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BACKGROUND AND OBJECTIVES - Obtaining IgM and IgG titres is important in numerous clinical situations, including solid-organ transplant, obstetrics, and for testing of out-of-group plasma-containing components. Tube method is the most prevalent testing modality, though it is both labour-intensive and known for intra- and inter-laboratory variability. The utility of automated gel testing as a method to improve both inter- and intra-laboratory reproducibility is unknown.
MATERIALS AND METHODS - Two academic centres participated in a study evaluating automated gel titreing. Group O plasma samples were used to measure titres of antibodies against ABO (IgM) with buffered gel cards and 4 minor and minor red-blood-cell antigens (IgG) anti-IgG gel cards. Multiple ORTHO VISION automated analyzers were used to assess inter-instrument variation. A subset of ABO (IgM) samples were compared between laboratories to evaluate inter-laboratory variability. Multiple samples were titred by tube and by automated gel technology to determine similarity of results.
RESULTS - Testing demonstrated no significant difference between analysers or between sites when performing automated titrations (P ≥ 0·99). Non-ABO IgG titres were evaluated and demonstrated little inter-instrument variability. The IgM anti-A and -B titres obtained by automated gel testing were neither consistently higher nor lower than tube titres. Greater than 90% of titre values were within one dilution.
CONCLUSION - Based on this study, our data suggest that titreing by automated gel testing is both highly reproducible (IgM and IgG) and does not differ significantly from manual tube testing results of direct agglutination (IgM).
© 2020 International Society of Blood Transfusion.
BACKGROUND - Circulating biomarkers can facilitate diagnosis and risk stratification for complex conditions such as heart failure (HF). Newer molecular platforms can accelerate biomarker discovery, but they require significant resources for data and sample acquisition.
OBJECTIVES - The purpose of this study was to test a pragmatic biomarker discovery strategy integrating automated clinical biobanking with proteomics.
METHODS - Using the electronic health record, the authors identified patients with and without HF, retrieved their discarded plasma samples, and screened these specimens using a DNA aptamer-based proteomic platform (1,129 proteins). Candidate biomarkers were validated in 3 different prospective cohorts.
RESULTS - In an automated manner, plasma samples from 1,315 patients (31% with HF) were collected. Proteomic analysis of a 96-patient subset identified 9 candidate biomarkers (p < 4.42 × 10). Two proteins, angiopoietin-2 and thrombospondin-2, were associated with HF in 3 separate validation cohorts. In an emergency department-based registry of 852 dyspneic patients, the 2 biomarkers improved discrimination of acute HF compared with a clinical score (p < 0.0001) or clinical score plus B-type natriuretic peptide (p = 0.02). In a community-based cohort (n = 768), both biomarkers predicted incident HF independent of traditional risk factors and N-terminal pro-B-type natriuretic peptide (hazard ratio per SD increment: 1.35 [95% confidence interval: 1.14 to 1.61; p = 0.0007] for angiopoietin-2, and 1.37 [95% confidence interval: 1.06 to 1.79; p = 0.02] for thrombospondin-2). Among 30 advanced HF patients, concentrations of both biomarkers declined (80% to 84%) following cardiac transplant (p < 0.001 for both).
CONCLUSIONS - A novel strategy integrating electronic health records, discarded clinical specimens, and proteomics identified 2 biomarkers that robustly predict HF across diverse clinical settings. This approach could accelerate biomarker discovery for many diseases.
Copyright © 2019 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
BACKGROUND - Convolutional neural networks (CNNs) are advanced artificial intelligence algorithms well suited to image classification tasks with variable features. These have been used to great effect in various real-world applications including handwriting recognition, face detection, image search, and fraud prevention. We sought to retrain a robust CNN with coronal computed tomography (CT) images to classify osteomeatal complex (OMC) occlusion and assess the performance of this technology with rhinologic data.
METHODS - The Google Inception-V3 CNN trained with 1.28 million images was used as the base model. Preoperative coronal sections through the OMC were obtained from 239 patients enrolled in 2 prospective chronic rhinosinusitis (CRS) outcomes studies, labeled according to OMC status, and mirrored to obtain a set of 956 images. Using this data, the classification layer of Inception-V3 was retrained in Python using a transfer learning method to adapt the CNN to the task of interpreting sinonasal CT images.
RESULTS - The retrained neural network achieved 85% classification accuracy for OMC occlusion, with a 95% confidence interval for algorithm accuracy of 78% to 92%. Receiver operating characteristic (ROC) curve analysis on the test set confirmed good classification ability of the CNN with an area under the ROC curve (AUC) of 0.87, significantly different than both random guessing and a dominant classifier that predicts the most common class (p < 0.0001).
CONCLUSION - Current state-of-the-art CNNs may be able to learn clinically relevant information from 2-dimensional sinonasal CT images with minimal supervision. Future work will extend this approach to 3-dimensional images in order to further refine this technology for possible clinical applications.
