Exploring the Potential of GlycolyticModulation in Myeloid-Derived Suppressor Cells for Immunotherapy and Disease Management

Recent advancements in various technologies have shed light on the critical role of metabolism in immune cells, paving the way for innovative disease treatment strategies through immunometabolism modulation. This review emphasizes the glucose metabolism of myeloid-derived suppressor cells (MDSCs), an emerging pivotal immunosuppressive factor especially within the tumor microenvironment. MDSCs, an immature and heterogeneous myeloid cell population, act as a double-edged sword by exacerbating tumors or mitigating inflammatory diseases through their immune-suppressive functions. Numerous recent studies have centered on glycolysis of MDSC, investigating the regulation of altered glycolytic pathways to manage diseases. However, the specific changes in MDSC glycolysis and their exact functions continue to be areas of ongoing discussion yet. In this paper, we review a range of current findings, including the latest research on the alteration of glycolysis in MDSCs, the consequential functional alterations in these cells, and the outcomes of attempts to modulate MDSC functions by regulating glycolysis. Ultimately, we will provide insights into whether these research efforts could be translated into clinical applications.

Got target?: computational methods for microRNA target prediction and their extension

MicroRNAs (miRNAs) are a class of small RNAs of 19-23 nucleotides that regulate gene expression through target mRNA degradation or translational gene silencing. The miRNAs are reported to be involved in many biological processes, and the discovery of miRNAs has been provided great impacts on computational biology as well as traditional biology. Most miRNA-associated computational methods comprise the prediction of miRNA genes and their targets, and increasing numbers of computational algorithms and web-based resources are being developed to fulfill the need of scientists performing miRNA research. Here we summarize the rules to predict miRNA targets and introduce some computational algorithms that have been developed for miRNA target prediction and the application of the methods. In addition, the issue of target gene validation in an experimental way will be discussed.

A Comparative Study on Medical Utilization between Urban and Rural Korea / 예방의학회지

This study was designed to compare the level of medical utilization between the urban and rural areas of Korea and to explain the differences between the two regions. Data from the National Health Interview survey performed by the korean Institute of Health & social Affairs in 1992 were used for this study utilizing a sample size of 21,841 people. The level of medical utilization such as the number of physician visits and the number of hospital admissions was compared between the regions with ANOVA. Various determinants for medical use were also compared by univariate analysis. Statistical models which included enabling factors, predisposing factors, need factors and region were constructed for bivariate analysis in order to further elucidate the level of medical utilization. The results were as follows: 1. There was greater medical use, both in terms of physician visits and inpatient care in the rural areas in spite of insufficient health resources. The particular reasons for higher medical utilization in rural areas were attributed to a higher number of initial physician visits as well as a longer the length of stay per hospital admission. Therefore, indicators representing the degree of met need (utilization/need) showed no significant difference between rural and urban areas in spite of the fact that the medical need is larger in rural areas. 2. Use of public health facilities received a significant portion of physician visits in the rural area. The government's effort to enhance primary health care through health centers, health subcenters and the nurse practitioner's post in rural areas has contributed to the increase of access to medical care in the rural areas. 3. There were some differences in the socio-demographic characteristics between two regions; There were more elderly people over the age of 65; unstable marital status, less education and lower incomes also characterized the rural areas. Therefore, among rural people, there were more predisposing factors for medical use. Additionally, need factors such as poor self-reported health status and high morbidity level were also high in the rural area. 4. In contrast it was learned that, the supply of health resources was mostly concentrated in the urban areas except for public health facilities. Therefore, geographical access to medical care was lower in the rural area both in terms travel time and travel cost. 5. The coefficient of the region variable was insignificant in the regression model which controlled the supply factor only However, utilization was significantly higher in urban areas if the model included predisposing factors and need factors in addition to the supply factor. The results were interpreted as rural people have greater medical needs.