ABSTRACT
We introduce machine learning (ML) to perform classification and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate neutrophil NETosis, a recently discovered process involved in multiple human diseases. CNNs achieved >94% in performance accuracy in differentiating NETotic from non-NETotic cells and vastly facilitated dose-response analysis and screening of the NETotic response in neutrophils from patients. Using only features learned from nuclear morphology, CNNs can distinguish between NETosis and necrosis and between distinct NETosis signaling pathways, making them a precise tool for NETosis detection. Furthermore, by using CNNs and tools to determine object dispersion, we uncovered differences in NETotic nuclei clustering between major NETosis pathways that is useful in understanding NETosis signaling events. Our study also shows that neutrophils from patients with sickle cell disease were unresponsive to one of two major NETosis pathways. Thus, we demonstrate the design, performance, and implementation of ML tools for rapid quantitative and qualitative cell analysis in basic science.
Subject(s)
Diagnostic Imaging/methods , Extracellular Traps/metabolism , Image Processing, Computer-Assisted/methods , Machine Learning , Neutrophils/pathology , Cell Death/physiology , Humans , Necrosis/metabolism , Necrosis/pathology , Neural Networks, Computer , Phenotype , Reactive Oxygen Species/metabolism , Signal Transduction/physiologyABSTRACT
OBJECTIVES: To describe statewide emergency medical service (EMS) protocols relating to identification, management, and reporting of elder abuse in the prehospital setting. DESIGN: Cross-sectional analysis. SETTING: Statewide EMS protocols in the United States. PARTICIPANTS: Publicly available statewide EMS protocols identified from published literature, http://EMSprotocols.org, and each state's public health website. MEASUREMENTS: Protocols were reviewed to determine whether elder abuse was mentioned, elder abuse was defined, potential indicators of elder abuse were listed, management of older adults experiencing abuse was described, and instructions regarding reporting were provided. EMS protocols for child abuse were reviewed in the same manner for the purpose of comparison. RESULTS: Of the 35 publicly available statewide EMS protocols, only 14 (40.0%) mention elder abuse. Of protocols that mention elder abuse, 6 (42.9%) define elder abuse, 10 (71.4%) describe indicators of elder abuse, 8 (57.1%) provide instruction regarding management, and 12 (85.7%) provide instruction regarding reporting. Almost twice as many states met each of these metrics for child abuse. CONCLUSION: Statewide EMS protocols for elder abuse vary in regard to identification, management, and reporting, with the majority of states having no content on this subject. Expansion and standardization of protocols may increase the identification of elder abuse.
