ANN-Based Integrated Risk Ranking Approach: A Case Study of Contaminants of Emerging Concern of Fish and Seafood in Europe

Current research development on food contaminants, future risks, regulatory regime and detection technologies: A systematic literature review

Food contaminants pose serious threats to public health, with profound negative impacts on the economy, society, and environment. However, there is a lack of timely and comprehensive reviews on the latest developments in food contaminants and effective measures to prevent contamination, particularly through novel intelligent detection technologies and regulatory regimes. This study addresses this knowledge gap by presenting a timely review of the literature, focusing on current types of food contaminants, advances in detection technologies, emerging risks, and the latest developments in regulatory frameworks. The study reviewed 116 relevant articles published between 2019 and 2024 and conducted a thematic analysis. The food contaminants were classified into three categories: biological, chemical, and physical. The study identified six key drivers of current and future food safety risks: demographic change, economic factors, environmental conditions, geopolitical shifts, consumer priorities, and technological advancements. Findings reveal the uneven understanding of contaminants of emerging concern, future drivers of contaminants of emerging concern, and their impact on the food system, the environment, and human health. These findings highlight the need for future research on systematically identifying and validating the regional differences in food contamination prevention measures and assessing the extent to which these differences impact the effectiveness of prevention, mitigation, and control efforts. The findings also call for more international cooperation in food contamination research and the active involvement of technology partners to facilitate the application of cutting-edge technologies in food contamination detection and control.

Common issues of data science on the eco-environmental risks of emerging contaminants

Data-driven approaches (e.g., machine learning) are increasingly used to replace or assist laboratory studies in the study of emerging contaminants (ECs). In the past ten years, an increasing number of models or approaches have been applied to ECs, and the datasets used are continuously enriched. However, there are large knowledge gaps between what we have found and the natural eco-environmental meaning. For most published reviews, the contents are organized by the types of ECs, but the common issues of data science, regardless of the type of pollutant, are not sufficiently addressed. To close or narrow the knowledge gaps, we highlight the following issues ignored in the field of data-driven EC research. Complicated biological and ecological data and ensemble models revealing mechanisms and spatiotemporal trends with strong causal relationships and without data leakage deserve more attention in the future. In addition, the matrix influence, trace concentration, and complex scenario have often been ignored in previous works. Therefore, an integrated research framework related to natural fields, ecological systems, and large-scale environmental problems, rather than relying solely on laboratory data-related analysis, is urgently needed. Beyond the current prediction purposes, data science can inspire the discovery of scientific questions, and mutual inspiration among data science, process and mechanism models, and laboratory and field research is a critical direction. Focusing on the above urgent and common issues related to data, frameworks, and purposes, regardless of the type of pollutant, data science is expected to achieve great advancements in addressing the eco-environmental risks of ECs.

Pharmacophore Modeling Using Machine Learning for Screening the Blood-Brain Barrier Permeation of Xenobiotics

Daily exposure to xenobiotics affects human health, especially the nervous system, causing neurodegenerative diseases. The nervous system is protected by tight junctions present at the blood-brain barrier (BBB), but only molecules with desirable physicochemical properties can permeate it. This is why permeation is a decisive step in avoiding unwanted brain toxicity and also in developing neuronal drugs. In silico methods are being implemented as an initial step to reduce animal testing and the time complexity of the in vitro screening process. However, most in silico methods are ligand based, and consider only the physiochemical properties of ligands. However, these ligand-based methods have their own limitations and sometimes fail to predict the BBB permeation of xenobiotics. The objective of this work was to investigate the influence of the pharmacophoric features of protein-ligand interactions on BBB permeation. For these purposes, receptor-based pharmacophore and ligand-based pharmacophore fingerprints were developed using docking and Rdkit, respectively. Then, these fingerprints were trained on classical machine-learning models and compared with classical fingerprints. Among the tested footprints, the ligand-based pharmacophore fingerprint achieved slightly better (77% accuracy) performance compared to the classical fingerprint method. In contrast, receptor-based pharmacophores did not lead to much improvement compared to classical descriptors. The performance can be further improved by considering efflux proteins such as BCRP (breast cancer resistance protein), as well as P-gp (P-glycoprotein). However, the limited data availability for other proteins regarding their pharmacophoric interactions is a bottleneck to its improvement. Nonetheless, the developed models and exploratory analysis provide a path to extend the same framework for environmental chemicals, which, like drugs, are also xenobiotics. This research can help in human health risk assessment by a priori screening for neurotoxicity-causing agents.

Source, fate, transport and modelling of selected emerging contaminants in the aquatic environment: Current status and future perspectives

The occurrence of emerging contaminants (ECs), such as pharmaceuticals and personal care products (PPCPs), perfluoroalkyl and polyfluoroalkyl substances (PFASs) and endocrine-disrupting chemicals (EDCs) in aquatic environments represent a major threat to water resources due to their potential risks to the ecosystem and humans even at trace levels. Mathematical modelling can be a useful tool as a comprehensive approach to study their fate and transport in natural waters. However, modelling studies of the occurrence, fate and transport of ECs in aquatic environments have generally received far less attention than the more widespread field and laboratory studies. In this study, we reviewed the current status of modelling ECs based on selected representative ECs, including their sources, fate and various mechanisms as well as their interactions with the surrounding environments in aquatic ecosystems, and explore future development and perspectives in this area. Most importantly, the principles, mathematical derivations, ongoing development and applications of various ECs models in different geographical regions are critically reviewed and discussed. The recommendations for improving data quality, monitoring planning, model development and applications were also suggested. The outcomes of this review can lay down a future framework in developing a comprehensive ECs modelling approach to help researchers and policymakers effectively manage water resources impacted by rising levels of ECs.

Contaminants of emerging concern (CECs) in aquaculture effluent: Insight into breeding and rearing activities, alarming impacts, regulations, performance of wastewater treatment unit and future approaches

The contamination of aquaculture products and effluents by contaminants of emerging concern (CECs) from the direct chemical use in aquaculture activities or surrounding industries is currently an issue of increasing concern as these CECs exert acute and chronic effects on living organisms. CECs have been detected in aquaculture water, sediment, and culture species, and antibiotics, antifoulants, and disinfectants are the commonly detected groups. Through accumulation, CECs can reside in the tissue of aquaculture products and eventually consumed by humans. Currently, effluents containing CECs are discharged to the surrounding environment while producing sediments that eventually contaminate rivers as receiving bodies. The rearing (grow-out) stages of aquaculture activities are issues regarding CECs-contamination in aquaculture covering water, sediment, and aquaculture products. Proper regulations should be imposed on all aquaculturists to control chemical usage and ensure compliance to guidelines for appropriate effluent treatment. Several techniques for treating aquaculture effluents contaminated by CECs have been explored, including adsorption, wetland construction, photocatalysis, filtration, sludge activation, and sedimentation. The challenges imposed by CECs on aquaculture activities are discussed for the purpose of obtaining insights into current issues and providing future approaches for resolving associated problems. Stakeholders, such as researchers focusing on environment and aquaculture, are expected to benefit from the presented results in this article. In addition, the results may be useful in establishing aquaculture-related CECs regulations, assessing toxicity to living biota, and preventing pollution.