Underage drinking continues to be an important public health problem and a challenge to the substance abuse prevention field. Community-based interventions designed to more rigorously control underage access to alcohol through retailer education and greater enforcement of underage drinking laws have been advocated as potentially effective strategies to help address this problem, but studies designed to evaluate such interventions are sparse. To address this issue we conducted a randomized trial involving 36 communities to test the combined effectiveness of five interrelated intervention components designed to reduce underage access to alcohol. The intervention was found to be effective in reducing the likelihood that retail clerks would sell alcohol to underage-looking buyers, but did not reduce underage drinking or the perceived availability of alcohol among high school students. Post hoc analyses, however, revealed significant associations between the level of underage drinking law enforcement in the intervention communities and reductions in both 30-day use of alcohol and binge drinking. The findings highlight the difficulty in reducing youth drinking even when efforts to curtail retail access are successful. Study findings also suggest that high intensity implementation of underage drinking law enforcement can reduce underage drinking. Any such effects of enhanced enforcement on underage drinking appear to be more directly attributable to an increase in perceived likelihood of enforcement and the resultant perceived inconveniences and/or sanctions to potential drinkers, than to a reduction in access to alcohol per se.

Identification of pancreatic ductal adenocarcinoma (PDAC) and precursor lesions in histological tissue slides can be challenging and elaborate, especially due to tumor heterogeneity. Thus, supportive tools for the identification of anatomical and pathological tissue structures are desired. Deep learning methods recently emerged, which classify histological structures into image categories with high accuracy. However, to date, only a limited number of classes and patients have been included in histopathological studies. In this study, scanned histopathological tissue slides from tissue microarrays of PDAC patients (n = 201, image patches n = 81.165) were extracted and assigned to a training, validation, and test set. With these patches, we implemented a convolutional neuronal network, established quality control measures and a method to interpret the model, and implemented a workflow for whole tissue slides. An optimized EfficientNet algorithm achieved high accuracies that allowed automatically localizing and quantifying tissue categories including pancreatic intraepithelial neoplasia and PDAC in whole tissue slides. SmoothGrad heatmaps allowed explaining image classification results. This is the first study that utilizes deep learning for automatic identification of different anatomical tissue structures and diseases on histopathological images of pancreatic tissue specimens. The proposed approach is a valuable tool to support routine diagnostic review and pancreatic cancer research.

Hypoxia is a hallmark of pancreatic cancer (PDAC) due to its compact and extensive fibrotic tumor stroma. Hypoxia contributes to high lethality of this disease, by inducing a more malignant phenotype and resistance to radiation and chemotherapy. Thus, non-invasive methods to quantify hypoxia could be helpful for treatment decisions, for monitoring, especially in non-resectable tumors, or to optimize personalized therapy. In the present study, we investigated whether tumor hypoxia in PDAC is reflected by diffusion-weighted magnetic resonance imaging (DW-MRI), a functional imaging technique, frequently used in clinical practice for identification and characterization of pancreatic lesions. DW-MRI assesses the tissue microarchitecture by measuring the diffusion of water molecules, which is more restricted in highly compact tissues. As reliable surrogate markers for hypoxia, we determined Blimp-1 (B-lymphocyte induced maturation protein), a transcription factor, as well as vascular endothelial growth factor (VEGF), which are up-regulated in response to hypoxia. In 42 PDAC patients, we observed a close association between restricted water diffusion in DW-MRI and tumor hypoxia in matched samples, as expressed by high levels of Blimp-1 and VEGF in tissue samples of the respective patients. In summary, our data show that DW-MRI is well suited for the evaluation of tumor hypoxia in PDAC and could potentially be used for the identification of lesions with a high hypoxic fraction, which are at high risk for failure of radiochemotherapy.

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Purpose: This study aimed to evaluate the potential of diffusion-weighted magnetic resonance imaging (DW-MRI) as imaging biomarker for epithelial-to-mesenchymal transition (EMT) in pancreatic ductal adenocarcinoma (PDAC). Methods: In forty-two patients, preoperative apparent diffusion coefficient (ADC) values of therapy-naive PDAC were compared with immunohistochemical expression profiles of the epithelial marker E-cadherin as well as mesenchymal transcription factors Runt-related transcription factor 2 (Runx2) and Zinc finger E-box-binding homeobox 1 (Zeb1), as determined by Allred immunoreactivity score. Results: We observed a significant positive rank correlation between the ADC and the E-cadherin Allred score (ρ = 0.553, p < 0.001) and significant negative rank correlations between the ADC and the Runx2 Allred score (ρ = -0.526, p < 0.001) as well as the Zeb1 Allred score (ρ = -0.710, p < 0.001). Compared to tumors with low ADC values < 1.3 µm 2 /s, tumors with ADC values ≥ 1.3 µm 2 /s had significantly higher Allred scores for E-cadherin (median, 4 versus 5; p < 0.001) and significantly lower Allred scores for Runx2 (median, 3 versus 2; p = 0.003) as well as Zeb1 (median, 4 versus 0; p < 0.001). Conclusion: In PDAC, tumor plasticity in terms of EMT is well reflected by ADC values from DW-MRI. In the near future, DW-MRI could be beneficial for identification of PDAC patients that might profit from personalized EMT-targeted therapies.