Users' perception of the OH-EpiCap evaluation tool based on its application to nine national antimicrobial resistance surveillance systems.

Introduction: Antimicrobial resistance (AMR) is a One Health (OH) challenge. To achieve or maintain an effective and efficient AMR surveillance system, it is crucial to evaluate its performance in meeting the proposed objectives, while complying with resource restrictions. The OH-EpiCap tool was created to evaluate the degree of compliance of hazard surveillance activities with essential OH concepts across the following dimensions: organization, operational activities, and impact of the surveillance system. We present feedback on the application of the OH-EpiCap tool from a user's perspective, based on the use of the tool to evaluate nine national AMR surveillance systems, each with different contexts and objectives. Methods: The OH-EpiCap was assessed using the updated CoEvalAMR methodology. This methodology allows the evaluation of the content themes and functional aspects of the tool and captures the user's subjective experiences via a strengths, weaknesses, opportunities, and threats (SWOT) approach. Results and Discussion: The results of the evaluation of the OH-EpiCap are presented and discussed. The OH-EpiCap is an easy-to-use tool, which can facilitate a fast macro-overview of the application of the OH concept to AMR surveillance. When used by specialists in the matter, an evaluation using OH-EpiCap can serve as a basis for the discussion of possible adaptations of AMR surveillance activities or targeting areas that may be further investigated using other evaluation tools.

Helping to heal nature and ourselves through human-rights-based and gender-responsive One Health.

The health of our planet and humanity is threatened by biodiversity loss, disease and climate crises that are unprecedented in human history, driven by our insatiable consumption and unsustainable production patterns, particularly food systems. The One Health approach is a pathway to synergistically addressing outcomes in term of health and sustainability, but gender issues at the One Health and biodiversity nexus are largely ignored. By examining the roles and responsibilities of Indigenous and Local People, and especially women, in conserving natural resources, and the social costs of living at the Human-Animal-Environment interface under current conservation strategies, we show that women bear a disproportionate health, poverty and climate burden, despite having pivotal roles in conserving biodiversity. To mitigate risks of emerging infectious diseases, food insecurity and climate change impacts, a gender perspective has previously been proposed, but implementation lags behind. Endemic zoonotic diseases, human-wildlife conflict and environmental pollution lack gender-sensitive frameworks. We demonstrate that women can be powerful agents for change at all levels of society, from communities to businesses, and policy-making institutions, but gender inequalities still persist. We develop a framework for mainstreaming a gender-responsive and rights-based One Health approach, in order to heal ourselves and nature. Using a leverage-points perspective, we suggest a change of paradigm, from the pursuit of GDP and over-consumption, to a focus on human well-being and their reconnection with healthy environments, using a One Health understanding of nature and health. We recommend learning from Indigenous People to re-position ourselves within nature and to better conserve biodiversity. We also propose integration of gender equity in leadership, the respect of human rights, women's rights (access to health care, healthy food, land tenure, natural resources, education, and economic opportunities), and the rights of nature, through the implementation of gender-responsive and rights-based One Health Action Plans, at policy-making level, in the private sector and the civil society. As the COVID-19 pandemic continues to unveil deep socio-economic inequities in the wealthiest economies and the vital role of nature in supporting our health, we argue to seize this opportunity to build back better and improve resilience and sustainability by using a gender-responsive and rights-based One Health approach.

Probabilistic approach for assessing cancer risk due to benzo[a]pyrene in barbecued meat: Informing advice for population groups.

