Similarity measuring between patient traces for clinical pathway analysis

Clinical pathways leave traces, described as activity sequences with regard to a mixture of various latent treatment behaviors. Measuring similarities between patient traces can profitably be exploited further as a basis for providing insights into the pathways, and complementing existing techniques of clinical pathway analysis, which mainly focus on looking at aggregated data seen from an external perspective. In this paper, a probabilistic graphical model, i.e., Latent Dirichlet Allocation, is employed to discover latent treatment behaviors of patient traces for clinical pathways such that similarities of pairwise patient traces can be measured based on their underlying behavioral topical features. The presented method, as a basis for further tasks in clinical pathway analysis, are evaluated via a real-world data-set collected from a Chinese hospital

Similarity measuring between patient traces for clinical pathway analysis

Clinical pathways leave traces, described as activity sequences with regard to a mixture of various latent treatment behaviors. Measuring similarities between patient traces can profitably be exploited further as a basis for providing insights into the pathways, and complementing existing techniques of clinical pathway analysis, which mainly focus on looking at aggregated data seen from an external perspective. In this paper, a probabilistic graphical model, i.e., Latent Dirichlet Allocation, is employed to discover latent treatment behaviors of patient traces for clinical pathways such that similarities of pairwise patient traces can be measured based on their underlying behavioral topical features. The presented method, as a basis for further tasks in clinical pathway analysis, are evaluated via a real-world data-set collected from a Chinese hospital

Research on prokaryocyte expression and biological activity of the core region of Bloom’s Syndrome protein

To establish an effective approach for inducing expression of the RecQ core of Bloom’s Syndrome protein (BLM642-1290) and assaying its biological activity in vitro, BLM642-1290 recombinant protein was expressed with IPTG at room temperature in Escherichia coli, and then the expressed product was assayed using SDS-PAGE and western blotting. After purification via affinity chromatography, DNA binding activity and unwinding activity of the protein were assessed by fluorescence polarization. Furthermore, the ATPase activity of the protein was also assayed using ultraviolet spectrophotometry based on PiColorLock Gold reagent. An effective expression method was established for BLM protein in E. coli. The obvious bioactivities of the protein were observed in binding to ssDNA or dsDNA, unwinding the dsDNA in the presence of ATP, as well as catalyzing ATP hydrolysis in the presence of ssDNA in vitro. The prokaryocyte expression method of BLM642-1290 was established successfully and the protein with biological activity was obtained from recombinant E.coli. This would be significant to provide a better understanding on BLM protein and facilitate the elucidation of mechanism of pathopoiesia in Bloom’s Syndrome.Keywords: BLM642-1290 protein, induced expression, enzymatic activit