery Disease
Healthcare 2022, 10(2), 232; https://doi.org/10.3390/healthcare10020232 - 26 Jan 2022
Cited by 1 | Viewed by 1492
Abstract
Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States [...] Read more.
Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives. Full article
Show Figures

Figure 1

Other

Opinion
Environment, Environmental Crimes, Environmental Forensic Medicine, Environmental Risk Management and Environmental Criminology
Healthcare 2022, 10(2), 263; https://doi.org/10.3390/healthcare10020263 - 29 Jan 2022
Viewed by 859
Abstract
Forensic medicine has always held the human environment, either seen as a source for pathological agents or the background of judicial events, in great consideration. The concept of the environment has evolved through time, expanding itself to include all the physical and virtual [...] Read more.
Forensic medicine has always held the human environment, either seen as a source for pathological agents or the background of judicial events, in great consideration. The concept of the environment has evolved through time, expanding itself to include all the physical and virtual sub-spaces in which we exist. We can nowadays talk of technoenvironmental reality; virtual spaces exploded because of the COVID-19 pandemic making us come to terms with the fact that those are the places where we work, where we socialize and, even, where we meet our doctors and can be cured. Artificial Intelligence (AI) has contributed to shaping new virtual realities that have got their own rules yet to be discovered, carved and respected. We already fight a daily battle to save our natural environment: along with the danger of green crimes, comes the need for environmental justice and environmental forensic medicine that will probably develop a forensic branch and an experimental branch, to implement our technical culture leading to definition of the real dimension of the risk itself to improve the role of legal medicine in the Environmental Risk Management. While green criminology addresses widespread green crimes, a virtual environment criminology will also develop, maybe with a contribution of AI in the justice field. For a sustainable life, the environmental revolution must rapidly take place, and there is the need for a new justice, a new forensic medicine and a new criminology too. Full article
(This article belongs to the Special Issue New Trends in Forensic and Legal Medicine)
Back to TopTop