New perspectives in the prediction of postoperative complications for high-risk ulcerative colitis patients: machine learning preliminary approach

OBJECTIVE: Patients with acute severe and medical refractory ulcerative colitis have a high risk of postoperative complications after total abdominal colectomy (TAC). The objective of this retrospective study is to use machine learning to analyze and predict short-term outcomes. PATIENTS AND METHODS: 32 patients with ulcerative colitis were treated with total abdominal colectomy between 2011 and 2017. Biographical data, preoperative therapy, blood chemistry, nutritional status, surgical technique, blood transfusion and preoperative length of stay were the features selected for the statistical analyses and were used as input for the machine learning algorithms to predict the rate of complications. RESULTS: Traditional statistical analysis showed an overall postoperative morbidity rate of 34% and a mortality rate of 3%. Preoperative low serum albumin levels (4 days), blood transfusions (≥1 unit) and body temperature (≥37.5°C) demonstrated a major impact on infectious morbidity with statistical significance (p<0.05). Patients treated with steroids and rescue therapy presented a higher risk of minor infectious complications (p<0.05). Evaluating only preoperative features, machine learning algorithms were able to predict minor postoperative complications with a high strike rate (84.3%), high sensitivity (87.5%) and high specificity (83.3%) during the testing phase. CONCLUSIONS: Machine learning is demonstrated to be useful in predicting the rate of minor postoperative complications in high-risk ulcerative colitis patients, despite the small sample size. It represents a major step forward in data analysis by implementing a retrospective study from a prospective point of view

Open Data as Open Educational Resources: Case studies of emerging practice

This collection presents the stories of our contributors’ experiences and insights, in order to demonstrate the enormous potential for openly-licensed and accessible datasets (Open Data) to be used as Open Educational Resources (OER). Open Data is an umbrella term describing openly-licensed, interoperable, and reusable datasets which have been created and made available to the public by national or local governments, academic researchers, or other organisations. These datasets can be accessed, used and shared without restrictions other than attribution of the intellectual property of their creators1.While there are various definitions of OER, these are generally understood as openly-licensed digital resources that can be used in teaching and learning. On the basis of these definitions, it is reasonable to assert that while Open Data is not always OER, it certainly becomes OER when used within pedagogical contexts. Yet while the question may appear already settled at the level of definition, the potential and actual pedagogical uses of Open Data appear to have been under-discussed. As open education researchers who take a wider interest in the various open ‘movements’, we have observed that linkages between them are not always strong, in spite of shared and interconnecting values. So, Open Data tends to be discussed primarily in relation to its production, storage, licensing and accessibility, but less often in relation to its practical subsequent uses. And, in spite of widespread understanding that use of the term ‘OER’ is actually context-dependent, and, therefore, could be almost all-encompassing, the focus of OER practice and research has tended to be on educator-produced learning materials. The search for relevant research literature in the early stages of this project turned up sources which discuss the benefits of opening data, and others advocating improving student engagement with data3, but on the topic of Open Data as an educational resource specifically, there appeared to be something of a gap

Peptides of the Constant Region of Antibodies Display Fungicidal Activity

Synthetic peptides with sequences identical to fragments of the constant region of different classes (IgG, IgM, IgA) of antibodies (Fc-peptides) exerted a fungicidal activity in vitro against pathogenic yeasts, such as Candida albicans, Candida glabrata, Cryptococcus neoformans, and Malassezia furfur, including caspofungin and triazole resistant strains. Alanine-substituted derivatives of fungicidal Fc-peptides, tested to evaluate the critical role of each residue, displayed unaltered, increased or decreased candidacidal activity in vitro. An Fc-peptide, included in all human IgGs, displayed a therapeutic effect against experimental mucosal and systemic candidiasis in mouse models. It is intriguing to hypothesize that some Fc-peptides may influence the antifungal immune response and constitute the basis for devising new antifungal agents

Rare ATG7 genetic variants predispose patients to severe fatty liver disease

Background &amp; Aims: Non-alcoholic fatty liver disease (NAFLD) is the leading cause of liver disorders and has a strong heritable component. The aim of this study was to identify new loci that contribute to severe NAFLD by examining rare variants. Methods: We performed whole-exome sequencing in individuals with NAFLD and advanced fibrosis or hepatocellular carcinoma (n = 301) and examined the enrichment of likely pathogenic rare variants vs. the general population. This was followed by validation at the gene level. Results: In patients with severe NAFLD, we observed an enrichment of the p.P426L variant (rs143545741 C&gt;T; OR 5.26, 95% CI 2.1-12.6; p = 0.003) of autophagy-related 7 (ATG7), which we characterized as a loss-of-function, vs. the general population, and an enrichment in rare variants affecting the catalytic domain (OR 13.9; 95% CI 1.9-612; p = 0.002). In the UK Biobank cohort, loss-of-function ATG7 variants increased the risk of cirrhosis and hepatocellular carcinoma (OR 3.30; 95% CI 1.1-7.5 and OR 12.30, 95% CI 2.6-36, respectively; p &lt;0.001 for both). The low-frequency loss-of-function p.V471A variant (rs36117895 T&gt;C) was also associated with severe NAFLD in the clinical cohort (OR 1.7; 95% CI 1.2-2.5; p = 0.003), predisposed to hepatocellular ballooning (p = 0.007) evolving to fibrosis in a Liver biopsy cohort (n = 2,268), and was associated with liver injury in the UK Biobank (aspartate aminotransferase levels, p &lt;0.001), with a larger effect in severely obese individuals in whom it was linked to hepatocellular carcinoma (p = 0.009). ATG7 protein localized to periportal hepatocytes, particularly in the presence of ballooning. In the Liver Transcriptomic cohort (n = 125), ATG7 expression correlated with suppression of the TNFα pathway, which was conversely upregulated in p.V471A carriers. Conclusions: We identified rare and low-frequency ATG7 loss-of-function variants that promote NAFLD progression by impairing autophagy and facilitating ballooning and inflammation. Lay summary: We found that rare mutations in a gene called autophagy-related 7 (ATG7) increase the risk of developing severe liver disease in individuals with dysmetabolism. These mutations cause an alteration in protein function and impairment of self-renewal of cellular content, leading to liver damage and inflammation

The use of open data as a material for learning

Open data has potential value as a material for use in learning activities. However, approaches to harnessing this are not well understood or in mainstream use in education. In this research, early adopters from a diverse range of educational projects and teaching settings were interviewed to explore their rationale for using open data in teaching, how suitable activity designs could be achieved, and the practical challenges of using open data. A thematic analysis was conducted to identify patterns and relationships in these open data-based practices that have already emerged. A document analysis of teaching materials and other related artefacts was used to augment and validate the findings. Drawing on this, common approaches and issues are identified, and a conceptual framework to support greater use of open data by educators is described. This paper also highlights where existing concepts in education and educational technology research, including inquiry-based learning, authenticity, motivation, dialogue, and personalisation can help us to understand the value and challenges of using open data in education