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We created a software tool that accurately removes all patient identifying information from various kinds of clinical data documents, including laboratory and narrative reports. We created the Medical De-identification System (MeDS), a software tool that de-identifies clinical documents, and performed 2 evaluations. Our first evaluation used 2,400 Health Level Seven (HL7) messages from 10 different HL7 message producers. After modifying the software based on the results of this first evaluation, we performed a second evaluation using 7,190 pathology report HL7 messages. We compared the results of MeDS de-identification process to a gold standard of human review to find identifying strings. For both evaluations, we calculated the number of successful scrubs, missed identifiers, and over-scrubs committed by MeDS and evaluated the readability and interpretability of the scrubbed messages. We categorized all missed identifiers into 3 groups: (1) complete HIPAA-specified identifiers, (2) HIPAA-specified identifier fragments, (3) non-HIPAA-specified identifiers (such as provider names and addresses). In the results of the first-pass evaluation, MeDS scrubbed 11,273 (99.06%) of the 11,380 HIPAA-specified identifiers and 38,095 (98.26%) of the 38,768 non-HIPAA-specified identifiers. In our second evaluation (status postmodification to the software), MeDS scrubbed 79,993 (99.47%) of the 80,418 HIPAA-specified identifiers and 12,689 (96.93%) of the 13,091 non-HIPAA-specified identifiers. Approximately 95% of scrubbed messages were both readable and interpretable. We conclude that MeDS successfully de-identified a wide range of medical documents from numerous sources and creates scrubbed reports that retain their interpretability, thereby maintaining their usefulness for research.
A Software Tool for Removing Patient Identifying Information from Clinical Documents
F. Jeff Friedlin, DO ? and Clement J. McDonald, MD 1
Transcriptome sequencing can be used to determine gene sequences and transcript abundance in non-model species, and the advent of next-generation sequencing (NGS) technologies has greatly decreased the cost and time required for this process. Transcriptome data are especially desirable in bamboo species, as certain members constitute an economically and culturally important group of mostly semelparous plants with remarkable flowering features, yet little bamboo genomic research has been performed. Here we present, for the first time, extensive sequence and transcript abundance data for the floral transcriptome of a key bamboo species, Dendrocalamus latiflorus, obtained using the Illumina GAII sequencing platform. Our further goal was to identify patterns of gene expression during bamboo flower development.
Approximately 96 million sequencing reads were generated and assembled de novo, yielding 146,395 high quality unigenes with an average length of 461 bp. Of these, 80,418 were identified as putative homologs of annotated sequences in the public protein databases, of which 290 were associated with the floral transition and 47 were related to flower development. Digital abundance analysis identified 26,529 transcripts differentially enriched between two developmental stages, young flower buds and older developing flowers. Unigenes found at each stage were categorized according to their putative functional categories. These sequence and putative function data comprise a resource for future investigation of the floral transition and flower development in bamboo species.
Our results present the first broad survey of a bamboo floral transcriptome. Although it will be necessary to validate the functions carried out by these genes, these results represent a starting point for future functional research on D. latiflorus and related species.
De Novo Sequencing and Characterization of the Floral Transcriptome of Dendrocalamus latiflorus (Poaceae: Bambusoideae)
Xue-Mei Zhang,# Lei Zhao,# Zachary Larson-Rabin,# De-Zhu Li, and Zhen-Hua Guo *Michael N. Nitabach, Editor
Human epidermal growth factor receptor (HER)-2-positive breast cancer accounts for ~25% of all breast cancer cases, has a high propensity for relapse, metastasis and drug resistance, and is associated with a poor prognosis. Therefore, it is necessary to develop more effective therapeutic targets for the treatment of HER-2-positive breast cancer. CD44+/CD24?/low is currently the most commonly used marker for breast cancer stem cells (CSCs), which are considered the main cause of drug resistance, relapse and metastasis. In the present study, the ratio of CD44+/CD24?/low cells was almost zero in SK-BR-3 cells; however, it was >90% in MDA-MB-231 cells, as determined by flow cytometry. Since SK-BR-3 and MDA-MB-231 cells both exhibit a strong propensity for invasion and migration, it was hypothesized that there may be other markers of CSCs in SK-BR-3 cells. Therefore, transcriptome sequencing was performed for SK-BR-3 and MDA-MB-231 cells. It was observed that several leukocyte differentiation antigens and other CSC markers were significantly more highly expressed in SK-BR-3 cells. Furthermore, the expression of aldehyde dehydrogenase (ALDH)1A3, CD164 and epithelial cell adhesion molecule (EpCAM) was higher in SK-BR-3 cells compared with in other subtypes of breast cell lines, as determined by reverse transcription-polymerase chain reaction and western blot analysis. In addition, the expression levels of ALDH1A3, ALDH3B2 and EpCAM were higher in HER-2-positive breast cancer compared with in paracancerous tissues and other subtypes of breast cancer, as determined by immunohistochemistry. The expression of β-catenin in the Wnt signaling pathway was lower in SK-BR-3 cells compared with in MDA-MB-231 cells, which may be used as a prognostic indicator for breast cancer. These findings may help identify novel CSC markers and therapeutic targets for HER-2-positive breast cancer.
cancer stem cell markers, signaling pathways, transcriptome sequencing, human epidermal growth factor receptor-2-positive breast cancer
Identification of new cancer stem cell markers and signaling pathways in HER-2-positive breast cancer by transcriptome sequencing
Lu Feng,1,* Shangke Huang,2,* Gaili An,3 Guanying Wang,1 Shanzhi Gu,4,* and Xinhan Zhao1,*