Human biomonitoring (HBM) complements air and surface measurements by integrating exposure from all routes and sources, strengthening occupational exposure assessment and control. In occupational settings, HBM can quantify exposure during routine work and nonroutine activities, evaluate controls, investigate incidents (potential overexposures), and support medical surveillance. To use HBM to its full potential, occupational health and safety professionals (OHPs) should adopt harmonized biomonitoring approaches reflecting best practice. This short communication presents the BASIC Guide series (Human Biomonitoring and Surveillance of Chemical Exposure in Occupational Settings), initiated by the International Society of Exposure Science Human Biomonitoring working group (ISES Europe HBM WG) as an integral part of the HBM Global Network. These chemical-specific practical documents operationalize the OECD (Organisation for Economic Co-operation and Development) occupational biomonitoring guidance, supporting the consistent implementation of exposure biomonitoring programs. Each BASIC Guide provides clear instructions on biomarker selection, sample handling, analytical methods, quality assurance, and result interpretation and communication. By translating international frameworks into actionable protocols, the BASIC Guides improve reproducibility and regulatory alignment in occupational HBM and enable more defensible exposure assessments worldwide.

The COVID-19 pandemic presented unprecedented challenges to global economies, revealing vulnerabilities and emphasizing the urgent need for predictive tools to support timely and informed decision-making. In this context, this study investigates the application of Multilayer Perceptron (MLP) artificial neural networks (ANN) to forecast four key economic indicators: Gross Domestic Product (GDP), unemployment rate, inflation rate, and tourism, in six European countries (Austria, France, Germany, Italy, Portugal, and Spain). Based on a time series of public health and economic data collected between January 2020 and May 2023.The methodology involved developing two forecasting models: (1) a joint model trained to predict all indicators simultaneously, and (2) independent models, where each economic indicator was forecasted using a dedicated network. Data preparation included population-adjusted normalization and country-specific normalization for GDP, followed by a sliding window approach using 30-day input periods. The dataset was split into training, validation, and testing subsets, and experiments were conducted with varying activation functions and hidden layer configurations within the MLP networks.The results show that both approaches achieved high predictive accuracy, with independent models performing better in forecasting GDP and Inflation, while the joint model delivered superior results for unemployment and tourism. The lowest mean absolute error (MAE) was recorded for tourism (0.0013) using an independent network, and the joint model achieved an MAE of 0.0018 for unemployment.These findings highlight that network architecture should be tailored to specific forecasting objectives, as different economic indicators benefit from distinct configurations. The study reinforces the potential of MLP networks as effective tools for economic forecasting during crisis periods.

BACKGROUND:Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants increasingly detected in indoor environments, yet information on PFAS in school settings remains scarce. OBJECTIVE:We characterized PFAS levels in primary school indoor air and estimated inhalation exposure among children and school staff. METHODS:We collected 36 indoor and 8 outdoor air samples from 8 primary schools in Porto (the second-largest city in Portugal) and analyzed them for 64 PFAS, including perfluoroalkyl acids (PFAAs) and PFAA precursors. RESULTS:PFAA precursors predominated over terminal PFAAs, accounting for 66.5% and 64.6% of total PFAS in indoor and outdoor air, respectively. Among PFAAs, perfluoroalkyl carboxylic acids (PFCAs) were the dominant class in both indoor and outdoor air (median: 3822.3 and 495.1 pg/m3, respectively), whereas polyfluoroalkyl phosphate esters (PAPs; median: 2811.6 and 732.0 pg/m3) and fluorotelomer alcohols (FTOHs; median: 1436.6 and 313.5 pg/m3) were the predominant PFAA precursors. Trifluoroacetic acid (TFA), perfluorooctane sulfonate (PFOS), nonafluoro-3,6-dioxaheptanoic acid (NFDHA), di-sulfonamido polyfluoroalkyl phosphate (diSAmPAP), and 8:2 fluorotelomer alcohol (8:2 FTOH) were the major individual PFAS detected indoors. All PFAS classes showed indoor enrichment relative to outdoor air. Children had higher body-weight-normalized inhalation exposure than adults. Under the low-exposure scenario, FTOHs contributed most to total EDI, whereas PFCAs predominated under the high-exposure scenario. CONCLUSION:Primary schools constitute important indoor microenvironments for PFAS exposure. Although PFAA precursors predominated in indoor air, PFAAs accounted for the largest share of estimated inhalation exposure under the high-exposure scenario, highlighting the importance of considering both PFAA precursors and PFAS in comprehensive exposure assessments.