BACKGROUND: Consumption of meat prepared by barbecuing is associated with risk of cancer due to formation of carcinogenic compounds including benzo[a]pyrene (BaP). Assessment of a population's risk of disease and people's individual probability of disease given specific consumer attributes may direct food safety strategies to where impact on public health is largest. The aim of this study was to propose a model that estimates the risk of cancer caused by exposure to BaP from barbecued meat in Denmark, and to estimate the probability of developing cancer in subgroups of the population given different barbecuing frequencies. METHODS: We developed probabilistic models applying two dimensional Monte Carlo simulation to take into account the variation in exposure given age and sex and in the individuals' sensitivity to develop cancer after exposure to BaP, and the uncertainty in the dose response model. We used the Danish dietary consumption survey, monitoring data of chemical concentrations, data on consumer behavior of frequency of barbecuing, and animal dose response data. FINDINGS: We estimated an average extra lifetime risk of cancer due to BaP from barbecued meat of 6.8 × 10-5 (95% uncertainty interval 2.6 × 10-7 - 7.0 × 10-4) in the Danish population. This corresponds to approximately one to 4,074 extra cancer cases over a lifetime, reflecting wide uncertainty. The impact per barbecuing event on the risk of cancer for men and women of low body weight was higher compared to higher bodyweight. However, the difference due to sex and bodyweight between subgroups are dwarfed by the uncertainty. INTERPRETATION: This study proposes a model that can be applied to other substances and routes of exposure, and allows for deriving the change in risk following a specific change in behaviour. The presented methodology can serve as a valuable tool for risk management, allowing for the formulation of behaviour advice targeted to specific sub-groups in the population.

A Systems Approach to Evaluate One Health Initiatives.

Challenges calling for integrated approaches to health, such as the One Health (OH) approach, typically arise from the intertwined spheres of humans, animals, and ecosystems constituting their environment. Initiatives addressing such wicked problems commonly consist of complex structures and dynamics. As a result of the EU COST Action (TD 1404) "Network for Evaluation of One Health" (NEOH), we propose an evaluation framework anchored in systems theory to address the intrinsic complexity of OH initiatives and regard them as subsystems of the context within which they operate. Typically, they intend to influence a system with a view to improve human, animal, and environmental health. The NEOH evaluation framework consists of four overarching elements, namely: (1) the definition of the initiative and its context, (2) the description of the theory of change with an assessment of expected and unexpected outcomes, (3) the process evaluation of operational and supporting infrastructures (the "OH-ness"), and (4) an assessment of the association(s) between the process evaluation and the outcomes produced. It relies on a mixed methods approach by combining a descriptive and qualitative assessment with a semi-quantitative scoring for the evaluation of the degree and structural balance of "OH-ness" (summarised in an OH-index and OH-ratio, respectively) and conventional metrics for different outcomes in a multi-criteria-decision-analysis. Here, we focus on the methodology for Elements (1) and (3) including ready-to-use Microsoft Excel spreadsheets for the assessment of the "OH-ness". We also provide an overview of Element (2), and refer to the NEOH handbook for further details, also regarding Element (4) (http://neoh.onehealthglobal.net). The presented approach helps researchers, practitioners, and evaluators to conceptualise and conduct evaluations of integrated approaches to health and facilitates comparison and learning across different OH activities thereby facilitating decisions on resource allocation. The application of the framework has been described in eight case studies in the same Frontiers research topic and provides first data on OH-index and OH-ratio, which is an important step towards their validation and the creation of a dataset for future benchmarking, and to demonstrate under which circumstances OH initiatives provide added value compared to disciplinary or conventional health initiatives.

Framework to Define Structure and Boundaries of Complex Health Intervention Systems: The ALERT Project.

Health intervention systems are complex and subject to multiple variables in different phases of implementation. This constitutes a concrete challenge for the application of translational science in real life. Complex systems as health-oriented interventions call for interdisciplinary approaches with carefully defined system boundaries. Exploring individual components of such systems from different viewpoints gives a wide overview and helps to understand the elements and the relationships that drive actions and consequences within the system. In this study, we present an application and assessment of a framework with focus on systems and system boundaries of interdisciplinary projects. As an example on how to apply our framework, we analyzed ALERT [an integrated sensors and biosensors' system (BEST) aimed at monitoring the quality, health, and traceability of the chain of the bovine milk], a multidisciplinary and interdisciplinary project based on the application of measurable biomarkers at strategic points of the milk chain for improved food security (including safety), human, and ecosystem health (1). In fact, the European food safety framework calls for science-based support to the primary producers' mandate for legal, scientific, and ethical responsibility in food supply. Because of its multidisciplinary and interdisciplinary approach involving human, animal, and ecosystem health, ALERT can be considered as a One Health project. Within the ALERT context, we identified the need to take into account the main actors, interactions, and relationships of stakeholders to depict a simplified skeleton of the system. The framework can provide elements to highlight how and where to improve the project development when project evaluations are required.

