Risk of colorectal cancer due to Streptococcus gallolyticus: a systematic review

Introduction: World Health Organization (2019) has declared colorectal cancer (CRC) as the second most common cancer in females and third in males, where the incidence seems to rise year by year. One of the very few potential pathogens specifically associated with malignant colonic diseases is Streptococcus gallolyticus (Sg). Sg is a part of the intestinal flora which formerly known as biotype I of Streptococcus bovis, belongs to Group D streptococci. Owing to only a few researches done in determining evidence to support Sg as a determinant of CRC, a systematic review is constructed. Materials and methods: Full-text articles on case-control and cohort studies published from 1st January 2010 to 1st October 2020 were searched using Google Scholar, PubMed and JSTOR. People of all age groups and Sg bacteraemia or colonisation were the type of participant and exposure used for the search strategy, respectively. Data collection was done by three reviewers and checked by two reviewers for discrepancies. All the papers were critically appraised using the STROBE statement. Qualitative synthesis was done by descriptive comparison, distribution of Sg according to stage comparison, method used for Sg detection comparison and risk of bias comparison. Result: Seven out of 11 articles that fulfil the eligibility criteria were selected. Four papers have low overall risk of bias due to low confounding or selection bias. Sg is found to be a risk factor for CRC from three papers studied, whereas the other four papers did not include the strength of association. Only two papers studied the association between the distribution of Sg and stages of CRC, where the results were contradictory from each other, making it to be inconclusive. The most common method used for Sg detection is a culturing technique, followed by molecular and biochemical techniques. Conclusion: There is insufficient evidence to prove the association between Sg bacteraemia as the risk factor for CRC as well as the association between the Sg distribution and stages of CRC. Culturing technique is the most common method used for the detection of bacteria, but it requires subsequent investigations to confirm the presence of Sg. Thus, it is recommended that more studies need to be done using strong statistical analysis to control for most of the confounders with comprehensive explanation and use of more methods in the detection of Sg

Role of the PI3-kinase/mTor pathway in the regulation of the stearoyl CoA desaturase (SCD1) gene expression by insulin in liver

The stearoyl-CoA desaturase 1 (SCD1) catalyzes the synthesis of monounsaturated fatty acids. This enzyme is a critical control point regulating hepatic lipogenesis and lipid oxidation. Therefore SCD1 may be a potential therapeutic target in the treatment of obesity and metabolic syndrome. Regulation of SCD1 expression occurs primarily at the level of transcription. In the present study, we characterized the insulin response elements (IREs) and the insulin signaling pathway mediating the regulation of SCD1 gene transcription in liver. In chicken embryo hepatocytes (CEH) and HepG2 cells, insulin stimulates SCD1 promoter activity by 2.5 folds. This activation is mediated by two different IREs on the chicken promoter, one localized between −1,975 and −1,610 bp and one between −372 and −297 bp. The latter binds both NF-Y and SREBP-1 transcription factors in response to insulin. We also demonstrated that insulin induction of SCD1 gene expression and promoter activity is abolished by pre-incubation of cells with specific inhibitors of both PI3-kinase (LY294002) and mTor (Rapamycin) or by over-expression of a dominant negative mutant of PI3-kinase. The PI3-kinase and mTor pathway mediates the insulin response on both IREs. In summary, insulin activates SCD1 gene expression in liver via a signaling pathway that involves PI3-kinase and mTor and the downstream transcription factors NF-Y and SREBP-1. Sentence summary: Insulin regulates SCD1 gene expression via two different IREs. The most 3′ IRE is localized between −372 and −297 bp and binds the NF-Y and SREBP-1 transcription factors in response to insulin. PI3-kinase and mTor mediate the action of insulin on both IREs

The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals

The healthcare sector in general and hospitals in particular represent a main application area for Data Envelopment Analysis (DEA). This paper reviews 262 papers of DEA applications in healthcare with special focus on hospitals and therefore closes a gap of over ten years that were not covered by existing review articles. Apart from providing descriptive statistics of the papers, we are the first to examine the research purposes of the publications. These research goals can be grouped into four distinct clusters according to our proposed framework. The four clusters are (1) Pure DEA efficiency analysis, i.e. performing a DEA on hospital data, (2) Developments or applications of new methodologies, i.e. applying new DEAy approaches on hospital data, (3) Specific management question, i.e. analyzing the effects of managerial specification, such as ownership, on hospital efficiency, and (4) Surveys on the effects of reforms, i.e. researching the impact of policy making, such as reforms of health systems, on hospital efficiency. Furthermore, we analyze the methodological settings of the studies and describe the applied models. We analyze the chosen inputs and outputs as well as all relevant downstream techniques. A further contribution of this paper is its function as a roadmap to important methodological literature and publications, which provide crucial information on the setup of DEA studies. Thus, this paper should be of assistance to researchers planning to apply DEA in a hospital setting by providing information on a) what has been published between 2005 and 2016, b) possible pitfalls when setting up a DEA analysis, and c) possible ways to apply the DEA analysis in practice. Finally, we discuss what could be done to advance DEA from a scientific tool to an instrument that is actually utilized by managers and policymakers