Life | Special Issue : Emerging Risk Factors Associated with Public Health

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Emerging Risk Factors Associated with Public Health

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A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Epidemiology".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 3126

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Special Issue Editors

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Dear Colleagues,

Emerging risk factors for public health are constantly changing and offer a significant challenge for public health around the world. These include climate change, antimicrobial resistance, mental health, urbanization, and digital health. Climate change poses a significant threat to public health through its effects on the environment, while antimicrobial resistance has led to the emergence of drug-resistant bacteria. Mental health is also an emerging risk factor, while urbanization can lead to overcrowding, poor air quality, inadequate sanitation, and increased exposure to violence and crime. Digital health technologies have the potential to transform healthcare, but also pose new risks such as data privacy and security, an over-reliance on technology, and unequal access to digital health services. Public health professionals must take a comprehensive and multidisciplinary approach to addressing these risk factors to prevent and mitigate their impact on individuals and communities, ultimately improving the health and well-being of populations around the world. In this Special Issue, advances in understanding the growing public health risk factors and applications for identifying and mitigating these risk factors will be addressed.

You may choose our Joint Special Issue in Healthcare.

Dr. Akshaya Srikanth Bhagavathula
Dr. Wubshet Hailu Tesfaye
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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

risk factors
epidemiology
public health
climate change
antimicrobial resistance
mental health
digital health
prevention
urbanization
health disparities

Published Papers (2 papers)

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Research

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16 pages, 1265 KiB

Open AccessArticle

Helicobacter pylori (H. pylori) Infection-Associated Dyslipidemia in the Asir Region of Saudi Arabia

by Mohammad Asrar Izhari, Omar A. Al Mutawa, Ali Mahzari, Essa Ajmi Alotaibi, Maher A. Almashary, Jaber Abdullah Alshahrani, Ahmed R. A. Gosady, Abdulrahman M Almutairi, Daifallah M. M. Dardari and Abdul Kareem A. AlGarni

Life 2023, 13(11), 2206; https://doi.org/10.3390/life13112206 - 13 Nov 2023

Cited by 1 | Viewed by 1495

Abstract

Objectives: H. pylori-associated dyslipidemia has been reported to be a major risk factor for atherosclerosis and coronary heart diseases. We aimed to investigate the association of the H. pylori infection with dyslipidemia. Methods: A retrospective case–control study was undertaken to evaluate H. pylori-associated dyslipidemia, where H. pylori-positive individuals were treated as the case group (n = 260) while H. pylori-negative individuals were considered as the control group (n = 250). The mean ± SD of the age of the patients included (n = 510) was 44.01 ± 13.58 years. Study subjects with a total cholesterol level of >5.17 mmol/L and/or a triglyceride level of >1.69 mmol/L and/or an LDL-C level of >2.59 mmol/L and/or an HDL-C level of <1 mmol/L in males and/or an HDL-C level of <1.3 mmol/L in females were defined as dyslipidemia. Descriptive (mean, standard deviation, median, and IQR) and inferential (t-test, chi-square test, and logistic regression) statistical analyses were undertaken using the R-base/R-studio (v-4.0.2)/tidyverse package. Univariate and bivariate logistic regressions were executed to calculate the crude and adjusted odds ratio along with the p-value. A p-value of <0.05 was the cut-off for statistical significance. We used ggplot2 for data visualization. Results: The differences in overall mean ± SD (H. pylori positive vs. negative) of the cholesterol (5.22 ± 1.0 vs. 5.49 ± 0.85, p < 0.01), triglyceride (1.66 ± 0.75 vs. 1.29 ± 0.71, p < 0.001), LDL-C (3.43 ± 0.74 vs. 3.26 ± 0.81, p < 0.05), and HDL-C (1.15 ± 0.30 vs. 1.30 ± 0.25, p < 0.001) levels were statistically significant. The cholesterol and LDL-C levels in ages >60, age = 30–60, in females, and LDL-C levels in males were not significantly different for the H. pylori-positive and -negative groups. The proportion (H. pylori positive vs. negative) of hypercholesterolemia (190/59.9% vs. 127/40% p < 0.01), hypertriglyceridemia (136/68% vs. 64/32% p < 0.001), high LDL-cholesterolemia levels (234/53% vs. 201/46% p < 0.01), and low HDL-cholesterolemia levels (149/71% vs. 60/28.7% p < 0.01) were statistically significant. The odds of having hypercholesterolemia (AOR: 2.64, 95%CI: 1.824–3.848, p < 0.001), hypertriglyceridemia (AOR: 3.24, 95%CI: 2.227–4.757, p < 0.001), an increased LDL-C level (AOR: 2.174, 95%CI: 1.309–3.684, p < 0.01), and a decreased HDL-C level (AOR: 4.2, 95%CI: 2.937–6.321, p < 0.001) were 2.64, 3.24, 2.17, and 4.2 times higher in the H. pylori-infected individuals as compared with the H. pylori-uninfected group. Conclusion: Our results demonstrate that an enhanced risk of dyslipidemia is associated with the H. pylori infection, which can aggrandize the atherosclerosis process. The evaluation of temporal variation in the lipid profile in H. pylori-infected individuals is recommended for the effective management of H. pylori-infected patients. Full article

(This article belongs to the Special Issue Emerging Risk Factors Associated with Public Health)

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13 pages, 455 KiB

Open AccessSystematic Review

Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic Review

by Hailun Liang, Meili Hu, Yuxin Ma, Lei Yang, Jie Chen, Liwei Lou, Chen Chen and Yuan Xiao

Life 2023, 13(9), 1911; https://doi.org/10.3390/life13091911 - 14 Sep 2023

Cited by 1 | Viewed by 1165

Abstract

Objective: For several years, computer technology has been utilized to diagnose lung nodules. When compared to traditional machine learning methods for image processing, deep-learning methods can improve the accuracy of lung nodule diagnosis by avoiding the laborious pre-processing step of the picture (extraction of fake features, etc.). Our goal is to investigate how well deep-learning approaches classify lung nodule malignancy. Method: We evaluated the performance of deep-learning methods on lung nodule malignancy classification via a systematic literature search. We conducted searches for appropriate articles in the PubMed and ISI Web of Science databases and chose those that employed deep learning to classify or predict lung nodule malignancy for our investigation. The figures were plotted, and the data were extracted using SAS version 9.4 and Microsoft Excel 2010, respectively. Results: Sixteen studies that met the criteria were included in this study. The articles classified or predicted pulmonary nodule malignancy using classification and summarization, using convolutional neural network (CNN), autoencoder (AE), and deep belief network (DBN). The AUC of deep-learning models is typically greater than 90% in articles. It demonstrated that deep learning performed well in the diagnosis and forecasting of lung nodules. Conclusion: It is a thorough analysis of the most recent advancements in lung nodule deep-learning technologies. The advancement of image processing techniques, traditional machine learning techniques, deep-learning techniques, and other techniques have all been applied to the technology for pulmonary nodule diagnosis. Although the deep-learning model has demonstrated distinct advantages in the detection of pulmonary nodules, it also carries significant drawbacks that warrant additional research. Full article

(This article belongs to the Special Issue Emerging Risk Factors Associated with Public Health)

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