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

Therapeutic drug failure poses a significant global health burden, particularly in developing regions with high environmental toxicant exposure. While traditionally attributed to factors such as substandard drugs, genetic polymorphisms, and non-compliance, emerging evidence implicates heavy metals as under-recognized modulators of drug efficacy. This systematic review assessed how heavy metals potentially interfere with the pharmacokinetics and pharmacodynamics of drugs. Following PRISMA guidelines, peer-reviewed articles published between 1999 and 2024 were retrieved from PubMed, ScienceDirect, Web of Science, Scopus and Google Scholar. Studies investigating in vivo, in vitro or clinical evidence of heavy metal interference with therapeutic drug action were eligible. Out of 139 studies identified, 20 studies met the inclusion criteria. Cadmium, lead and mercury were the most implicated metals. Approximately 60% of studies demonstrated that oxidative stress could induce loss of therapeutic drug efficacy, while 40% showed enzyme inhibition (notably CYP3A4 and CYP2C9) or transporter impairment. Experimental models linked these effects to anti-hypertensives, antidiabetics, and drugs used in the management of metabolic disorders. Environmental heavy metal exposures may hinder therapeutic responses through oxidative and metabolic disruption. Integrating environmental exposure assessment into global pharmacovigilance and clinical pharmacology could enhance drug response predictability and treatment success.

Nanoplastics (1-1000 nm) (NPs) represent a novel and insidious class of emerging contaminants with the potential to profoundly disrupt gut microbial ecology and accelerate the spread of antibiotic resistance two critical and converging threats to global health. While prior studies have examined the toxicokinetics of NPs and their general microbial interactions, this review provides the first comprehensive synthesis specifically focused on the nexus between NPs, gut dysbiosis, and the propagation of antibiotic resistance genes (ARGs). This review highlights how NPs alter gut microbiota composition, suppressing beneficial microbes while fostering opportunistic pathogens and how such imbalances may contribute to human health issues. Importantly, emerging evidence also suggests that NPs may serve as unrecognized vectors for horizontal gene transfer (HGT), enabling the rapid dissemination of ARGs via conjugation, transformation, transduction, and extracellular vesicles within the gastrointestinal tract. In addition, this review also identifies urgent methodological gaps in detecting NPs in biological matrices and the environment, as well as assessing their mechanistic impacts, calling for innovation in analytical approaches. By presenting an interdisciplinary perspective that bridges nanotoxicology, microbiome science, and antimicrobial resistance, this article sheds light on an underexplored yet urgent frontier in environmental health, offering novel insights to guide future research, risk assessment, and policy development.

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.

This review aims to critically assess the carcinogenic and mutagenic risks associated with firefighting, with a focus on cancer incidence, cancer mortality and other chronic health outcomes. Firefighting is widely recognized as a high-risk occupation due to repeated exposure to hazardous substances, including polycyclic aromatic hydrocarbons (PAHs) and other combustion by-products. An extensive review of empirical studies, meta-analyses, and institutional reports was conducted, considering not only direct fireground exposure but also risks arising from contaminated personal protective equipment (PPE) and indoor air pollution within fire stations. The findings establish a consistent link between firefighting and elevated risks of multiple cancers and chronic diseases. The complexity of modern -firefighting -environments, amplified by evolving materials, under--ventilated fires, and inadequate decontamination practices, further intensifies these risks. The review highlights the urgent need for advanced protective gear, standardized decontamination protocols, routine health surveillance, and policy reforms. It underscores the importance of multidisciplinary collaboration in developing effective strategies to safeguard the health of firefighters, who play a vital role in ensuring public safety.

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.

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.

Early exposure to bisphenol A (BPA) is associated with increased asthma prevalence. To examine the effects of the most widely used BPA substitute, bisphenol S (BPS), on the development of childhood asthma, we conducted the study using the mouse model of early exposure. To simulate the burden from the human chronic exposure, we used a short-term exposure with 10 µg/ml BPS in the drinking water of female BALB/c mice (F0) from one week before pregnancy until the weaning of F1 pups. The pups were sensitized with low doses of ovalbumin (OVA) injection on postnatal day 4 and inhalation of OVA two weeks later. Twenty-four hours after the last inhalation, allergen-specific IgE and IgG1 levels, airway inflammation, and hyperresponsiveness were assessed. Non-OVA-sensitized females were mated with non-exposed male mice for the next generation at eight weeks of age. The resulting pups were sensitized, and the asthma phenotype was examined up to F4. Pups exposed to BPS displayed an asthma phenotype in response to their sensitization. We observed enhanced asthma phenotype in the F1-F4 derived from BPS-exposed F0 females compared to those derived from non-exposed females. Maternal exposure to BPS caused the multigenerational effects on the development of experimental asthma.

Environmental contaminants potentially associated with thyroid cancer are found in Superfund sites, areas the United States Environmental Protection Agency (EPA) has listed for cleanup. This study investigated the association between thyroid cancer incidence rates and Superfund density for sites containing certain contaminants that have been found to be potentially associated with thyroid cancer. Public data on thyroid cancer incidence rates (2015-2019; U.S. Centers for Disease Control and Prevention) and Superfund sites (1980-2014; EPA) were used. Multiple linear regressions were conducted to account for potential confounders, including income, race, and ethnicity. The results showed significant, positive correlations between thyroid cancer incidence rates and log Superfund density in counties with at least one Superfund site for total incidence (B = 0.897, SE = 0.169, p < 0.001), female incidence (B = 1.363, SE = 0.169, p < 0.001), and male incidence (B = 0.438, SE = 0.169, p = 0.010). On sensitivity tests of all counties, including those with zero sites, only female thyroid cancer incidence rate was positively, significantly associated with log Superfund density (B = 0.264, SE = 0.053, p < 0.001). Future studies may assess thyroid cancer incidence, Superfund sites, and covariates at the individual and community levels.

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.