© 2018 ARS-AAOA, LLC.
OBJECTIVE - Changes in microvascular perfusion have been reported in many diseases, yet the functional significance of altered perfusion is often difficult to determine. This is partly because commonly used techniques for perfusion measurement often rely on either indirect or by-hand approaches.
METHODS - We developed and validated a fully automated software technique to measure microvascular perfusion in videos acquired by fluorescence microscopy in the mouse gastrocnemius. Acute perfusion responses were recorded following intravenous injections with phenylephrine, SNP, or saline.
RESULTS - Software-measured capillary flow velocity closely correlated with by-hand measured flow velocity (R = 0.91, P < 0.0001). Software estimates of capillary hematocrit also generally agreed with by-hand measurements (R = 0.64, P < 0.0001). Detection limits range from 0 to 2000 μm/s, as compared to an average flow velocity of 326 ± 102 μm/s (mean ± SD) at rest. SNP injection transiently increased capillary flow velocity and hematocrit and made capillary perfusion more steady and homogenous. Phenylephrine injection had the opposite effect in all metrics. Saline injection transiently decreased capillary flow velocity and hematocrit without influencing flow distribution or stability. All perfusion metrics were temporally stable without intervention.
CONCLUSIONS - These results demonstrate a novel and sensitive technique for reproducible, user-independent quantification of microvascular perfusion.
© 2018 John Wiley & Sons Ltd.
OBJECTIVES - Registry-based clinical research in nephrolithiasis is critical to advancing quality in urinary stone disease management and ultimately reducing stone recurrence. A need exists to develop Health Insurance Portability and Accountability Act (HIPAA)-compliant registries that comprise integrated electronic health record (EHR) data using prospectively defined variables. An EHR-based standardized patient database-the Registry for Stones of the Kidney and Ureter (ReSKU™)-was developed, and herein we describe our implementation outcomes.
MATERIALS AND METHODS - Interviews with academic and community endourologists in the United States, Canada, China, and Japan identified demographic, intraoperative, and perioperative variables to populate our registry. Variables were incorporated into a HIPAA-compliant Research Electronic Data Capture database linked to text prompts and registration data within the Epic EHR platform. Specific data collection instruments supporting New patient, Surgery, Postoperative, and Follow-up clinical encounters were created within Epic to facilitate automated data extraction into ReSKU.
RESULTS - The number of variables within each instrument includes the following: New patient-60, Surgery-80, Postoperative-64, and Follow-up-64. With manual data entry, the mean times to complete each of the clinic-based instruments were (minutes) as follows: New patient-12.06 ± 2.30, Postoperative-7.18 ± 1.02, and Follow-up-8.10 ± 0.58. These times were significantly reduced with the use of ReSKU structured clinic note templates to the following: New patient-4.09 ± 1.73, Postoperative-1.41 ± 0.41, and Follow-up-0.79 ± 0.38. With automated data extraction from Epic, manual entry is obviated.
CONCLUSIONS - ReSKU is a longitudinal prospective nephrolithiasis registry that integrates EHR data, lowering the barriers to performing high quality clinical research and quality outcome assessments in urinary stone disease.
Numerous compounds stimulate rodent β-cell proliferation; however, translating these findings to human β-cells remains a challenge. To examine human β-cell proliferation in response to such compounds, we developed a medium-throughput in vitro method of quantifying adult human β-cell proliferation markers. This method is based on high-content imaging of dispersed islet cells seeded in 384-well plates and automated cell counting that identifies fluorescently labeled β-cells with high specificity using both nuclear and cytoplasmic markers. β-Cells from each donor were assessed for their function and ability to enter the cell cycle by cotransduction with adenoviruses encoding cell cycle regulators cdk6 and cyclin D3. Using this approach, we tested 12 previously identified mitogens, including neurotransmitters, hormones, growth factors, and molecules, involved in adenosine and Tgf-1β signaling. Each compound was tested in a wide concentration range either in the presence of basal (5 mM) or high (11 mM) glucose. Treatment with the control compound harmine, a Dyrk1a inhibitor, led to a significant increase in Ki-67 β-cells, whereas treatment with other compounds had limited to no effect on human β-cell proliferation. This new scalable approach reduces the time and effort required for sensitive and specific evaluation of human β-cell proliferation, thus allowing for increased testing of candidate human β-cell mitogens.
