RESUMEN
Respiratory disease in cattle is a significant global concern, yet current diagnostic methods are limited, and there is a lack of crush-side tests for detecting active disease. To address this gap, we propose utilizing electrical impedance tomography (EIT), a non-invasive imaging technique that provides real-time visualization of lung ventilation dynamics. The study included adult cattle from farms in Western Australia. The cattle were restrained in a crush. A standardized respiratory scoring system, which combined visual, auscultation, and clinical scores, was conducted by two non-conferring clinicians for each animal. The scores were blinded and averaged. During assessment, an EIT electrode belt was placed around the thorax. EIT recordings of ten suitable breaths were taken for analysis before the cattle were released back to the herd. Based on the combined examination scoring, the cattle were categorized as having healthy or diseased lungs. To allow visual interpretation of each breath and enable the creation of the quartile ventilation ratio (VQR), Flow/Tidal Impedance Variation curves (F/TIV) were generated for each breath. The analysis focused on two EIT variables: The novel VQR over time during inhalation and exhalation and global expiratory impedance (TIVEXP) adjusted by breath length. A mixed effects model was used to compare these variables between healthy and diseased cattle. Ten adult cattle of mixed ages were used in the current analysis. Five cattle were scored as healthy and five as diseased. There was a significant difference in the examination scores between the healthy and diseased group (P = 0.03). A significant difference in VQR during inhalation (P = 0.03) was observed between the healthy and diseased groups. No difference was seen in VQR over time during exhalation (P = 0.3). The TIVEXP was not different between groups (P = 0.36). In this study, EIT was able to detect differences in inhalation mechanics when comparing healthy and diseased cattle as defined via clinical examination, highlighting the clinical utility of EIT.
RESUMEN
The airway epithelium of children with asthma is characterized by aberrant repair that may be therapeutically modifiable. The development of epithelial-targeting therapeutics that enhance airway repair could provide a novel treatment avenue for childhood asthma. Drug discovery efforts utilizing high-throughput live cell imaging of patient-derived airway epithelial culture-based wound repair assays can be used to identify compounds that modulate airway repair in childhood asthma. Manual cell tracking has been used to determine cell trajectories and wound closure rates, but is time consuming, subject to bias, and infeasible for high-throughput experiments. We therefore developed software, EPIC, that automatically tracks low-resolution low-framerate cells using artificial intelligence, analyzes high-throughput drug screening experiments and produces multiple wound repair metrics and publication-ready figures. Additionally, unlike available cell trackers that perform cell segmentation, EPIC tracks cells using bounding boxes and thus has simpler and faster training data generation requirements for researchers working with other cell types. EPIC outperformed publicly available software in our wound repair datasets by achieving human-level cell tracking accuracy in a fraction of the time. We also showed that EPIC is not limited to airway epithelial repair for children with asthma but can be applied in other cellular contexts by outperforming the same software in the Cell Tracking with Mitosis Detection Challenge (CTMC) dataset. The CTMC is the only established cell tracking benchmark dataset that is designed for cell trackers utilizing bounding boxes. We expect our open-source and easy-to-use software to enable high-throughput drug screening targeting airway epithelial repair for children with asthma.
