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Various models and datasets related to aflatoxins in the maize and dairy production chain have been developed and used but they have not yet been linked with each other. This study aimed to investigate the impacts of climate change on aflatoxin B1 production in maize and its consequences on aflatoxin M1 contamination in dairy cow’s milk, using a full chain modelling approach. To this end, available models and input data were chained together in a modelling framework. As a case study, we focused on maize grown in Eastern Europe and imported to the Netherlands to be fed–as part of dairy cows’ compound feed–to dairy cows in the Netherlands. Three different climate models, one aflatoxin B1 prediction model and five different carryover models were used. For this particular case study of East European maize, most of the calculations suggest an increase (up to 50%) of maximum mean aflatoxin M1 in milk by 2030, except for one climate (DMI) model suggesting a decrease. Results from all combinations of carryover and climate models suggest a similar or slight increase (up to 0.6%) of the chance of finding aflatoxin M1 in milk above the EC limit of 0.05 μg/kg by 2030. Results varied mainly with the climate model data and carryover model considered. The model framework infrastructure is flexible so that forecasting models for other mycotoxins or other food safety hazards as well as other production chains, together with necessary input databases, can easily be included as well. This modelling framework for the first time links datasets and models related to aflatoxin B1 in maize and related aflatoxin M1 the dairy production chain to obtain a unique predictive methodology based on Monte Carlo simulation. Such an integrated approach with scenario analysis provides possibilities for policy makers and risk managers to study the effects of changes in the beginning of the chain on the end product. [ABSTRACT FROM AUTHOR]

Colon carcinoma is one of the leading causes of death from cancer and is characterized by a heterogenic pool of cells with distinct differentiation patterns. Recently, it was reported that a population of undifferentiated cells from a primary tumor, so-called cancer stem cells (CSC), can reconstitute the original tumor on xenotransplantation. Here, we show that spheroid cultures of these colon CSCs contain expression of CD133, CD166, CD44, CD29, CD24, Lgr5, and nuclear I3-catenin, which have all been suggested to mark the (cancer) stem cell population. More importantly, by using these spheroid cultures or freshly isolated tumor cells from multiple colon carcinomas, we now provide compelling evidence to indicate that the capacity to propagate a tumor with all differentiated progeny resides in a single CSC. Single-cell-cloned CSCs can form an adenocarcinoma on xenotransplantation but do not generate the stroma within these tumors. Moreover, they can selfrenew and are capable of multilineage differentiation. Further analysis indicated that the lineage decision is dictated by phosphoinositide 3-kinase (P13K) signaling in CSC5. These data support the hypothesis that tumor hierarchy can be traced back to a single CSC that contains multilineage differentiation capacity, and provides clues to the regulation of differentiation in colon cancers in vivo. [ABSTRACT FROM AUTHOR]

Cancer has long been viewed as an exclusively genetic disorder. The model of carcinogenesis, postulated by Nowell and Vogelstein, describes the formation of a tumor by the sequential accumulation of mutations in oncogenes and tumor suppressor genes. In this model, tumors are thought to consist of a heterogeneous population of cells that continue to acquire new mutations, resulting in a highly dynamic process, with clones that out compete others due to increased proliferative or survival capacity. However, novel insights in cancer stem cell research suggest another layer of complexity in the process of malignant transformation and preservation. It has been reported that only a small fraction of the cancer cells in a malignancy have the capacity to propagate the tumor upon transplantation into immuno-compromised mice. Those cells are termed ‘cancer stem cells’ (CSC) and can be selected based on the expression of cell surface markers associated with immature cell types. In this review, we will critically discuss these novel insights in CSC-related research. Where possible we integrate these results within the genetic model of cancer and illustrate that the CSC model can be considered an extension of the classic genetic model rather than a contradictory theory. Finally, we discuss some of the most controversial issues in this field.Cell Death and Differentiation (2008) 15, 947–958; doi:10.1038/cdd.2008.20; published online 15 February 2008 [ABSTRACT FROM AUTHOR]

Background: Checklists can reduce medical errors. However, the effectiveness of checklists is hampered by lack of acceptance and compliance. Recently, a new type of checklist with dynamic properties has been created to provide more specific checklist items for each individual patient. Our purpose in this simulation-based study was to investigate a newly developed intelligent dynamic clinical checklist (DCC) for the intensive care unit (ICU) ward round.Methods: Eligible clinicians were invited to participate as volunteers. Highest achievable scores were established for six typical ICU scenarios to determine which items must be checked. The participants compared the DCC with the local standard of care. The primary outcomes were the caregiver satisfaction score and the percentages of checked items overall and of critical items requiring a direct intervention.Results: In total, 20 participants were included, who performed 116 scenarios. The median percentage of checked items was 100.0% with the DCC and 73.6% for the scenarios completed with local standard of care ( P <0.001). Critical items remained unchecked in 23.1% of the scenarios performed with local standard of care and 0.0% of the scenarios where the DCC was available ( P <0.001). The mean satisfaction score of the DCC was 4.13 out of 5.Conclusions: This simulation study indicates that an intelligent DCC significantly increases compliance with best practice by reducing the percentage of unchecked items during ICU ward rounds, while the user satisfaction rate remains high. Real-life clinical research is required to evaluate this new type of checklist further. [ABSTRACT FROM AUTHOR]

Background: There are profound individual differences in clinical outcomes between colorectal cancers (CRCs) presenting with identical stage of disease. Molecular stratification, in conjunction with the traditional TNM staging, is a promising way to predict patient outcomes. We investigated the interconnectivity between tumor stage and tumor biology reflected by the Consensus Molecular Subtypes (CMSs) in CRC, and explored the possible value of these insights in patients with stage II colon cancer.Methods: We performed a retrospective analysis using clinical records and gene expression profiling in a meta-cohort of 1040 CRC patients. The interconnectivity of tumor biology and disease stage was assessed by investigating the association between CMSs and TNM classification. In order to validate the clinical applicability of our findings we employed a meta-cohort of 197 stage II colon cancers.Results: CMS4 was significantly more prevalent in advanced stages of disease (stage I 9.8% versus stage IV 38.5%, p < 0.001). The observed differential gene expression between cancer stages is at least partly explained by the biological differences as reflected by CMS subtypes. Gene signatures for stage III-IV and CMS4 were highly correlated (r = 0.77, p < 0.001). CMS4 cancers showed an increased progression rate to more advanced stages (CMS4 compared to CMS2: 1.25, 95% CI: 1.08-1.46). Patients with a CMS4 cancer had worse survival in the high-risk stage II tumors compared to the total stage II cohort (5-year DFS 41.7% versus 100.0%, p = 0.008).Conclusions: Considerable interconnectivity between tumor biology and tumor stage in CRC exists. This implies that the TNM stage, in addition to the stage of progression, might also reflect distinct biological disease entities. These insights can potentially be utilized to optimize identification of high-risk stage II colon cancers. [ABSTRACT FROM AUTHOR]