User:Rnelson2021/Biomarker

Prognostic

·        A prognostic biomarker provides information about the patients overall outcome, with treatment or therapeutic intervention having little to no affect in improving a patient’s condition. One example of a prognostic biomarkers in clinical research, is the use of mutated PIK3CA in the study of metastatic breast cancer. As noted by the results in Figure 2A, the mutation is prognostic since its presence in the patient endure the same outcome regardless of the treatment method used.

·        Women who had the PIK3CA mutation before treatment, had the lowest average survival rate. The decline in the groups containing the mutant occurred quicker and in a much steeper decline. The independent nature of the prognostic factor allows researcher to study the disease or condition in its natural state. This makes it easier to observe these abnormal biological processes and speculate on how to correct them. Prognostic factors are often used in combination with predictive variables in therapeutics studies, to examine how effective different treatments are in curing specific diseases or cancer. As opposed to predictive biomarkers, prognostic do not rely on any explanatory variables, thus allowing for independent examination of the underlying disease or condition.

Digital

·        One major current use of digital biomarkers involves keeping track of regular brain activity. Specific neural indicators can be measured by devices to evaluate patients for any neuro abnormalities. The data collected can determine the patients disease probability or condition. Digital biomarkers are currently being used in conjugation with Artificial intelligence (A.I.) in order to recognize symptoms for Mild Cognitive Impairment (MCI). While patients carryout everyday tasks (IADL), computers are using machine learning to observe and detect any deviation from normal behavior. These markers are used as signs or indicators of cognitive decline.