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Andreas Seyfang

Publications and source records attributed to Andreas Seyfang.

5 recordsLinked to original sources
PubMed2003

The ERV-9 LTR enhancer is not blocked by the HS5 insulator and synthesizes through the HS5 site non-coding, long RNAs that regulate LTR enhancer function.

A solitary long terminal repeat (LTR) of ERV-9 human endogenous retrovirus is located upstream of the HS5 site in the human beta-globin locus control region and possesses unique enhancer activity in erythroid K562 cells. In cells transfected with plasmid LTR-HS5-epsilonp-GFP, the LTR enhancer activates the GFP reporter gene and is not blocked by the interposed HS5 site, which has been reported to have insulator function. The LTR enhancer initiates synthesis of long RNAs from the LTR promoter through the intervening HS5 site into the epsilon-globin promoter and the GFP gene. Synthesis of the sense, long LTR RNAs is correlated with high level synthesis of GFP mRNA from the epsilon-globin promoter. Mutations of the LTR promoter and/or the epsilon-globin promoter show that (i) the LTR enhancer can autonomously initiate synthesis of LTR RNAs independent of the promoters and (ii) the LTR RNAs are not processed into GFP mRNA or translated into GFP. However, reversing the orientation of the LTR in plasmid (LTR)rev-HS5-epsilonp-GFP, thus reversing the direction of synthesis of LTR RNAs in the antisense direction away from the epsilon-globin promoter and GFP gene drastically reduces the level of GFP mRNA and thus LTR enhancer function. The results suggest that the LTR-assembled transcription machinery in synthesizing non-coding, LTR RNAs can reach the downstream epsilon-globin promoter to activate transcription of the GFP gene.

Base Sequence↗
PubMed2002

Combining diagnosis and treatment using ASBRU.

Traditionally, diagnosis and treatment have been seen as two distinct tasks. Consequently, most approaches to computer supported health care focus on one of the two-mostly on diagnosis or rather on the interpretation of measurements which is much better understood and formalised. However, in practice diagnosis and treatment overlap and influence each other in many ways. Combinations range from repeatedly going through the diagnosis-treatment loop over a period of time to permanent monitoring of the patients' health condition as it is done in intensive care units. In this article we describe how to model these combinations using the clinical protocol-representation language ASBRU. It implements treatment steps in a hierarchy of skeletal, time-oriented plans. Diagnosis can either be described in a declarative way in the conditions, under which treatment steps are taken or it can be modelled explicitly as plans of their own right. We demonstrate our approach using examples taken from the American Association of Paediatricians' guideline for the treatment of hyperbilirubinemia in the new-born.

Clinical Protocols↗
PubMed2002

Development and evaluation of VIE-PNN, a knowledge-based system for calculating the parenteral nutrition of newborn infants.

Calculating the daily changing composition of parenteral nutrition for small newborn infants is troublesome and time consuming routine work in neonatal intensive care. The task needs expertise and experience and is prone to inherent calculation errors. We designed VIE-PNN (Vienna Expert System for Parenteral Nutrition of Neonates), a knowledge-based system (KBS) in order to reduce daily routine work and calculation errors. VIE-PNN was redesigned several times because the clinicians accepted the system only when it saved time. The most recent version of VIE-PNN uses an Hypertext Markup Language (HTML)-based client-server architecture and is integrated into the intranet of the local patient data management system. Since more than 3 years all parenteral nutrition plans are calculated using VIE-PNN. Evaluating the system's performance and the users contentedness, we compared 50 nutrition plans calculated in parallel using VIE-PNN or a hand-held calculator, retrospectively analyzed more than 5000 nutrition plans stored in VIE-PNNs database and evaluated a user questionnaire. Nutrition plans were calculated in a mean time of 2.4 versus 7.1min using VIE-PNN or the hand-held calculator. Errors and omissions in the nutrition plans were detected in 22% versus 56% and errors in the VIE-PNN's plans occurring only with interactively changed values. Reviews of stored plans show that a mean of 4 out of 16 parameters were interactively changed. VIE-PNN was well accepted. Most important reasons for the successful operation of VIE-PNN in the daily routine work were time savings and robustness of the system.

Artificial Intelligence↗
PubMedDate not supplied

Comparing computer-interpretable guideline models: a case-study approach.

OBJECTIVES: Many groups are developing computer-interpretable clinical guidelines (CIGs) for use during clinical encounters. CIGs use "Task-Network Models" for representation but differ in their approaches to addressing particular modeling challenges. We have studied similarities and differences between CIGs in order to identify issues that must be resolved before a consensus on a set of common components can be developed. DESIGN: We compared six models: Asbru, EON, GLIF, GUIDE, PRODIGY, and PROforma. Collaborators from groups that created these models represented, in their own formalisms, portions of two guidelines: American College of Chest Physicians cough guidelines [correction] and the Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. MEASUREMENTS: We compared the models according to eight components that capture the structure of CIGs. The components enable modelers to encode guidelines as plans that organize decision and action tasks in networks. They also enable the encoded guidelines to be linked with patient data-a key requirement for enabling patient-specific decision support. RESULTS: We found consensus on many components, including plan organization, expression language, conceptual medical record model, medical concept model, and data abstractions. Differences were most apparent in underlying decision models, goal representation, use of scenarios, and structured medical actions. CONCLUSION: We identified guideline components that the CIG community could adopt as standards. Some of the participants are pursuing standardization of these components under the auspices of HL7.

Decision Support Systems, Clinical↗