Urinary Thromboxane B2 in Cardiac Transplant Patients as a Screening Method of Rejection

Noninvasive methods for regular monitoring of cardiac transplant patients for acute rejection are preferable to the only currently accepted method involving frequent endomyocardial biopsies. Thromboxane A₂ (TXA₂) is synthesized in large amounts by monocytes/macrophages during organ graft rejection. It enhances T-lymphocyte clonal expansion and cytotoxic function as well as upregulating the major histocompatibility class II expression on antigen presenting cells. Experimentally increased urinary excretion of TXA₂ metabolites is associated with cardiac transplant rejection. We therefore compared urinary immunoreactive thromboxane B₂ (i-TXB₂) levels to the rejection score of the endomyocardial biopsies. In addition we graded the degree of activated lymphocytes in peripheral blood. Urinary i-TXB₂ was significantly higher in patients exhibiting medium to severe rejection than in patients without rejection (1236 ± 372 vs. 526 ± 57 pg/mL). The urine i-TXB₂ (704 ± 48 pg/mL) of all patients who participated in this study, whose endomyocardial biopsy indicated rejection, was also significantly higher than in the non-rejecting group. Increased levels of urine i-TXB₂ were associated with increased biopsy scores. Circulating activated lymphocytes was also significantly increased in patients with moderate/severe rejection compared to patients with no rejection (66 ± 11 vs. 39 ± 4 per mm (3)) (p < 0.01). Further, this study shows that urine i-TXB₂ is associated with increased endomyocardial biopsy scores (acute rejection scores) and blood lymphocyte activation. Thus we conclude that urine i-TXB₂ may be of potential value as a diagnostic screening test for helping identify cardiac transplant patients undergoing acute rejection.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Subwavelength-Sized Plasmonic Structures for Wide-Field Optical Microscopic Imaging with Super-Resolution

We propose a wide-field super-resolved optical microscopic imaging technique based on subwavelength slit arrays embedded in a thin silver film to generate surface plasmon (SP) standing wave interference patterns. These fringes carrying high spatial frequency information serve as excitation profiles to excite the nanoscale fluorescence objects. The super-resolved fluorescence density distribution is reconstructed from a weight sum of a series of fluorescence images with differently phase-shifted SP standing wave illumination. Simulation and experimental results show that the lateral resolution of the reconstructed fluorescence density image is enhanced by 0.28?λ _SP in two dimensions, which is twofold better than that of conventional high numerical aperture fluorescence microscopy. This technique benefits from a grating coupler to offer a simple way for the generation and phase shift of SP standing wave excitation profiles in two dimensions. The flat configuration, wide field, and noninvasive nature make this approach suitable for real-time analyzing the fine details of bio-samples in biochip applications.     

Computational Biomarker Pipeline from Discovery to Clinical Implementation: Plasma Proteomic Biomarkers for Cardiac Transplantation

Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.     

Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging

This work aimed at combining different segmentation approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method. Then, we used an unsupervised statistical evaluation, the extended Regression Without Truth (eRWT) that ranks different methods according to their accuracy in estimating a specific biomarker in a population. The segmentation accuracy was evaluated by estimating six cardiac function parameters resulting from the left ventricle contour delineation using a public cardiac cine MRI database. Eight different segmentation methods, including three expert delineations and five automated methods, were considered, and sixteen combinations of the automated methods using STAPLE were investigated. The supervised and unsupervised evaluations demonstrated that in most cases, STAPLE results provided better estimates than individual automated segmentation methods. Overall, combining different automated segmentation methods improved the reliability of the segmentation result compared to that obtained using an individual method and could achieve the accuracy of an expert.     

Mitochondrial Oscillations and Waves in Cardiac Myocytes: Insights from Computational Models

Periodic cellwide depolarizations of mitochondrial membrane potential (Ψ_M) which are triggered by reactive oxygen species (ROS) and propagated by ROS-induced ROS release (RIRR) have been postulated to contribute to cardiac arrhythmogenesis and injury during ischemia/reperfusion. Two different modes of RIRR have been described: Ψ_M oscillations involving ROS-sensitive mitochondrial inner membrane anion channels (IMAC), and slow depolarization waves related to mitochondrial permeability transition pore (MPTP) opening. In this study, we developed a computational model of mitochondria exhibiting both IMAC-mediated RIRR and MPTP-mediated RIRR, diffusively coupled in a spatially extended network, to study the spatiotemporal dynamics of RIRR on Ψ_M. Our major findings are: 1), as the rate of ROS production increases, mitochondria can exhibit either oscillatory dynamics facilitated by IMAC opening, or bistable dynamics facilitated by MPTP opening; 2), in a diffusively-coupled mitochondrial network, the oscillatory dynamics of IMAC-mediated RIRR results in rapidly propagating (∼25 μm/s) cellwide Ψ_M oscillations, whereas the bistable dynamics of MPTP-mediated RIRR results in slow (0.1-2 μm/s) Ψ_M depolarization waves; and 3), the slow velocity of the MPTP-mediated depolarization wave is related to competition between ROS scavenging systems and ROS diffusion. Our observations provide mechanistic insights into the spatiotemporal dynamics underlying RIRR-induced Ψ_M oscillations and waves observed experimentally in cardiac myocytes.     

NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures

The National Cancer Institute (NCI) Cancer Imaging Program organized two related workshops on June 26–27, 2013, entitled “Correlating Imaging Phenotypes with Genomics Signatures Research” and “Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems.” The first workshop focused on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources. These workshops linked clinical and scientific requirements of currently known phenotypic and genotypic cancer biology characteristics with imaging phenotypes that underpin genomics and clinical outcomes. The group generated a set of recommendations to NCI leadership and the research community that encourage and support development of the emerging radiogenomics research field to address short-and longer-term goals in cancer research.     

Diagnostic Performance of Echocardiography for the Detection of Acute Cardiac Allograft Rejection: A Systematic Review and Meta-Analysis

Objective

Many studies have addressed the diagnostic performance of echocardiography to evaluate acute cardiac allograft rejection compared with endomyocardial biopsy. But the existence of heterogeneity limited its clinical application. Thus, we conducted a comprehensive, systematic literature review and meta-analysis for the purpose.

Methods

Studies prior to September 1, 2014 identified by Medline/PubMed, EMBASE and Cochrance were examined by two independent reviews. We conducted meta-analysis by using Meta-DiSc 1.4 software. An assessment tool of QUADAS-2 was applied to evaluate the risk of bias and applicability of the studies.

Results

Thirty studies met the inclusion criteria of meta-analysis. The four parameters of pressure half time, isovolumic relaxation time, index of myocardial performance and late diastolic mitral annular motion velocity were included in the meta-analysis, with a pooled diagnostic odds ratio of 10.43, 6.89, 15.95 and 5.68 respectively, and the area under the summary receiver operating characteristic curves value of 0.829, 0.599, 0.871 and 0.685 respectively.

Conclusion

The meta-analysis and systematic review demonstrate that no single parameter of echocardiography showed a reliable diagnostic performance for acute cardiac allograft rejection. A result of echocardiography for ACAR should be comprehensively considered by physicians in the context of clinical presentations and imaging feature.     

The kSORT Assay to Detect Renal Transplant Patients at High Risk for Acute Rejection: Results of the Multicenter AART Study

BackgroundDevelopment of noninvasive molecular assays to improve disease diagnosis and patient monitoring is a critical need. In renal transplantation, acute rejection (AR) increases the risk for chronic graft injury and failure. Noninvasive diagnostic assays to improve current late and nonspecific diagnosis of rejection are needed. We sought to develop a test using a simple blood gene expression assay to detect patients at high risk for AR.ConclusionsThe kSORT blood QPCR assay is a noninvasive tool to detect high risk of AR of renal transplants.Please see later in the article for the Editors'' Summary     

Ultrasound Imaging Versus Morphopathology in Cardiovascular Diseases: The Heart Failure

Background

Exercise echocardiography (EE) is an established method to diagnose coronary artery disease (CAD). Chronotropic incompetence (CI) during the EE may be a marker of myocardial ischemia. The purpose of this investigation was to evaluate the additive value of CI during EE in CAD diagnosis.

Methods

Between 2000 and 2006, 4042 patients (1900 men with a mean age of 56 ± 11 years) were evaluated by EE. Based on the heart rate (HR) reached during the exercise test, the subjects were divided into two groups: G1 group – 490 patients who failed to achieve 85% of the maximal age-predicted HR, and G2 group – 3552 patients who were able to achieve 85% of the maximal age-predicted HR. Clinical characteristics, left ventricular wall motion abnormalities – wall motion score index (WMSI) – and coronary angiography (CA) were the parameters compared between the two groups.

Results

The left ventricular wall motion abnormalities were more frequent in G1 group than in G2 group (54% versus 26%; P < 0.00001). WMSI was higher in G1 group than in G2 group, both at rest (1.06 ± 0.17 versus 1.02 ± 0.09; P < 0.0001) and after exercise (1.12 ± 0.23 versus 1.04 ± 0.21; P < 0.0001). In G1 group, 82% of the patients with positive EE for myocardial ischemia presented obstructive coronary, compared to 71% (P = 0.03) in G2 group.

Conclusion

CI is associated with a higher frequency of myocardial ischemia during EE, reinforcing the concept that CI is a marker of the severity of myocardial ischemia.     

Differentially Expressed RNA from Public Microarray Data Identifies Serum Protein Biomarkers for Cross-Organ Transplant Rejection and Other Conditions

Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers.