Extrapolation Model Research Articles

Development of a bioavailability-based acute effects assessment method for nickel.

This study aimed to develop a bioavailability-based effects assessment method for nickel (Ni) to derive acute freshwater environmental thresholds in Europe. The authors established a reliable acute freshwater Ni ecotoxicity database covering 63 different freshwater species, and the existing acute Ni bioavailability models for invertebrates were revised. A single average invertebrate bioavailability model was proposed, in which the protective effects of Ca2+ and Mg2+ on Ni2+ toxicity were integrated as a single-site competition effect at the Ni biotic ligand. The biotic ligand stability constants for Ca2+ and Mg2+ (log KCaBL = 3.80 and log KMgBL = 3.32) were derived by averaging these parameters from three existing cladoceran models. A pH extension was also integrated into the average invertebrate bioavailability model to reflect the increase in free Ni2+ toxicity observed greater than pH 8.0. The proposed invertebrate model has further been validated using an extensive dataset of acute toxicity data covering 15 different invertebrate species. Evaluating the extrapolation of the invertebrate model to plant species revealed significant uncertainty about the applicability of the acute Ni bioavailability models for plants. The newly developed acute invertebrate model was used alongside the existing acute fish and algae bioavailability models to support an acute bioavailability normalization approach for Ni. By combining these bioavailability models with the acute toxicity dataset for Ni, a normalized species sensitivity distribution approach is proposed to derive site-specific acute environmental thresholds, expressed by the HC5L(E)C50 (i.e., dissolved Ni concentration resulting in at least 50% effect for 5% of the species). The applicability ranges of the acute Ni bioavailability normalization approach are estimated to be valid for approximately 70% of European freshwaters. The proposed approach serves as a basis to incorporate bioavailability into the compliance evaluation relative to acute environmental threshold values for Ni in Europe.

Expression of NKp46 and other activating inhibitory receptors on uterine endometrial NK cells in females with various reproductive failures: A review.

Uterine endometrial natural killer (uNK) cells represent major leukocytes in the mid-secretory phase of the cell cycle, and their number is further increased during early pregnancy. The activating and inhibitory receptors expressed on their surface mediate various functions of uNK cells, such as cytotoxicity, cytokine production, spiral artery remodeling, and self-recognition. This study reviewed the most recent information (PubMed database, 175 articles included) regarding the activating and inhibitory receptors on uNK cells in human females with healthy pregnancies and the evidence indicating their significance in various reproductive failures. Numerous studies have indicated that the natural cytotoxic receptors, killer cell immunoglobulin-like receptors, and C-type lectin receptors, particularly those expressed on uNK cells, play crucial roles in successful pregnancy. As studies on human uNK cells are limited owing to the low availability of fertile samples, and the extrapolation of animal models has certain limitations, the invivo role of uNK cells has not yet been fully elucidated. However, immunotherapies focusing on modulating uNK cell function have been controversial in terms of pregnancy outcomes. Further research is required to elucidate the role of uNK cells in reproduction.

Extrapolative machine learning models for copolymers

ML models extrapolate better when trained on large data sets.

Simultaneous Prediction of Multiple Unmeasured Muscle Activations Through Synergy Extrapolation.

Estimating muscle forces is crucial for understanding joint dynamics and improving rehabilitation strategies, particularly for patients with neurological disorders who suffer from impaired muscle function. Muscle forces are directly proportional to muscle activations, which can be obtained using electromyography (EMG). EMG-driven modeling estimates muscle forces and joint moments from muscle activations. While surface muscles' activations can be obtained using surface electrodes, deep muscles require invasive methods and are not readily available for real-time applications. This study aims to extend our previously developed method for a single unmeasured muscle to a comprehensive approach for the simultaneous prediction of multiple unmeasured muscle activations in the upper extremity using muscle synergy extrapolation and EMG-driven modeling. By employing non-negative matrix factorization to decompose known EMG data into synergy components, the activations of unmeasured muscles are reconstructed with high accuracy by minimizing differences between joint moments obtained by EMG-driven modeling and inverse dynamics. This methodology is validated through experimentally collected muscle activations, demonstrating over 90% correlation with EMG signals in various scenarios.