Application of ERICA index to evaluation of soil ecosystem health according to sustainability threshold for chemical impact.

The aim of the improved ERICA model for risk assessment (Boriani et al., 2010) is to give an instrument able to measure the effect of xenobiotics introduced into the environment. This will be of great help for "green" processes and sustainable industries and may help to advertise their products as safe for the environment following impact assessment. In this work we have added new indicators and scoring systems to be used in particular with attention for the soil compartment. Even though it is partly starting to be considered by some legislations, there is still an open debate to assess if a compound added to a certain scenario will increase risk for human beings and the environment. The prolonged environmental occurrence introduces uncertainty regarding the presence and properties of degradation products and cumulative effects from multiple substances present in the environment. Tools capable of efficiently coping with this issue may prove useful for stakeholders. For instance, industries able to show that their substances present good characteristics also related to fate and transport properties may document the added value of environmental friendly products. Furthermore, the use of these tools may lead to awareness by industries of minimizing the environmental impact of the whole production chain. In the present study we show how the instrument ERICA may work by addressing multiple sources of exposure. An improved version of ERICA and in particular its parameter EF (fate and transport of chemical compounds into the environment) is described in this paper and is applied to a scenario of two veterinarian pharmaceutical compounds: Sulfadiazine (SDZ) and Toltrazuril and their metabolites present in the environment. Results show that the new EF parameter is able to prioritize the chemical compounds better than the previous version with respect to their ability to degrade or not into the environment.

Assessment and validation of the CAESAR predictive model for bioconcentration factor (BCF) in fish.

BACKGROUND: Bioconcentration factor (BCF) describes the behaviour of a chemical in terms of its likelihood of concentrating in organisms in the environment. It is a fundamental property in recent regulations, such as the European Community Regulation on chemicals and their safe use or the Globally Harmonized System for classification, labelling and packaging. These new regulations consider the possibility of reducing or waiving animal tests using alternative methods, such as in silico methods. This study assessed and validated the CAESAR predictive model for BCF in fish. RESULTS: To validate the model, new experimental data were collected and used to create an external set, as a second validation set (a first validation exercise had been done just after model development). The performance of the model was compared with BCFBAF v3.00. For continuous values and for classification purposes the CAESAR BCF model gave better results than BCFBAF v3.00 for the chemicals in the applicability domain of the model. R² and Q² were good and accuracy in classification higher than 90%. Applying an offset of 0.5 to the compounds predicted with BCF close to the thresholds, the number of false negatives (the most dangerous errors) dropped considerably (less than 0.6% of chemicals). CONCLUSIONS: The CAESAR model for BCF is useful for regulatory purposes because it is robust, reliable and predictive. It is also fully transparent and documented and has a well-defined applicability domain, as required by REACH. The model is freely available on the CAESAR web site and easy to use. The reliability of the model reporting the six most similar compounds found in the CAESAR dataset, and their experimental and predicted values, can be evaluated.

ERICA: A multiparametric toxicological risk index for the assessment of environmental healthiness.

A risk assessment strategy considering the impact of chemicals on the whole ecosystem has been developed in order to create a sound and useful method for quantifying and comparing the global risk posed by the main different hazardous chemicals found in the environment. This index, called Environmental Risk Index for Chemical Assessment (ERICA), merges in a single number the environmental assessment, the human health risk assessment and the uncertainty due to missing or uncertain data. ERICA uses a dedicated scoring system with parameters for the main characteristics of the pollutants. The main advantage is that it preserves a simple approach by condensing in this single value an analysis of the risk for the area under observation. ERICA quantifies and compares the global risk posed by hazardous chemicals found in the environment and can be considered a diagnostic and prognostic method for environmental contaminants in critical and potentially dangerous sites, such as incinerators, landfills and industrial areas or in broader geographical areas. The application of the proposed integrated index provides a preliminary quantitative analysis of possible environmental alert due to the presence of one or some pollutants in the investigated site. This paper presents the method and the equations behind the index and a first case study based on the Italian legislation and a pilot study located on the Italian seacoast.

A new hybrid system of QSAR models for predicting bioconcentration factors (BCF).

The aim was to develop a reliable and practical quantitative structure-activity relationship (QSAR) model validated by strict conditions for predicting bioconcentration factors (BCF). We built up several QSAR models starting from a large data set of 473 heterogeneous chemicals, based on multiple linear regression (MLR), radial basis function neural network (RBFNN) and support vector machine (SVM) methods. To improve the results, we also applied a hybrid model, which gave better prediction than single models. All models were statistically analysed using strict criteria, including an external test set. The outliers were also examined to understand better in which cases large errors were to be expected and to improve the predictive models. The models offer more robust tools for regulatory purposes, on the basis of the statistical results and the quality check on the input data.

Regulatory perspectives in the use and validation of QSAR. A case study: DEMETRA model for Daphnia toxicity.

The DEMETRA acute toxicity model toward the water flea (Daphnia magna)was used as a case studyto outline a validation method compatible with regulatory use. Reliability, predictive power, uncertainty, and applicability were verified with an external test set of pesticides. Predictions for this external set using the DEMETRA model, developed ad hoc for pesticides, were compared with the results using ECOSAR and TOPKAT as benchmarks. The evaluation considered statistical parameters and the presence of errors, with their size and sign. DEMETRA gave good statistical predictions, and the maximum error of the outliers was lower than that with the other two models. DEMETRA gave a limited number of false negatives, and the use of defined rules indicated the level of uncertainty was acceptable.

Structural features of diverse ligands influencing binding affinities to estrogen alpha and estrogen beta receptors. Part II. Molecular descriptors calculated from conformation of the ligands in the complex resulting from previous docking study.

A QSAR study is reported, in which the relationship between chemical structure of a set of compounds and the binding affinity to human estrogen receptor alpha and beta (ER-alpha and ER-beta) is modelled. Counterpropagation neural networks are used to predict experimental binding affinity of a range of substances. Several compounds as estrogenic chemicals, phytoestrogens, and natural and synthetic estrogens are treated with a structure-based approach that involves the protein structure. The conformations obtained with a docking methodology are used to calculate molecular descriptors. The models are built up with the neural network training procedure, which encodes the information present in molecular descriptors and related binding affinities of the pre-selected training set of compounds. In order to reach the best possible models, a selection of the descriptors using genetic algorithm was conducted. The selection was directed by the error in the prediction of binding affinities of compounds from the test set. The final models obtained for estrogen receptor alpha and beta were tested with an external validation set and were compared with the models obtained from a receptor-independent approach reported in the accompanying paper.

Structural features of diverse ligands influencing binding affinities to estrogen alpha and estrogen beta receptors. Part I: Molecular descriptors calculated from minimal energy conformation of isolated ligands.

We report a neural network modeling approach combined with genetic algorithm for prediction of experimental binding affinity to human Estrogen Receptor alpha and beta (ER-alpha and ER-beta) of a diverse set of chemicals. The counterpropagation artificial neural network is used as a modeling method. Structural features of ligands having the strongest influence to the binding affinities were investigated. The molecular descriptors have been selected in the variable selection procedure based on the genetic algorithm (GA). The 3D descriptors of molecular structures were calculated for the minimal energy conformation of isolated ligands. All the optimized models were tested by an internal and an external set of compounds. The models served for classification and prediction of binding affinities. The optimized models were 100% correct in the classification part, where the active molecules were separated from the inactive ones. The best predictive model of active molecules was assessed with the internal test set yielding the error in prediction RMS = 0.12, while the predictions for the external test set contain some outliers, which are ascribed to the incompatibility of individual compounds concerning the structural domain of our model. The influence of the receptor on the conformation of the ligands in the ligand-protein complex is described and discussed in the accompanying paper.

A protocol to select high quality datasets of ecotoxicity values for pesticides.

The key to any QSAR model is the underlying dataset. In order to construct a reliable dataset to develop a QSAR model for pesticide toxicity, we have derived a protocol to critically evaluate the quality of the underlying data. In developing an appropriate protocol that would enable data to be selected in constructing a QSAR, we concentrated on one toxicity end point, the 96 h LC50 from the acute rainbow trout study. This end point is key in pesticide regulation carried out under 91/414/EEC. The dataset used for this exercise was from the US EPA-OPP database.