Risk Assessment Approach Research Articles

Predictions of tissue concentrations of myclobutanil, oxyfluorfen, and pronamide in rat and human after oral exposures via GastroPlusTM physiologically based pharmacokinetic modelling

ABSTRACT Heritage agrochemicals like myclobutanil, oxyfluorfen, and pronamide, are extensively used in agriculture, with well-established studies on their animal toxicity. Yet, human toxicity assessment relies on conventional human risk assessment approaches including the utilization of animal-based ADME (Absorption, Distribution, Metabolism, and Excretion) data. In recent years, Physiologically Based Pharmacokinetic (PBPK) modelling approaches have played an increasing role in human risk assessment of many chemicals including agrochemicals. This study addresses the absence of PBPK-type data for myclobutanil, oxyfluorfen, and pronamide by generating in vitro data for key input PBPK parameters (Caco-2 permeability, rat plasma binding, rat blood to plasma ratio, and rat liver microsomal half-life), followed by generation of PBPK models for these three chemicals via the GastroPlusTM software. Incorporating these experimental input parameters into PBPK models, the prediction accuracy of plasma AUC (area under curve) was significantly improved. Validation against rat oral administration data demonstrated substantial enhancement. Steady-state plasma concentrations (Css) of pronamide aligned well with published data using measured PBPK parameters. Following validation, parent-based tissue concentrations for these agrochemicals were predicted in humans and rats after single or 30-day repeat exposure of 10 mg/kg/day. These predicted concentrations contribute valuable information for future human toxicity risk assessments of these agrochemicals.

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

Abstract Deep learning has changed the approach of urban environmental risk assessment and management. These methods enable solid models for large data sets, enabling early identification, prediction, and description of environmental risks. The current work analyses the advances in deep learning for urban environmental hazard assessments and disaster studies to provide monitoring, management, and mitigation measures. It reports the improvement in self-supervised learning, transformer architectures, persistent learning, attention mechanisms, adversarial robustness, associated learning, meta-learning, and multimodal learning within the domain of urban environmental hazard analysis. These approaches allow the creation of robust models for handling vast data volumes, facilitating early detection, prediction, and characterisation of diverse environmental threats. This trends analysis for urban applications will bring insights for connecting deep-learning models for effective and proactive approaches to tackle urban environmental hazards and disasters.

Systemic risk capability assessment methodology: A new approach for evaluating inter-connected risks in seaport ecosystems

The ever-increasing systemic risks posed by disasters and the impacts of climate change have emerged as formidable challenges that demand comprehensive assessment and understanding. This study endeavours to address this critical need by introducing the innovative Systemic Risk Capability Assessment (SRCA) methodology. Unlike traditional risk assessment approaches, SRCA is uniquely designed to not only assess systemic risk but also operationalize its management, making it particularly suited for safeguarding critical infrastructure, with a specific focus on seaports. The SRCA methodology, offers a quasi-quantitative framework that goes beyond conventional risk assessment, enabling a deeper understanding of the dynamics and interdependencies inherent to seaport ecosystems. In the pursuit of demonstrating the practicality and versatility of the SRCA methodology, this study applies the model to two hypothetical ports, accompanied by the utilization of synthetic data. By doing so, it elucidates the step-by-step analysis facilitated by SRCA, highlighting its potential for enhancing the resilience of seaports to systemic risks. The results underscore the limitations of traditional risk assessment methodologies when confronted with the complexity of systemic risks in seaports, thereby emphasizing the significance of the proposed SRCA methodology. In conclusion, this research contributes significantly to the field of systemic risk management, particularly within the critical infrastructure domain.

Financial Performance and Bankruptcy Risk Analysis: An Application on Private Health Insurance Companies in Turkey

This research evaluates the correlation between the financial performances of seven insurance companies operating in Turkey from 2018 to 2022 and their risk of bankruptcy. In the study, data obtained from the year-end financial statements of the companies were used, weights were assigned to the criteria determined by the ENTROPY methodology, and the performance rankings of the companies were obtained using the WASPAS method. The Altman-Z model was applied to determine the risk of bankruptcy. The decisive criteria in the financial performance ranking are profitability ratios such as Asset Profitability, Equity Profitability, and Net Profit Margin. While Turkey Insurance and Anadolu Insurance's leadership positions in the sector were identified, Ray Insurance and Mapfre Insurance were determined as the companies showing the weakest performance. Although the research shows that financial performance affects the risk of bankruptcy, it also reveals that it is not the sole factor in determining the likelihood of bankruptcy. In this context, the evaluation of financial performance in the insurance industry should be considered as part of a more holistic risk assessment approach.

New Approach Methodologies (NAMs) for ad hoc human health risk assessment of food and non-food products - Proceedings of a workshop

RIVM convened a workshop on the use of New Approach Methodologies (NAMs) for the ad hoc human health risk assessment of food and non-food products. Central to the workshop were two case studies of marketed products with a potential health concern: the botanical Tabernanthe iboga which is used to facilitate mental or spiritual insight or to (illegally) treat drug addiction and is associated with cardiotoxicity, and dermal creams containing female sex hormones, intended for use by perimenopausal women to reduce menopause symptoms without medical supervision. The workshop participants recognized that data from NAM approaches added valuable information for the ad hoc risk assessment of these products, although the available approaches were inadequate to derive health-based guidance values. Recommendations were provided on how to further enhance and implement NAM approaches in regulatory risk assessment, specifying both scientific and technical aspects as well as stakeholder engagement aspects.

Risk Prioritization in A Manufacturing Project with Fuzzy SWARA and Fuzzy MOORA Methods

Businesses must constantly change and develop to keep up with customer needs and changes in the market. Not being able to adapt to change or not being ready enough can bring risks. These changes and risks can affect both the process and the outputs of the process. It is essential to be prepared for the possible risks experienced in the operations, especially in project-based, production-to-order enterprises, to meet the customer's demands and to comply with the delivery date. Businesses must implement an effective risk assessment process to achieve this. This study implemented a risk prioritization for a new business project that produces energy storage systems by having to order. Changes must be made in the production process for an additional feature to be added to the product in line with the customer's request. The risks posed by the change and the short deadline were analyzed and prioritized. A new model was developed by developing the classical Decision Matrix Risk Assessment (DMRA) method, one of the risk assessment methods to evaluate the ten identified risks. The "severity" component of the classical DMRA method is divided into three sub-components (the severity of delivery time, the severity of cost, and the severity of quality) to be evaluated more specifically. Moreover, since the weights of these sub-components on the risks are different, the components are weighted with the Fuzzy SWARA method. The weights obtained from the Fuzzy SWARA method are used in the Fuzzy MOORA method, and the risks are prioritized. Thus, the effectiveness of the classical risk assessment approach has increased by detailing the "severity" component of the risk assessment components and weighting and prioritizing the components using multi-criteria decision-making (MCDM) techniques.

Optimizing natural gas pipeline risk assessment using hybrid fuzzy bayesian networks and expert elicitation for effective decision-making strategies

Natural gas pipelines are susceptible to external and internal risk factors, such as corrosion, environmental conditions, external interferences, construction and design faults, and equipment failures. Bayesian Networks (BN) is a promising risk assessment approach widely used to evaluate these risk factors. One of BN's inherent limitations is its inability to accurately capture statistical dependencies and causal relationships, which can be overcome by incorporating expert elicitation into BN. To account for uncertainty and vagueness in assessing pipeline failure risks, fuzzy set theory (FST) can be combined with BN, commonly known as Fuzzy Bayesian Networks (FBN). This study developed an FBN framework that uses linguistic variables to calculate fuzzy probability (FPr) through domain expert elicitation, and crisp probabilities (CPr) are computed using historical incident data from the Pipeline and Hazardous Materials Safety Administration (PHMSA). Based on the findings from the case study of the Midwest region of the USA, external factors, i.e., third-party interference, outside force, and other incidents, significantly impact pipeline performance and reliability. Diagnosis inference indicates that in the Midwest region of the USA, pipeline material and age are critical factors leading to corrosion failure by threatening pipeline integrity. The findings from this study suggested that a targeted risk mitigation strategy is paramount for minimizing the risks associated with pipeline networks.

