Predicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell lines

<p>Abstract</p> <p>Background</p> <p>Drugs can influence the whole metabolic system by targeting enzymes which catalyze metabolic reactions. The existence of interactions between drugs and metabolic reactions suggests a potential way to discover drug targets.</p> <p>Results</p> <p>In this paper, we present a computational method to predict new targets for approved anti-cancer drugs by exploring drug-reaction interactions. We construct a Drug-Reaction Network to provide a global view of drug-reaction interactions and drug-pathway interactions. The recent reconstruction of the human metabolic network and development of flux analysis approaches make it possible to predict each metabolic reaction's cell line-specific flux state based on the cell line-specific gene expressions. We first profile each reaction by its flux states in NCI-60 cancer cell lines, and then propose a kernel k-nearest neighbor model to predict related metabolic reactions and enzyme targets for approved cancer drugs. We also integrate the target structure data with reaction flux profiles to predict drug targets and the area under curves can reach 0.92.</p> <p>Conclusions</p> <p>The cross validations using the methods with and without metabolic network indicate that the former method is significantly better than the latter. Further experiments show the synergism of reaction flux profiles and target structure for drug target prediction. It also implies the significant contribution of metabolic network to predict drug targets. Finally, we apply our method to predict new reactions and possible enzyme targets for cancer drugs.</p

Evolving Landscape of Cancer Survivorship Research: An Analysis of the Journal of Cancer Survivorship, 2007-2020

Introduction: Since 2007, the Journal of Cancer Survivorship (JCSU) has published hundreds of papers to advance cancer survivorship care and science across the globe. The aim of this study was to provide an analysis of papers published by the JCSU from March 1st, 2007 until December 31, 2020. Methods: Characteristics (locations, study type, cancer type, keywords assigned by original authors) of all included articles were extracted, coded and analysis using a range of bibliometric programs including EndNote X9, NVivo v.R1.3, and VOSviewer, v.1.616. Journal Impact Factor and citation counts of each manuscript were downloaded from Clarivate Journal Citation Reports and Scopus® respectively. Results: Published papers were predominantly from the USA, Australia, and the United Kingdom. While breast cancer is the dominant cancer type, a range of different cancer types and populations with mixed-cancer types have been addressed. Cross-sectional designs were the most used. JCSU’s impact experienced steady growth between 2011-15 and stabilized recently (2016-20), at 3.296 (1-year) and 3.830 (5-year). Keyword co-occurrence analyses indicated prominent themes including quality of life, physical activity, late effects, follow-up care, and psychosocial aspects of cancer survivorship. Conclusions: JCSU has made a significant contribution by disseminating knowledge in cancer survivorship. This study provides insight into JCSU’s success to date and recommends further diversification and directions for practice areas that are novel or have received less attention by the cancer survivorship community

Sleep promotes analogical transfer in problem solving

Analogical problem solving requires using a known solution from one problem to apply to a related problem. Sleep is known to have profound effects on memory and information restructuring, and so we tested whether sleep promoted such analogical transfer, determining whether improvement was due to subjective memory for problems, subjective recognition of similarity across related problems, or by abstractgeneralisation of structure. In Experiment 1, participants were exposed to a set of source problems. Then, after a 12-h period involving sleep or wake, they attempted target problems structurally related to the source problems but with different surface features. Experiment 2 controlled for time of day effects by testing participants either in the morning or the evening. Sleep improved analogical transfer, but effects were not due to improvements in subjective memory or similarity recognition, but rather effects of structural generalisation across problems

Evolving landscape of cancer survivorship research: An analysis of the journal of cancer survivorship, 2007–2020

Purpose To provide an analysis of papers published by the Journal of Cancer Survivorship (JCSU) from March 1, 2007 (its inception) until December 31, 2020. Methods Characteristics (locations, study type, cancer type, keywords assigned by original authors) of all included articles were extracted into EndNote X9 and were coded and analyzed using Excel, NVivo v.R1.3 and VOSviewer, v.1.616. Journal Impact Factor and citation counts of each manuscript were downloaded from Clarivate Journal Citation Reports and Scopus®, respectively. Results Published papers are predominantly from the USA, Australia, and the UK. While breast cancer continues to be the dominant cancer type, a range of different cancer types and populations with mixed-cancer types have been addressed in the journal. Cross-sectional designs were the most used. JCSU’s impact factor experienced a steady growth between 2011 and 2015 and stabilized over recent years (2016–2020), at 3.296 (1 year) and 3.830 (5 years). Keyword co-occurrence analyses indicated prominent themes including quality of life, physical activity, late effects, follow-up care, and psychosocial aspects of cancer survivorship. Conclusions JCSU has made a significant contribution thus far by disseminating knowledge in cancer survivorship. This paper provides insights of JCSU’s success to date and recommends further diversification and directions for practice areas that are novel or have received less attention by the cancer survivorship community. Implications for Cancer Survivors This journal stands ready to publish new information that informs the cancer survivorship community on the multidimensional nature of cancer and facilitates translation into quality care across many different settings and across the globe

Relationship Between Inflation and Real Economic Growth in Rwanda

peer reviewedThis study examines the impact of economic stability measures (inflation and unemployment rates) on real gross domestic product (GDP) in Rwanda. It uses quarterly data for the period of 2000Q1–2015Q4 collected from the Ministry of Finance and Economic Planning, Central Bank of Rwanda and the National Institute of Statistics of Rwanda (NISR). This study concludes that inflation and unemployment have a long-run negative and significant relationship on real gross domestic product. In the long run, the coefficients are not significant at the 5% level; it is only the inflation coefficient and error which are significant. Real gross domestic product increases when inflation reduces with a p-value of 0.00266; real gross domestic product increases when unemployment reduces with a p-value of 0.09882. The coefficient from the error correction model means that the effect of the shock will reduce by 0.0483% each quarter, meaning that the effect of the shock will reduce by 19.32% in each 4th quarter. This further means that it will end at 20 quarters, that is, after a five-year period. It has to be highlighted that there is a weak relationship between real gross domestic product and both inflation and unemployment rates

Implications of Fiscal and Financial Policies on Unlocking Green Finance and Green Investment

10.1007/978-981-10-8710-3_32-1Handbook of Green Finance: Energy Security and Sustainable development1-3

Implications of Fiscal and Financial Policies on unlocking Green Finance and Green Investment

ADBI Working Paper Series8611-3