The initial excitement and growth of biobanks worldwide has faced several challenges related to logistics, ethics, and utility,1,2 which call into question the long-term feasibility of a relationship with the traditional biobank. Perhaps it already feels one-sided, transactional, and unbalanced when trying to fulfill the many needs of a research study design.
Isolating unique cell populations for further experimental analyses can answer critical research questions in translational and clinical research—empowering observational and therapeutic studies. A popular approach is to isolate the heterogeneous population of cells in peripheral blood mononuclear cells (PBMCs) using the Ficoll method, followed by further purification to yield unique immune cell subsets, such as activated T cells for further downstream analyses.
From autoimmune diseases to neurological conditions to pathogenic infections, researchers studying associated immune-mediated mechanisms hope to uncover novel modulatory approaches for therapeutic intervention. To that end, purified human peripheral blood mononuclear cells (PBMCs) represent a heterogeneous population of cells, including B cells (~15%), T cells (~70%), monocytes (~5%), and natural killer (NK) cells (~10%) that can provide valuable phenotypic and functional information.1 But many factors influence PBMC purification quality.
Access to samples is essential to researchers’ experiments and assays. This is important when researchers are developing a proof of concept and they need a small set of samples, but it’s also important throughout the duration of studies. At the beginning of their studies, access to data allows researchers to “get their feet” wet and assess their ability to produce real-world data.
We at Sanguine Bioscience have partnered with multiple biotech and pharmaceutical companies, such as Vir Biotechnology and EpiVax, Inc, to facilitate over 23 research studies aimed at gaining a better understanding of COVID-19. Although the overarching goal of this research is to develop treatments and vaccines, valuable information can be obtained about the virus, its patterns of infection, and the effectiveness of community response to the epidemic.
Longitudinal studies are powerful tools in medical research armamentarium. Researchers gain valuable information following the same group of people with repeated measured variables over time. This type of research provides rates of change of continuous variable(s) over weeks, months, or years allowing researchers to assess patterns in human behavior or cause and effect relationships.
Research on the human microbiome has burgeoned in recent years. Microbiome imbalances have been linked to chronic conditions, such as diabetes, cardiovascular disease, and depression. But scientists have also uncovered evidence of the microbiome’s crucial role in infectious diseases, including COVID-19, pointing to it as a promising target for treating them.
Historically, medical research data obtained from other sources outside of traditional clinical trials were viewed with a heavy dose of skepticism even before evaluation of the methodology. Although this real-world data (RWD) was widely used to satisfy safety monitoring and post-drug approval regulation requirements, many clinicians viewed RWD and the real-world evidence (RWE) it generated as unverified and unreliable data mining explorations with drug marketing objectives.
Hemolysis is the rupture of red blood cells, and it has multiple causes, can happen at any time, both in vivo and in vitro. Hemolysis Can Endanger Your Results Hemolysis can certainly impact study results, and has a particularly well-documented impact on potassium concentration measurements.
The Benefits and Challenges of Real-World Data to Support Traditional Clinical Trial Initiatives Historically, medical research data obtained from other sources outside of traditional clinical trials were viewed with a heavy dose of skepticism even before evaluation of the methodology. Although this real-world data (RWD) was widely used to satisfy safety monitoring and post-drug approval regulation requirements, many clinicians viewed RWD and the real-world evidence (RWE) it generated as unverified and unreliable data mining explorations with drug marketing objectives.