Optimizing NFDRS by developing custom fuel models for wildfire risk assessment in Golestan Province, NE Iran

Hormesis: from marginalization to mainstream: A case for hormesis as the default dose-response model in risk assessment

BackgroundOccupational hazards such as solvents and noise in the electronics industry are serious. Although various occupational health risk assessment models have been applied in the electronics industry, they have only been used to assess the risks of individual job positions. Few existing studies have focused on the total risk level of critical risk factors in enterprises.MethodsTen electronics enterprises were selected for this study. Information, air samples and physical factor measurements were collected from the selected enterprises through on-site investigation, and then the data were collated and samples were tested according to the requirements of Chinese standards. The Occupational Health Risk Classification and Assessment Model (referred to as the Classification Model), the Occupational Health Risk Grading and Assessment Model (referred to as the Grading Model), and the Occupational Disease Hazard Evaluation Model were used to assess the risks of the enterprises. The correlations and differences between the three models were analyzed, and the results of the models were validated by the average risk level of all of the hazard factors.ResultsHazards with concentrations exceeding the Chinese occupational exposure limits (OELs) were methylene chloride, 1,2-dichloroethane, and noise. The exposure time of workers ranged from 1 to 11 h per day and the frequency of exposure ranged from 5 to 6 times per week. The risk ratios (RRs) of the Classification Model, the Grading Model and the Occupational Disease Hazard Evaluation Model were 0.70 ± 0.10, 0.34 ± 0.13, and 0.65 ± 0.21, respectively. The RRs for the three risk assessment models were statistically different (P < 0.001), and there were no correlations between them (P > 0.05). The average risk level of all of the hazard factors was 0.38 ± 0.18, which did not differ from the RRs of the Grading Model (P > 0.05).ConclusionsThe hazards of organic solvents and noise in the electronics industry are not negligible. The Grading Model offers a good reflection of the actual risk level of the electronics industry and has strong practicability.

Financial integrity is fundamental to the stability and sustainability of global financial systems, requiring robust internal audit mechanisms, comprehensive risk assessment models, and effective governance frameworks. This paper explores the role of an advanced internal audit risk assessment and governance model in enhancing financial integrity, addressing key challenges in financial oversight, and mitigating risks associated with fraud, regulatory non-compliance, and unethical financial practices. It begins by examining the theoretical foundations of internal auditing and governance, highlighting key models and frameworks that shape financial oversight practices. The paper then delves into the evolution of risk assessment methodologies, emphasizing the integration of artificial intelligence, predictive analytics, and data-driven auditing techniques to enhance risk detection and mitigation. A critical aspect of this study is the development of a sophisticated internal audit risk assessment model that leverages technological advancements to strengthen financial oversight. The paper outlines essential components of an effective risk assessment framework, emphasizing the role of data analytics, automated compliance monitoring, and industry benchmarks in improving financial transparency. Additionally, it presents case studies and industry best practices that demonstrate the effectiveness of enhanced risk assessment models in preventing financial irregularities. The governance framework proposed in this paper underscores the importance of corporate transparency, ethical leadership, regulatory compliance, and strong internal controls in ensuring financial integrity. It evaluates the impact of regulatory policies, including the Sarbanes-Oxley Act, Basel Accords, and anti-money laundering frameworks, on corporate governance structures. Furthermore, the study highlights the role of cybersecurity risk management and blockchain-based audit mechanisms in strengthening financial accountability. Future research opportunities in audit risk assessment and governance are also discussed, with a focus on AI-driven audit systems, blockchain transparency solutions, behavioral governance models, and global regulatory harmonization. The paper concludes by providing strategic policy recommendations for financial institutions, regulatory bodies, and corporate entities, advocating for the integration of advanced analytics, enhanced whistle-blower protections, stronger cybersecurity governance, and cross-border regulatory cooperation. By implementing these measures, organizations can fortify financial integrity, mitigate systemic risks, and build a more transparent and resilient financial ecosystem.

