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

As the 16^th most common cancer globally, oral cancer yearly accounts for some 355,000 new cases. This study underlines that an early diagnosis can improve the prognosis and cut down on mortality. It discloses a multifaceted approach to the detection of oral cancer, including clinical examination, biopsies, imaging techniques, and the incorporation of artificial intelligence and deep learning methods. This study is distinctive in that it provides a thorough analysis of the most recent AI-based methods for detecting oral cancer, including deep learning models and machine learning algorithms that use convolutional neural networks. By improving the precision and effectiveness of cancer cell detection, these models eventually make early diagnosis and therapy possible. This study also discusses the importance of techniques in image pre-processing and segmentation in improving image quality and feature extraction, an essential component of accurate diagnosis. These techniques have shown promising results, with classification accuracies reaching up to 97.66% in some models. Integrating the conventional methods with the cutting-edge AI technologies, this study seeks to advance early diagnosis of oral cancer, thus enhancing patient outcomes and cutting down on the burden this disease is imposing on healthcare systems.

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.

Nitrate contamination in drinking water poses significant health risks, particularly in rapidly urbanizing areas of developing countries. This study presents an integrated computational and graphical approach to evaluate the geochemistry and health risks of nitrate-contaminated water for six age groups in Southeast, Nigeria. The research employed a detailed methodology combining water nutrient pollution index (WNPI), nitrate pollution index (NPI), water pollution index (WPI), geochemical plotting techniques, stoichiometry, and health risk computations. Water samples from several locations were analyzed for physicochemical parameters and nitrate concentrations. Results revealed predominantly acidic conditions and varying levels of nitrate contamination. Geochemical analysis indicated that silicate weathering and ion exchange processes were the primary influences on water chemistry. The WPI identified 14.29% of samples as "extremely polluted" (WPI > 1), while the WNPI classified 7.14% of samples as "moderately polluted" (WNPI > 1). However, the NPI categorized the samples as safe, indicating low nitrate inputs from anthropogenic sources. Health risk assessments indicated low-moderate risks, with the highest total hazard index of 0.839 for the 6-12 months age group; thus, higher vulnerability for infants. Oral exposure was found to be the dominant pathway, contributing over 99.90% to the total risk. This research provides crucial insights for achieving the Sustainable Development Goals (SDGs) related to water quality and public health protection. The integrated approach offers a robust framework for water resource management and interventions in risk-prone areas. Future research should focus on expanding the spatial coverage, incorporating sensitivity analyses, and exploring advanced technologies for real-time monitoring and predictive modeling of water quality.

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.

Monosodium glutamate (MSG) is a food additive that enhances the palatability of foods, thus its frequent use both domestically and industrially. Based on the dose-factor, frequency, and duration of exposure, MSG may provoke adverse health outcomes both in animals and humans. The present report aims at providing a comprehensive analysis of the scientifically proven untoward health effects of MSG. To achieve our aim, we adopted the PRISMA guidelines and checklist and searched four databases (Scopus, Web of Science, PubMed, and Google Scholar) from 2014 to 2024. Retrieved research papers were critically appraised for quality using the ARRIVE and Joanna Briggs (JB) checklists and data analysis was conducted via the narrative synthesis method. Our analysis reveals that though MSG is generally considered safe at low doses; however, high doses and repeated exposure to MSG are associated with embryotoxicity and teratogenicity, obesity, cardiotoxicity, hepatotoxicity, kidney toxicity, neurotoxicity, endothelial dysfunction, reproductive toxicities, alteration of lipid, and glucose metabolism. Thus, chronic exposure to MSG may be of human pathological importance. The findings of the present narrative synthesis provide a rationale for informed decisions on the use and labeling of this widely used food additive.

Pyrrolizidine alkaloids (PAs) form a family of toxic and carcinogenic phytochemicals found in plants worldwide. The metabolism of toxic PAs, both in vivo and in vitro, generates four (±)-6,7-dihydro-7-hydroxy-1-hydroxymethyl-5H-pyrrolizine (DHP)-derived DNA adducts, namely, DHP-dG-3, DHP-dG-4, DHP-dA-3, and DHP-dA-4, as documented in previous research. We have proposed that these DHP-DNA adducts play a pivotal role in the induction of liver tumor by PAs in rats and mice, serving as potential common biological biomarkers for PA exposure and carcinogenesis. In this study, we found that the metabolism of PAs and PA N-oxides by human liver microsomes, in the presence of calf thymus DNA, results in the formation of DNA adducts. This process serves as a convenient and biologically significant platform for investigating the structure-carcinogenicity relationships of PAs.

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 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.

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.