ABSTRACT
Purpose@#Pioglitazone, an antihyperglycemic drug, is widely used in diabetes mellitus patients with insulin resistance. Although pioglitazone is known to have a potential link to bladder cancer (BC), there have been contradictory results. This present study is designed to understand the regulatory mechanisms that drive the effects of pioglitazone on the bladder epithelial cells. @*Methods@#Labeled liquid chromatography-tandem mass spectrometry-based proteomics profiling characterized the global proteomes of normal human bladder epithelial cells treated with or without pioglitazone. @*Results@#This approach detected approximately 5,769 proteins in total. Of those 5,769 proteins, 124 were identified as being differentially expressed due to pioglitazone treatment. Further analysis identified 95 upregulated and 29 downregulated proteins (absolute log2 fold change >0.58 and P-value<0.05). The following functional gene enrichment analysis suggested that pioglitazone may be altering a few select biological processes, such as gene/chromatin silencing, by downregulating BMI1 (B lymphoma Mo-MLV insertion region 1 homolog), a polycomb complex protein. Further cell-based assays showed that cell adhesion molecules, epithelial-mesenchymal transition markers, and major signaling pathways were significantly downregulated by pioglitazone treatment. @*Conclusions@#These experimental results revealed the proteomic and biological alterations that occur in normal bladder cells in response to pioglitazone. These findings provided a landscape how bladder proteome is influenced by pioglitazone, which suggests the potential adverse effects of diabetes drugs and their links to bladder dysfunctions.
ABSTRACT
OBJECTIVE: Opioid analgesics, for postoperative pain management, are an indispensable group of medication; however, they also have a variety of adverse drug reactions (ADR). Multimodal methods, combining non-opioid analgesics with opioid analgesics, have been investigated to increase the effects of analgesics and reduce ADR with opioid-sparing effects. The purpose of this study was to compare the effects of patient-controlled analgesia (PCA) with fentanyl alone, and PCA with fentanyl and intravenous (i.v.) propacetamol to determine the effects of pain control, cumulative opioid usage, and opioid ADR. METHODS: The subjects were patients who underwent total knee arthroplasty at the Seoul Veterans hospital from January 1, 2015 to December 31, 2016. The study period was from postoperative day 0 (POD0) to day 3 (POD3), and the retrospective study was conducted using electronic medical records. RESULTS: Pain severity was significantly low at POD1 (p = 0.017), POD2 (p = 0.003), and POD3 (p = 0.002) in the multimodal group. The fentanyl only group frequently reported both moderate and severe pain at a statistically significant level. This was consistent with the analysis of the pro re nata (PRN) intramuscular analgesia usage at the time of numerical rating scale (NRS) 4 and above. The opioid-sparing effect confirmed that the average opioid dose equivalent to i.v. morphine dose was 9.4 mg more than that used for the multimodal group in the fentanyl only group. The ADRs and length of stay between the two groups were not statistically different. CONCLUSION: The results of this study suggest that the combination therapy of fentanyl and i.v. propacetamol is superior to fentanyl monotherapy.
ABSTRACT
The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms.