Myocardial ischemia (MI) resulting in infarction is an important cause of mortality and morbidity worldwide. Acute ischaemia rapidly impairs myocardial contractile function. Myocardial dysfunction persisting for several hours after transient non-lethal ischaemia, eventually resulting in full functional recovery is termed asmyocardial stunning. Hibernation is now thought to be the consequence of repetitive bouts of ischaemia and stunning due to normally occurring increases in myocardial metabolic demand in the setting of significant coronary stenoses. We need robust investigations to identify and treat MI early. Myocardial perfusion imaging has established itself as the earliest investigation that can identify ischemia with or without infarction in an acute setting. However, recent data reveals that metabolic changes precede perfusion abnormalities during an ischemic or infarction episode; thus the renewed interest in the myocardial metabolic imaging.Concept of myocardial metabolic imaging is gaining momentum with the wider availability of positron emitting radio isotopes. Myocardial metabolism has been widely studied using 123I-BMIPP [15- (p-Iodophenyl) -3-methylpentadecanoic acid] and BMIPP was synonymous for ischemic memory imaging, (IMI). It was based on the fact that one can capture the still picture of the ongoing ischemic insult as a “memory image ” past the acute episode. BMIPP imaging was primarily based on its ability to memorize the area at risk for a couple of weeks, even after reperfusion therapy. We aim to elaborate in this review the various radiotracers that can be used to identify myocardial metabolic disorders at its inception so that it may be possible to provide early management options.

Introduction: Glioblastoma is one of the common brain cancers that has a high mortality rate. In this study, the genes present in the metabolic pathways of glioblastoma stem cells were examined and nominated using bioinformatics analysis. Method: In this study, an appropriate dataset was selected for analysis by referring to GEO database. This dataset included gene expression profiles in stem cells isolated from glioblastoma patients. Gene clusters with high and low expression were categorized. Rich databases such as Enrichr, STRING, and GEPIA were used for more accurate data evaluation. Finally, candidate genes were isolated. Results: 1250 genes were highly expressed in cholesterol biosynthesis, inositol triphosphate metabolism, geranyl diphosphate metabolism, zymosterol biosynthesis, and phosphatidylinositol metabolism, and 1030 genes were low in chondroitin sulfate, dermatan sulfate, N acetyl glucosamine, and glycolysis pathways. After evaluating the relationship between protein networks, genes with high and low expression were selected. All these genes were observed in the survival curve, in about 20 months. The survival rate of patients was less than 10%. Conclusion: The results of this study showed that the DHCR7 gene had a significant increase in expression. However, the ENO2 gene had a significant decrease in expression. The rest of the genes had a relative increase and decrease in expression.

Background: Risk of colorectal cancer (CRC) is defined by genetic predisposition and environmental factors that often co-occur and interact, resulting in diversiform biological reactions. The present study attempted to investigate transcriptome alteration and adaptation associated with CRC progression. Methods: The study consisted of patients who presented at Memorial Sloan-Kettering Cancer Center, Guangzhou, China with a colonic neoplasm in 1992-2004. Microarray GSE41258 of the study was acquired from Gene Expression Omnibus and 253 included microarrays were categorized by groups of normal colon, early primary tumor, lymph node metastases primary tumor, advanced primary tumor and distant metastases. Short Time-series Expression Miner (STEM) was applied to discover tumor grade-dependent gene expression patterns. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out to explore functional enrichment of differential expression genes (DEGs). Results: Overall, 2870 significant DEGs were screened out on all groups. Six significant grade-dependent gene expression patterns were statistically significant. DEGs in all significant patterns were mainly assembled in GO terms of metastases and deterioration of tumor, epithelial proteins and cytokines, and protein binding and bridging. DEGs in profile 0 down-regulated with higher tumor grade, prominently enriched in KEGG pathways of metabolism. Conclusion: Besides many well-known colorectal cancer-related pathways, DEGs of profiles especially those down-regulated with CRC progression, clustered in various metabolic pathways including starch and sucrose metabolism, fatty acid metabolism, nitrogen metabolism, as well as xenobiotics biotransformation that link to tumorigenesis, demonstrating the impairment of physiological metabolic pathways in the context of tumor progression. These results gave a high potential for therapeutic strategies.