Results are only as good as the samples used to generate them. Good sample in, good data out. In fact, analysis of uncontrolled samples can generate misleading data and cause setbacks in research efforts costing time, money, and possible loss of precious samples.
Detection of low-abundance serum proteins associated with prediabetes for predictive and prognostic purposes
Diabetes mellitus refers to a family of metabolic disorders that are characterized by elevated blood glucose concentrations, or hyperglycemia. The International Diabetes Federation estimated that in 2015 there were 415 million diabetes cases worldwide in the 20-79 year old age group and predicted that number to increase to 640 million by 2040.
Advanced age is the single greatest risk factor for disease; however, aging is a complex and multifactorial process, and its mechanisms are still poorly understood. It has been shown that aging activates common transcriptional patterns across most organs and tissues, such as inflammatory, stress response and transcriptional regulation pathways1, suggesting the changes are systemic and interrelated. Exposing a young, healthy organism to plasma from old mice, for example, slows down tissues regeneration in a manner that resembles aging2. Conversely, exposure to plasma from young blood is capable of restoring youthful phenotypes 3,4. Understanding the primary factors that affect tissue and organ function on a cellular level may lead to developing treatments that can help people stay healthy and live longer.
Circulating protein biomarkers are a promising avenue for predicting patient response to cancer immunotherapies
Cancer immunotherapy relies on activating or enhancing the antitumor immune response and has taken a central position in cancer treatment modalities. While offering a generally safer and more efficacious alternative to standard chemotherapy and radiotherapy, not all patients respond to immunotherapy, and combination immunotherapies increase cost of treatment and heighten the risk for toxicity-related adverse events. An understanding of the molecular factors that contribute to clinical outcomes could enable improved selection of subject cohorts; therefore, identifying predictive biomarkers of immunotherapy response has become a growing focus of immuno-oncology research.
Four letters, four compounds – adenine (A), cytosine (C), guanine (G), and thymine (T) – are the basic code “letters” of our genetic material, DNA. As elegant as the genetic code is, it is also quite dense with information, leading to many questions about how genetic variations affect our biology or health. How can the answers be found amongst all that tightly packed information?
The battle against coronavirus disease 2019 (COVID-19) only begins with a test to identify who is infected and who is not. To accelerate an effective response, we have to move beyond the initial diagnosis to prognosis: Who is most susceptible to developing serious, life-threatening symptoms? Currently, there is no way to tell whether an infected individual (or an individual who may become infected) will be asymptomatic or require hospitalization. This wide variation in disease severity is, at least in part, a reflection of the myriad changes that occur within a person’s body as it fights the virus, which in turn reflect the unique biological makeup of that individual.
Detection of low-abundance serum proteins associated with cardiovascular diseases for prognostic purposes
As the leading cause of death in the United States and worldwide, cardiovascular disease (CVD) includes a family of diseases that affect the heart and blood vessels. The primary origin of CVD is most often atherosclerotic in nature, in which fatty plaque deposits line arteries and obstruct circulation of blood. The etiology of CVD is multifactorial and is shaped by the interaction of genes and environment and further influenced by age and gender…
NASH and NAFLD are characterized by distinct sets of protein signatures. In our latest white paper, we discuss how researchers have used the SomaScan Assay to characterize and predict NASH non-invasively, even beating the best available clinical model. Download the white paper to learn more about how to get the most out of your NASH and NAFLD clinical samples with the world’s most impactful protein assay.