Natural Language Processing to Identify Pulmonary Nodules and Extract Nodule Characteristics From Radiology Reports.

BACKGROUND: There is an urgent need for population-based studies on managing patients with pulmonary nodules. RESEARCH QUESTION: Is it possible to identify pulmonary nodules and associated characteristics using an automated method? STUDY DESIGN AND METHODS: We revised and refined an existing natural language processing (NLP) algorithm to identify radiology transcripts with pulmonary nodules and greatly expanded its functionality to identify the characteristics of the largest nodule, when present, including size, lobe, laterality, attenuation, calcification, and edge. We compared NLP results with a reference standard of manual transcript review in a random test sample of 200 radiology transcripts. We applied the final automated method to a larger cohort of patients who underwent chest CT scan in an integrated health care system from 2006 to 2016, and described their demographic and clinical characteristics. RESULTS: In the test sample, the NLP algorithm had very high sensitivity (98.6%; 95% CI, 95.0%-99.8%) and specificity (100%; 95% CI, 93.9%-100%) for identifying pulmonary nodules. For attenuation, edge, and calcification, the NLP algorithm achieved similar accuracies, and it correctly identified the diameter of the largest nodule in 135 of 141 cases (95.7%; 95% CI, 91.0%-98.4%). In the larger cohort, the NLP found 217,771 reports with nodules among 717,304 chest CT reports (30.4%). From 2006 to 2016, the number of reports with nodules increased by 150%, and the mean size of the largest nodule gradually decreased from 11 to 8.9 mm. Radiologists documented the laterality and lobe (90%-95%) more often than the attenuation, calcification, and edge characteristics (11%-14%). INTERPRETATION: The NLP algorithm identified pulmonary nodules and associated characteristics with high accuracy. In our community practice settings, the documentation of nodule characteristics is incomplete. Our results call for better documentation of nodule findings. The NLP algorithm can be used in population-based studies to identify pulmonary nodules, avoiding labor-intensive chart review.

Neural stem cells genetically-modified to express neprilysin reduce pathology in Alzheimer transgenic models.

INTRODUCTION: Short-term neural stem cell (NSC) transplantation improves cognition in Alzheimer's disease (AD) transgenic mice by enhancing endogenous synaptic connectivity. However, this approach has no effect on the underlying beta-amyloid (Aß) and neurofibrillary tangle pathology. Long term efficacy of cell based approaches may therefore require combinatorial approaches. METHODS: To begin to examine this question we genetically-modified NSCs to stably express and secrete the Aß-degrading enzyme, neprilysin (sNEP). Next, we studied the effects of sNEP expression in vitro by quantifying Aß-degrading activity, NSC multipotency markers, and Aß-induced toxicity. To determine whether sNEP-expressing NSCs can also modulate AD-pathogenesis in vivo, control-modified and sNEP-NSCs were transplanted unilaterally into the hippocampus of two independent and well characterized transgenic models of AD: 3xTg-AD and Thy1-APP mice. After three months, stem cell engraftment, neprilysin expression, and AD pathology were examined. RESULTS: Our findings reveal that stem cell-mediated delivery of NEP provides marked and significant reductions in Aß pathology and increases synaptic density in both 3xTg-AD and Thy1-APP transgenic mice. Remarkably, Aß plaque loads are reduced not only in the hippocampus and subiculum adjacent to engrafted NSCs, but also within the amygdala and medial septum, areas that receive afferent projections from the engrafted region. CONCLUSIONS: Taken together, our data suggest that genetically-modified NSCs could provide a powerful combinatorial approach to not only enhance synaptic plasticity but to also target and modify underlying Alzheimer's disease pathology.

Soluble aß promotes wild-type tau pathology in vivo.

Growing evidence suggests that soluble Aß species can drive Alzheimer disease (AD) pathogenesis by inducing a cascade of events including tau hyperphosphorylation, proteasome impairment, and synaptic dysfunction. However, these studies have relied largely on in vitro approaches to examine the role of soluble Aß in AD. In particular, it remains unknown whether soluble Aß oligomers can facilitate the development of human wild-type tau pathology in vivo. To address this question, we developed a novel transgenic model that expresses low levels of APP with the Arctic familial AD mutation to enhance soluble Aß oligomer formation in conjunction with wild-type human tau. Using a genetic approach, we show that reduction of ß-site APP cleaving enzyme (BACE) in these ArcTau mice decreases soluble Aß oligomers, rescues cognition, and, more importantly, reduces tau accumulation and phosphorylation. Notably, BACE reduction decreases the postsynaptic mislocalization of tau in ArcTau mice and reduces the association between NMDA receptors and PSD-95. These studies provide critical in vivo evidence for a strong mechanistic link between soluble Aß, wild-type tau, and synaptic pathology.