Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.

Purpose: Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to extract oncologists' documentation of stage in the national Veterans Affairs (VA) Healthcare System.

Methods: Using nationwide electronic health record (EHR) and cancer registry data from the VA Corporate Data Warehouse, we developed and validated a rule-based NLP algorithm to extract oncologist-determined MM stage.

Matching Patients to Accelerate Clinical Trials (MPACT): Enabling Technology for Oncology Clinical Trial Workflow.

Clinical trial enrollment is impeded by the significant time burden placed on research coordinators screening eligible patients. With 50,000 new cancer cases every year, the Veterans Health Administration (VHA) has made increased access for Veterans to high-quality clinical trials a priority. To aid in this effort, we worked with research coordinators to build the MPACT (Matching Patients to Accelerate Clinical Trials) platform with a goal of improving efficiency in the screening process.

Machine learning-based natural language processing to extract PD-L1 expression levels from clinical notes.

PD-L1 expression is used to determine oncology patients' response to and eligibility for immunologic treatments; however, PD-L1 expression status often only exists in unstructured clinical notes, limiting ability to use it in population-level studies. We developed and evaluated a machine learning based natural language processing (NLP) tool to extract PD-L1 expression values from the nationwide Veterans Affairs electronic health record system. The model demonstrated strong evaluation performance across multiple levels of label granularity.

Effect of Dipyridamole on Membrane Energization and Energy Transfer in Chromatophores of Rba. sphaeroides.

Effect of dipyridamole (DIP) at concentrations up to 1 mM on fluorescent characteristics of light-harvesting complexes LH2 and LH1, as well as on conditions of photosynthetic electron transport chain in the bacterial chromatophores of Rba. sphaeroides was investigated. DIP was found to affect efficiency of energy transfer from the light-harvesting complex LH2 to the LH1-reaction center core complex and to produce the long-wavelength ("red") shift of the absorption band of light-harvesting bacteriochlorophyll molecules in the IR spectral region at 840-900 nm.