[Probiotic drug therapy with E. coli strain Nissle 1917 (EcN): results of a prospective study of the records of 3,807 patients]. / Probiotische Arzneimitteltherapie mit E. coli Stamm Nissle 1917 (EcN): Ergebnisse einer prospektiven Datenerhebung mit 3,807 Patienten.

INTRODUCTION: Living microorganisms that enter the gut in an active state and exert a positive influence on the host are called probiotics. Numerous experimental and clinical studies were performed recently and confirm both the efficacy and modes of action of probiotic drugs. PATIENTS AND METHODS: In a post-marketing-surveillance study with the probiotic Escherichia Coli strain Nissle 1917 (EcN) data on the range of indications as well as on efficacy and tolerance were gathered prospectively in 446 centres. The intended treatment duration was limited to a maximum of 12 weeks. RESULTS: EcN was used in 3,807 patients with more than 20 different indications, n = 3,511 of whom had gastrointestinal complaints: Among others, 1,067 patients presented with chronically recurring (n = 728) or protracted diarrhoea (n = 339), 415 with inflammatory bowel disease, 679 with irritable bowel syndrome, and 253 with chronic constipation. The overall efficacy was assessed as good to very good by an average of 81.4 % of the therapists. The stool frequency and consistency as well as the symptoms of meteorism and abdominal pain were improved in very many patients. Suspected cases of side effects were documented in only 2.8 % of the patients. CONCLUSION: EcN is frequently used in practice for the treatment of various, mostly gastrointestinal, complaints and is well tolerated.

[Quantitative image analysis in pulmonary pathology - digitalization of preneoplastic lesions in human bronchial epithelium (author's transl)]. / Quantitative Bildanalyse in der Lungenpathologie - Digitalisierung präneoplastischer Veränderungen der menschlichen Bronchialschleimhuat.

The report concerns the first phase of a quantitative study of normal and abnormal bronchial epithelium with the objective of establishing the digitalization of histologic patterns. Preparative methods, data collecting and handling, and further mathematical analysis are described. In cluster and discriminatory analysis the digitalized histologic features can be used to separate and classify the individual cases into the respective diagnostic groups.