The conversion of aflatoxin B1 in feed consumed by cows into aflatoxin M1 in their milk poses a challenge to food safety, with milk being a popular staple food and because of the harmful effects of these compounds. This study aimed at reviewing scientific information about the extent of carry-over of AFB1 from feed to milk. A range of studies reported about correlations of carry-over with different factors, particularly with milk yield and AFB1 intake. The extent of carry-over considerably varies, being 1–2% on average, which may be as high as 6% in the case of increased milk production. Specific factors influencing transfer rates, including milk yield, somatic cell counts, aflatoxin B1 intake, source of contamination, seasonal effects, particle size of feed, and the effects of certain interventions, namely vaccination and the use of adsorbents, are identified as the most important and are discussed in this review. The different mathematical formulas describing carry-over and instances of their application are reviewed as well. These carry-over equations may lead to largely different results, and no single carry-over equation can be suggested as the best one. While the exact quantification of carry-over is difficult as the process depends on many factors, including individual variabilities between animals, the intake of aflatoxin B1 and milk yield seem to be the most important factors influencing the excreted amount of aflatoxin M1 and the rate of carry-over.

Antimicrobial resistance (AMR) is a critical global health concern, with the food production sector, particularly poultry production, playing a significant role in the spread of resistant bacteria. This study aims to identify and analyze the key factors influencing antimicrobial use (AMU) and thereby/or the spread of AMR within the poultry production chain, spanning from farm to consumer. A scoping literature review was conducted to investigate factors influencing AMU and/or the emergence and dissemination of AMR in poultry production. Knowledge synthesis was conducted from 69 relevant publications covering data from 41 countries. A total of 327 factors were identified from the various studies. Most factors (221) were related to knowledge, attitudes, and practices (KAPs) concerning farms. The other significant group, with 57 factors, involved intentional misuse or poor practices related to antimicrobials. The findings highlight the complexity of factors influencing AMR in poultry production, including financial pressures on farmers and varied training effectiveness. Integrated and tailored interventions are essential. Drug/feed sellers’ practices require stronger regulations, while economic motivations and policy gaps must be addressed. Systematic management of these interactions and further research on environmental and genetic influences are crucial for effective strategies to combat this pressing public health challenge.

Environmental conditions are increasingly recognized as important contributors to the emergence and spread of antimicrobial resistance (AMR), yet early detection of high-risk situations remains difficult. This study developed a data-driven framework to identify anomalous environmental profiles associated with potential AMR risk. Using an unsupervised anomaly detection method (Isolation Forest) applied to multivariate indicators—including pesticide use, land use change, precipitation, and crop type—we detected unusual environmental patterns without prior AMR data. The anomaly detection analysis highlighted pesticide use, population density, land use change, and fertilizer application as the dominant environmental factors, together explaining the largest share of variation in anomaly scores (each contributing around one-quarter to one-third of the model’s decisions). In the subset of anomalous cases, fertilizer and pesticide intensity exerted the strongest negative impact, confirming their role as key drivers of atypical environmental profiles. Extreme precipitation and crop-specific production patterns also emerged as influential in certain cases. These results show that our interpretable framework can both rank global drivers and reveal context-dependent risks, thereby enabling the development of early-warning strategies for AMR surveillance.

Forecasting antimicrobial resistance (AMR) is critical for public health, yet most models neglect the interconnected nature of agricultural systems. Focusing on ciprofloxacin resistance in Campylobacter jejuni—a leading foodborne pathogen in poultry—this study aims to develop a probabilistic framework for identifying high-risk environmental conditions. We employed a graph-based machine learning and Bayesian approach, integrating and discretizing data from international databases. An exploratory classification with XGBoost and SVC was followed by core analysis using a Generalized Naive Bayes (GNB) model for feature selection and a Bayesian Network (BN) to uncover conditional dependencies. The GNB model identified pesticides, land use, and precipitation as key features. The BN revealed a complex web of interactions, showing that resistance probability is highly context-dependent. Precipitation was a critical effect modifier; for example, expanded land use correlated with an 18.3% increase in resistance probability during dry conditions but a 73.7% decrease during wet periods. Scenarios with low and high precipitation were associated with high risk, indicating multiple environmental pathways. Our results demonstrate that Bayesian networks can effectively model the complex, non-linear relationships driving AMR. Ciprofloxacin resistance emerges from system-wide interactions rather than isolated factors. This approach provides a valuable framework for generating hypotheses and supports the development of early-warning systems for targeted antimicrobial stewardship in poultry production.

Fumonisins are mycotoxins commonly found on corn and other grains, and have been linked to several health concerns. The aim of this study is to estimate the exposure of the Hungarian population to fumonisins from regular food consumption. Fumonisin B1 and B2 concentrations were determined in commercially available corn-, wheat- and rice-based foods. Daily intakes on an individual level were calculated deterministically based on recent individual food consumption data distributions, and the average contamination of the concerned food categories. The most significant sources of fumonisins were corn flour, cornmeal, cornflakes, other corn-based products, and wheat-based products (average total fumonisin contents in the middle bound scenario were 0.115, 0.074, 0.052, 0.091 and 0.077 mg/kg, respectively). In cases where the concentration values measured below the analytical determination limits, these were substituted by half of the corresponding limits (middle bound scenario). Mean and high (95th percentile) exposures to fumonisins B1 + B2 were 0.101 and 0.258 µg/bw kg/day for adults, and 0.313 and 0.744 µg/bw kg/day for children, respectively. Our results show that about 1.5% of children’s exposure could exceed the tolerable daily intake limit (TDI) of fumonisins, established by EFSA in 2018, meaning that potential health risks cannot be ruled out for a low proportion of consumers. We found that besides corn-based products, wheat-based food products could also contribute to total intake, due to their high consumption levels. Constant monitoring of fumonisin levels in corn- and wheat-based products is recommended to safeguard consumer health.

The identification of emerging risks in the food chain is essential for proactive food safety management in a rapidly evolving global environment. In this study, we applied a structured emerging risk identification framework supported by text mining of food safety news collected from the Europe Media Monitor (EMM) to analyse signals from 2022 to 2023. The workflow combines automated news collection, topic detection, and expert evaluation within a multi-stage filtering system. Using this approach, 164 emerging risks and drivers were identified during the study period. Chemical risks—including pesticide-related issues, per- and polyfluoroalkyl substances, and microplastics—were among the most frequently detected topics, while sustainability-related developments such as alternative proteins and novel packaging technologies emerged as major drivers of change. Comparison with the 2020–2021 period indicates a marked increase in signals related to chemical contaminants and sustainability. These findings highlight the value of combining automated text mining of food safety news with expert-based evaluation to support timely emerging risk identification in the food chain.