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PURPOSE - Tissue compression during ultrasound imaging leads to error in the location and geometry of subsurface targets during soft tissue interventions. We present a novel compression correction method, which models a generic block of tissue and its subsurface tissue displacements resulting from application of a probe to the tissue surface. The advantages of the new method are that it can be realized independent of preoperative imaging data and is capable of near-video framerate compression compensation for real-time guidance.
METHODS - The block model is calibrated to the tip of any tracked ultrasound probe. Intraoperative digitization of the tissue surface is used to measure the depth of compression and provide boundary conditions to the biomechanical model of the tissue. The tissue displacement field solution of the model is inverted to nonrigidly transform the ultrasound images to an estimation of the tissue geometry prior to compression. This method was compared to a previously developed method using a patient-specific model and within the context of simulation, phantom, and clinical data.
RESULTS - Experimental results with gel phantoms demonstrated that the proposed generic method reduced the mock tumor margin modified Hausdorff distance (MHD) from 5.0 ± 1.6 to 2.1 ± 0.7 mm and reduced mock tumor centroid alignment error from 7.6 ± 2.6 to 2.6 ± 1.1mm. The method was applied to a clinical case and reduced the in vivo tumor margin MHD error from 5.4 ± 0.1 to 2.9 ± 0.1mm, and the centroid alignment error from 7.2 ± 0.2 to 3.8 ± 0.4 mm.
CONCLUSIONS - The correction method was found to effectively improve alignment of ultrasound and tomographic images and was more efficient compared to a previously proposed correction.
We report herein application of an in situ material strategy to attenuate allograft T cell responses in a skin transplant mouse model. Functionalized peptidic membranes were used to impede trafficking of donor antigen-presenting cells (dAPCs) from skin allografts in recipient mice. Membranes formed by self-assembling peptides (SAPs) presenting antibodies were found to remain underneath grafted skins for up to 6 days. At the host-graft interface, dAPCs were targeted by using a monoclonal antibody that binds to a class II major histocompatibility complex (MHC) molecule (I-A(d)) expressed exclusively by donor cells. Using a novel cell labeling near-infrared nanoemulsion, we found more dAPCs remained in allografts treated with membranes loaded with anti-I-A(d) antibodies than without. In vitro, dAPCs released from skin explants were found adsorbed preferentially on anti-I-A(d) antibody-loaded membranes. Recipient T cells from these mice produced lower concentrations of interferon-gamma cultured ex vivo with donor cells. Taken together, the data indicate that the strategy has the potential to alter the natural course of rejection immune mechanisms in allogeneic transplant models.
Copyright © 2014 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
BACKGROUND - Anesthesiology residencies are developing trainee assessment tools to evaluate 25 milestones that map to the six core competencies. The effort will be facilitated by development of automated methods to capture, assess, and report trainee performance to program directors, the Accreditation Council for Graduate Medical Education and the trainees themselves.
METHODS - The authors leveraged a perioperative information management system to develop an automated, near-real-time performance capture and feedback tool that provides objective data on clinical performance and requires minimal administrative effort. Before development, the authors surveyed trainees about satisfaction with clinical performance feedback and about preferences for future feedback.
RESULTS - Resident performance on 24,154 completed cases has been incorporated into the authors' automated dashboard, and trainees now have access to their own performance data. Eighty percent (48 of 60) of the residents responded to the feedback survey. Overall, residents "agreed/strongly agreed" that they desire frequent updates on their clinical performance on defined quality metrics and that they desired to see how they compared with the residency as a whole. Before deployment of the new tool, they "disagreed" that they were receiving feedback in a timely manner. Survey results were used to guide the format of the feedback tool that has been implemented.
CONCLUSION - The authors demonstrate the implementation of a system that provides near-real-time feedback concerning resident performance on an extensible series of quality metrics, and which is responsive to requests arising from resident feedback about desired reporting mechanisms.
OBJECTIVE - Healthcare analytics research increasingly involves the construction of predictive models for disease targets across varying patient cohorts using electronic health records (EHRs). To facilitate this process, it is critical to support a pipeline of tasks: (1) cohort construction, (2) feature construction, (3) cross-validation, (4) feature selection, and (5) classification. To develop an appropriate model, it is necessary to compare and refine models derived from a diversity of cohorts, patient-specific features, and statistical frameworks. The goal of this work is to develop and evaluate a predictive modeling platform that can be used to simplify and expedite this process for health data.
METHODS - To support this goal, we developed a PARAllel predictive MOdeling (PARAMO) platform which (1) constructs a dependency graph of tasks from specifications of predictive modeling pipelines, (2) schedules the tasks in a topological ordering of the graph, and (3) executes those tasks in parallel. We implemented this platform using Map-Reduce to enable independent tasks to run in parallel in a cluster computing environment. Different task scheduling preferences are also supported.
RESULTS - We assess the performance of PARAMO on various workloads using three datasets derived from the EHR systems in place at Geisinger Health System and Vanderbilt University Medical Center and an anonymous longitudinal claims database. We demonstrate significant gains in computational efficiency against a standard approach. In particular, PARAMO can build 800 different models on a 300,000 patient data set in 3h in parallel compared to 9days if running sequentially.
CONCLUSION - This work demonstrates that an efficient parallel predictive modeling platform can be developed for EHR data. This platform can facilitate large-scale modeling endeavors and speed-up the research workflow and reuse of health information. This platform is only a first step and provides the foundation for our ultimate goal of building analytic pipelines that are specialized for health data researchers.
Copyright © 2013 Elsevier Inc. All rights reserved.
PURPOSE - Head motion continues to be a major source of artifacts and data quality degradation in MRI. The goal of this work was to develop and demonstrate a novel technique for prospective, 6 degrees of freedom (6DOF) rigid body motion estimation and real-time motion correction using inductively coupled wireless nuclear magnetic resonance (NMR) probe markers.
METHODS - Three wireless probes that are inductively coupled with the scanner's RF setup serve as fiducials on the subject's head. A 12-ms linear navigator module is interleaved with the imaging sequence for head position estimation, and scan geometry is updated in real time for motion compensation. Flip angle amplification in the markers allows the use of extremely small navigator flip angles (∼1°). A novel algorithm is presented to identify marker positions in the absence of marker specific receive channels. Motion correction is demonstrated in high resolution 2D and 3D gradient recalled echo experiments in a phantom and humans.
RESULTS - Significant improvement of image quality is demonstrated in phantoms and human volunteers under different motion conditions.
CONCLUSION - A novel real-time 6DOF head motion correction technique based on wireless NMR probes is demonstrated in high resolution imaging at 7 Tesla.
Copyright © 2013 Wiley Periodicals, Inc.
BACKGROUND - Pneumococcal vaccination is an effective strategy to prevent invasive pneumococcal disease in the elderly. Emergency department (ED) visits present an underutilized opportunity to increase vaccination rates; however, designing a sustainable vaccination program in an ED is challenging. We examined whether an information technology supported approach would provide a feasible and sustainable method to increase vaccination rates in an adult ED.
METHODS - During a 1-year period we prospectively evaluated a team-oriented, workflow-embedded reminder system that integrated four different information systems. The computerized triage application screened all patients 65 years and older for pneumococcal vaccine eligibility with information from the electronic patient record. For eligible patients the computerized provider order entry system reminded clinicians to place a vaccination order, which was passed to the order tracking application. Documentation of vaccine administration was then added to the longitudinal electronic patient record. The primary outcome was the vaccine administration rate in the ED. Multivariate logistic regression analysis was used to estimate the odds ratios and their 95% confidence intervals, representing the overall relative risks of ED workload related variables associated with vaccination rate.
