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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 13287

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Special Issue Editors

Dr. Ashutosh Pandey

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Guest Editor

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
Interests: environmental toxicology; regulatory toxicology; nanotoxicology; protein glycosylation rare genetic disorders; congenital disorders of glycosylation

Dr. Abhinava K. Mishra

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Co-Guest Editor

Department of Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA 93106, USA
Interests: signal transduction; immunotherapy; clinical informatics; health data visualization
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Special Issue Information

Dear Colleagues,

As a multidisciplinary approach, environmental toxicology covers a broad segment of toxicological studies, including risk assessment, regulatory toxicology, and environmental and human health exposure. Studies that assess the dangers of living organisms’ exposure to environmental pollutant/xenobiotics demonstrate the potential impact that these substances have on human health. The development of new assessment methods, approaches and model systems has advanced our understanding in this field. Outcomes from such studies not only scientifically explain the potential health hazards but also facilitate the development of preventive measures and policies for human health based on the acquisition and curation of public health informatics data. The recent COVID-19 outbreak has posed new challenges for public health assessment and management. The integration of machine learning, artificial intelligence, big data analysis, digital health technologies and epidemiological tools for outbreak surveillance, reporting and management has not only helped improve clinical decision support and critical care but also population-level health outcomes.

This issue of IJERPH aims to cover studies that concern risk assessment for human health hazards, including emerging environmental pollutants/xenobiotics using human population samples or any relevant in vivo or in vitro model system approaches. This issue also focuses on public health informatics-based approaches for epidemiological prediction, surveillance, data analysis, reporting and outbreak management. The research may also encompass the development of new and improved modeling-based approaches, AI, big data and machine learning for next-generation and community medicine.

In this Special Issue, we invite researchers in the field of toxicology (including environmental toxicology and regulatory toxicology), public health, information technology and epidemiology, as well as healthcare professionals, clinicians, data scientists and policymakers, among others, to submit original research articles, systematic reviews, mini-reviews, letters and communication or opinion pieces related to the proposed focus areas.

Dr. Ashutosh Pandey
Dr. Abhinava K. Mishra
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

toxicological assessment
health hazards
risk assessment
human population cohorts
public health statistics
health information management
digital health technologies
epidemiological modeling
clinical informatics
health data visualization
big data and machine learning in health informatics

Published Papers (7 papers)

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Research

23 pages, 6718 KiB

Open AccessArticle

Analysis and Prediction of COVID-19 Multivariate Data Using Deep Ensemble Learning Methods

by Shruti Sharma, Yogesh Kumar Gupta and Abhinava K. Mishra

Int. J. Environ. Res. Public Health 2023, 20(11), 5943; https://doi.org/10.3390/ijerph20115943 - 24 May 2023

Cited by 2 | Viewed by 1994

Abstract

The global economy has suffered losses as a result of the COVID-19 epidemic. Accurate and effective predictive models are necessary for the governance and readiness of the healthcare system and its resources and, ultimately, for the prevention of the spread of illness. The primary objective of the project is to build a robust, universal method for predicting COVID-19-positive cases. Collaborators will benefit from this while developing and revising their pandemic response plans. For accurate prediction of the spread of COVID-19, the research recommends an adaptive gradient LSTM model (AGLSTM) using multivariate time series data. RNN, LSTM, LASSO regression, Ada-Boost, Light Gradient Boosting and KNN models are also used in the research, which accurately and reliably predict the course of this unpleasant disease. The proposed technique is evaluated under two different experimental conditions. The former uses case studies from India to validate the methodology, while the latter uses data fusion and transfer-learning techniques to reuse data and models to predict the onset of COVID-19. The model extracts important advanced features that influence the COVID-19 cases using a convolutional neural network and predicts the cases using adaptive LSTM after CNN processes the data. The experiment results show that the output of AGLSTM outperforms with an accuracy of 99.81% and requires only a short time for training and prediction. Full article

(This article belongs to the Special Issue Recent Advances in Toxicological Risk Assessment and Public Health Informatics for Environmental and Occupational Health Hazards)

