Based on change in weight, change in blood glucose, and age at onset of diabetes, we developed and validated a model to determine risk of PC in patients with NOD based on glycemic status (END-PAC model). An independent prospective study is needed to further validate this model, which could contribute to early detection of PC.

Up to 85% of patients with pancreatic cancer have diabetes or hyperglycaemia, which frequently manifests as early as 2–3 years before a diagnosis of pancreatic cancer. Conversely, patients with new-onset diabetes have a 5–8-fold increased risk of being diagnosed with pancreatic cancer within 1–3 years of developing diabetes. Emerging evidence now indicates that pancreatic cancer causes diabetes. As in type 2 diabetes, β-cell dysfunction and peripheral insulin resistance are seen in pancreatic cancer-induced diabetes. However, unlike in patients with type 2 diabetes, glucose control worsens in patients with pancreatic cancer in the face of ongoing, often profound, weight loss. Diabetes and weight loss, which precede cachexia onset by several months, are paraneoplastic phenomena induced by pancreatic cancer. Although the pathogenesis of these pancreatic cancer-induced metabolic alterations is only beginning to be understood, these are likely mechanisms to promote the survival and growth of pancreatic cancer in a hostile and highly desmoplastic microenvironment. Interestingly, these metabolic changes could enable early diagnosis of pancreatic cancer, if they can be distinguished from the ones that occur in patients with type 2 diabetes. One such possible biomarker is adrenomedullin, which is a potential mediator of β-cell dysfunction in pancreatic cancer-induced diabetes.

The content and quality of medical information available on video sharing websites such as YouTube is not known. We analyzed the source and quality of medical information about Ebola hemorrhagic fever (EHF) disseminated on YouTube and the video characteristics that influence viewer behavior. An inquiry for the search term ‘Ebola’ was made on YouTube. The first 100 results were arranged in decreasing order of “relevance” using the default YouTube algorithm. Videos 1–50 and 51–100 were allocated to a high relevance (HR), and a low relevance (LR) video group, respectively. Multivariable logistic regression models were used to assess the predictors of a video being included in the HR vs. LR groups. Fourteen videos were excluded because they were parodies, songs or stand-up comedies (n = 11), not in English (n = 2) or a remaining part of a previous video (n = 1). Two scales, the video information and quality and index and the medical information and content index (MICI) assessed the overall quality, and the medical content of the videos, respectively. There were no videos from hospitals or academic medical centers. Videos in the HR group had a higher median number of views (186,705 vs. 43,796, p < 0.001), more ‘likes’ (1119 vs. 224, p < 0.001), channel subscriptions (208 vs. 32, p < 0.001), and ‘shares’ (519 vs. 98, p < 0.001). Multivariable logistic regression showed that only the ‘clinical symptoms’ component of the MICI scale was associated with a higher likelihood of a video being included in the HR vs. LR group.(OR 1.86, 95 % CI 1.06–3.28, p = 0.03). YouTube videos presenting clinical symptoms of infectious diseases during epidemics are more likely to be included in the HR group and influence viewers behavior.

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