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Abstract AimsNeural network classifiers can detect aortic stenosis (AS) using limited cardiac ultrasound images. While networks perform very well using cart-based imaging, they have never been tested or fine-tuned for use with focused cardiac ultrasound (FoCUS) acquisitions obtained on handheld ultrasound devices. Methods and resultsProspective study performed at Tufts Medical Center. All patients ≥65 years of age referred for clinically indicated transthoracic echocardigraphy (TTE) were eligible for inclusion. Parasternal long axis and parasternal short axis imaging was acquired using a commercially available handheld ultrasound device. Our cart-based AS classifier (trained on ∼10 000 images) was tested on FoCUS imaging from 160 patients. The median age was 74 (inter-quartile range 69–80) years, 50% of patients were women. Thirty patients (18.8%) had some degree of AS. The area under the received operator curve (AUROC) of the cart-based model for detecting AS was 0.87 (95% CI 0.75–0.99) on the FoCUS test set. Last-layer fine-tuning on handheld data established a classifier with AUROC of 0.94 (0.91–0.97). AUROC during temporal external validation was 0.97 (95% CI 0.89–1.0). When performance of the fine-tuned AS classifier was modelled on potential screening environments (2 and 10% AS prevalence), the positive predictive value ranged from 0.72 (0.69–0.76) to 0.88 (0.81–0.97) and negative predictive value ranged from 0.94 (0.94–0.94) to 0.99 (0.99–0.99) respectively. ConclusionOur cart-based machine-learning model for AS showed a drop in performance when tested on handheld ultrasound imaging collected by sonographers. Fine-tuning the AS classifier improved performance and demonstrates potential as a novel approach to detecting AS through automated interpretation of handheld imaging. 

The differentiation of specialized infection cells, called appressoria, from polarized germ tubes of the blast fungus Magnaporthe oryzae, requires remarkable remodeling of cell polarity and architecture, yet our understanding of this process remains incomplete. Here we investigate the behavior and role of cell-end marker proteins in appressorium remodeling and hyphal branch emergence. We show that the SH3 domain-containing protein Tea4 is required for the normal formation of an F-actin ring at Tea1-GFP-labeled polarity nodes, which contributes to the remodeling of septin structures and repolarization of the appressorium. Further, we show that Tea1 localizes to a cortical structure during hyphal septation which, unlike contractile septin rings, persists after septum formation, and, in combination with other polarity determinants, likely spatially regulates branch emergence. Genetic loss of Tea4 leads to mislocalization of Tea1 at the hyphal apex and with it, impaired growth directionality. In contrast, Tea1 is largely depleted from septation events in Δ tea4 mutants and branching and septation are significantly reduced. Together, our data provide new insight into polarity remodeling during infection-related and vegetative growth by the blast fungus.