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Toxics
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Big Data, Machine and Deep Learning Methods for Transformative Approaches...
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Big Data, Machine and Deep Learning Methods for Transformative Approaches in Toxicology

A special issue of Toxics (ISSN 2305-6304). This special issue belongs to the section "Novel Methods in Toxicology Research".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 9590

Special Issue Editor

Prof. Dr. Rashid Mehmood

E-Mail Website
Guest Editor
High Performance Computing Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Interests: high-performance computing (HPC); big data; AI and IoT with applications in smart cities, healthcare, transportation, logistics, and toxicology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Big data and artificial intelligence (AI) approaches, including machine and deep learning, are playing an increasingly important role in toxicology research. This field is still in its infancy, and more research is needed in areas such as predictive toxicology, adverse drug reactions, toxicity pathway analysis, environmental toxicology, image analysis, toxicity prediction for occupational and environmental exposure, risk assessment, and AI model explainability. Multi-omics data integration, real-time toxicity monitoring, customized toxicology, human–computer interfaces, ethics and laws, high-throughput screening, medication repurposing, and toxicity prediction across species are among the other fields of research. The purpose of these investigations is to increase the accuracy, robustness, and interpretability of AI models in order to inform risk assessments, improve decision-making in chemical control and public health, and understand the molecular causes of toxicity. Text mining of the scientific literature is one example of how big data and AI may be utilized in toxicology research to uncover new possible targets for drug development by extracting information from publications and identifying commonly cited compounds and biological processes relevant to toxicity.

Social media and digital media can also help toxicology research by collecting large amounts of data on people's interactions with toxic compounds, validating the results of AI models, identifying emerging trends, monitoring environmental exposure, engaging the public in discussions, polling public opinion, and enabling participatory toxicology approaches. However, problems of data privacy, dependability, and bias must be addressed to assure the data's credibility. These platforms can also be used in forensic toxicology to collect information about a person's drug use, create a timeline of drug use, and track a person's movements, but the challenge is to verify the information through other means and not rely on it as the sole basis for any forensic toxicology conclusions.

In the domains indicated above, this Special Issue asks for research on transformative big data, social and digital media, and AI approaches to toxicology. The current research in this context is in its infancy and requires more exploration from this multidisciplinary community.

Prof. Dr. Rashid Mehmood
Guest Editor

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. Toxics 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 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

  • big data in toxicology
  • machine and deep learning in toxicology
  • social media and digital media in toxicology
  • multi-omics data integration (e.g., genomics, proteomics, metabolomics)
  • multimedia (e.g., image, voice, video, natural language) analysis in toxicology
  • predictive and personalized toxicology
  • adverse drug reactions
  • data privacy, dependability, and bias in toxicology
  • interpretability and explainability of AI models in toxicology
  • ethics and regulations in toxicology

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Published Papers (3 papers)

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Research

17 pages, 5054 KiB  
Article
Deep Learning-Based Automatic Duckweed Counting Using StarDist and Its Application on Measuring Growth Inhibition Potential of Rare Earth Elements as Contaminants of Emerging Concerns
by Kevin Adi Kurnia, Ying-Ting Lin, Ali Farhan, Nemi Malhotra, Cao Thang Luong, Chih-Hsin Hung, Marri Jmelou M. Roldan, Che-Chia Tsao, Tai-Sheng Cheng and Chung-Der Hsiao
Toxics 2023, 11(8), 680; https://doi.org/10.3390/toxics11080680 - 8 Aug 2023
Cited by 4 | Viewed by 2864
Abstract
In recent years, there have been efforts to utilize surface water as a power source, material, and food. However, these efforts are impeded due to the vast amounts of contaminants and emerging contaminants introduced by anthropogenic activities. Herbicides such as Glyphosate and Glufosinate [...] Read more.
In recent years, there have been efforts to utilize surface water as a power source, material, and food. However, these efforts are impeded due to the vast amounts of contaminants and emerging contaminants introduced by anthropogenic activities. Herbicides such as Glyphosate and Glufosinate are commonly known to contaminate surface water through agricultural industries. In contrast, some emerging contaminants, such as rare earth elements, have started to enter the surface water from the production and waste of electronic products. Duckweeds are angiosperms from the Lemnaceae family and have been used for toxicity tests in aquatic environments, mainly those from the genus Lemna, and have been approved by OECD. In this study, we used duckweed from the genus Wolffia, which is smaller and considered a good indicator of metal pollutants in the aquatic environment. The growth rate of duckweed is the most common endpoint in observing pollutant toxicity. In order to observe and mark the fronds automatically, we used StarDist, a machine learning-based tool. StarDist is available as a plugin in ImageJ, simplifying and assisting the counting process. Python also helps arrange, manage, and calculate the inhibition percentage after duckweeds are exposed to contaminants. The toxicity test results showed Dysprosium to be the most toxic, with an IC50 value of 14.6 ppm, and Samarium as the least toxic, with an IC50 value of 279.4 ppm. In summary, we can provide a workflow for automatic frond counting using StarDist integrated with ImageJ and Python to simplify the detection, counting, data management, and calculation process. Full article
(This article belongs to the Special Issue Big Data, Machine and Deep Learning Methods for Transformative Approaches in Toxicology)
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14 pages, 1381 KiB  
Article
Disruptive Technologies for Learning and Further Investigation of the Potential Toxicity Produced by Titanium in the Human Body during the COVID-19 Pandemic Period
by Mădălin Dorel Țap, Cristina Stanciu (Neculau), George Popescu and Octavia-Sorina Honțaru
Toxics 2023, 11(6), 523; https://doi.org/10.3390/toxics11060523 - 9 Jun 2023
Cited by 1 | Viewed by 1313
Abstract
Titanium is considered to be a biocompatible material and is used to a great extent in the pharmaceutical and oral implantology fields. While initially, specialists considered that its use does not cause adverse effects on the human body, as time has gone by, [...] Read more.
Titanium is considered to be a biocompatible material and is used to a great extent in the pharmaceutical and oral implantology fields. While initially, specialists considered that its use does not cause adverse effects on the human body, as time has gone by, it has become clear that its use can lead to the development of certain diseases. The objective of this study was to identify the way in which digital technologies have the capacity to facilitate information regarding the potential long-term harm caused by titanium device toxicity during the COVID-19 pandemic. In this study, a regression model was developed to identify how a series of independent variables have the ability to influence the dependent variable (respondents’ perceptions of how new web technologies have the ability to help future physicians to facilitate information absorption with regard to potential titanium toxicity). The results illustrated that new technologies have the potential to support both the learning process on this topic and the innovation activity by discovering new solutions that will gradually lead to the reduction of the side effects of titanium used in the pharmaceutical and oral implantology fields. Full article
(This article belongs to the Special Issue Big Data, Machine and Deep Learning Methods for Transformative Approaches in Toxicology)
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38 pages, 9065 KiB  
Article
Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses
by Sarah Alswedani, Rashid Mehmood, Iyad Katib and Saleh M. Altowaijri
Toxics 2023, 11(3), 287; https://doi.org/10.3390/toxics11030287 - 20 Mar 2023
Cited by 4 | Viewed by 4379
Abstract
Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment; however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental [...] Read more.
Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment; however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives: Drugs and Treatments, Causes and Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined six macro-parameters (Diseases and Disorders, Individual Factors, Social and Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for a social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media. Full article
(This article belongs to the Special Issue Big Data, Machine and Deep Learning Methods for Transformative Approaches in Toxicology)
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