Risk assessment models are important tools in economic analysis of infectious disease. Risk assessment is the science of identifying and understanding hazards (unwanted events), of estimating the likelihood of these events occurring and of estimating the consequences if they do occur. Disease outcome tree models can organize medical data to estimate the probability of lifetime outcomes due to food-borne pathogens. Economists can then estimate public health costs for these outcomes. If the damage to society is significant, a cost-benefit analysis of public and private control options can be conducted. After the options for controlling pathogens in food production and distribution are determined, scenario models can be combined with probabilistic risk assessment (PRA) models to estimate quantitatively the impact of alternative risk-reducing options. Both individual companies seeking to control pathogens better and the public regulators setting priorities among food-borne hazards can use these risk assessment models. The ‘market failure’, due to limited information about the presence of pathogens in food, has a strong impact on economic incentives. Some remedies to the information problems are suggested. How economic incentives affect investment in both short- and long-term control options is also discussed. The various risk assessment models presented here are applicable to economic analysis of any infectious disease.

When the effect of two soil erosion features, in this study, gully headcuts (GHs) and pipe collapses (PCs), is investigated jointly, a new comprehensive concept related to their controlling factors can be found. The objective of this paper to evaluate susceptibility to these two features in the hilly region of the Golestan Province (NE Iran), which may be helpful in land management and the sustainable development of the region. The maps of the controlling factors of GHs and PCs were constructed and the random forest algorithm was used to prioritize the factors controlling the occurrence of these two soil erosion features. The GHs and PCs susceptibility maps were prepared using the random forest model in the R software and validated applying the receiver operating characteristic curves, fourfold plot, and Cohen's kappa index, whereas the susceptibility map of the two soil erosion features was prepared by overlapping these maps. The results of factor importance analysis have indicated that land use, slope, and silt content are the most important factors in the occurrence of PCs, whereas slope gradient, silt content, and distance to streams are the most important in GHs occurrence. This means that steep and uncultivated slopes in the study area are most susceptible to GHs and PCs. The susceptibility models of GHs and PCs have excellent accuracy, that is, the area under the receiver operating characteristic curve values of GHs and PCs models were 0.960 and 0.935, respectively. The susceptibility map of GHs and PCs analysed jointly has shown that 66% of the area is not susceptible to any of these soil erosion features, whereas 15% is susceptible to both of them. The places susceptible to the two types of soil erosion features (GHs and PCs) indicate the areas where there is a high probability of GHs retreat due to the PCs. This study has confirmed that it is feasible to forecast the spatial behaviour of GHs and PCs occurrence and development. This is particularly preferred when the developed methods are applied for the susceptibility evaluation under various mitigation scenarios.

Study on risk assessment models for the aggregation of vehicles transporting hazardous chemicals

Obesity and Cumulative Inflammatory Burden: A Valuable Risk Assessment Parameter in Caring for Dental Patients

Development of good modelling practice for physiologically based pharmacokinetic models for use in risk assessment: The first steps

Absorption modeling is an excellent strategic fit to perform a risk assessment for relative bioavailability (RBA) studies as it provides direct input into the question that is at the core of the RBA decision, namely, how does the absorption of the test drug product compare to the reference and is it likely to be different enough to justify an RBA study. The main limitation to absorption modeling in risk assessment is the inherent uncertainty associated with modeling. The extent to which the absorption modeling is integrated into the risk assessment should depend on the level of confidence in the modeling. It is difficult, however, to quantify the level of confidence on a case by case basis. The effective application of absorption modeling for RBA risk assessment therefore requires a general understanding of when modeling is expected to be reliable and also how to build reliability directly into the modeling. This paper describes a framework for effective modeling in RBA risk assessment that is based on four fundamental building blocks: (1) relate severity of drug product change and API properties to reliability of modeling, (2) use critical model variables to express the critical differences in the drug products, (3) generate a fraction-absorbed response surface expressed in terms of the critical model variables to evaluate the relative performance of the drug products, and (4) tie the first three building blocks together by following good model building practices that assure the highest quality model is built. The building blocks are demonstrated by a simple but common example of a change in solid state from free base to HCl salt.

