Gastrointestinal Disease Outbreak Detection Using Multiple Data Streams from Electronic Medical Records

Background: Passive reporting and laboratory testing delays may limit gastrointestinal (GI) disease outbreak detection. Healthcare systems routinely collect clinical data in electronic medical records (EMRs) that could be used for surveillance. This study's primary objective was to identify data streams from EMRs that may perform well for GI outbreak detection. Methods: Zip code-specific daily episode counts in 2009 were generated for 22 syndromic and laboratory-based data streams from Kaiser Permanente Northern California EMRs, covering 3.3 million members. Data streams included outpatient and inpatient diagnosis codes, antidiarrheal medication dispensings, stool culture orders, and positive microbiology tests for six GI pathogens. Prospective daily surveillance was mimicked using the space-time permutation scan statistic in single and multi-stream analyses, and space-time clusters were identified. Serotype relatedness was assessed for isolates in two Salmonella clusters. Results: Potential outbreaks included a cluster of 18 stool cultures ordered over 5 days in one zip code and a Salmonella cluster in three zip codes over 9 days, in which at least five of six cases had the same rare serotype. In all, 28 potential outbreaks were identified using single stream analyses, with signals in outpatient diagnosis codes most common. Multi-stream analyses identified additional potential outbreaks and in one example, improved the timeliness of detection. Conclusions: GI disease-related data streams can be used to identify potential outbreaks when generated from EMRs with extensive regional coverage. This process can supplement traditional GI outbreak reports to health departments, which frequently consist of outbreaks in well-defined settings (e.g., day care centers and restaurants) with no laboratory-confirmed pathogen. Data streams most promising for surveillance included microbiology test results, stool culture orders, and outpatient diagnoses. In particular, clusters of microbiology tests positive for specific pathogens could be identified in EMRs and used to prioritize further testing at state health departments, potentially improving outbreak detection

Hybrid cellular Potts model for solid tumor growth

We present a hybrid computational framework whose aim is to reproduce and analyze the early growth of a solid tumor. The model couples an extended version of the discrete Cellular Potts Model, used to represent the phenomenological behavior of malignant cells, with a continuous approach of reaction-diffusion equations, employed to describe the evolution of microscopic variables, as the growth factors and the matrix proteins present in the host tissue and the proteolytic enzymes secreted by the tumor. The behavior of each cancer cell is determined by a balance of interaction forces, such as homotypic (cell-cell) and heterotypic (cell-matrix) adhesions and haptotaxis, and is mediated by its molecular state, which regulates the motility and proliferation rate. The resulting model captures the different phases of the development of the tumor mass, i.e. its exponential growth and the subsequent stabilization in a steady-state due to limitations in vital molecules. The proposed approach also predicts the influence on the cancer morphology of changes in specific intercellular adhesive mechanisms

Advanced characterisation techniques for nanostructures

This chapter presents some of the most important currently utilised techniques for the characterisation of nanostructures and nanoparticles. The techniques presented here are grouped into categories of topology, internal structure and compositional investigation. Topological techniques presented here include field emission scanning electron microscopy (FESEM), scanning probe microscopy (SPM), optical microscopy (confocal and NSOM) and particle size distribution with dynamic light scattering (DLS). Internal structure techniques presented include transmission electron microscope (TEM), magnetic resonance force microscope (MRFM) and X-ray diffraction (XRD). Compositional techniques presented include X-ray photoelectron spectroscopy (XPS), energy dispersive X-ray spectroscopy (EDS), secondary ion mass spectroscopy (SIMS) and Auger electron spectroscopy (AES). To highlight the current capabilities and applications of these techniques, case studies from recent literature are presented