Virtual control groups (VCGs) represent an approach in which historical control data (HCD) from previously conducted toxicological animal studies are used to replace animals in current control groups (CCGs). When using HCD to replace CCGs with VCGs, it is essential to carefully match the HCD to the animals of the treatment group and the study-specific criteria in order to construct an initial pool of suitable control animals. VCGs can then be generated by sampling animals from this filtered HCD pool. When constructing VCGs in toxicological contexts, often only studies conducted within the last 5 years are considered in order to avoid shifts in data due to, e.g., genetic drift in the animals. However, such a fixed retrospective time window may not correspond to periods with stable observations. Changes in experimental conditions, such as diet composition, anesthetic procedures, or other experimental conditions, may influence certain endpoints, making observations unsuitable for use as virtual controls. To address this, we propose a new statistical approach that identifies the time interval during which observations remain stable. This approach is based on a method that detects atypical changes (changepoints) over time. The estimated most recent changepoint indicates the start of the time interval with stable values, from which observations may be considered appropriate for VCG construction. We show that the proposed method performs well on both real data and in a simulation study that generates observations from multiple studies over time with artificial changepoints introduced at predefined locations under different scenarios. The method reliably detects changepoints when the mean shifts are moderately large and when an adequate number of observations are available after the changepoint. To facilitate practical application, an interactive R Shiny application implementing the proposed methodology was developed and is available at https://shiny.statistik.tu-dortmund.de/app/CPstable.
Soil in the area of the former antimony mine near the village of Lisa (Ivanjica municipality) was analysed in this study. The contents of potentially toxic elements (PTEs), as well as their relation to the natural background and the prescribed limit values, indicate that As exceeded the limit values in 63% of the samples, while in 37% of the samples it exceeded the remediation values. Using multivariate methods, including Network analysis, compositional data analysis, and positive matrix factorisation, three main sources of PTEs were identified. Arsenic was identified as the dominant pollutant, primarily associated with anthropogenic sources, but also partly with pedological factors. The health risk assessment showed that As is the main contributor to the overall risk, particularly for children, for whom the average health index (HI) is 2.89, almost three times higher than the permissible value. Regarding carcinogenic risk, As was also identified as a key factor, with an average value of 6.10E-05, indicating a notable risk. The highest contribution to HI comes from the anthropogenic factor, both for non-carcinogenic risk in children and for carcinogenic risk, while no significant risk was observed for the geogenic factor.
BackgroundAir pollution is one of the leading global environmental health risks, associated with millions of premature deaths annually. It represents a substantial share of the global disease burden. Despite well-established epidemiological associations, the underlying molecular mechanisms remain only partially understood. There is a need for systematic approaches toward mechanistic understanding that connect particulate matter exposure to downstream pathways and pathophysiological effects.ResultsWe present ExposomeMap-PM (https://disease-maps.io/markopolo), a model of mechanistic progression from fine and ultrafine particle exposure. ExposomeMap-PM includes 304 entities and 497 processes across five mechanistic blocks. The model identifies key components, including alveolar deposition, oxidative stress, barrier disruption, mitochondrial dysfunction, and pro-inflammatory signalling. Adverse outcomes are associated with conditions such as asthma, chronic obstructive pulmonary disease, atherosclerosis and neurodegeneration. Differential-expression values from transcriptomics and proteomics datasets were overlaid onto protein, mRNA and gene nodes of ExposomeMap-PM, providing a pathway-level view of particle-induced effects. In this application, based on a single reference particle type and a short 3-day exposure, the map nodes captured a significantly enriched share of the differentially expressed transcripts: 1.50-fold over size-matched random gene sets, permutation p = 0.0074. The proteomics overlay pointed in the same direction, 1.13-fold. This indicates that the transcriptomic response is concentrated within the mechanistic scope of the map.MethodsThe approach combines the adverse outcome pathway concept from toxicology with the disease map concept from systems biomedicine. The model was constructed in CellDesigner using Systems Biology Graphical Notation standard and made available online via the MINERVA Platform to facilitate exploration and reuse.ConclusionThe ExposomeMap-PM provides a mechanistic layer of understanding, a missing step between epidemiological evidence of particle-induced harmful effects and the molecular events that drive it and connect to outcomes. This reusable and extendable resource bridges toxicological and biomedical perspectives, advancing the understanding of how fine and ultrafine particles contribute to the onset and progression of complex diseases. It supports community annotation and is accessible online for browsing, community feedback and data visualisation.
N-Nitroso-methylphenylamine (NMPA) is a rat esophageal carcinogen that has been found in food, tobacco and drugs. In this study, NMPA was administered via drinking water to Big Blue transgenic rats over 28 days following methods recommended in OECD TG 488; benzo[a]pyrene (B[a]P) was administered as the positive control. The esophagus, liver, and bone marrow were collected and analyzed for mutation using both the cII mutation assay and Duplex Sequencing (DS). NMPA treatment induced tissue-specific mutations, with the esophagus exhibiting the highest mutagenic response in both the cII and DS assays, followed by the liver; bone marrow remained relatively unaffected, showing no increase in the cII assay and only a slight elevation when analyzed by DS. The DS mutational spectrum for NMPA was characterized by a broadly distributed elevation across all base substitution types and in all trinucleotide contexts, distinguishing it from other small-molecule nitrosamines. Notably, DS detected fold-increases in NMPA mutagenesis greater than those detected by the transgenic rodent (TGR) assay, with a maximum 7.5-fold increase in genomic mutation frequency in the esophagus compared to the 5.6-fold increase in the cII gene. These data suggest that NMPA is an organ-specific mutagen, and that its mutagenic potency corresponds with its carcinogenicity in the primary target tissue and other tissues.
Environmental risk assessment (ERA) of pesticides is essential for protecting non-target organisms and supporting sustainable agricultural practices. This study links exposure and effect models for the prediction of pesticide risk and performs a global sensitivity analysis to identify the most influential parameters for maximum concentrations and risk indices. We coupled the FOCUS surface water Step 3 model (TOXSWA combined with PRZM) with the General Unified Threshold model of Survival (GUTS) to translate time-resolved exposure profiles into lethal profiles (LP50) values, capturing time-dependent toxicity and recovery processes. This approach offers a dynamic and mechanistically-based quantification of risk compared to traditional static risk quotients, which are based on single time-point concentrations. After prescreening 20 model parameters using a Morris sensitivity analysis, we selected the sorption coefficient and water degradation time as substance-specific properties, water depth as a key water-body parameter, and catchment size to capture the influence of loadings from agricultural practices for further in-depth analyses. Sobol sensitivity analyses of these four parameters were performed to assess the global sensitivity to simultaneous changes in these parameters. We found that while the maximum predicted environmental concentrations (PECmax) are primarily driven by water depth and catchment size, risk quantified by LP50 values is most sensitive to catchment size and so to loadings. Furthermore, degradation time does not influence PECmax, but its sensitivity increased for LP50 values associated with slower kinetics. These findings show that parameters to which peak exposure is most sensitive are not necessarily the same as those to which a time-resolved risk metric is most sensitive. Whether this added mechanistic detail improves the prediction of effects in the field remains to be tested by benchmarking against monitoring data and simpler metrics.
