Journal of Environmental Science and Health Part C-Toxicology and Carcinogenesis最新文献

The escalating apprehension surrounding the carcinogenic potential of chemicals emphasizes the imperative need for efficient methods of assessing carcinogenicity. Conventional experimental approaches such as in vitro and in vivo assays, albeit effective, suffer from being costly and time-consuming. In response to this challenge, new alternative methodologies, notably machine learning and deep learning techniques, have attracted attention for their potential in developing carcinogenicity prediction models. This article reviews the progress in predicting carcinogenicity using various machine learning and deep learning algorithms. A comparative analysis on these developed models reveals that support vector machine, random forest, and ensemble learning are commonly preferred for their robustness and effectiveness in predicting chemical carcinogenicity. Conversely, models based on deep learning algorithms, such as feedforward neural network, convolutional neural network, graph convolutional neural network, capsule neural network, and hybrid neural networks, exhibit promising capabilities but are limited by the size of available carcinogenicity datasets. This review provides a comprehensive analysis of current machine learning and deep learning models for carcinogenicity prediction, underscoring the importance of high-quality and large datasets. These observations are anticipated to catalyze future advancements in developing effective and generalizable machine learning and deep learning models for predicting chemical carcinogenicity.

Disinfecting swimming pool water plays a crucial role in preventing the spread of harmful bacteria. However, the interaction between disinfectants and precursors can lead to the formation of potentially disinfection by-products (DBPs). Prolonged exposure to these DBPs may pose health risks. This review study investigates recent research advancements concerning the formation, exposure, and regulation of DBPs within swimming pools. It also provides an overview of existing models that predict DBPs generation in pools, highlighting their limitations. The review explores the mechanisms behind DBPs formation under different disinfectant and precursor conditions. It specifically discusses two types of models that simulate the production of these by-products. Compared to drinking water, swimming pool water presents unique challenges for model development due to its complex mix of external substances, human activities, and environmental factors. Existing models can be categorized as empirical or mechanistic. Empirical models focus on water quality parameters and operational practices, while mechanistic models delve deeper into the kinetics of DBPs generation and the dynamic nature of these compounds. By employing these models, it becomes possible to minimize DBPs production, optimize equipment design, enhance operational efficiency, and manage mechanical ventilation systems effectively.

Recent discoveries of microplastics in cities, suburbs, and even remote locations, far from microplastic source regions, have raised the possibility of long-distance transmission of microplastics in many ecosystems. A little is known scientifically about the threat that it posed to the environment by microplastics. The problem's apparent size necessitates the rapid development of reliable scientific advice regarding the ecological risks of microplastics. These concerns are brought on by the lack of consistent sample and identification techniques, as well as the limited physical analysis and understanding of microplastic pollution. This review provides insight regarding some unaddressed issues about the occurrence, fate, movement, and impact of microplastics, in general, with special emphasis on primary microplastics. The approaches taken in the earlier investigations have been analyzed and different recommendations for future research have been suggested.

Since usnic acid was first isolated in 1844 as a prominent secondary lichen metabolite, it has been used for various purposes worldwide. Usnic acid has been claimed to possess numerous therapeutic properties, including antimicrobial, anti-inflammatory, antiviral, anti-proliferative, and antipyretic activities. Approximately two decades ago, crude extracts of usnic acid or pure usnic acid were marketed in the United States as dietary supplements for aiding in weight loss as a "fat-burner" and gained popularity in the bodybuilding community; however, hepatotoxicity was documented for some usnic acid containing products. The US Food and Drug Administration (FDA) received numerous reports of liver toxicity associated with the use of dietary supplements containing usnic acid, leading the FDA to issue a warning letter in 2001 on a product, LipoKinetix. The FDA also sent a recommendation letter to the manufacturer of LipoKinetix, resulting in the withdrawal of LipoKinetix from the market. These events triggered investigations into the hepatotoxicity of usnic acid and its mechanisms. In 2008, we published a review article titled "Usnic Acid and Usnea Barbata Toxicity". This review is an updated version of our previous review article and incorporates additional data published since 2008. The purpose of this review is to provide a comprehensive summary of the understanding of the liver toxicity associated with usnic acid, with a particular focus on the current understanding of the putative mechanisms of usnic acid-related hepatotoxicity.

In the rapidly evolving field of artificial intelligence (AI), explainability has been traditionally assessed in a post-modeling process and is often subjective. In contrary, many quantitative metrics have been routinely used to assess a model's performance. We proposed a unified formular named PERForm, by incorporating explainability as a weight into the existing statistical metrics to provide an integrated and quantitative measure of both predictivity and explainability to guide model selection, application, and evaluation. PERForm was designed as a generic formula and can be applied to any data types. We applied PERForm on a range of diverse datasets, including DILIst, Tox21, and three MAQC-II benchmark datasets, using various modeling algorithms to predict a total of 73 distinct endpoints. For example, AdaBoost algorithms exhibited superior performance (PERForm AUC for AdaBoost is 0.129 where Linear regression is 0) in DILIst prediction, where linear regression outperformed other models in the majority of Tox21 endpoints (PERForm AUC for linear regression is 0.301 where AdaBoost is 0.283 in average). This research marks a significant step toward comprehensively evaluating the utility of an AI model to advance transparency and interpretability, where the tradeoff between a model's performance and its interpretability can have profound implications.

Public health concerns on surface and groundwater contamination worldwide have increased. Sachet water contamination has also raised serious concerns across many developing countries. While previous studies attempted to address this issue, this review takes a different approach by utilizing a comprehensive analysis of physicochemical parameters, heavy metals, and microbial loads tested in sachet water across Nigeria's six geopolitical zones, within the period of 2020-2023. In this review study, over 50 articles were carefully analyzed. Collected data unveiled regional variations in the quality of sachet water across Nigeria. Noteworthy concerns revolve around levels of pH, total hardness, magnesium, calcium, nickel, iron, lead, mercury, arsenic, and cadmium. Fecal contamination was also identified as a significant issue, with the prevalence of several pathogens like Escherichia coli, Salmonella typhi, Enterobacter cloacae, Staphylococcus aureus, and Enterococcus faecalis. The manufacturing, delivery, storage, and final sale of sachet water, as well as poor environmental hygiene, were identified as potential contamination sources. The intake of contaminated sachet water exposes the citizens to waterborne and carcinogenic diseases. While the sachet water industry keeps growing and making profits, it is apparent that improvement calls made by previous studies, regarding the quality of water produced, have not been paid serious attention.

There has been growing interest in the use of human-derived metabolically competent cells for genotoxicity testing. The HepaRG cell line is considered one of the most promising cell models because it is TP53-proficient and retains many characteristics of primary human hepatocytes. In recent years, HepaRG cells, cultured in both a traditional two-dimensional (2D) format and as more advanced in-vivo-like 3D spheroids, have been employed in assays that measure different types of genetic toxicity endpoints, including DNA damage, mutations, and chromosomal damage. This review summarizes published studies that have used HepaRG cells for genotoxicity assessment, including cell model evaluation studies and risk assessment for various compounds. Both 2D and 3D HepaRG models can be adapted to several high-throughput genotoxicity assays, generating a large number of data points that facilitate quantitative benchmark concentration modeling. With further validation, HepaRG cells could serve as a unique, human-based new alternative methodology for in vitro genotoxicity testing.

Increasing public interest has resulted in the widespread use of non-pharmaceutical cannabidiol (CBD) products. The sales of CBD products continue to rise, accompanied by concerns regarding unsubstantiated benefits, lack of product quality control, and potential health risks. Both animal and human studies have revealed a spectrum of toxicological effects linked to the use of CBD. Adverse effects related to exposure of humans to CBD include changes in appetite, gastrointestinal discomfort, fatigue, and elevated liver aminotransferase enzymes. Animal studies reported changes in organ weight, reproduction, liver function, and the immune system. This review centers on human-derived data, including clinical studies and in vitro investigations. Animal studies are also included when human data is not available. The objective is to offer an overview of CBD-related hepatotoxicity, metabolism, and potential CBD-drug interactions, thereby providing insights into the current understanding of CBD's impact on human health. It's important to note that this review does not serve as a risk assessment but seeks to summarize available information to contribute to the broader understanding of potential toxicological effects of CBD on the liver.

The Cancer Genome Atlas (TCGA) and its patient-derived multi-omics datasets have been the backbone of cancer research, and with novel approaches, it continues to shed new insight into the disease. In this study, we delved into a method of multi-omics integration of patient datasets and the association of biological pathways related to the disease. First, across thirty-three types of cancer present in TCGA, we merged genomic mutations and drug response datasets and filtered for the viable variant-drug response combinations available in TCGA, containing more than three samples for each drug response label with RNA sequencing (RNA-seq) and genomic methylation data available for each patient. We identified two distinct combinations in TCGA, one being pancreatic adenocarcinoma patients with/without rs121913529 variant in KRAS gene treated with gemcitabine, and the other low-grade glioma with/without rs121913500 variant in IDH1 gene administered with temozolomide. In these two groups, different patterns of gene expression were observed in the pathways often associated with cancer progression, such as mTOR and PDGF between the patients with complete response and progressive disease. Our result will provide yet another example of the relevance of these biological pathways in cancer drug response and a way for multi-omics integration in cancer datasets.

The escalation of technological advancements, coupled with the increased use of hazardous chemicals, has emerged as a significant concern for human health. Exposure to environmental pollutants like heavy metals and pesticides (insecticides, herbicides and fungicides) is known to significantly contribute to various health problems, particularly affecting reproductive health. Disturbances in reproductive potential and reproductive toxicity in males are particularly worrisome. Existing literature suggests that exposure to these environmental pollutants significantly alters male reproductive parameters. Thus, it is imperative to thoroughly analyze, comprehend, and evaluate their impact on male reproductive toxicity. Oxidative stress and disruptions in redox equilibrium are major factors through which these pollutants induce changes in sperm parameters and affect the reproductive system. Insecticides, fungicides, and herbicides act as endocrine disruptors, interfering with the secretion and function of reproductive hormones such as testosterone and luteinizing hormone (LH), consequently impacting spermatogenesis. Additionally, heavy metals are reported to bio-accumulate in reproductive organs, acting as endocrine disruptors and triggering oxidative stress. The co-operative association of these pollutants can lead to severe damage. In this comprehensive review, we have conducted an in-depth analysis of the impact of these environmental pollutants on the male reproductive system, shedding light on the underlying mechanisms of action.