Semi-quantification of renal perfusion using 99mTc-DTPA in systolic heart failure: a feasibility study.

BACKGROUND: Renal scintigraphy with 99mTc-diethylenetriaminepentaacetic acid (DTPA) may be used to study renal perfusion (RP) in heart failure (HF) patients. The goal of this study was to establish a new method to assess RP in patients with systolic HF. METHODS: In this retrospective, single-center, observational study, 86 subjects with left ventricular ejection fraction ≤ 45% and 31 age-matched subjects without HF underwent renal scintigraphy with 99mTc-DTPA. Patients with HF were classified into two categories according to the New York Heart Association (NYHA) functional class, i.e., moderate HF with NYHA functional class I or II and severe HF with NYHA functional class III or IV. The first-pass time-activity curve of the renal scintigraph was recorded. The GFR was determined by Gates' method. The time to peak perfusion activity (Tp), the slope of the perfusion phase (Sp), the slope of the washout phase (Sw), and glomerular filtration rate (GFR) in the study were obtained. Differences between groups were assessed by one-way analysis of variance with the Bonferroni post hoc test and rank-sum test. RESULTS: RP in HF was impaired despite comparable GFRs between the control and HF groups. RP in HF was characterized by a longer Tp and a shallower Sp and Sw. The primary parameter (Tp) was significantly prolonged in patients with HF (41.63 ± 12.22 s in severe HF vs. 26.95 ± 6.26 s in moderate HF vs. 17.84 ± 3.17 s in control, P < 0.001). At a cutoff point of 22 s, there was a high sensitivity (0.895) and specificity (0.935) in identifying patients with HF. CONCLUSIONS: Renal scintigraphy with 99mTc-DTPA may represent a new and useful method to noninvasively monitor RP abnormalities in HF.

In silico identification of common and specific signatures in coronary heart diseases.

Coronary heart disease (CHD) is on the increase in developing countries, where lifestyle choices such as smoking, bad diet, and no exercise contribute and increase the incidence of high blood pressure and high cholesterol levels to cause CHD. Through utilization of a biomarker-based approach for developing interventions, the aim of the study was to identify differentially expressed genes (DEGs) and their association and impact on various bio-targets. The microarray datasets of both healthy and CHD patients were analyzed to identify the DEGs and their interactions using Gene Ontology, PANTHER, Reactome, and STRING (for the possible associated genes with multiple targets). Our data mining approach suggests that the DEGs were associated with molecular functions, including protein binding (75%) and catalytic activity (56%); biological processes such as cellular process (83%), biological regulation (57%), and metabolic process (44%); and cellular components such as cell (65%) and organelle (58%); as well as other associations including apoptosis, inflammatory, cell development and metabolic pathways. The molecular functions were further analyzed, and protein binding in particular was analyzed using network analysis to determine whether there was a clear association with CHD and disease. The ingenuity pathway analysis revealed pathways related to cell cholesterol biosynthesis, the immune system including cytokinin signaling, in which, the understanding of DEGs is crucial to predict the advancement of preventive strategies. Results of the present study showed that, there is a need to validate the top DEGs to rule out their molecular mechanism in heart failure caused by CHD.