Subhepatically located appendicitis due to adhesions: a case report

<p>Abstract</p> <p>Introduction</p> <p>Acute appendicitis occurs frequently and is a major indication for acute abdominal surgery. Subhepatic appendicitis has rarely been reported and is more difficult to diagnose.</p> <p>Case presentation</p> <p>A 71-year-old man with multiple medical comorbidities presented with undifferentiated right abdominal pain. Diagnostic difficulty was encountered due to subhepatic mal-location of the appendix and subsequently atypical presentation for acute appendicitis.</p> <p>Conclusion</p> <p>Subhepatic anatomical location of the appendix makes it more difficult to diagnose acute appendicitis at any age, including in older adults.</p

Probabilistic classification of acute myocardial infarction from multiple cardiac markers

Logistic regression and Gaussian mixture model (GMM) classifiers have been trained to estimate the probability of acute myocardial infarction (AMI) in patients based upon the concentrations of a panel of cardiac markers. The panel consists of two new markers, fatty acid binding protein (FABP) and glycogen phosphorylase BB (GPBB), in addition to the traditional cardiac troponin I (cTnI), creatine kinase MB (CKMB) and myoglobin. The effect of using principal component analysis (PCA) and Fisher discriminant analysis (FDA) to preprocess the marker concentrations was also investigated. The need for classifiers to give an accurate estimate of the probability of AMI is argued and three categories of performance measure are described, namely discriminatory ability, sharpness, and reliability. Numerical performance measures for each category are given and applied. The optimum classifier, based solely upon the samples take on admission, was the logistic regression classifier using FDA preprocessing. This gave an accuracy of 0.85 (95% confidence interval: 0.78–0.91) and a normalised Brier score of 0.89. When samples at both admission and a further time, 1–6 h later, were included, the performance increased significantly, showing that logistic regression classifiers can indeed use the information from the five cardiac markers to accurately and reliably estimate the probability AMI

Localized Giant Inflammatory Polyposis of the Ileocecum Associated with Crohn's Disease: Report of a Case

Although inflammatory polyposis is one of the common complications in patients with inflammatory bowel disease, it is rare that each poly grows up to more than 1.5 cm. We describe a case of localized giant inflammatory polyposis of the ileocecum associated with Crohn's disease. A 40-year-old man who had been followed for 28 years because of Crohn's disease was hospitalized for right lower abdominal pain after meals. Barium enema and colonoscopy showed numerous worm-like polyps in the ascending colon which grew up to the hepatic flexure of the colon from the ileocecum, causing an obstruction of the ileocecal orifice. Since histology of a biopsy specimen taken from the giant polyps showed no dysplasia, he was diagnosed with ileus due to the localized giant inflammatory polyposis. A laparoscopically assisted ileocecal resection was performed. The resected specimen showed that the giant polyps grew up into the ileocecum. Histological examination revealed inflammatory polyposis without neoplasm. Generally, conservative treatment is indicated for localized giant inflammatory polyposis because this lesion is regarded as benign. However, occasionally serious complications arise, requiring surgical treatment

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams