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Objective: To describe the reusable transformation process of electronic health records (EHR), claims, and prescriptions data into Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM), together with challenges faced and solutions implemented. Materials and Methods: We used Estonian national health databases that store almost all residents' claims, prescriptions, and EHR records. To develop and demonstrate the transformation process of Estonian health data to OMOP CDM, we used a 10% random sample of the Estonian population (n = 150 824 patients) from 2012 to 2019 (MAITT dataset). For the sample, complete information from all 3 databases was converted to OMOP CDM version 5.3. The validation was performed using open-source tools. Results: In total, we transformed over 100 million entries to standard concepts using standard OMOP vocabularies with the average mapping rate 95%. For conditions, observations, drugs, and measurements, the mapping rate was over 90%. In most cases, SNOMED Clinical Terms were used as the target vocabulary. Discussion: During the transformation process, we encountered several challenges, which are described in detail with concrete examples and solutions. Conclusion: For a representative 10% random sample, we successfully transferred complete records from 3 national health databases to OMOP CDM and created a reusable transformation process. Our work helps future researchers to transform linked databases into OMOP CDM more efficiently, ultimately leading to better real-world evidence.
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AIMS: The intraportal pancreatic islet transplantation (IPIT) model of diabetic rats is an insulin mediated model of hepatocarcinogenesis characterized by the induction of clear cell foci (CCF) of altered hepatocytes, which are pre-neoplastic lesions excessively storing glycogen (glycogenosis) and exhibiting activation of the AKT/mTOR protooncogenic pathway. In this study, we transferred the IPIT model to the mouse and combined it with the knockout of the transcription factor carbohydrate responsive element binding protein (chREBP). METHODS: C57BL/6J Wild-type (WT) and chREBP-knockout (chREBP-KO) mice (n = 297) were matched to 16 groups (WT/ chREBP-KO, experimental/control, streptozotocine-induced diabetic/not diabetic, one/four weeks). Experimental groups received the intraportal transplantation of 70 pancreatic islets. Liver and pancreatic tissue was examined using histology, morphometry, enzyme- and immunohistochemistry and electron microscopy. RESULTS: CCF emerged in the liver acini downstream of the transplanted islets. In comparison to WT lesions, CCF of chREBP-KO mice displayed more glycogen accumulation, reduced activity of the gluconeogenic enzyme glucose-6-phosphatase, decreased glycolysis, lipogenesis and reduced levels of the AKT/mTOR cascade members. Proliferative activity of CCF was â¼two folds higher in WT mice than in chREBP-KO mice. CONCLUSIONS: The IPIT model is applicable to mice, as murine CCF resemble preneoplastic liver lesions from this hepatocarcinogenesis model in the rat in terms of morphological, metabolic and molecular alterations and proliferative activity, which is diminished after chREBP knockout. chREBP appears to be an essential component of AKT/mTOR mediated cell proliferation and the metabolic switch from a glycogenotic to lipogenic phenotype in precursor lesions of hepatocarcinogenesis.
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Protein microarrays are valuable tools for protein assays. Reducing spot sizes from micro- to nano-scale facilitates miniaturization of platforms and consequently decreased material consumption, but faces inherent challenges in the reduction of fluorescent signals and compatibility with complex solutions. Here we show that vertical arrays of nanowires (NWs) can overcome several bottlenecks of using nanoarrays for extraction and analysis of proteins. The high aspect ratio of the NWs results in a large surface area available for protein immobilization and renders passivation of the surface between the NWs unnecessary. Fluorescence detection of proteins allows quantitative measurements and spatial resolution, enabling us to track individual NWs through several analytical steps, thereby allowing multiplexed detection of different proteins immobilized on different regions of the NW array. We use NW arrays for on-chip extraction, detection and functional analysis of proteins on a nano-scale platform that holds great promise for performing protein analysis on minute amounts of material. The demonstration made here on highly ordered arrays of indium arsenide (InAs) NWs is generic and can be extended to many high aspect ratio nanostructures.
