HDL in Endocrine Carcinomas: Biomarker, Drug Carrier, and Potential Therapeutic

High-density lipoprotein (HDL) have long been studied for their protective role against cardiovascular diseases, however recently relationship between HDL and cancer came into focus. Several epidemiological studies have shown an inverse correlation between HDL-cholesterol (HDL-C) and cancer risk, and some have even implied that HDL-C can be used as a predictive measure for survival prognosis in for specific sub-population of certain types of cancer. HDL itself is an endogenous nanoparticle capable of removing excess cholesterol from the periphery and returning it to the liver for excretion. One of the main receptors for HDL, scavenger receptor type B-I (SR-BI), is highly upregulated in endocrine cancers, notably due to the high demand for cholesterol by cancer cells. Thus, the potential to exploit administration of cholesterol-free reconstituted or synthetic HDL (sHDL) to deplete cholesterol in endocrine cancer cell and stunt their growth of use chemotherapeutic drug loaded sHDL to target payload delivery to cancer cell has become increasingly attractive. This review focuses on the role of HDL and HDL-C in cancer and application of sHDLs as endocrine cancer therapeutics

HDL in Endocrine Carcinomas: Biomarker, Drug Carrier, and Potential Therapeutic

High-density lipoprotein (HDL) have long been studied for their protective role against cardiovascular diseases, however recently relationship between HDL and cancer came into focus. Several epidemiological studies have shown an inverse correlation between HDL-cholesterol (HDL-C) and cancer risk, and some have even implied that HDL-C can be used as a predictive measure for survival prognosis in for specific sub-population of certain types of cancer. HDL itself is an endogenous nanoparticle capable of removing excess cholesterol from the periphery and returning it to the liver for excretion. One of the main receptors for HDL, scavenger receptor type B-I (SR-BI), is highly upregulated in endocrine cancers, notably due to the high demand for cholesterol by cancer cells. Thus, the potential to exploit administration of cholesterol-free reconstituted or synthetic HDL (sHDL) to deplete cholesterol in endocrine cancer cell and stunt their growth of use chemotherapeutic drug loaded sHDL to target payload delivery to cancer cell has become increasingly attractive. This review focuses on the role of HDL and HDL-C in cancer and application of sHDLs as endocrine cancer therapeutics

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Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155986/1/art41237.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155986/2/art41237_am.pd

High‐Density Lipoprotein in Lupus: Disease Biomarkers and Potential Therapeutic Strategy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152788/1/art41059_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152788/2/art41059.pd

Battle of GLP-1 delivery technologies

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) belong to an important therapeutic class for treatment of type 2 diabetes. Six GLP-1 RAs, each utilizing a unique drug delivery strategy, are now approved by the Food and Drug Administration (FDA) and additional, novel GLP-1 RAs are still under development, making for a crowded marketplace and fierce competition among the manufacturers of these products. As rapid elimination is a major challenge for clinical application of GLP-1 RAs, various half-life extension strategies have been successfully employed including sequential modification, attachment of fatty-acid to peptide, fusion with human serum albu- min, fusion with the fragment crystallizable (Fc) region of a monoclonal antibody, sustained drug delivery sys- tems, and PEGylation. In this review, we discuss the scientific rationale of the various half-life extension strategies used for GLP-1 RA development. By analyzing and comparing different approved GLP-1 RAs and those in development, we focus on assessing how half-life extending strategies impact the pharmacokinetics, pharmacodynamics, safety, patient usability and ultimately, the commercial success of GLP-1 RA products. We also anticipate future GLP-1 RA development trends. Since similar drug delivery strategies are also applied for de- veloping other therapeutic peptides, we expect this case study of GLP-1 RAs will provide generalizable concepts for the rational design of therapeutic peptides products with extended duration of action

Available and missing data to model impact of climate change on European forests

Climate change is expected to cause major changes in forest ecosystems during the 21st century and beyond. To assess forest impacts from climate change, the existing empirical information must be structured, harmonised and assimilated into a form suitable to develop and test state-of-the-art forest and ecosystem models. The combination of empirical data collected at large spatial and long temporal scales with suitable modelling approaches is key to understand forest dynamics under climate change. To facilitate data and model integration, we identified major climate change impacts observed on European forest functioning and summarised the data available for monitoring and predicting such impacts. Our analysis of c. 120 forest-related databases (including information from remote sensing, vegetation inventories, dendroecology, palaeoecology, eddy-flux sites, common garden experiments and genetic techniques) and 50 databases of environmental drivers highlights a substantial degree of data availability and accessibility. However, some critical variables relevant to predicting European forest responses to climate change are only available at relatively short time frames (up to 10-20 years), including intra-specific trait variability, defoliation patterns, tree mortality and recruitment. Moreover, we identified data gaps or lack of data integration particularly in variables related to local adaptation and phenotypic plasticity, dispersal capabilities and physiological responses. Overall, we conclude that forest data availability across Europe is improving, but further efforts are needed to integrate, harmonise and interpret this data (i.e. making data useable for non-experts). Continuation of existing monitoring and networks schemes together with the establishments of new networks to address data gaps is crucial to rigorously predict climate change impacts on European forests. © 2019 The Author(s

Available and missing data to model impact of climate change on European forests

Climate change is expected to cause major changes in forest ecosystems during the 21st century and beyond. To assess forest impacts from climate change, the existing empirical information must be structured, harmonised and assimilated into a form suitable to develop and test state-of-the-art forest and ecosystem models. The combination of empirical data collected at large spatial and long temporal scales with suitable modelling approaches is key to understand forest dynamics under climate change. To facilitate data and model integration, we identified major climate change impacts observed on European forest functioning and summarised the data available for monitoring and predicting such impacts. Our analysis of c. 120 forest-related databases (including information from remote sensing, vegetation inventories, dendroecology, palaeoecology, eddy-flux sites, common garden experiments and genetic techniques) and 50 databases of environmental drivers highlights a substantial degree of data availability and accessibility. However, some critical variables relevant to predicting European forest responses to climate change are only available at relatively short time frames (up to 10-20 years), including intra-specific trait variability, defoliation patterns, tree mortality and recruitment. Moreover, we identified data gaps or lack of data integration particularly in variables related to local adaptation and phenotypic plasticity, dispersal capabilities and physiological responses. Overall, we conclude that forest data availability across Europe is improving, but further efforts are needed to integrate, harmonise and interpret this data (i.e. making data useable for non-experts). Continuation of existing monitoring and networks schemes together with the establishments of new networks to address data gaps is crucial to rigorously predict climate change impacts on European forests.Peer reviewe