Porterfieldsauer8270

Introduction Automated image analysis provides an objective, quantitative, and reproducible method of measurement of biomarkers. Image quantification is particularly well suited for the analysis of tissue microarrays which has played a major pivotal role in the rapid assessment of molecular biomarkers. Data acquired from grinding up bulk tissue samples miss spatial information regarding cellular localization; therefore, methods that allow for spatial cell phenotyping at high resolution have proven to be valuable in many biomarker discovery assays. Here, we focus our attention on breast cancer as an example of a tumor type that has benefited from quantitative biomarker studies using tissue microarray format.Areas covered The history of immunofluorescence and immunohistochemistry and the current status of these techniques, including multiplexing technologies (spectral and non-spectral) and image analysis software will be addressed. Finally, we will turn our attention to studies that have provided proof-of-principle evidence that have been impacted from the use of these techniques.Expert opinion Assessment of prognostic and predictive biomarkers on tissue sections and TMA using Quantitative immunohistochemistry is an important advancement in the investigation of biologic markers. The challenges in standardization of quantitative technologies for accurate assessment are required for adoption into routine clinical practice.Introduction Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the United States with a single-digit 5-year survival rate despite advances in understanding the genetics and biology of the disease. Glycogen synthase kinase-3α (GSK-3α) and GSK-3β are serine/threonine kinases that localize to the cytoplasm, mitochondria and nucleus. Although they are highly homologous within their kinase domains and phosphorylate an overlapping set of target proteins, genetic studies have shown that GSK-3β regulates the activity of several proteins that promote neoplastic transformation. Significantly, GSK-3β is progressively overexpressed during PDAC development where it participates in tumor progression, survival and chemoresistance. Thus, GSK-3β has become an attractive target for treating PDAC.Areas covered This review summarizes the mechanisms regulating GSK-3β activity, including upstream translational and post-translational regulation, as well as the downstream targets and their functions in PDAC cell growth, metastasis and chemoresistance.Expert opinion The activity of GSK-3 kinases are considered cell- and context-specific. In PDAC, oncogenic KRas drives the transcriptional expression of the GSK-3β gene, which has been shown to regulate cancer cell proliferation and survival, as well as resistance to chemotherapy. A1874 chemical Thus, the combination of GSK-3 inhibitors with chemotherapeutic drugs could be a promising strategy for PDAC.For centuries plants have been intensively utilized as reliable sources of food, flavoring, agrochemical and pharmaceutical ingredients. However, plant natural habitats are being rapidly lost due to climate change and agriculture. Plant biotechnology offers a sustainable method for the bioproduction of plant secondary metabolites using plant in vitro systems. The unique structural features of plant-derived secondary metabolites, such as their safety profile, multi-target spectrum and "metabolite likeness," have led to the establishment of many plant-derived drugs, comprising approximately a quarter of all drugs approved by the Food and Drug Administration and/or European Medicinal Agency. However, there are still many challenges to overcome to enhance the production of these metabolites from plant in vitro systems and establish a sustainable large-scale biotechnological process. These challenges are due to the peculiarities of plant cell metabolism, the complexity of plant secondary metabolite pathways, and the correct selection of bioreactor systems and bioprocess optimization. In this review, we present an integrated overview of the possible avenues for enhancing the biosynthesis of high-value marketable molecules produced by plant in vitro systems. These include metabolic engineering and CRISPR/Cas9 technology for the regulation of plant metabolism through overexpression/repression of single or multiple structural genes or transcriptional factors. The use of NMR-based metabolomics for monitoring metabolite concentrations and additionally as a tool to study the dynamics of plant cell metabolism and nutritional management is discussed here. Different types of bioreactor systems, their modification and optimal process parameters for the lab- or industrial-scale production of plant secondary metabolites are specified.Introduction The safety of de-escalation of empirical antimicrobial therapy is largely based on observational data, with many reporting protective effects on mortality. As there is no plausible biological explanation for this phenomenon, it is most probably caused by confounding by indication.Areas covered We evaluate the methodology used in observational studies on the effects of de-escalation of antimicrobial therapy on mortality. We extended the search for a recent systematic review and identified 52 observational studies. The heterogeneity in study populations was large. Only 19 (36.5%) studies adjusted for confounders and four (8%) adjusted for clinical stability during admission, all as a fixed variable. All studies had methodological limitations, most importantly the lack of adjustment for clinical stability, causing bias toward a protective effect.Expert opinion The methodology used in studies evaluating the effects of de-escalation on mortality requires improvement. We depicted all potential confounders in a directed acyclic graph to illustrate all associations between exposure (de-escalation) and outcome (mortality). Clinical stability is an important confounder in this association and should be modeled as a time-varying variable. We recommend to include de-escalation as time-varying exposure and use inverse-probability-of-treatment weighted marginal structural models to properly adjust for time-varying confounders.