Hypoxia-inducible factor-1alpha is a critical mediator of hypoxia induced apoptosis in cardiac H9c2 and kidney epithelial HK-2 cells

<p>Abstract</p> <p>Background</p> <p>Hypoxia inducible factor-1 (HIF-1) is a transcription factor that functions to maintain cellular homeostasis in response to hypoxia. There is evidence that HIF-1 can also trigger apoptosis, possibly when cellular responses are inadequate to meet energy demands under hypoxic conditions.</p> <p>Methods</p> <p>Cardiac derived H9c2 and renal tubular epithelial HK-2 cells expressing either the wild type oxygen regulated subunit of HIF-1 (pcDNA3-Hif-1α) or a dominant negative version that lacked both DNA binding and transactivation domains (pcDNA3-DN-Hif-1α), were maintained in culture and exposed to hypoxia. An RNA interference approach was also employed to selectively knockdown expression of Hif-1α. Apoptosis was analyzed in both H9c2 and HK-2 cells by Hoechst and TUNEL staining, caspase 3 activity assays and activation of pro-apoptotic Bcl2 family member Bax.</p> <p>Results</p> <p>Overexpression of pcDNA3-DN-Hif-1α led to a significant reduction in hypoxia -induced apoptosis (17 ± 2%, <it>P </it>< 0.01) in H9c2 cells compared to both control-transfected and wild type Hif-1α transfected cells. Moreover, selective ablation of HIF-1α protein expression by RNA interference in H9c2 cells led to 55% reduction of caspase 3 activity and 46% reduction in the number of apoptotic cells as determined by Hoechst 33258 staining, after hypoxia. Finally, upregulation of the pro-apoptotic protein, Bax, was found in H9c2 cells overexpressing full-length pcDNA3-HA-HIF-1α exposed to hypoxia. In HK-2 cells overexpression of wild-type Hif-1α led to a two-fold increase in Hif-1α levels during hypoxia. This resulted in a 3.4-fold increase in apoptotic cells and a concomitant increase in caspase 3 activity during hypoxia when compared to vector transfected control cells. HIF-1α also induced upregulation of Bax in HK-2 cells. In addition, introduction of dominant negative Hif-1α constructs in both H9c2 and HK-2 -cells led to decreased active Bax expression.</p> <p>Conclusion</p> <p>These data demonstrate that HIF-1α is an important component of the apoptotic signaling machinery in the two cell types.</p

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase&nbsp;1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation&nbsp;disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age&nbsp; 6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score&nbsp; 652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc&nbsp;= 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N&nbsp;= 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in&nbsp;Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in&nbsp;Asia&nbsp;and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

The IDENTIFY study: the investigation and detection of urological neoplasia in patients referred with suspected urinary tract cancer - a multicentre observational study

Objective To evaluate the contemporary prevalence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC] and renal cancer) in patients referred to secondary care with haematuria, adjusted for established patient risk markers and geographical variation. Patients and Methods This was an international multicentre prospective observational study. We included patients aged ≥16 years, referred to secondary care with suspected urinary tract cancer. Patients with a known or previous urological malignancy were excluded. We estimated the prevalence of bladder cancer, UTUC, renal cancer and prostate cancer; stratified by age, type of haematuria, sex, and smoking. We used a multivariable mixed-effects logistic regression to adjust cancer prevalence for age, type of haematuria, sex, smoking, hospitals, and countries. Results Of the 11 059 patients assessed for eligibility, 10 896 were included from 110 hospitals across 26 countries. The overall adjusted cancer prevalence (n = 2257) was 28.2% (95% confidence interval [CI] 22.3–34.1), bladder cancer (n = 1951) 24.7% (95% CI 19.1–30.2), UTUC (n = 128) 1.14% (95% CI 0.77–1.52), renal cancer (n = 107) 1.05% (95% CI 0.80–1.29), and prostate cancer (n = 124) 1.75% (95% CI 1.32–2.18). The odds ratios for patient risk markers in the model for all cancers were: age 1.04 (95% CI 1.03–1.05; P < 0.001), visible haematuria 3.47 (95% CI 2.90–4.15; P < 0.001), male sex 1.30 (95% CI 1.14–1.50; P < 0.001), and smoking 2.70 (95% CI 2.30–3.18; P < 0.001). Conclusions A better understanding of cancer prevalence across an international population is required to inform clinical guidelines. We are the first to report urinary tract cancer prevalence across an international population in patients referred to secondary care, adjusted for patient risk markers and geographical variation. Bladder cancer was the most prevalent disease. Visible haematuria was the strongest predictor for urinary tract cancer

Evaluating a Novel Summary Visualization for Clinical Trial Reports: A Usability Study.