An Application of Logistic Regression in Bank Lending Prediction: A Machine Learning Perspective

Logistic regression has been widely applied in bank lending prediction due to its simplicity, efficiency, and interpretability. This paper provides a review of the theoretical foundations, practical applications, and recent advancements of logistic regression in this domain. Through case studies and literature analysis, the author focuses on the challenges faced by logistic regression in the modeling process, such as data imbalance and nonlinear relationships, as well as optimization strategies like regularization and ensemble learning. Despite the proliferation of machine learning methods, logistic regression remains the preferred choice and benchmark for credit risk modeling, thanks to its robust predictive performance and regulatory friendliness. Looking forward, the author argues that the integration of logistic regression with other machine learning techniques, coupled with domain knowledge, will further enhance its applicability and predictive power. However, striking a balance between model complexity and interpretability, while improving model robustness and extrapolation, remains an important area for future research. This paper offers new perspectives and insights for credit risk management practices in banks and other financial institutions.

Updated cost-effectiveness analysis of tislelizumab in combination with chemotherapy for the first-line treatment of advanced gastric cancer or gastroesophageal junction adenocarcinoma.

The RATIONALE-305 trial demonstrated that tislelizumab in combination with chemotherapy regimens was more beneficial than chemotherapy regimens alone in the treatment of patients with advanced gastric cancer or gastroesophageal junction adenocarcinoma (GC/GEJC). This study aimed to evaluate the cost-effectiveness of tislelizumab combination chemotherapy in the treatment of advanced GC/GEJC from the perspective of the Chinese health service system. A three-state partition survival model was constructed to evaluate the economics of tislelizumab combined with chemotherapy as the first-line treatment of advanced GC/GEJC. Clinical data were collected from the RATIONALE-305 trial, and the incremental cost-effectiveness ratio (ICER) was calculated using quality-adjusted life years (QALYs) as the output index. The stability of the results was verified using sensitivity and subgroup analyses. In addition, scenario analysis was conducted for the model simulation time and different parameter extrapolation models. The results of basic analysis showed an increase of 0.31 QALYs in the tislelizumab group compared with the placebo group (1.53 QALYs vs 1.22 QALYs), and a concomitant increase in cost of 10,326.68 USD, with an ICER of 33,876.38 USD/QALY, which is less than the current Chinese willingness-to-pay threshold (36,924.80 USD/QALY). Sensitivity analyses demonstrated that the utility values of progression-free survival, progressive disease and the price of capecitabine had a greater impact on the model. Subgroup analysis revealed that combination therapy was equally cost-effective in people with a program death ligand 1 tumor area positivity score of ≥5%. From the perspective of the Chinese health service system, the treatment of advanced GC/GEJC with tislelizumab combined with chemotherapy has a cost-effective advantage over chemotherapy alone.

Variability in resistance training trajectories of breast cancer patients undergoing therapy

PurposeIn resistance training (RT), the change in volume-load from training sessions (TS) to TS is an indicator of training progress. Resulting growth trajectories are likely to differ between individuals. Understanding this variation is important for exercise planning in general, but even more for clinical populations. We investigated this variation in breast cancer patients undergoing treatment.MethodsData of 69 patients from two randomized controlled trails were investigated. They conducted a 12-week RT program. We fitted a quadratic Bayesian regression model to the baseline standardized volume-load over the course of the intervention. We allowed all parameters to vary both between exercises and between individuals.ResultsWe observed a positive linear component of 0.093 (95% uncertainty interval (UI) 0.058 to 0.120) and a negative quadratic component of − 0.002 (95% UI -0.008 to 0.001) for the mean trajectory of the change in volume-load. For the different exercises, we observed a dispersion for both the linear (0.043, 95% UI 0.018 to 0.082) and the quadratic component (0.002, 95% UI < 0.001 to 0.004). Variation between individual appears to be approximately four times larger. We also observed between-exercise variation within individuals. Extrapolation of the regression model indicates training progression stagnates after 20.6 TS (95% UI 14.8 to 44.4).ConclusionThere is substantial variation in RT response between breast cancer patients undergoing tumor therapy and in-between exercises. The non-linear trajectory indicates that training progression will eventually plateau, demanding periodization and timely modification.Trial registrationBEATE Study: NCT01106820, Date: April 20, 2010; BEST Study: NCT01468766, Date: November 9, 2011.

Covid-19 mortality: the Proportionality Hypothesis

AbstractWe introduce and provide evidence to support the Proportionality Hypothesis which states that Covid-19 infection fatality rates are approximately proportional to all-cause death rates by age and subgroup (e.g., socio-economic class). We also show that vaccination played a very significant role in preventing people infected with Covid-19 from needing to be hospitalised, since it reduced the average severity of an infection. Death rates involving Covid-19 were very significantly lower for people in the fully vaccinated group compared to the unvaccinated group. During the pandemic, death rates from other causes were in some cases reduced (e.g., flu and pneumonia), in some cases unchanged (e.g., lung cancer) and in some cases elevated (e.g., heart disease). We discuss the implications of our findings both for potential adjustments to extrapolative mortality models which allow for future pandemics in a way that is consistent with the Proportionality Hypothesis and for insurance companies in terms of both modelling extreme scenarios and the design of mortality catastrophe bonds.

ПРОГНОЗУВАННЯ ПОКАЗНИКІВ РОЗВИТКУ ПІДПРИЄМСТВ МАЛОГО І СЕРЕДНЬОГО БІЗНЕСУ

The article considers issues related to analyzing the activities of small and medium-sized businesses under martial law. It is established that under such conditions, small and medium-sized enterprises occupy a special place in ensuring the functioning of the domestic economy, acting as one of the primary forms of management and contributing to the processes of demonopolization and privatization. Such enterprises contribute to developing innovative activity, ensuring the market's saturation with goods and services, creating jobs, and forming competition in the market. They are one of the primary sources of income generation for the country. Predictive analytics for small and medium-sized enterprises, in particular, identifying trends in the number of such enterprises and the number of people employed in them, is essential in the strategic planning of their activities. It is established that trend extrapolation models are an effective tool for solving such problems. The article analyzes approaches to modeling the activities of small and medium-sized businesses. It showed various forecasting tools, particularly regression models, production functions, trend extrapolation models, etc. For practical application, the study used trend models that estimate the forecast values of indicators under the complex influence of all factors. The article contains an assessment of the development trends of small and medium-sized businesses, as well as the calculation of forecast values for 2024 for the indicators of the number of enterprises and the number of employees at enterprises, according to which their values are expected to increase. The results obtained during the study and the conclusions formed can be used by state administration bodies, particularly the Khmelnytskyi region, as an analytical basis when reviewing the program of socio-economic development.

Radar Echo Extrapolation Based on Translator Coding and Decoding Conditional Generation Adversarial Network

In response to the shortcomings of current spatiotemporal prediction models, which frequently encounter difficulties in temporal feature extraction and the forecasting of medium to high echo intensity regions over extended sequences, this study presents a novel model for radar echo extrapolation that combines a translator encoder-decoder architecture with a spatiotemporal dual-discriminator conditional generative adversarial network (STD-TranslatorNet). Initially, an image reconstruction network is established as the generator, employing a combination of a temporal attention unit (TAU) and an encoder–decoder framework. Within this architecture, both intra-frame static attention and inter-frame dynamic attention mechanisms are utilized to derive attention weights across image channels, thereby effectively capturing the temporal evolution of time series images. This approach enhances the network’s capacity to comprehend local spatial features alongside global temporal dynamics. The encoder–decoder configuration further bolsters the network’s proficiency in feature extraction through image reconstruction. Subsequently, the spatiotemporal dual discriminator is crafted to encapsulate both temporal correlations and spatial attributes within the generated image sequences. This design serves to effectively steer the generator’s output, thereby augmenting the realism of the produced images. Lastly, a composite multi-loss function is proposed to enhance the network’s capability to model intricate spatiotemporal evolving radar echo data, facilitating a more comprehensive assessment of the quality of the generated images, which in turn fortifies the network’s robustness. Experimental findings derived from the standard radar echo dataset (SRAD) reveal that the proposed radar echo extrapolation technique exhibits superior performance, with average critical success index (CSI) and probability of detection (POD) metrics per frame increasing by 6.9% and 7.6%, respectively, in comparison to prior methodologies.

Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting.

QSPR modeling to predict surface tension of psychoanaleptic drugs using the hybrid DA-SVR algorithm

A robust Quantitative Structure-Property Relationship (QSPR) model was presented to predict the surface tension property of psychoanaleptic (psychostimulant and antidepressant) drugs. A dataset of 112 molecules was utilized, and three feature selection methods were applied: genetic algorithm combined with Ordinary Least Squares (GA-OLS), Partial Least Squares (GA-PLS), and Support Vector Machines (GA-SVM), each identifying ten pertinent AlvaDesc descriptors. The models were constructed using the Dragonfly Algorithm combined with the Support Vector Regressor (DA-SVR), with the GA-SVM-based model emerging as the top performer. Rigorous statistical metrics validate its superior predictive accuracy (R2 = 0.98142, Q2LOO = 0.98142, RMSE = 1.12836, AARD = 0.78746). Furthermore, an external test set of ten compounds was employed for model validation and extrapolation, along with assessing the applicability domain, further underscoring the model’s reliability. The selected descriptors (X0Av, VE1sign_B(e), ATSC1e, MATS6v, P_VSA_ppp_A, TDB01u, E1s, R2m+, N-067, SssO) collectively elucidate the key structural factors influencing surface tension in the studied drugs. The model provides excellent predictions and can be used to determine the surface tension of new psychoanaleptic drugs. Its outcomes will guide the design of novel medications with targeted surface tension properties.

Water density–pressure–temperature modeling for drilling fluids in real-time HPHT applications

Accurate equations for extrapolating the density of water to varying pressures and temperatures are important for the petroleum industry. Water is a key component in drilling fluids, and extrapolation models of water are essential for calculating the density of the fluid as a whole for different depths in the well. This is further of paramount importance to be able to keep the pressure in the well within safety limits. This paper shows and explains how density–pressure–temperature (DPT) models referred to in the field of Petroleum are not well-suited for high-pressure, high-temperature (HPHT) wells which are becoming more and more common. It also discusses DPT- and pressure–volume–temperature (PVT) models from other disciplines and demonstrates that models with the accuracy needed for HPHT wells, are excessively complex and have unnecessary long execution times. For many challenging wells, hydraulic models run today in parallel with the operation in real-time and may constitute one of several components in a digital twin. In such applications it is important that each component runs as efficiently as possible, such that the entire system is able to deliver its results in time. In this work, new polynomial DPT models are constructed, that fill this gap in water DPT modeling. I.e., they are designed for being used for calculating the water density in real-time, and cover HPHT wells with pressure up to 200MPa and temperature up to 260°C. It is demonstrated that it is possible to construct polynomial models that are 1−2 decades faster than the industry standard used for comparison, while also being more accurate over the pressure and temperature range relevant to HPHT wells. If the accuracy can be slightly relaxed, a new model that is 2−3 orders of magnitude faster than the industry standard is proposed.

DiffREE: feature-conditioned diffusion model for radar echo extrapolation

DiffREE: feature-conditioned diffusion model for radar echo extrapolation

Assessing the Costs of Intravenous Push Waste in Intraoperative Areas Through Observation: A Multi-site Study.

The costs associated with proper disposal, management, and regulatory compliance of controlled substances in healthcare systems are substantial. In the context of the current opioid crisis, and given the high abuse potential of controlled substances, it is imperative that waste is minimized and waste procedures are followed to ensure safe disposal of controlled substances. This study aims to quantify the costs associated with fentanyl, hydromorphone, morphine, midazolam, and ketamine waste in intraoperative areas through a multi-site observational analysis. The study used an observational design across various hospital procedural and post-procedural units in the Southwest Florida region of the United States. Automated and non-automated workflows for wasting controlled substances were compared. As with a previous study conducted by Hertig et al., waste was evaluated as (1) the quantity (mg/μg) of medication disposed defined as 'pharmaceutical waste' or 'product waste' (PW); and (2) workforce time associated with the waste disposal process defined as 'workforce time waste' (WTW). Secondary measures include workforce costs associated with the waste disposal process. The product waste analysis was conducted between October and December 2023. The workforce time waste analysis was examined over a 10-day period in January and February 2024. A yearly extrapolation model was applied to cost data. The findings revealed substantial costs linked to both PW and WTW, emphasizing the financial burden of controlled substance waste. Study data validated previous literature describing the extent of fentanyl, hydromorphone, and morphine waste while documenting significant amounts of midazolam and ketamine waste. The combined annual waste cost for the two study hospitals was estimated at US$56,557, with workforce time accounting for 36%-50% of this total cost. This study provides vital insights into the financial and operational impact of medication waste in procedural and post-procedural areas, supporting ongoing efforts to minimize waste, ensuring the safe and effective use of controlled substances. Future research should explore the impact of medication waste across diverse healthcare settings and the cost implications associated with pharmacy professionals in the waste compliance process.

Detailed multivariate comparisons of environments with mobility oriented parity

Multivariate model projections onto conditions that differ from those under which the models were calibrated can result in uncertainty owing to response extrapolation. Therefore, interpretations of models transferred to conditions not analogous to those of model calibration must be done carefully to avoid misleading conclusions. A good practice when producing model transfers is to assess the degree of dissimilarity between calibration and transfer conditions. The mobility oriented parity (MOP) metric is a tool commonly used to quantify such dissimilarities. Our study elucidated the details of MOP, and expanded the interpretability of results when transfer conditions are not analogous to those of model calibration. We implemented a more detailed characterization of non-analogous conditions previously only identified as outside calibration ranges. In addition, we quantified the sensitivity of MOP to the density and position of reference conditions in environmental space, and explored the effect of sample size on MOP results. We show that detailed characterizations of non-analogous conditions can help to determine which variables are different between the two areas and how. This information can be used to understand causes of environmental dissimilarity and to identify variables that could be excluded from ecological niche modeling owing to extrapolation risks associated with them. Our results also show that dissimilarity values calculated in comparisons offer good indicators of how reliable interpretations can be in areas with conditions not analogous to those of reference. The tools developed and implemented in this study provide a useful, quantitatively efficient means of characterizing extrapolation uncertainty in model transfer studies, a best practice when using multivariate ecological niche models to make predictions about species’ distributions. An extended version of the mobility oriented parity metric helps identify variables for which risks of model extrapolations can be expected. Distinct MOP metrics used to characterize dissimilarity show comparable general patterns in transfer areas, despite differences in values. As MOP results are influenced by background sampling size, the definition of this sample should be aimed to appropriately characterize calibration areas.

Multi-population mortality modeling with economic, environmental and lifestyle variables

AbstractThe premise of multipopulation mortality models is that pooling multiple populations can help identify more stable trends and diminish statistical noise. However, many existing models fail to contextualize mortality trends, treating them as isolated phenomena. This article introduces a comprehensive multipopulation mortality model that incorporates a broad spectrum of economic, environmental, and lifestyle factors to predict mortality trends. The factors are obtained with principal components analysis, extending current models which employ external variables beyond GDP. The model is applied to 33 countries present in the Human Mortality Database, divided into 9 clusters. Expanding the scope of covariates improves model fit for 29 countries out of 33 compared to GDP alone, and consistently outperforms the Li-Lee model. Furthermore, forecasting accuracy surpasses that of the Li-Lee model across various jump-off years and matches or exceeds models limited to GDP as a covariate. This study advances the field by demonstrating that a multipopulation approach, enriched with a wide array of covariates, significantly refines mortality forecasts, challenging the reliance on extrapolative or GDP-only models. It offers actuarial practitioners and policymakers a more nuanced tool for scenario planning, emphasizing the interconnectedness of mortality rates with broader socio-economic and environmental factors.

In Vitro Hepatic Clearance Evaluations of Per- and Polyfluoroalkyl Substances (PFAS) across Multiple Structural Categories.

Toxicokinetic (TK) assays and in vitro-in vivo extrapolation (IVIVE) models are New Approach Methods (NAMs) used to translate in vitro points of departure to exposure estimates required to reach equivalent blood concentrations. Per- and polyfluoroalkyl substances (PFAS) are a large chemical class with wide-ranging industrial applications for which only limited toxicity data are available for human health evaluation. To address the lack of TK data, a pooled primary human hepatocyte suspension model was used with targeted liquid chromatography-mass spectrometry to investigate substrate depletion for 54 PFAS. A median value of 4.52 μL/(min x million cells) was observed across those that showed significant clearance, with 35 displaying no substrate depletion. Bayesian modeling propagated uncertainty around clearance values for use in IVIVE models. Structural evaluations showed the fluorotelomer carboxylic acids were the only PFAS carboxylates showing appreciable clearance, and per- and polyfluorosulfonamides were more readily metabolized than other PFAS sulfonates. Biotransformation product prediction, using the chemical transformation simulator, suggested hydrolysis of PFAS sulfonamides to more stable sulfonic acids, which is an important consideration for exposure modeling. This effort greatly expands the PFAS in vitro toxicokinetic dataset, enabling refined TK modeling, in silico tool development, and NAM-based human health evaluations across this important set of emerging contaminants.