A series of physiologically based toxicokinetic (PBTK) models for tebuconazole were developed in four species, rat, rabbit, rhesus monkey, and human. The developed models were analyzed with respect to the application of the models in higher tier human risk assessment, and the prospect of using such models in risk assessment of cumulative and aggregate exposure is discussed. Relatively simple and biologically sound models were developed using available experimental data as parameters for describing the physiology of the species, as well as the absorption, distribution, metabolism, and elimination (ADME) of tebuconazole. The developed models were validated on in vivo half-life data for rabbit with good results, and on plasma and tissue concentration-time course data of tebuconazole after i.v. administration in rabbit. In most cases, the predicted concentration levels were seen to be within a factor of 2 compared to the experimental data, which is the threshold set for the use of PBTK simulation results in risk assessment. An exception to this was seen for one of the target organs, namely, the liver, for which tebuconazole concentration was significantly underestimated, a trend also seen in model simulations for the liver after other nonoral exposure scenarios. Possible reasons for this are discussed in the article. Realistic dietary and dermal exposure scenarios were derived based on available exposure estimates, and the human version of the PBTK model was used to simulate the internal levels of tebuconazole and metabolites in the human body for these scenarios. By a variant of the models where the R(-)- and S(+)-enantiomers were treated as two components in a binary mixture, it was illustrated that the inhibition between the two tebuconazole enantiomers did not affect the simulation results for these realistic exposure scenarios. The developed models have potential as an important tool in risk assessment.

The available inhalation toxicity information for acrylic acid (AA) suggests that lesions to the nasal cavity, specifically olfactory degeneration, are the most sensitive end point for developing a reference concentration (RfC). Advances in physiologically based pharmacokinetic (PBPK) modeling, specifically the incorporation of computational fluid dynamic (CFD) models, now make it possible to estimate the flux of inhaled chemicals within the nasal cavity of experimental species, specifically rats. The focus of this investigation was to apply an existing CFD-PBPK hybrid model in the estimation of an RfC to determine the impact of incorporation of this new modeling technique into the risk assessment process. Information provided in the literature on the toxicity and mode of action for AA was used to determine the risk assessment approach. A comparison of the approach used for the current U.S. Environmental Protection Agency (U.S. EPA) RfC with the approach using the CFD-PBPK hybrid model was also conducted. The application of the CFD-PBPK hybrid model in a risk assessment for AA resulted in an RfC of 79 ppb, assuming a minute ventilation of 13.8 l/min (20 m(3)/day) in humans. This value differs substantially from the RfC of 0.37 ppb estimated for AA by the U.S. EPA before the PBPK modeling advances became available. The difference in these two RfCs arises from many factors, with the main difference being the species selected (mouse vs. rat). The choice to conduct the evaluation using the rat was based on the availability of dosimetry data in this species. Once these data are available in the mouse, an assessment should be conducted using this information. Additional differences included the methods used for estimating the target tissue concentration, the uncertainty factors (UFs) applied, and the application of duration and uncertainty adjustments to the internal target tissue dose rather than the external exposure concentration.

Toxicokinetics is the study of kinetics of absorption, distribution, metabolism, and excretion of a xenobiotic under the conditions of toxicity evaluation. Conventional toxicokinetics uses the hypothetical compartments, and the model is composed of rate equations that describe the time course of drug and chemical disposition. The utility of toxicokinetics in toxicity evaluation and interpretation of animal toxicology data is emerging as an important tool in product discovery and development. With implementation of the International Conference on Harmonization (ICH) guidelines on systemic exposure and dose selection, toxicokinetics have been integrated in routine toxicity evaluations. Although traditional compartmental/noncompartmental models are generally adequate for assessing systemic exposure, they are unable to the predict time course of drug disposition in target tissues and often fail to relate systemic drug levels to a biological response. Physiologically based toxicokinetic (PB-TK) models address this deficiency of traditional compartmental models. PB-TK models are the kinetic models of the uptake and disposition of chemicals based on rates of biochemical reactions, physiological and anatomical characteristics. These models, when developed appropriately, can predict target organ drug distribution in different species under variety of conditions. This minireview discusses the basic principles, and applications of traditional compartmental toxicokinetic and physiologically based toxicokinetics (PB-TK) models in drug development and risk assessment. Special emphasis will be placed on discussion related to interpretation of the ICH guidelines related to toxicokinetics and the utility of toxicokinetics data in dose selection for toxicity and carcinogenicity studies. The utility of PB-TK models in risk assessment of methylene chloride, vinyl chloride, retinoic acid, dioxin, and inhaled organic esters is discussed.

Biologically based dose-response (BBDR) models predict health outcomes (response) resulting from the presence of a toxicant at a biological target (dose). The benefits of BBDR models are many, and research programs are increasingly focusing on mechanistic research to support model development; however, progress has been slow. Impediments to progress include the complexity of dose response modeling, the need for a multidisciplinary team and consistent funding support, and difficulty in identifying and extracting the needed data. Of immediate concern is the lack of transparency of published models to the supporting data and literature, difficulty in accessing model code and simulation conditions sufficient to allow independent replication of results, and absence of well-defined quality criteria. Suggestions are presented to improve the development and use of BBDR models in risk assessment and to address the above limitations. Examples from BBDR models for methylmercury neurotoxicity and 5-fluorouracil embryotoxicity are presented to illustrate the suggestions including what kinds of databases are needed to support model development and transparency, quality assurance for modeling, and how the internet can advance database development and collaboration within the biological modeling community.

Advances in the technology of human cell and tissue culture and the increasing availability of human tissue for laboratory studies have led to the increased use of in vitro human tissue models in toxicology and pharmacodynamics studies and in quantitative modeling of metabolism, pharmacokinetic behavior, and transport. In recognition of the potential importance of such models in toxicological risk assessment, the Society of Toxicology sponsored a workshop to evaluate the current status of human cell and tissue models and to develop consensus recommendations on the use of such models to improve the scientific basis of risk assessment. This report summarizes the evaluation by invited experts and workshop attendees of the current status of such models for prediction of human metabolism and identification of drug-drug interactions, prediction of human toxicities, and quantitative modeling of pharmacokinetic and pharmaco-toxicodynamic behavior. Consensus recommendations for the application and improvement of current models are presented.

<b>Background:</b> Usual pulmonary arterial hypertension (PAH) risk assessment (i.e., used to predict survival) models are based on statistical analysis which do not consider the relationship between variables, assuming their linear association with outcome. <b>Aims:</b> To be free of this bias, we aimed to build a PHORA2.0 invasive and noninvasive risk assessment model using machine learning method. <b>Methods:</b> Clinical data set from 7 PAH trials (GRIPHON, SERAPHIN, EARLY, COMPASS-2 and 3, MAESTRO, TRANSIT-1) with 147 variables (clinical, biological, hemodynamic) were harmonized. Random Forrest (RF) was used to identify variable importance in predicting outcome. To build the Bayesian Network (BN), variables were selected based on both clinical knowledge, RF minimal depth (selected when &lt;8). The outcome was 1-year survival. Both noninvasive and invasive BN were then validated using 10-fold cross validation. <b>Results:</b> 2,870 patients were included (mean age 43 years old, 77% female, 50% idiopathic or heritable PAH) with a 1-year mortality of 14%. The invasive and noninvasive BN included 13 and 12 variables (8 overlaps). AUC were 0.83 and 0.80, respectively. BN shows the existence (arrow direction) and weight (arrow thickness) of the relationships between these variables and with the outcome. <b>Conclusion:</b> BN provides powerful method to build PAH risk assessment model. Although important, invasive parameters provided minimal discrimination increase. Future addition of genomic and imaging data may continue to enhance noninvasive PHORA 2.0 model.