[Primary cutaneous lymphoma-a case series of 163 patients]. / Primär kutane Lymphome ­ eine Fallserie von 163 Patienten.

BACKGROUND: In addition to a broad and clinically diverse spectrum of known primary cutaneous lymphomas, for which an incidence of 1-3:100,000 is postulated, each year further entities are specified and defined. The goal is the presentation of a case series from daily clinical routine. METHODS: Over a period of 6 years and 2 months, patients consulting the Department of Dermatology, Medical Center University of Freiburg, were registered. Subsequently, collectives of mycosis fungoides (MF), Sezary syndrome (SS), CD30+ lymphoproliferative diseases, single cases with rare primary cutaneous lymphomas, and subcollectives of B­cell lymphomas were examined. The high number of MF cases allowed the additional quantitative analyses of the types of therapies used in this group. RESULTS: Yearly 16-25 new diagnoses of primary cutaneous lymphoma are made. The evaluation of 163 primary cutaneous lymphoma revealed 111 cases with MF (68.1%), including 9 particular variants, 15 primary cutaneous CD30+ lymphoproliferative diseases (9.2%) dominated by 10 lymphomatoid papulosis (LyP), in addition to 5 primary cutaneous anaplastic large cell lymphoma (PCALCL), 6 SS (3.68%), and 24 cutaneous B­cell lymphomas (14-72%). Three cases with rare primary cutaneous T/NK cell lymphomas are addressed in detail. In all, 82% of MF cases were stage IA and IB. The descending use of therapies for stage I-III included steroids and diverse UV therapies followed by bexarotene, interferon-α, methotrexate, and extracorporal photophoresis. CONCLUSIONS: Diagnoses of cutaneous lymphomas belong to a vast spectrum of differential diagnoses. This registry describes frequent findings and shows rare variants. You can only diagnose what you know; accordingly, a collection of case reports, which we wish to encourage, can help in processing and specification of entities.

A model qualification method for mechanistic physiological QSP models to support model-informed drug development.

Mechanistic physiological modeling is a scientific method that combines available data with scientific knowledge and engineering approaches to facilitate better understanding of biological systems, improve decision-making, reduce risk, and increase efficiency in drug discovery and development. It is a type of quantitative systems pharmacology (QSP) approach that places drug-specific properties in the context of disease biology. This tutorial provides a broadly applicable model qualification method (MQM) to ensure that mechanistic physiological models are fit for their intended purposes.

Text mining in livestock animal science: introducing the potential of text mining to animal sciences.

In biological research, establishing the prior art by searching and collecting information already present in the domain has equal importance as the experiments done. To obtain a complete overview about the relevant knowledge, researchers mainly rely on 2 major information sources: i) various biological databases and ii) scientific publications in the field. The major difference between the 2 information sources is that information from databases is available, typically well structured and condensed. The information content in scientific literature is vastly unstructured; that is, dispersed among the many different sections of scientific text. The traditional method of information extraction from scientific literature occurs by generating a list of relevant publications in the field of interest and manually scanning these texts for relevant information, which is very time consuming. It is more than likely that in using this "classical" approach the researcher misses some relevant information mentioned in the literature or has to go through biological databases to extract further information. Text mining and named entity recognition methods have already been used in human genomics and related fields as a solution to this problem. These methods can process and extract information from large volumes of scientific text. Text mining is defined as the automatic extraction of previously unknown and potentially useful information from text. Named entity recognition (NER) is defined as the method of identifying named entities (names of real world objects; for example, gene/protein names, drugs, enzymes) in text. In animal sciences, text mining and related methods have been briefly used in murine genomics and associated fields, leaving behind other fields of animal sciences, such as livestock genomics. The aim of this work was to develop an information retrieval platform in the livestock domain focusing on livestock publications and the recognition of relevant data from cattle and pigs. For this purpose, the rather noncomprehensive resources of pig and cattle gene and protein terminologies were enriched with orthologue synonyms, integrated in the NER platform, ProMiner, which is successfully used in human genomics domain. Based on the performance tests done, the present system achieved a fair performance with precision 0.64, recall 0.74, and F(1) measure of 0.69 in a test scenario based on cattle literature.

Prediction of 60 day case-fatality after aneurysmal subarachnoid haemorrhage: results from the International Subarachnoid Aneurysm Trial (ISAT).

Aneurysmal subarachnoid haemorrhage (aSAH) is a devastating event with substantial case-fatality. Our purpose was to examine which clinical and neuro-imaging characteristics, available on admission, predict 60 day case-fatality in aSAH and to evaluate performance of our prediction model. We performed a secondary analysis of patients enrolled in the International Subarachnoid Aneurysm Trial (ISAT), a randomised multicentre trial to compare coiling with clipping in aSAH patients. Multivariable logistic regression analysis was used to develop a prognostic model to estimate the risk of dying within 60 days from aSAH based on clinical and neuro-imaging characteristics. The model was internally validated with bootstrapping techniques. The study population comprised of 2,128 patients who had been randomised to either endovascular coiling or neurosurgical clipping. In this population 153 patients (7.2%) died within 60 days. World Federation of Neurosurgical Societies (WFNS) grade was the most important predictor of case-fatality, followed by age, lumen size of the aneurysm and Fisher grade. The model discriminated reasonably between those who died within 60 days and those who survived (c statistic = 0.73), with minor optimism according to bootstrap re-sampling (optimism corrected c statistic = 0.70). Several strong predictors are available to predict 60 day case-fatality in aSAH patients who survived the early stage up till a treatment decision; after external validation these predictors could eventually be used in clinical decision making.

@neurIST - chronic disease management through integration of heterogeneous data and computer-interpretable guideline services.

This paper presents an overview of computerised decision support for clinical practice. The concept of computer-interpretable guidelines is introduced in the context of the @neurIST project, which aims at supporting the research and treatment of asymptomatic unruptured cerebral aneurysms by bringing together heterogeneous data, computing and complex processing services. The architecture is generic enough to adapt it to the treatment of other diseases beyond cerebral aneurysms. The paper reviews the generic requirements of the @neurIST system and presents the innovative work in distributing executable clinical guidelines.