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11 pages, 1013 KiB

Open AccessArticle

Mutational Analysis of EGFR Mutations in Non-Small Cell Lung Carcinoma—An Indian Perspective of 212 Patients

by Amrit Kaur Kaler, Khushi Patel, Harshali Patil, Yash Tiwarekar, Bijal Kulkarni, Meenal Hastak, Nivetha Athikari, Samrudhi Rane, Ankita Nikam, Smita Umarji, Imran Shaikh, Sandeep Goyle and Rajesh Mistry

Int. J. Environ. Res. Public Health 2023, 20(1), 758; https://doi.org/10.3390/ijerph20010758 - 31 Dec 2022

Cited by 6 | Viewed by 2184

Abstract

Lung cancer is the world’s leading cause of cancer-related deaths. Epidermal growth factor receptor (EGFR) is one of the critical oncogenes and plays a significant role in tumor proliferation and metastasis. Patients with sensitizing mutations in the EGFR gene have better clinical outcomes when treated with tyrosine kinase inhibitors (TKI). This study expands our knowledge of the spectrum of EGFR mutations among lung cancer patients in the Indian scenario. This is a retrospective descriptive study of all newly diagnosed patients with lung cancer in tertiary care hospital in India. All the samples were subjected to real-time PCR (q-PCR) analysis and confirmation of rare novel mutations was done using Sanger sequencing. Clinicopathological characteristics, mutational EGFR status, and location on the exon and metastatic sites were evaluated. An analysis of total 212 samples showed mutations in 38.67% of cases. Among these, five (5.9%) samples had mutations in exon 18, 41 (48.8%) samples had mutations in exon 19, 12 (14.28%) samples had mutations in exon 20, and 26 (30.95%) samples had mutations in exon 21. Eleven (13.41%) were found to be uncommon EGFR mutations. Additionally, six (21.4%) samples that had EGFR mutations were also positive for brain metastasis. Future testing on bigger panels will help to characterize the incidence of genetic mutations and to determine the appropriate targeted treatment choices for NSCLC patients. Full article

(This article belongs to the Special Issue Recent Advances in Toxicological Risk Assessment and Public Health Informatics for Environmental and Occupational Health Hazards)

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17 pages, 3834 KiB

Open AccessArticle

COVID-19 Public Opinion: A Twitter Healthcare Data Processing Using Machine Learning Methodologies

by Shweta Agrawal, Sanjiv Kumar Jain, Shruti Sharma and Ajay Khatri

Int. J. Environ. Res. Public Health 2023, 20(1), 432; https://doi.org/10.3390/ijerph20010432 - 27 Dec 2022

Cited by 6 | Viewed by 2033

Abstract

The COVID-19 pandemic has shattered the whole world, and due to this, millions of people have posted their sentiments toward the pandemic on different social media platforms. This resulted in a huge information flow on social media and attracted many research studies aimed at extracting useful information to understand the sentiments. This paper analyses data imported from the Twitter API for the healthcare sector, emphasizing sub-domains, such as vaccines, post-COVID-19 health issues and healthcare service providers. The main objective of this research is to analyze machine learning models for classifying the sentiments of people and analyzing the direction of polarity by considering the views of the majority of people. The inferences drawn from this analysis may be useful for concerned authorities as they work to make appropriate policy decisions and strategic decisions. Various machine learning models were developed to extract the actual emotions, and results show that the support vector machine model outperforms with an average accuracy of 82.67% compared with the logistic regression, random forest, multinomial naïve Bayes and long short-term memory models, which present 78%, 77%, 68.67% and 75% accuracy, respectively. Full article

(This article belongs to the Special Issue Recent Advances in Toxicological Risk Assessment and Public Health Informatics for Environmental and Occupational Health Hazards)

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14 pages, 2520 KiB

Open AccessArticle

Toxicity Assessment of Curculigo orchioides Leaf Extract Using Drosophila melanogaster: A Preliminary Study

by Sharanya Kushalan, Leonard Clinton D’Souza, Khyahrii Aloysius, Anurag Sharma and Smitha Hegde

Int. J. Environ. Res. Public Health 2022, 19(22), 15218; https://doi.org/10.3390/ijerph192215218 - 18 Nov 2022

Cited by 4 | Viewed by 1827

Abstract

Curculigo orchioides is used in Indian and Chinese traditional medicinal systems for various health benefits. However, its toxicological effects are mostly unknown. This study assesses the potential toxicity of aqueous leaf (A.L.) extract of C. orchioides using Drosophila melanogaster as an experimental model. Preliminary phytochemical tests were followed by the Fourier transform infrared (FTIR) tests to identify the functional group in the A.L. extract of C. orchioides. Drosophila larvae/adults were exposed to varying concentrations of C. orchioides A.L. extract through diet, and developmental, lifespan, reproduction, and locomotory behaviour assays were carried out to assess the C. orchioides toxicity at organismal levels. The cellular toxicity of A.L. extract was examined by analysing the expression of heat shock protein (hsps), reactive oxygen species (ROS) levels, and cell death. The FTIR analysis showed the presence of functional groups indicating the presence of secondary metabolites like saponins, phenolics, and alkaloids. Exposure to A.L. extract during development resulted in reduced emergence and wing malformations in the emerged fly. Furthermore, a significant reduction in reproductive performance and the organism’s lifespan was observed when adult flies were exposed to A.L. extract. This study indicates the adverse effect of C. orchioides A.L. extract on Drosophila and raises concerns about the practice of indiscriminate therapeutic use of plant extracts. Full article

(This article belongs to the Special Issue Recent Advances in Toxicological Risk Assessment and Public Health Informatics for Environmental and Occupational Health Hazards)

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14 pages, 2688 KiB

Open AccessArticle

Maternal Plasma Glycerophospholipids LC-PUFA Levels Have a Sex-Specific Association with the Offspring’s Cord Plasma Glycerophospholipids-Fatty Acid Desaturation Indices at Birth

by Sowmya Giriyapura Vamadeva, Nagalakshmi Bhattacharyya and Kunal Sharan

Int. J. Environ. Res. Public Health 2022, 19(22), 14850; https://doi.org/10.3390/ijerph192214850 - 11 Nov 2022

Viewed by 1242

Abstract

Fatty acid desaturases, the enzymes responsible for the production of unsaturated fatty acids (FA) in fetal tissues, are known to be influenced by maternal-placental supply of nutrients and hormones for their function. We hypothesize that there could be a gender-specific regulation of unsaturated FA metabolism at birth, dependent on the maternal fatty acid levels. In this study, 153 mother-newborn pairs of uncomplicated and ‘full-term’ pregnancies were selected and the FA composition of plasma glycerophospholipids (GP) was quantified by gas chromatography. The FA composition of mother blood plasma (MB) was compared with the respective cord blood plasma (CB) of male newborns or female newborns. Product to substrate ratios were estimated to calculate delta 5 desaturase (D5D), delta 6 desaturase (D6D) and delta 9 stearoyl-CoA-desaturase (D9D/SCD) indices. Pearson correlations and linear regression analyses were employed to determine the associations between MB and CB pairs. In the results, the male infant’s MB-CB association was positively correlated with the SCD index of carbon-16 FA, while no correlation was seen for the SCD index of carbon-18 FA. Unlike for males, the CB-D5D index of female neonates presented a strong positive association with the maternal n-6 long chain-polyunsaturated FA (LC-PUFA), arachidonic acid. In addition, the lipogenic desaturation index of SCD18 in the CB of female new-borns was negatively correlated with their MB n-3 DHA. In conclusion, sex-related differences in new-borns’ CB desaturation indices are associated with maternal LC-PUFA status at the time of the birth. This examined relationship appears to predict the origin of sex-specific unsaturated FA metabolism seen in later life. Full article

(This article belongs to the Special Issue Recent Advances in Toxicological Risk Assessment and Public Health Informatics for Environmental and Occupational Health Hazards)

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11 pages, 3323 KiB

Open AccessArticle

Surveillance and Molecular Characterization of SARS-CoV-2 Infection in Non-Human Hosts in Gujarat, India

by Dinesh Kumar, Sejalben P. Antiya, Sandipkumar S. Patel, Ramesh Pandit, Madhvi Joshi, Abhinava K. Mishra, Chaitanya G. Joshi and Arunkumar C. Patel

Int. J. Environ. Res. Public Health 2022, 19(21), 14391; https://doi.org/10.3390/ijerph192114391 - 3 Nov 2022

Cited by 3 | Viewed by 1780

Abstract

Since December 2019, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has been spreading worldwide, triggering one of the most challenging pandemics in the human population. In light of the reporting of this virus in domestic and wild animals from several parts of the world, a systematic surveillance study was conceptualized to detect SARS-CoV-2 among species of veterinary importance. Nasal and/or rectal samples of 413 animals (dogs n= 195, cattle n = 64, horses n = 42, goats n = 41, buffaloes n = 39, sheep n = 19, cats n = 6, camels n = 6, and a monkey n = 1) were collected from different places in the Gujarat state of India. RNA was extracted from the samples and subjected to RT-qPCR-based quantification of the target sequences in viral nucleoprotein (N), spike (S), and ORF1ab genes. A total of 95 (23.79%) animals were found positive, comprised of n = 67 (34.35%) dogs, n= 15 (23.43%) cattle, and n = 13 (33.33%) buffaloes. Whole SARS-CoV-2 genome sequencing was done from one sample (ID-A4N, from a dog), where 32 mutations, including 29 single-nucleotide variations (SNV) and 2 deletions, were detected. Among them, nine mutations were located in the receptor binding domain of the spike (S) protein. The consequent changes in the amino acid sequence revealed T19R, G142D, E156-, F157-, A222V, L452R, T478K, D614G, and P681R mutations in the S protein and D63G, R203M, and D377Y in the N protein. The lineage assigned to this SARS-CoV-2 sequence is B.1.617.2. Thus, the present study highlights the transmission of SARS-CoV-2 infection from human to animals and suggests being watchful for zoonosis. Full article

(This article belongs to the Special Issue Recent Advances in Toxicological Risk Assessment and Public Health Informatics for Environmental and Occupational Health Hazards)

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17 pages, 3880 KiB

Open AccessArticle

Survival and Virulence Potential of Drug-Resistant E. coli in Simulated Gut Conditions and Antibiotic Challenge

by Vankadari Aditya, Akshatha Kotian, Anisha Sanil, Poidal Mohammed-ali Thaseena, Indrani Karunasagar and Vijaya Kumar Deekshit

Int. J. Environ. Res. Public Health 2022, 19(19), 12805; https://doi.org/10.3390/ijerph191912805 - 6 Oct 2022

Cited by 1 | Viewed by 1588

Abstract

The gut forms a vital niche for the survival and replication of drug-resistant E. coli; however, the role of gut conditions on drug-resistant and sensitive E. coli is not clearly understood. The study aims to understand the effect of in vitro gut conditions on the spread of antibiotic resistance among E. coli and their ability to adapt to gut conditions. In this study, a multidrug-resistant (J51) and a sensitive (J254) E. coli isolate were exposed to a series of in vitro gut conditions and their growth pattern, virulence gene expression and invasion ability were studied. Further, the effect of antibiotic under in vitro gut conditions was also studied. Bile significantly affected the growth of the isolates, and the addition of iron chelator extended the lag phase of the sensitive isolate. Each in vitro gut condition had a differential effect on the expression of virulence genes in both the isolates. Further, the resistant isolate could adhere to and invade Caco2 cell lines better than the sensitive isolate. Most of the downregulated genes showed increased expression upon ciprofloxacin shock under in vitro gut conditions. The transcriptomics study revealed that exposure to bile, led to the downregulation of genes involved in different metabolic pathways. Further downregulation of metabolic pathways on ciprofloxacin shock was also observed. The downregulation of metabolic pathways could be a part of the global response played by the bacteria to adapt to harsh conditions. Reverting these fluctuated pathways could prove to be a novel strategy in combating AMR threat. Overall, bile, in high and low temperature conditions, showed a significant effect on modulating virulence gene expression on the antibiotic challenge. Thus, it is essential to consider the impact of gut conditions on gut pathogens, such as E. coli, before prescribing antimicrobial therapy during infection. Full article

(This article belongs to the Special Issue Recent Advances in Toxicological Risk Assessment and Public Health Informatics for Environmental and Occupational Health Hazards)

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