Contributions of clinical trials are captured in published reports that are unstructured and often require extensive manual review to gain a deeper understanding of the study itself. Our goal is to increase comprehension and decrease the time necessary to understand these reports through the use of visualization tools. In this paper, we specify and evaluate the visualization of a previously developed representation as well as gain insight from user input for further development. The usability experiment consisted of a two-arm study with users either having or not having access to the visualization. A user questionnaire was used to measure time spent and accuracy in comprehension; intuitiveness and reproducibility of the visualization; and preferences. We found that having the visualization required on average 28.1% less time (25.8&nbsp;min vs. 35.8&nbsp;min, p=0.01) while maintaining similar accuracy (73.7% vs. 67.0%). Users were then asked to create their own visualizations, with their visualizations averaging 86.1% similar to the gold standard. All participants either preferred the visualization over the status quo or preferred both equally. These results demonstrate that novel visualizations for trial reports could provide time savings and achieve similar accuracy as reviewing the paper itself. Understanding the strength and quality of clinical trials can be alleviated with a visualization that makes content explicit

A formal representation for numerical data presented in published clinical trial reports.

Assessing the quality of and integrating clinical trial reports are necessary to practice evidence-based medicine. In particular, the numerical data is essential to understanding the strength and quality of the clinical trial study. In this paper, we present a formal representation for standardizing numerical data in published clinical trial reports, and our efforts towards developing computational tools to capture and visualize this representation. The approach includes two aspects: a process model used to precisely define experimental context behind the numerical value; and a spreadsheet, an intuitive and familiar tool used to organize numerical data. We demonstrated this representation using clinical trial reports on non-small cell lung cancer (NSCLC). We performed a preliminary evaluation to determine the usefulness of this formalism for identifying the characteristics, quality and significance of a clinical trial. Our initial results demonstrate that the representation is sufficiently expressive to capture reported numerical information in published papers

Knowledge-Based Image Retrieval with Spatial and Temporal Constructs

A knowledge-based approach to retrieve medical images by feature and content with spatial and temporal constructs is developed. Selected objects of interest in a medical image (e.g. x-ray, MR image) are segmented, and contours are generated from these objects. Features (e.g. shape, size, texture) and content (e.g. spatial relationships among objects) are extracted and stored in a feature and content database. Knowledge about image features can be expressed as a hierarchical structure called a Type Abstraction Hierarchy (TAH). The high-level nodes in the TAH represent more general concepts than low-level nodes. Thus, traversing along TAH nodes allows approximate matching by feature and content if an exact match is not available. TAHs can be generated automatically by clustering algorithms based on feature values in the databases and hence are scalable to large collections of image features. Further, since TAHs are generated based on user classes and applications, they are context- and u..

Imaging-based observational databases for clinical problem solving: the role of informatics

Imaging has become a prevalent tool in the diagnosis and treatment of many diseases, providing a unique in vivo, multi-scale view of anatomic and physiologic processes. With the increased use of imaging and its progressive technical advances, the role of imaging informatics is now evolving—from one of managing images, to one of integrating the full scope of clinical information needed to contextualize and link observations across phenotypic and genotypic scales. Several challenges exist for imaging informatics, including the need for methods to transform clinical imaging studies and associated data into structured information that can be organized and analyzed. We examine some of these challenges in establishing imaging-based observational databases that can support the creation of comprehensive disease models. The development of these databases and ensuing models can aid in medical decision making and knowledge discovery and ultimately, transform the use of imaging to support individually-tailored patient care

Problem-centric Organization and Visualization of Patient Imaging and Clinical Data

A patient’s electronic medical record contains a large amount of unstructured textual information. As patient records become increasingly dense owing to an aging population and increased occurrence of chronic diseases, a tool is needed to help organize and navigate patient data in a way that facilitates a clinician’s ability to understand this information and that improves efficiency. A system has been developed for physicians that summarizes clinical information from a patient record. This system provides a gestalt view of the patient’s record by organizing information about each disease along four dimensions (axes): time (eg, disease progression over time), space (eg, tumor in left frontal lobe), existence (eg, certainty of existence of a finding), and causality (eg, response to treatment). A display is generated from information provided by radiology reports and discharge summaries. Natural language processing is used to identify clinical abnormalities (problems, symptoms, findings) from these reports as well as associated properties and relationships. This information is presented in an integrated format that organizes extracted findings into a problem list, depicts the information on a timeline grid, and provides direct access to relevant reports and images. The goal of this system is to improve the structure of clinical information and its presentation to the physician, thereby simplifying the information retrieval and knowledge discovery necessary to bridge the gap between acquiring raw data and making an informed diagnosis

Assessing Variability in Brain Tumor Segmentation to Improve Volumetric Accuracy and Characterization of Change*

Brain tumor analysis is moving towards volumetric assessment of magnetic resonance imaging (MRI), providing a more precise description of disease progression to better inform clinical decision-making and treatment planning. While a multitude of segmentation approaches exist, inherent variability in the results of these algorithms may incorrectly indicate changes in tumor volume. In this work, we present a systematic approach to characterize variability in tumor boundaries that utilizes equivalence tests as a means to determine whether a tumor volume has significantly changed over time. To demonstrate these concepts, 32 MRI studies from 8 patients were segmented using four different approaches (statistical classifier, region-based, edge-based, knowledge-based) to generate different regions of interest representing tumor extent. We showed that across all studies, the average Dice coefficient for the superset of the different methods was 0.754 (95% confidence interval 0.701–0.808) when compared to a reference standard. We illustrate how variability obtained by different segmentations can be used to identify significant changes in tumor volume between sequential time points. Our study demonstrates that variability is an inherent part of interpreting tumor segmentation results and should be considered as part of the interpretation process