Mancozeb (MZ) is a widely used dithiocarbamate fungicide associated with environmental and developmental toxicity. The present study investigated the effects of waterborne exposure to increasing concentrations of MZ on embryonic and larval development of zebrafish (Danio rerio). Embryos were exposed to four concentrations of MZ (45, 90, 180, and 360µg/L) from fertilization until 96hours post-fertilization (hpf). Morphological and morphometric analyses, heart rate evaluation, developmental outcome assessment, cell viability (MTT assay), and oxidative-sensitive fluorescence (DCF assay) were performed. Exposure to MZ induced concentration-dependent developmental toxicity characterized by reduced larval growth, pigmentation alterations, developmental delay, body curvature, pericardial edema, and impaired hatching. Significant alterations in cardiac activity were observed, with larvae from T1-T3 exhibiting reduced heart rates, whereas T4 larvae displayed heart rate values similar to controls. MZ exposure also increased the frequency of teratogenic outcomes and early mortality. Additionally, exposed larvae exhibited reduced cellular viability and altered oxidative stress profiles, suggesting disruption of redox homeostasis during embryogenesis. Collectively, these findings demonstrate that waterborne exposure to MZ induces embryotoxic, cardiotoxic, and oxidative alterations in zebrafish larvae, reinforcing concerns regarding the developmental toxicity and environmental risks associated with chronic exposure to this fungicide.
Graphene-family nanomaterials (GFNs) comprise structurally and chemically diverse two-dimensional materials with broad applications in biomedicine, environmental technologies, energy, sensing, and advanced materials. Their expanding use has increased the need for reliable approaches to characterize and predict potential biological and environmental hazards. However, meaningful comparison of GFN toxicity data remains difficult because biological responses depend on interacting material characteristics, including lateral dimensions, layer number, defects, oxidation, surface functionalization, charge, morphology, aggregation, and dispersion state, together with exposure conditions, dosimetry, biological models, and endpoint definitions. This review examines how standardized descriptors can provide a quantitative basis for connecting GFN characteristics with toxicological responses and constructing reproducible predictive datasets. Structural, chemical, surface, colloidal, electronic, computational, exposure and biological descriptors are considered within an integrated framework that distinguishes intrinsic material properties from exposure-state characteristics. Particular emphasis is placed on descriptor harmonization, dataset curation, data provenance, and consistent reporting of measurement and dose information. The review also discusses machine-learning approaches for modelling nonlinear and multidimensional relationships between GFN descriptors and toxicity, emphasising the significance of proper feature selection, validation, applicability-domain assessment and uncertainty quantification. Because GFN datasets may contain limited numbers of independently characterized materials and substantial variation in experimental protocols, predictive performance should be interpreted in the context of data quality, validation design, and applicability-domain limitations. Explainable machine learning is considered as a means of identifying influential descriptors and generating mechanistically testable hypotheses rather than causal conclusions. Building on these principles, a standardized predictive framework is proposed that links characterization, descriptor harmonization, toxicity datasets, validated and explainable machine learning, experimental confirmation, and iterative model refinement. The framework provides a foundation for using predictive insights to guide material modification and support Safe-by-Design and Safe-and-Sustainable-by-Design strategies. Overall, standardized descriptors and traceable data practices can help advance GFN nanotoxicology from heterogeneous retrospective comparisons toward reproducible, interpretable, and experimentally testable prediction.
There are strong links between routine exposure to aerosolized cleaning products and epidemiologically observed adverse respiratory health outcomes, such as occupational asthma. However, because safety assessment relies largely on constituent-level information and mixture-classification rules, mechanistic data on the effects of complete cleaning formulations remain limited. In this study three commercially available all-purpose spray cleaners were investigated for early, localized cellular effects using an in vitro primary bronchial epithelial cells (PBEC) Air-Liquid Interface (ALI) model across three concentrations (10, 100, and 1000 ppm (v/v)). Transcriptional markers for oxidative stress, barrier integrity, and innate immunity were evaluated after 6 h of exposure, followed by protein quantification for IL-6, IL-8, MMP-9, and TIMP-1 at 18 h post-exposure period. Exposure to these three cleaning product formulations showed a persistent downregulation in GPX1 transcription across all products, indicating alterations in redox balance. Furthermore, one of the investigated formulations induced marked transcriptional changes in genes associated with epithelial barrier integrity (CDH1, CLDN1, CLDN4, and OCLN). Additionally, IL-8 secretion levels were significantly increased for all formulations. These findings demonstrated that these spray cleaners induce cellular responses in bronchial epithelial models in a formulation-dependent manner. By investigating distinct cellular pathways critical to integrity and their responses following chemical assault, these findings provide insights into the pathogenic processes of respiratory effects associated to cleaning-product-exposures.