An open-source hyperpolarizer producing (13)C hyperpolarized contrast agents using parahydrogen induced polarization (PHIP) for biomedical and other applications is presented. This PHIP hyperpolarizer utilizes an Arduino microcontroller in conjunction with a readily modified graphical user interface written in the open-source processing software environment to completely control the PHIP hyperpolarization process including remotely triggering an NMR spectrometer for efficient production of payloads of hyperpolarized contrast agent and in situ quality assurance of the produced hyperpolarization. Key advantages of this hyperpolarizer include: (i) use of open-source software and hardware seamlessly allowing for replication and further improvement as well as readily customizable integration with other NMR spectrometers or MRI scanners (i.e., this is a multiplatform design), (ii) relatively low cost and robustness, and (iii) in situ detection capability and complete automation. The device performance is demonstrated by production of a dose (∼2-3 mL) of hyperpolarized (13)C-succinate with %P13C ∼ 28% and 30 mM concentration and (13)C-phospholactate at %P13C ∼ 15% and 25 mM concentration in aqueous medium. These contrast agents are used for ultrafast molecular imaging and spectroscopy at 4.7 and 0.0475 T. In particular, the conversion of hyperpolarized (13)C-phospholactate to (13)C-lactate in vivo is used here to demonstrate the feasibility of ultrafast multislice (13)C MRI after tail vein injection of hyperpolarized (13)C-phospholactate in mice.
BACKGROUND - Aversive olfactory classical conditioning has been the standard method to assess Drosophila learning and memory behavior for decades, yet training and testing are conducted manually under exceedingly labor-intensive conditions. To overcome this severe limitation, a fully automated, inexpensive system has been developed, which allows accurate and efficient Pavlovian associative learning/memory analyses for high-throughput pharmacological and genetic studies.
NEW METHOD - The automated system employs a linear actuator coupled to an odorant T-maze with airflow-mediated transfer of animals between training and testing stages. Odorant, airflow and electrical shock delivery are automatically administered and monitored during training trials. Control software allows operator-input variables to define parameters of Drosophila learning, short-term memory and long-term memory assays.
RESULTS - The approach allows accurate learning/memory determinations with operational fail-safes. Automated learning indices (immediately post-training) and memory indices (after 24h) are comparable to traditional manual experiments, while minimizing experimenter involvement.
COMPARISON WITH EXISTING METHODS - The automated system provides vast improvements over labor-intensive manual approaches with no experimenter involvement required during either training or testing phases. It provides quality control tracking of airflow rates, odorant delivery and electrical shock treatments, and an expanded platform for high-throughput studies of combinational drug tests and genetic screens. The design uses inexpensive hardware and software for a total cost of ∼$500US, making it affordable to a wide range of investigators.
CONCLUSIONS - This study demonstrates the design, construction and testing of a fully automated Drosophila olfactory classical association apparatus to provide low-labor, high-fidelity, quality-monitored, high-throughput and inexpensive learning and memory behavioral assays.
Copyright © 2015 Elsevier B.V. All rights reserved.
OBJECTIVE - Assessment of medical trainee learning through pre-defined competencies is now commonplace in schools of medicine. We describe a novel electronic advisor system using natural language processing (NLP) to identify two geriatric medicine competencies from medical student clinical notes in the electronic medical record: advance directives (AD) and altered mental status (AMS).
MATERIALS AND METHODS - Clinical notes from third year medical students were processed using a general-purpose NLP system to identify biomedical concepts and their section context. The system analyzed these notes for relevance to AD or AMS and generated custom email alerts to students with embedded supplemental learning material customized to their notes. Recall and precision of the two advisors were evaluated by physician review. Students were given pre and post multiple choice question tests broadly covering geriatrics.
RESULTS - Of 102 students approached, 66 students consented and enrolled. The system sent 393 email alerts to 54 students (82%), including 270 for AD and 123 for AMS. Precision was 100% for AD and 93% for AMS. Recall was 69% for AD and 100% for AMS. Students mentioned ADs for 43 patients, with all mentions occurring after first having received an AD reminder. Students accessed educational links 34 times from the 393 email alerts. There was no difference in pre (mean 62%) and post (mean 60%) test scores.
CONCLUSIONS - The system effectively identified two educational opportunities using NLP applied to clinical notes and demonstrated a small change in student behavior. Use of electronic advisors such as these may provide a scalable model to assess specific competency elements and deliver educational opportunities.
Copyright © 2015 Elsevier Inc. All rights reserved.
Cell-matrix adhesions are of great interest because of their contribution to numerous biological processes, including cell migration, differentiation, proliferation, survival, tissue morphogenesis, wound healing, and tumorigenesis. Adhesions are dynamic structures that are classically defined on two-dimensional (2D) substrates, though the need to analyze adhesions in more physiologic three-dimensional (3D) environments is being increasingly recognized. However, progress has been greatly hampered by the lack of available tools to analyze adhesions in 3D environments. To address this need, we have developed a platform for the automated analysis, segmentation, and tracking of adhesions (PAASTA) based on an open source MATLAB framework, CellAnimation. PAASTA enables the rapid analysis of adhesion dynamics and many other adhesion characteristics, such as lifetime, size, and location, in 3D environments and on traditional 2D substrates. We manually validate PAASTA and utilize it to quantify rate constants for adhesion assembly and disassembly as well as adhesion lifetime and size in 3D matrices. PAASTA will be a valuable tool for characterizing adhesions and for deciphering the molecular mechanisms that regulate adhesion dynamics in 3D environments.