Clinical application value of Ultrafast Pulse Wave Velocity in early cardiovascular injury of Immunoglobulin A nephropathy.

To investigate the application and the related influencing factors of ultrafast pulse wave velocity (ufPWV) in theevaluation of carotid artery elasticity in patients with Immunoglobulin A nephropathy (IgAN). A totalof 156 IgAN patients and 50 healthy individuals were selected as the control group. The ufPWV technique was employed tomeasure carotid arterial elasticity parameters, including carotid intima-media thickness (cIMT), pulse wave velocity at thebeginning of systole (BS-PWV) and pulse wave velocity at the end of systole (ES-PWV). Across the three groups(control group, IgAN patients with normal renal function, and IgAN patients with renal dysfunction) there was an increasingtrend observed in cIMT, BS-PWV, and ES-PWV. Additionally, ES-PWV exhibited higher sensitivity than BS-PWV. Correlationanalysis revealed that BS-PWV and ES-PWV were positively correlated with age, body mass index (BMI), systolic bloodpressure (SBP), high-sensitivity C-reactive protein (hs-CRP), creatinine (Cr), blood urea nitrogen (BUN), and uric acid (UA)and negatively correlated with estimated glomerular filtration rate (eGFR). The ufPWV technique allows for therapid and direct measurement of arterial elasticity parameters in the neck and represents a novel approach for the early diagnosisand quantitative assessment of arterial stiffness risk in IgAN patients.

Metallomic mapping of gut and brain in heavy metal exposed earthworms: A novel paradigm in ecotoxicology

This study explored the uptake of lead in the epigeic earthworm Dendrobaena veneta exposed to 0, 1000, and 2500 μg Pb/g soil. The soil metal content was extracted using strong acid digestion and water leaching, and analysed by means of Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to estimate absolute and bioavailable concentrations of metals in the soil. The guts and heads of lead-exposed earthworms were processed into formalin-fixed and paraffin embedded sections for high-resolution multi-element metallomic imaging via Laser Ablation ICP-MS (LA-ICP-MS). Metallomic maps of phosphorus, zinc, and lead were produced at 15-μm resolution in the head and gut of D. veneta. Additional 4-μm resolution metallomic maps of the earthworm brains were taken, revealing the detailed localisation of metals in the brain. The Pb bioaccumulated in the chloragogenous tissues of the earthworm in a dose-dependent manner, making it possible to track the extent of soil contamination. The bioaccumulation of P and Zn in earthworm tissues was independent of Pb exposure concentration. This approach demonstrates the utility of LA-ICP-MS as a powerful approach for ecotoxicology and environmental risk assessments.

ATT&CK-based Advanced Persistent Threat attacks risk propagation assessment model for zero trust networks

In recent years, the growing frequency and intensity of Advanced Persistent Threats (APTs) have significantly undermined the legitimacy and financial stability of government agencies, enterprises, and other entities. Moreover, these attacks have shown the inherent vulnerabilities in conventional border defense strategies. The emergence of the zero trust network architecture can be attributed to the increasing complexity of the cyber threat landscape. With the application of risk assessment, this paper effectively tackles the challenges posed by conventional network defense limitations and enhances the efficiency of the access control decision-making process. Nevertheless, the existing risk assessment approaches primarily focus on conventional security assessment objectives, which exhibits a deficiency in the ability to dynamically assess APT attacks. The Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) Framework introduced in this paper is a novel approach to mitigating APT attacks. This paper aims to mine and analyze the frequent item set and correlation of cyber threat penetration attack techniques. The paper also intends to construct an attack technique relationship diagram and develop a tactical prediction model for cyber threat penetration attacks using the Markov chain model. Finally, our study aims to establish a risk propagation model for APT threats based on the aforementioned model. The approach presented in this paper significantly enhances the capacity of zero trust networks in addressing sophisticated cyber threats.

Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs : A Risk Prediction Study.

Guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) recommend a risk calculator (ASCVD risk score) to estimate 10-year risk for major adverse cardiovascular events (MACE). Because the necessary inputs are often missing, complementary approaches for opportunistic risk assessment are desirable. To develop and test a deep-learning model (CXR CVD-Risk) that estimates 10-year risk for MACE from a routine chest radiograph (CXR) and compare its performance with that of the traditional ASCVD risk score for implications for statin eligibility. Risk prediction study. Outpatients potentially eligible for primary cardiovascular prevention. The CXR CVD-Risk model was developed using data from a cancer screening trial. It was externally validated in 8869 outpatients with unknown ASCVD risk because of missing inputs to calculate the ASCVD risk score and in 2132 outpatients with known risk whose ASCVD risk score could be calculated. 10-year MACE predicted by CXR CVD-Risk versus the ASCVD risk score. Among 8869 outpatients with unknown ASCVD risk, those with a risk of 7.5% or higher as predicted by CXR CVD-Risk had higher 10-year risk for MACE after adjustment for risk factors (adjusted hazard ratio [HR], 1.73 [95% CI, 1.47 to 2.03]). In the additional 2132 outpatients with known ASCVD risk, CXR CVD-Risk predicted MACE beyond the traditional ASCVD risk score (adjusted HR, 1.88 [CI, 1.24 to 2.85]). Retrospective study design using electronic medical records. On the basis of a single CXR, CXR CVD-Risk predicts 10-year MACE beyond the clinical standard and may help identify individuals at high risk whose ASCVD risk score cannot be calculated because of missing data. None.

Antibiotic resistome in landfill leachate and impact on groundwater

Landfill leachate is a hotspot in antibiotic resistance development. However, little is known about antibiotic resistome and host pathogens in leachate and their effects on surrounding groundwater. Here, metagenomic sequencing was used to explore profiles, host bacteria, environmental risks and influencing factors of antibiotic resistome in raw and treated leachate and surrounding groundwater of three landfills. Results showed detection of a total of 324 antibiotic resistance genes (ARGs). The ARGs conferring resistance to multidrug (8.8 %–25.7 %), aminoglycoside (13.1 %–39.2 %), sulfonamide (10.0 %–20.9 %), tetracycline (5.7 %–34.4 %) and macrolide-lincosamide-streptogramin (MLS, 5.3 %–29.5 %) were dominant in raw leachate, while multidrug resistance genes were the major ARGs in treated leachate (64.1 %–83.0 %) and groundwater (28.7 %–76.6 %). Source tracking analysis suggests non-negligible influence of leachate on the ARGs in groundwater. The pathogens including Acinetobacter pittii, Pseudomonas stutzeri and P. alcaligenes were the major ARG-carrying hosts. Variance partitioning analysis indicates that the microbial community, abiotic variables and their interaction contributed most to the antibiotic resistance development. Our results shed light on the dissemination and driving mechanisms of ARGs from leachate to the groundwater, indicating that a comprehensive risk assessment and efficient treatment approaches are needed to deal with ARGs in landfill leachate and nearby groundwater. Environmental implicationsAntibiotic resistance genes are found abundant in the landfill sites, and these genes could be disseminated into groundwater via leaching of wastewater and infiltration of leachate. This results in deterioration of groundwater quality and human health risks posed by these ARGs and related pathogens. Thus measures should be taken to minimize potential negative impacts of landfills on the surrounding environment.