In this work and the literature, voice signals can be classified as periodic (type 1) or either some periodicity (type 2) and chaos (type 3). This work aims to classify signs into types 1, 2 or 3 to be subsequently applied in a classification system for pathological/control signs. The original dataset is composed of 466 type 1 individuals, 900 type 2 individuals, and 84 type 3 individuals classified by an otolaryngologist. 15

Despite widespread use of polybrominated diphenyl ethers (PBDEs) and other brominated flame retardants (BFRs) in indoor environments, data on children's BFR exposure in Portuguese schools and associated health effects remain limited. We aimed to characterize indoor air BFR concentrations in 20 Portuguese primary schools, examine grade-specific exposure patterns, and investigate the associations between BFR concentrations and childhood asthma, wheezing, and eczema. Air BFRs were measured using passive sampling in 80 classrooms over 28 days. Health outcomes (lifetime asthma history, current asthma, wheezing, and eczema) were obtained from standardized parental questionnaires. Multilevel logistic regression models accounting for school-level clustering evaluated associations between log-transformed concentrations and health outcomes, adjusting for relevant confounders. The sum of BFR (∑BFR) concentrations ranged between 28.12 and 154.54 pg/m³ . Replacement BFRs, particularly decabromodiphenyl ethane (DBDPE), were dominant contributors, while BDE-47 and BDE-209 remained prevalent among legacy compounds. Grade 1 students exhibited higher exposure to several BFRs than older students. Per log10-unit increase, BDE-209 was associated with higher odds of lifetime asthma history (OR=2.13; 1.09-5.11) and wheezing (OR=1.75; 1.17-3.18), while an increase in ∑BFRs (OR=1.93; 1.29-3.91) was associated with higher odds of current asthma. Furthermore, a log10-unit increase in ∑non-BDEs was associated with a significantly higher eczema odds (OR=2.88; 1.65-5.46). Children in schools are exposed to legacy and replacement BFRs, with patterns varying per grade. The prominence of replacement compounds underscores an ongoing transition in indoor BFR profiles, reinforcing the need for targeted mitigation strategies and continued evaluation of potential health risks to children.

A pandemic’s socioeconomic disruption can result in deaths due to deprivation, suicide, violence, and trauma, in addition to virus-related consequences. This study seeks to map the scientific literature on the effects of mitigation measures for respiratory virus outbreaks on healthcare and economic systems across countries, as well as the models used to predict those effects. The primary objectives were to identify the main contributions in this field and delineate the major research pathways that can inform a future research agenda. The study uses bibliometric analysis, keyword co-occurrence analysis, and cluster analysis. Keyword linkages were examined to identify possible trends across the retrieved papers. Hierarchical cluster analysis was also applied to categorize related papers into distinct groups. The results facilitated the identification and classification of multiple theoretical perspectives derived from primary research across seven major approaches: (i) economic parameters affected by the COVID-19 crisis; (ii) healthcare crisis management; (iii) predictions of government interventions’ impact on the healthcare system; (iv) impacts of influenza virus in a global economic scenario; (v) general impacts of outbreaks in European and Asia Pacific countries; (vi) operating statistical stability in data analysis; and (vii) statistical trends regarding healthcare in a global economy over a pandemic crisis. Overall, the review synthesizes the main themes in the literature and highlights priority areas related to economic systems, healthcare systems, and predictive modeling. The findings highlight the strong interconnections among economic stability, healthcare system resilience, and public policy, while identifying key health and economic parameters that may inform predictive models assessing the effects of mitigation measures.

Rotary dry cutting and rectifying of ceramic tiles are sources of fine particulate matter (PM2.5) and nanoparticles (NPs). These activities are typically carried out inside industrial facilities during the manufacturing process, as well as outdoors and in residential indoor spaces during the installation phase, where mitigation measures are seldom implemented. This work aimed to understand the particle formation and release mechanisms, as well as particle properties (physical, chemical, and toxicological) and potential impacts on human health and the environment, for particles generated during ceramic tile rotary dry cutting operations. Aerosols were characterised in terms of particle number and mass concentrations, chemical composition, morphology and in vitro cytotoxicity. Two types of commercially available and representative tiles were tested in controlled chamber experiments: porous and non-porous ceramic body tiles (referred to in this work as A and B types, respectively). Results evidenced the release of fine particles and NPs during dry cutting of both materials, in comparable concentrations (20.000-45.000/cm3, 1-min average). However, the particle size distribution was significantly finer from A tiles (70% of the particle number concentration was nanosized (<100 nm)) in comparison to B tiles (<20%). While airborne particle chemical profiles were similar for both types of materials in the coarser size fractions (>0.6 mu m), in the smaller size fractions (<0.6 mu m) larger differences were observed. The chemical composition of airborne aerosols was consistent with that of the deposited dust. In vitro cytotoxicity responses evidenced statistically significant differences between exposure to aerosols from both types of tiles: cell viability was lower after exposure to aerosols from A tiles (50% at the original concentration) compared to those from B tiles, which exhibited high cell viability regardless of the aerosol concentration. Overall, results evidenced NP formation and release during rotary dry cutting of ceramic tiles, varying physical-chemical and cytotoxic profiles as a function of the material being processed, and highlight this activity as a potential health hazard in scenarios where prevention and mitigation measures are not implemented.

Enniatins (ENNs) and beauvericin (BEA) are cyclic hexadepsipeptide fungal metabolites which have demonstrated antibiotic, antimycotic, and insecticidal activities. The substantial toxic potentials of these mycotoxins are associated with their ionophoric molecular properties and relatively high lipophilicities. ENNs occur extensively in grain and grain-derived products and are considered a food safety issue by the European Food Safety Authority (EFSA). The tolerable daily intake and maximum levels for ENNs in humans and animals remain unestablished due to key toxicological and toxicokinetic data gaps, preventing full risk assessment. Aiming to find critical data gaps impeding hazard characterization and risk evaluation, this review presents a comprehensive summary of the existing information from in vitro and in vivo studies on toxicokinetic characteristics and cytotoxic, genotoxic, immunotoxic, endocrine, reproductive and developmental effects of the most prevalent ENN analogues (ENN A, A1, B, B1) and BEA. The missing information identified showed that additional studies on ENNs and BEA have to be performed before sufficient data for an in-depth hazard characterisation of these mycotoxins become available.

BACKGROUND:Portugal is among the European Union countries more devastated by forest fires. Wildland firefighters are at the forefront of this battle, facing exposure to a wide range of harmful pollutants. Epidemiological studies have highlighted a potential link between occupational firefighting exposure and several diseases, including cancer. To date, very few studies have explored the biological mechanisms associated with such exposure. The present longitudinal study aims to assess changes in early effect biomarkers following wildland firefighters' occupational exposure to a real wildfire event. METHODS:Paired blood samples from 59 healthy Portuguese wildland firefighters were collected at two different time points: before wildfire season and after a fire event during wildfire season. Sociodemographic variables (e.g., age, sex) and work-related factors (e.g., years of service) were assessed via a self-reported questionnaire. Levels of early effect biomarkers, such as primary DNA damage and oxidative DNA damage (oxidised purines) were assessed via comet assay. DNA double-strand breaks (DSBs) were evaluated by phosphorylated H2AX (γH2AX). Moreover, hydroxylated polycyclic aromatic hydrocarbon metabolites (OHPAHs) and metal(loid)s were quantified in urine samples. The influence of urinary OHPAHs, urinary metal(loid)s, and other exposure-related factors (e.g., firefighting duration) on changes (Δ) in early effect biomarkers (post-vs. baseline levels) was investigated. RESULTS:Firefighting activities led to a significant increase in both primary DNA damage and oxidative DNA damage by 22 % (95 % CI: 1.11-1.35; p < 0.05) and 23 % (95 % CI: 1.04-1.45; p < 0.05), respectively. Results from linear regression revealed that per each unit increase of urinary 2-hydroxyfluorene (2-OHFlu) (μmol/mol creatinine), the risk of ⧍ oxidative DNA damage increased by 20 % [FR: 1.20 (1.09-1.32); p < 0.01]. Additionally, each unit increase in urinary cesium (Cs) (μg/L) resulted in a significant 4 % increase in Δ primary DNA damage [FR: 1.04 (1.01-1.06); p < 0.05] and a 3 % increase in Δ oxidative DNA damage [FR: 1.03 (1.01-1.05); p < 0.05]. Post-exposure levels of γH2AX were significantly correlated with urinary 2-OHFlu levels assessed after firefighting (r = 0.30; p < 0.05). Furthermore, exposure duration and reported breathing difficulties during firefighting were significantly associated with increased levels of primary DNA damage. CONCLUSION:Results obtained provide insights into the potential human health effects of wildland firefighting occupational exposure at the genetic and molecular levels, offering new and important mechanistic data. These findings are crucial for implementing health and safety measures, recommendations, and best practices to mitigate occupational risks and protect the health of wildland firefighters.

OBJECTIVES:Despite being a commensal of human skin, Cutibacterium acnes plays an important role in the pathogenesis of Acne vulgaris, with the loss of bacterial phylotype diversity being related to disease progression. This study evaluated differences in the virulence profile of C. acnes strains from different phylotypes and investigated the possible phylotype-selective efficacy of essential oils (EOs) from Thymus x citriodorus (TC), Thymus mastichina, and Cistus ladanifer compared to Melaleuca alternifolia (tea tree), focusing on both planktonic and biofilm growth forms, the latter being related with pathogenesis and treatment resistance. METHODS:One collection strain and seven clinical isolates were classified into phylotypes using multiplex-touchdown PCR. Virulence trait differences across phylotypes were evaluated by studying antibiotic resistance, biofilm formation, porphyrin production, and lipase activity. EOs were tested for minimum inhibitory/bactericidal concentrations (MIC/MBC) and effects on biofilm biomass and metabolic activity. RESULTS:Strains from phylotype IA1 were higher biofilm and lipase producers than phylotype II strains. Regarding EO's efficacy, TC EO presented lower planktonic MIC values for all strains compared to the other EOs, presenting a smaller difference in MIC values across phylotypes. TC EO was able to similarly reduce biofilm biomass and metabolic activity in phylotype IA1 clinical strains, being effective at lower concentrations compared with the remaining EOs. CONCLUSIONS:Not all virulence traits were phylotype-related, highlighting the multifactorial nature of the disease. TC EO showed a relevant anti-acne potential, outperforming tea tree EO (a species with a commercial claim for Acne vulgaris) against both planktonic and biofilm growth forms of C. acnes.

Human biomonitoring (HBM) provides an integrated chemical exposures assessment considering all routes and sources of exposure. The accurate interpretation and comparability of biomarkers of exposure and effect depend on harmonized, quality-assured sampling, processing, and analysis. Currently, the lack of broadly accepted guidance on minimum information required for collecting and reporting HBM data, hinders comparability between studies. Furthermore, it prevents HBM from reaching its full potential as a reliable approach for assessing and managing the risks of human exposure to chemicals.The European Chapter of the International Society of Exposure Science HBM Working Group (ISES Europe HBM working group) has established a global human biomonitoring community network (HBM Global Network) to develop a guidance to define the minimum information to be collected and reported in HBM, called the “Minimum Information Requirements for Human Biomonitoring (MIR-HBM)”. This work builds on previous efforts to harmonize HBM worldwide.The MIR-HBM guidance covers all phases of HBM from the design phase to the effective communication of results. By carefully defining MIR for all phases, researchers and health professionals can make their HBM studies and programs are robust, reproducible, and meaningful. Acceptance and implementation of MIR-HBM Guidelines in both the general population and occupational fields would improve the interpretability and regulatory utility of HBM data. While implementation challenges remain—such as varying local capacities, and ethical and legal differences at the national levels, this initiative represents an important step toward harmonizing HBM practice and supports an ongoing dialogue among policymakers, legal experts, and scientists to effectively address these challenges. Leveraging the data and insights from HBM, policymakers can develop more effective strategies to protect public health and ensure safer working environments.

ObjectivesThis protocol outlines the development of a systematic evidence map (SEM) on genetic and epigenetic alterations associated with human prenatal tobacco exposure. The SEM will identify and synthetize epidemiological data on periconceptional and prenatal tobacco exposure associated with genetic (e.g., DNA damage) and epigenetic (e.g., DNA methylation) outcomes. Furthermore, it will describe the available evidence, highlight knowledge clusters, and identify gaps for future research.MethodsBibliographic databases and grey literature sources will be searched, complemented by reference mining. Predefined inclusion and exclusion criteria will guide study inclusion. Data extraction will include population, exposure, comparator, outcome, funding, study design, confounding factors, and statistical methods. Summarization will include a narrative review, graphical visualization, and an interactive, queryable and open-access table.ResultsA pilot study assessed the protocol’s feasibility, testing key components, including screening, data extraction, and eligibility criteria. Findings confirmed that the methodology is workable and reliable.ConclusionThis protocol supports a rigorous, reproducible, transparent SEM, aligned with international standards. The comprehensive mapping will support research prioritization and inform public health policies targeting maternal and child health.

Firefighters face increased risks of developing cardio-respiratory diseases and cancer. This study aimed, for the first time, to simultaneously characterize several biomarkers of effect (lung injury by Clara cell 16 -CC16, lipid peroxidation by 8-isoprostane-8-iso, and DNA oxidation by 8-hydroxy-2-deoxyguanosine-8-OHdG) and exposure (polycyclic aromatic hydrocarbons metabolites - 6 OHPAHs and 17 metal(loid)s) in (pre- and post-exposure) paired urine samples of wildland firefighters, while exploring their inter-/intra-associations and accounting for tobacco consumption. Wildfire combat influenced the levels of CC16 (+39 %), 8-iso (+33 %), 8-OHdG (-13 to +19 %), individual and sum of OHPAHs (+75-211 %), and metal(loid)s (up to 43 %, p > 0.05: lithium, zinc, antimony, and lead); post-exposure increments were more evident among non-smokers. Post-exposure (individual and sum) OHPAHs and some metal(loid)s (copper, cadmium, barium, antimony, copper, lead, zinc, selenium, and rubidium) were positively associated with CC16, 8-iso and/or 8-OHdG (0.609 < r < 0.838; 0.001 < p < 0.047). Spearman's correlations and principal component analysis highlighted CC16 as the best discriminant effect biomarker of wildland firefighting, correlating positively with individual and sum of OHPAHs, cadmium, barium and copper (0.647 < r < 0.764; 0.006 < p < 0.031). Cumulative exposure to wildfires and tobacco contributed to positive correlations (0.587 < r < 0.715; 0.009 < p < 0.045) between lipid peroxidation and arsenic, antimony, lead, and copper, and between DNA oxidation and lead. Smoking firefighters presented higher OHPAHs baseline concentrations (2- to 14-fold), and lung injury and DNA oxidation induced by cadmium, copper, strontium, cesium, barium and thallium (0.661 < r < 0.709; 0.022 < p < 0.038). Given firefighter's carcinogenic risks, performing similar studies in larger groups is crucial to enhance risk assessment by establishing a well-defined panel of effect and exposure biomarkers.

Cerium dioxide nanoparticles (CeO 2 NP), or nanoceria, are versatile materials with interesting properties for industry and medicine fields, particularly redox properties and catalytic activity. Because of their distinctive features, they have gained high attention in biomedical and pharmacological research to be employed in drug delivery, tissue regeneration, radioprotection, or diagnostic imaging. However, previous works reported that nanoceria may also induce reactive oxygen species (ROS) under certain conditions, leading to cellular stress, cellular damage, or cell death. In this study, the effects of CeO 2 NP on cell viability and morphology as well as their influence on oxidative stress (both oxidant and ROS scavenging capacities) were investigated in nervous system cells (SH-SY5Y neuronal and A172 glial cells) treated with a wide range of CeO 2 NP concentrations (1–100 µg/mL) for several treatment times. Results obtained showed that, despite being stable in time and effectively internalized by both cell types, CeO 2 NP did not produce significant decrease in viability, evaluated by MTT assay, morphological alterations, or intrinsic cell-free ROS, but they generated cellular ROS limited to longer exposure periods. Furthermore, CeO 2 NP demonstrated a certain intrinsic ability to scavenge ROS generated by H 2 O 2 in both tested cell types, more pronounced in neuronal cells. These results confirm the good biocompatibility of nanoceria on human nervous system cells and support further exploring their potential use in biomedicine field, particularly for those therapeutic and diagnostic applications related to the nervous system.

Despite being previously banned due to long-term health effects, Per- and polyfluoroalkyl substances (PFAS) remain widespread in the environment, accumulating in animals and humans. This systematic review and meta-analysis explores associations between exposure to PFAS and asthma onset, wheezing, atopic dermatitis, and eczema in children and adolescents while addressing exposure timing and sex-specific differences. After comprehensive search conducted in several databases, including risk of bias, study heterogeneity, and quality of evidence evaluation, the review included 28 observational studies, most with low risk of bias in all domains. PFAS exposure was not significantly associated with asthma onset (OR:1.03, CI:0.99;1.07), but revealed significantly lower association in the prenatal period (OR:0.97, CI:0.94;0.99), higher in the postnatal period (OR:1.20, CI:1.07;1.35), and no differences among sexes. PFAS exposure (mainly prenatal) was associated with 4 % significantly lower odds of wheezing (OR:0.96, CI:0.94;0.98), higher in girls (OR:0.94, CI:0.91;0.98) than in boys (OR:0.97, CI:0.94;1.00). No significant impact was noted on atopic dermatitis (OR:1.04, CI:0.94;1.16), while PFAS exposure was associated with 8 % significantly lower eczema odds (OR:0.92, CI:0.89;0.96). Evidence was insufficient to perform sensitivity analyses on atopic dermatitis and eczema. Additional research is needed on the impact of synergistic and co-exposure to other pollutants on children and adolescents' health.

This paper uses Multi-Layer Perceptron (MLP) neural networks to explore wind turbine power output prediction. Accurate forecasting of wind energy production is critical for grid stability and optimizing energy systems. The study compares various prediction techniques, including physical, statistical, and hybrid methods. The methodology employs real-world data sourced and uses records from 2016–2017. Data preprocessing includes filtering, seasonal decomposition, time series analysis, and dividing the dataset into training, validation, and testing sets. The model’s structure and hyperparameters were carefully tuned, employing 144 samples from the produced power as input, representing 24-hour cycles, to forecast the next hour. The study evaluated multiple MLP configurations, varying in hidden layer sizes and training strategies, to identify the optimal architecture for short-term wind power forecasting. The evaluation uses statistical metrics to assess prediction accuracy, including RMSE, NRMSE, and R2. Early stopping and randomized dataset splits were evaluated to increase model performance and robustness. The models obtained results between 94-95

Research on nanoparticle (NP) release and potential exposure can be assessed through experimental field campaigns, laboratory simulations, and prediction models. However, risk assessment models are typically designed for manufactured NP (MNP) and have not been adapted for incidental NP (INP) properties. A notable research gap is identifying NP sources and their chemical, physical, and toxicological properties, especially in real-world settings. This work aims to provide insights into the release and physico-chemical properties of INP while contributing to improving models for INP release. INP release was evaluated through a case study in a ceramic tile firing facility, where aerosol (10 nm - 10 μm) properties were determined. The Control Banding (CB) Nanotool model was applied to test outputs based on provided input parameters. RESULTS: demonstrate the constant generation and release of INP during tile firing, with NP concentrations up to 68711/cm³ and mean diameters of 37 nm, with 95% smaller than 100 nm. Particle morphology was mostly spherical, suggesting nucleation from precursor gases as the main formation mechanism. INP chemical composition was driven by primary ceramic components, while trace elements like Ni and Ti exhibited size-dependent patterns. In vitro cell viability tests indicated low to medium cytotoxicity of PM2 aerosols, decreasing human alveolar epithelial cell viability in a concentration-dependent manner. Applying the risk model with varying input parameters revealed that the risk level (RL) based on severity scores decreased when aerosol size distribution data were used, illustrating the model's sensitivity to input variables. We conclude on the need for comprehensive experimental datasets to support risk assessment models and achieve effective risk management strategies in real-world scenarios.

Occupational exposure as a firefighter is classified as carcinogenic to humans. However, information on the biological effects of wildland firefighting remains limited. This study aimed to assess genotoxicity in a group of wildland firefighters and evaluate the contribution of total concentration of urinary hydroxylated polycyclic aromatic hydrocarbons (ΣOHPAHs) to selected endpoints. A group of 59 northern Portuguese wildland firefighters (mean age: 35.5 ± 9.0 years) was evaluated before and during the wildfire season. Sociodemographic, lifestyle, occupational-related information was collected via questionnaire. The micronucleus (MN) assay in buccal and urothelial cells was applied to paired samples to assess the association with exposure, as indicated by urinary ΣOHPAHs levels. The risk of MN formation in urothelial cells was over twofold higher during the wildfire season [Frequency Ratio (FR): 2.13, 95 % CI: 1.99-2.27, p = 0.01]. A 35 % increase in MN frequency in buccal cells was observed during the wildfire season (FR: 1.35, 95 % CI: 0.76-2.40, p > 0.05). Urinary ΣOHPAHs exhibited a significant positive association with MN‰ in urothelial cells (FR:1.04, 95 % CI: 1.01-1.08, p < 0.05). This study provides the first assessment of MN frequency in urothelial cells of wildland firefighters, offering novel evidence of genotoxic risks and potential long-term health impacts associated with wildland firefighting.

Schizophrenia is a severe mental illness affecting over 20 million people worldwide, significantly impairing quality of life and daily functioning. Current diagnostic methods rely heavily on subjective assessments and interactions between doctors and patients, leaving room for potential misdiagnoses. Recent advancements in technology have introduced non-invasive, fast, and user-friendly approaches, such as machine learning, to support psychiatric diagnosis. In this study, spectral features extracted from speech samples of individuals with and without schizophrenia were analyzed. Using an ensemble bagged tree model, we achieved an accuracy of 96.3