Analysis of referral patterns,predictive accuracy,and impact on patient management of myocardial perfusion imaging in a new nuclear cardiology laboratory

BACKGROUND: Most of the published data on myocardial perfusion imaging (MPI) come from large tertiary-referral medical centers with extensive experience in cardiac imaging as well as a large volume of procedures. Whether the results of MPI remain as reliable in new nuclear cardiology laboratories with smaller volumes of procedures is unknown. The purpose of this study was to analyze the referral patterns, predictive accuracy, and impact of MPI on clinical practice in a newly opened nuclear cardiology laboratory. METHODS AND RESULTS: We performed a prospective study on all patients referred for MPI at our nuclear cardiology laboratory during its first year of operation. Patients were followed up for 3 months after the MPI study to determine whether they underwent coronary angiography. The study population consisted of 334 patients. Their mean age was 56 +/- 10 years, and 80% were men. Of the patients, 30% were asymptomatic, 29% had angina, and only 6% had recent acute myocardial infarction or unstable angina. Fifty-one patients (fifteen percent) were subsequently referred for coronary angiography. The positive and negative predictive values of MPI were 91% and 86%, respectively. The presence of reversible perfusion defects (P =.02) and the presence of multiple perfusion defects (P =.01) on MPI were the most important determinants of subsequent referral to coronary angiography. CONCLUSIONS: MPI stress testing retains its high diagnostic accuracy in a new nuclear cardiology laboratory with a relatively small volume of procedures. Furthermore, MPI findings in this population had a strong impact on the clinical practice of the referring physicians in terms of subsequent referral to coronary angiography.     

Hallmarks of Metabolic Reprogramming and Their Role in Viral Pathogenesis

Metabolic reprogramming is a hallmark of cancer and has proven to be critical in viral infections. Metabolic reprogramming provides the cell with energy and biomass for large-scale biosynthesis. Based on studies of the cellular changes that contribute to metabolic reprogramming, seven main hallmarks can be identified: (1) increased glycolysis and lactic acid, (2) increased glutaminolysis, (3) increased pentose phosphate pathway, (4) mitochondrial changes, (5) increased lipid metabolism, (6) changes in amino acid metabolism, and (7) changes in other biosynthetic and bioenergetic pathways. Viruses depend on metabolic reprogramming to increase biomass to fuel viral genome replication and production of new virions. Viruses take advantage of the non-metabolic effects of metabolic reprogramming, creating an anti-apoptotic environment and evading the immune system. Other non-metabolic effects can negatively affect cellular function. Understanding the role metabolic reprogramming plays in viral pathogenesis may provide better therapeutic targets for antivirals.