Critical loads for alkalization in terrestrial ecosystems

Critical loads are a risk assessment approach that has supported large decreases in atmospheric acidic deposition globally. In Canada, SOx emissions fell by approximately 70 % between 1990 and 2021, whereas total particulate matter (TPM) emissions increased by about 40 %, mostly after 2010. Base cations are a major component of TPM, and critical load models consider base cation deposition as beneficial to ecosystems insomuch as it reduces the risk of acidification. However, close to point sources, high levels of alkaline dust deposition have altered soil chemistry and caused an undesirable shift in ecosystem state; something that critical loads are designed to prevent. In this study, the simple mass balance model (SMB) was modified with the objective of preventing base cation accumulation in soil above an acceptable threshold. The concept was applied to a forested site close to large emission sources of sulphur, nitrogen, and base cations in the Oil Sands region of Alberta, Canada. At this site, base cation leaching measured at 25 cm was approximately three times higher than estimated background leaching and exceeded combined SO4 + NO3 leaching. The critical load for alkalization was exceeded under each scenario considered in this study, although the exceedance was marginal if all N in current deposition was assumed to leach from soil. While this framework can easily be applied to regional and national critical load efforts, the main uncertainties of the proposed approach include base cation deposition estimates, assumptions regarding the behavior of N in soil, the selection of an appropriate Alkle(crit) and the long-term immobilization of deposited base cations in soil.

Agent-based risk modeling of domino effects in urban LNG stations

As the transfer hub between natural gas and users, urban LNG stations (ULSs) are the important link of the whole LNG industry chain. Due to special geographical location and various process equipment, potential risks in ULSs, especially domino effect accidents, usually lead to catastrophic consequences and social impacts. In this study, a dynamic risk assessment model for analyzing domino effects in urban LNG stations (DRADL) based on agent modelling and simulation is proposed. Tanks and tank cars as well as vaporizers and boilers in ULSs are modeled. A set of physical process and effect models are employed as the simulation process in this paper. Considering the lightweight nature and narrow flammability limit of LNG vapor cloud, a cyclic process is applied to judge the consequences of vapor cloud dispersion, which could avoid overestimating the results. A real urban LNG station is taken as a case study for modeling its potential domino effect risk. The results demonstrate the effectiveness and the advantages of the proposed ABMS approach for LNG dynamic risk assessment. The proposed DRADL model takes into account the combined effects of explosion and fire and LNG characteristics, which can provide guidance for risk management and rescue operation of domino effect accidents in urban LNG stations.

A predictive approach for myocardial infarction risk assessment using machine learning and big clinical data

Myocardial infarction is one of the most common cardiovascular diseases in emergency departments. Early prevention of this dangerous condition significantly impacts public health and considerable socioeconomic outcomes. The emergence of electronic health records (EHR) and the availability of real-world clinical data have provided opportunities to improve the quality and efficiency of healthcare by using artificial intelligence tools. In this study, we focus on the early recognition of risk factors, which can provide valuable information for early prediction of myocardial infarction and promoting a healthy life. Based on a big clinical dataset, we develop a predictive analytics approach for myocardial infarction. A vital step in efficient prediction is assessing the significance of input features, their relationships and their contributions to the disease. Therefore, we adopted statistical techniques, principal component analysis (PCA) and feature engineering. To reveal patterns and insights on our dataset, we implemented machine learning (ML) models varying from classical to more sophisticated: decision trees (DT), random forests (RF), gradient boosting algorithms (GBoost, LightGBM, CatBoost, and XGBoost) and deep neural networks (DNN). The imbalance-data issue is tackled by employing random under-sampling technique. The light gradient boosting model (LightGBM) with feature engineering on the balanced dataset is the best prediction performance achieved in this study.

Meta-metabolomic responses of river biofilms to cobalt exposure and use of dose-response model trends as an indicator of effects

The response of the meta-metabolome is rarely used to characterize the effects of contaminants on a whole community. Here, the meta-metabolomic fingerprints of biofilms were examined after 1, 3 and 7 days of exposure to five concentrations of cobalt (from background concentration to 1 × 10−5 M) in aquatic microcosms. The untargeted metabolomic data were processed using the DRomics tool to build dose-response models and to calculate benchmark-doses. This approach made it possible to use 100% of the chemical signal instead of being limited to the very few annotated metabolites (7%). These benchmark-doses were further aggregated into an empirical cumulative density function. A trend analysis of the untargeted meta-metabolomic feature dose-response curves after 7 days of exposure suggested the presence of a concentration range inducing defense responses between 1.7 × 10−9 and 2.7 × 10−6 M, and of a concentration range inducing damage responses from 2.7 × 10−6 M and above. This distinction was in good agreement with changes in the other biological parameters studied (biomass and chlorophyll content). This study demonstrated that the molecular defense and damage responses can be related to contaminant concentrations and represents a promising approach for environmental risk assessment of metals.

DeepRisk: A deep learning approach for genome-wide assessment of common disease risk

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant interest. Although widely applied, traditional polygenic risk scoring methods fall short, as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms (SNPs). This presents a limitation, as genetic diseases often arise from complex interactions between multiple SNPs. To address this challenge, we developed DeepRisk, a biological knowledge-driven deep learning method for modeling these complex, nonlinear associations among SNPs, to provide a more effective method for scoring the risk of common diseases with genome-wide genotype data. Evaluations demonstrated that DeepRisk outperforms existing PRS-based methods in identifying individuals at high risk for four common diseases: Alzheimer's disease, inflammatory bowel disease, type 2 diabetes, and breast cancer.

Challenges and opportunities in collaborative approaches to responding to intimate partner violence: insights from social workers and police in Sweden

ABSTRACT This qualitative study explores the opportunities and challenges of collaboration experienced by social workers and police officers when dealing with cases of intimate partner violence (IPV) and stalking. The study aims to examine their collaborative approaches in risk assessment and risk management by identifying the structures, supports, and foundations crucial for effective collaboration. Our data, collected from twelve interviews and one focus group interview with social workers and police officers, reveals that collaboration was facilitated by assigning specific roles to involved parties, proximity, structure and professionalism. These key factors emerged as crucial and contributing to the effectiveness of the collaborative efforts. Practitioners should consider integrating these key elements into their practices to enhance and improve collaboration when addressing cases of IPV and stalking. The study underscores the need for a well-defined framework and support structures to optimise the collaborative response to such complex and sensitive issues.

Addition of selected cereal grains as non-dairy ingredients to dairy products: A microbiological risk assessment approach

The addition of cereal grains to dairy products in the dairy industry has the potential to contaminate final products with pathogenic and spoilage microorganisms. In this study, the microbial risks involved in the addition of cereal grains to dairy products with low, intermediate, and high-water activity/moisture content were assessed using a semi-quantitative risk assessment method.The results showed that the most critical microbiological hazard in the selected cereal grains is Bacillus cereus (B. cereus) due to its ability to form spores and persist in cereal grains. The addition of cereal grains to dairy products with high water activity/moisture content such as liquid breakfast products were found to pose the highest theoretical risk, and processing mitigations, such as UHT, would need to be implemented. The results of this study have identified some knowledge gaps in conducting risk assessments and have also provided background information about the microbial risks involved in the addition of cereal grains to dairy products.

Global multi-hazard risk assessment in a changing climate

Natural hazards pose significant risks to people and assets in many regions of the world. Quantifying associated risks is crucial for many applications such as adaptation option appraisal and insurance pricing. However, traditional risk assessment approaches have focused on the impacts of single hazards, ignoring the effects of multi-hazard risks and potentially leading to underestimations or overestimations of risks. In this work, we present a framework for modelling multi-hazard risks globally in a consistent way, considering hazards, exposures, vulnerabilities, and assumptions on recovery. We illustrate the approach using river floods and tropical cyclones impacting people and physical assets on a global scale in a changing climate. To ensure physical consistency, we combine single hazard models that were driven by the same climate model realizations. Our results show that incorporating common physical drivers and recovery considerably alters the multi-hazard risk. We finally demonstrate how our framework can accommodate more than two hazards and integrate diverse assumptions about recovery processes based on a national case study. This framework is implemented in the open-source climate risk assessment platform CLIMADA and can be applied to various hazards and exposures, providing a more comprehensive approach to risk management than conventional methods.