Hindcasting and updating Landsat-based forest structure mapping across years to support forest management and planning

Forest vegetation mapping that integrates forest inventory data with multispectral remote sensing data provides valuable geospatial products for public land management agencies, but resource managers may require rapid updating of maps as new imagery becomes available (updating) or retrospective mapping for times prior to forest inventory plot measurement (hindcasting). While forest attribute mapping using Landsat multispectral imagery is common, the accuracy of applying models outside of reference epoch to support long-term forest monitoring is not normally quantified. We examine whether a Landsat-based mapping approach can support robust, temporally consistent multivariate mapping of forest structure and composition data in support of forest management planning and landscape analysis. Specifically, we ask: how accurate forest attribute mapping was when hindcasting or updating outside of a period of time when forest inventory plot data were available (reference epoch)? In the western Cascade Mountains of Oregon and California, USA, we used the gradient nearest neighbor approach to annually impute USDA Forest Inventory and Analysis (FIA) plot data (2001–2016) to all 30-m forested pixels based on temporally smoothed Landsat multispectral imagery (1986–2021), including basal area, canopy cover, quadratic mean diameter of dominant trees, stand height, and the density of large diameter trees. We made extrapolations from models fit to a 10-year reference epoch to both earlier periods (2001–2006 hindcast) and to later period (2011–2016 update) and quantified prediction accuracies relative to models based on the full data (2001–2016). To evaluate the influence of spatial scale on hindcasting and updating, we compared full and extrapolation model predictions at pixel-level (0.09 ha) and hexagon-level (780 ha).At the plot-level, we found no strong differences between the full and extrapolation model predictions for R2 and mean error nor among predicted vs. observed regression coefficients. At the pixel-level, average differences due to hindcasting and updating were near zero, though differences varied up to 20 % across pixels. At the hexagon-level, the range in map differences was small (+/- 5 %), but hindcasting resulted in lesser forest attribute predictions. We observed greater variability in pixel-level and hexagon-level prediction differences when hindcasting or updating was temporally further away from the reference period. Using 2001 hindcast and 2016 updated maps as a case study, we found that with hindcasting and updating map differences were spatially aggregated across the study region. Our results support Landsat-based hindcasting and updating of forest attribute mapping beyond the time period covered by forest plot data. Our results suggest aggregating data to coarse spatial resolutions may minimize differences due to hindcasting and updating. Further research is needed to identify the key drivers for prediction differences to improve the accuracy of both hindcasting and updating as a basis for forest monitoring.

A novel approach to triazole fungicides risk characterization: Bridging human biomonitoring and computational toxicology

Brazil stands as the world's leading coffee producer, where the extensive use of pesticides is economically critical yet poses health and environmental risks due to their non-selective mechanisms of action. Specifically, triazole fungicides are widely used in agriculture to manage fungal diseases and are known to disrupt mammalian CYP450 and liver microsomal enzymes. This research establishes a framework for risk characterization of human exposure to triazole fungicides by internal-dose biomonitoring, biochemical marker measurements, and integration of high-throughput screening (HTS) data via computational toxicology workflows from the Integrated Chemical Environment (ICE). Volunteers from the southern region of Minas Gerais, Brazil, were divided into two groups: farmworkers and spouses occupationally and environmentally exposed to pesticides from rural areas (n = 140) and individuals from the urban area to serve as a comparison group (n = 50). Three triazole fungicides, cyproconazole, epoxiconazole, and triadimenol, were detected in the urine samples of both men and women in the rural group. Androstenedione and testosterone hormones were significantly reduced in the farmworker group (Mann-Whitney test, p < 0.0001). The data show a significant inverse association of testosterone with cholesterol, LDL, VLDL, triglycerides, and glucose and a direct association with HDL (Spearman's correlation, p < 0.05). In the ICE workflow, active in vitro HTS assays were identified for the three measured triazoles and three other active ingredients from the pesticide formulations. The curated HTS data confirm bioactivities predominantly related to steroid hormone metabolism, cellular stress processes, and CYP450 enzymes impacted by fungicide exposure at occupationally and environmentally relevant concentrations based on the in vitro to in vivo extrapolation models. These results characterize the potentially significant human health risk, particularly from the high frequency and intensity of exposure to epoxiconazole. This study showcases the critical role of biomonitoring and utility of computational tools in evaluating pesticide exposure and minimizing the risk.