Mycotoxin exposure and human cancer risk: A systematic review of epidemiological studies

In recent years, there has been an increasing interest in investigating the carcinogenicity of mycotoxins in humans. This systematic review aims to provide an overview of data linking exposure to different mycotoxins with human cancer risk. Publications (2019 and earlier) of case–control or longitudinal cohort studies were identified in PubMed and EMBASE. These articles were then screened by independent reviewers and their quality was assessed according to the Newcastle–Ottawa scale. Animal, cross‐sectional, and molecular studies satisfied criteria for exclusion. In total, 14 articles were included: 13 case–control studies and 1 longitudinal cohort study. Included articles focused on associations of mycotoxin exposure with primary liver, breast, and cervical cancer. Overall, a positive association between the consumption of aflatoxin‐contaminated foods and primary liver cancer risk was verified. Two case–control studies in Africa investigated the relationship between zearalenone and its metabolites and breast cancer risk, though conflicting results were reported. Two case–control studies investigated the association between hepatocellular carcinoma and fumonisin B1 exposure, but no significant associations were observed. This systematic review incorporates several clear observations of dose‐dependent associations between aflatoxins and liver cancer risk, in keeping with IARC Monograph conclusions. Only few human epidemiological studies investigated the associations between mycotoxin exposures and cancer risk. To close this gap, more in‐depth research is needed to unravel evidence for other common mycotoxins, such as deoxynivalenol and ochratoxin A. The link between mycotoxin exposures and cancer risk has mainly been established in experimental studies, and needs to be confirmed in human epidemiological studies to support the evidence‐based public health strategies.     

Artificial intelligence: consciousness and conscience

AI & SOCIETY - Our society is in the middle of the AI revolution. We discuss several applications of AI, in particular medical causality, where deep-learning neural networks screen through big...     

Guide to Quality Control,Second Revised English Edition

We introduce a very general “forward” method of data analysis that starts from a small, robustly chosen subset of the data and shows the effect of adding observations by a forward search. Powerful diagnostic procedures result: The observations are ordered by their agreement with the proposed transformation, masking is overcome, and the inferential effect of each observation is clear. We apply the resulting method to the transformation of both univariate and multivariate data. Other applications of the forward search are mentioned.