BackgroundA promising therapeutic strategy for amyotrophic lateral sclerosis (ALS) is the use of cell-based therapies that can protect motor neurons and thereby retard disease progression. We recently showed that a single large dose (25×106 cells) of mononuclear cells from human umbilical cord blood (MNC hUCB) administered intravenously to pre-symptomatic G93A SOD1 mice is optimal in delaying disease progression and increasing lifespan. However, this single high cell dose is impractical for clinical use. The aim of the present pre-clinical translation study was therefore to evaluate the effects of multiple low dose systemic injections of MNC hUCB cell into G93A SOD1 mice at different disease stages.Methodology/Principal FindingsMice received weekly intravenous injections of MNC hUCB or media. Symptomatic mice received 106 or 2.5×106 cells from 13 weeks of age. A third, pre-symptomatic, group received 106 cells from 9 weeks of age. Control groups were media-injected G93A and mice carrying the normal hSOD1 gene. Motor function tests and various assays determined cell effects. Administered cell distribution, motor neuron counts, and glial cell densities were analyzed in mouse spinal cords. Results showed that mice receiving 106 cells pre-symptomatically or 2.5×106 cells symptomatically significantly delayed functional deterioration, increased lifespan and had higher motor neuron counts than media mice. Astrocytes and microglia were significantly reduced in all cell-treated groups.Conclusions/SignificanceThese results demonstrate that multiple injections of MNC hUCB cells, even beginning at the symptomatic disease stage, could benefit disease outcomes by protecting motor neurons from inflammatory effectors. This multiple cell infusion approach may promote future clinical studies.

Background: Symptoms are a core concept of nursing interest. Large-scale secondary data reuse of notes in electronic health records (EHRs) has the potential to increase the quantity and quality of symptom research. However, the symptom language used in clinical notes is complex. A need exists for methods designed specifically to identify and study symptom information from EHR notes.Objectives: We aim to describe a method that combines standardized vocabularies, clinical expertise, and natural language processing to generate comprehensive symptom vocabularies and identify symptom information in EHR notes. We piloted this method with five diverse symptom concepts: constipation, depressed mood, disturbed sleep, fatigue, and palpitations.Methods: First, we obtained synonym lists for each pilot symptom concept from the Unified Medical Language System. Then, we used two large bodies of text (clinical notes from Columbia University Irving Medical Center and PubMed abstracts containing Medical Subject Headings or key words related to the pilot symptoms) to further expand our initial vocabulary of synonyms for each pilot symptom concept. We used NimbleMiner, an open-source natural language processing tool, to accomplish these tasks and evaluated NimbleMiner symptom identification performance by comparison to a manually annotated set of nurse-and physician-authored common EHR note types.Results: Compared to the baseline Unified Medical Language System synonym lists, we identified up to 11 times more additional synonym words or expressions, including abbreviations, misspellings, and unique multiword combinations, for each symptom concept. Natural language processing system symptom identification performance was excellent.Discussion: Using our comprehensive symptom vocabularies and NimbleMiner to label symptoms in clinical notes produced excellent performance metrics. The ability to extract symptom information from EHR notes in an accurate and scalable manner has the potential to greatly facilitate symptom science research.