RESULTS - Among 3371 patients 65 years old and older screened at triage 1309 (38.8%) were up-to-date with pneumococcal vaccination and 2062 (61.2%) were eligible for vaccination. Of the eligible patients, 621 (30.1%) consented to receive the vaccination during their ED visit. Physicians received prompts for 428 (68.9%) patients. When prompted, physicians declined to order the vaccine in 192 (30.9%) patients, while 222 (10.8%) of eligible patients actually received the vaccine. The computerized reminder system increased vaccination rate from a baseline of 38.8% to 45.4%. Vaccination during the ED visit was associated younger age (OR: 0.972, CI: 0.953-0.991), Caucasian race (OR: 0.329, CI: 0.241-0.448), and longer ED boarding times (OR: 1.039, CI: 1.013-1.065).
CONCLUSION - The integrated informatics solution seems to be a feasible and sustainable model to increase vaccination rates in a challenging ED environment.
Copyright © 2011 Elsevier Ltd. All rights reserved.
Gastric adenocarcinoma is the second leading cause of cancer death worldwide. Epstein-Barr virus (EBV) is present in the malignant cells of approximately 10% of cases. It is unclear whether EBV is being missed in some gastric adenocarcinomas due to insensitive test methods or partial EBV genome loss. In this study, we screened 113 gastric adenocarcinomas from low- and high-incidence regions (United States and Central America) for the presence of EBV using a battery quantitative real-time PCR (Q-PCR) assays targeting disparate segments of the EBV genome (BamH1W, EBNA1, LMP1, LMP2, BZLF1, EBER1) and histochemical stains targeting EBV-encoded RNA (EBER), the latent proteins LMP1 and LMP2, and the lytic proteins BMRF1 and BZLF1. EBV DNA was detected by Q-PCR in 48/75 United States cancers (64%) and in 38/38 Central American cancers (100%), which was a significant difference. EBER was localized to malignant epithelial cells in 8/48 (17%) United States and 3/38 (8%) Central American cancers. Viral loads were considerably higher for EBER-positive vs EBER-negative cancers (mean 162 986 vs 62 EBV DNA copies per 100,000 cells). A viral load of 2000 copies per 100,000 cells is recommended as the threshold distinguishing EBER-positive from EBER-negative tumors. One infected cancer selectively failed to amplify the LMP2 gene because of a point mutation, whereas another cancer had an atypical pattern of Q-PCR positivity suggesting deletion of large segments of the EBV genome. Three different viral latency profiles were observed in the cancers based on constant expression of EBER and focal or variable expression of LMP1 or LMP2, without lytic protein expression. We conclude that EBV DNA levels generally reflect EBER status, and a panel of at least two Q-PCR assays is recommended for sensitive identification of infected cancers.
Studies were undertaken to compare and contrast the two-dimensional protein profiles of epithelial and stromal cells from hyperplastic human prostate to establish the protein composition of the two major cellular components of the prostate. Epithelial and stromal cells were isolated from human prostate obtained from patients undergoing open prostatectomy for benign prostatic hyperplasia (BPH). Proteins, isolated from the two cell populations and separated by two-dimensional (2D) electrophoresis, were analyzed by silver staining, fluorography of [35S]-methionine-labeled proteins, and immunoprotein blotting. Isolated prostatic epithelial cells, but not stromal cells, contained cytokeratin polypeptides 5, 6, 7, 8, 13, 14, 15, 16, 17, 18, and 19. Although vimentin could not be identified in silver stained 2D gels and fluorographs of cultured prostatic epithelial cells, a low level of immunoreactivity was noted following immunoblot analysis of epithelial cells proteins by the use of an anti-vimentin polyclonal. Vimentin was prominently expressed in cultured prostatic stromal cells and could be identified on silver stained 2D gels, fluorographs, and immunoblots of stroma-derived proteins. In addition, stromal marker proteins SM1, SM2, and SM3 were identified in 2D gels of stromal cells to distinguish them from epithelial cells. These studies demonstrate (1) the two-dimensional protein profile and cytokeratin polypeptide composition of cultured epithelial cells from hyperplastic human prostate and (2) the 2D protein profile of cultured prostatic stromal cells and identification of specific stromal marker proteins.