Machine learning to detect the SINEs of cancer.

We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a machine learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to 7615 samples from 5178 individuals, 2073 with solid cancer and the remainder without cancer.

Interrogation of Electrochemical Aptamer-Based Sensors via Peak-to-Peak Separation in Cyclic Voltammetry Improves the Temporal Stability and Batch-to-Batch Variability in Biological Fluids.

Electrochemical, aptamer-based (E-AB) sensors support continuous, real-time measurements of specific molecular targets in complex fluids such as undiluted serum. They achieve these measurements by using redox-reporter-modified, electrode-attached aptamers that undergo target binding-induced conformational changes which, in turn, change electron transfer between the reporter and the sensor surface. Traditionally, E-AB sensors are interrogated via pulse voltammetry to monitor binding-induced changes in transfer kinetics.

Alkanethiol Monolayer End Groups Affect the Long-Term Operational Stability and Signaling of Electrochemical, Aptamer-Based Sensors in Biological Fluids.

Electrochemical aptamer-based (E-AB) sensors achieve highly precise measurements of specific molecular targets in untreated biological fluids. This unique ability, together with their measurement frequency of seconds or faster, has enabled the real-time monitoring of drug pharmacokinetics in live animals with unprecedented temporal resolution. However, one important weakness of E-AB sensors is that their bioelectronic interface degrades upon continuous electrochemical interrogation-a process typically seen as a drop in faradaic and an increase in charging currents over time.

Open Source Software for the Real-Time Control, Processing, and Visualization of High-Volume Electrochemical Data.

Electrochemical sensors are major players in the race for improved molecular diagnostics due to their convenience, temporal resolution, manufacturing scalability, and their ability to support real-time measurements. This is evident in the ever-increasing number of health-related electrochemical sensing platforms, ranging from single-measurement point-of-care devices to wearable devices supporting immediate and continuous monitoring. In support of the need for such systems to rapidly process large data volumes, we describe here an open-source, easily customizable, multiplatform compatible program for the real-time control, processing, and visualization of electrochemical data.