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Optical coherence tomography angiography (OCTA) is an emerging non-invasive technique for imaging the retinal vasculature. While there are many promising clinical applications for OCTA, determination of image quality remains a challenge. We developed a deep learning-based system using a ResNet152 neural network classifier, pretrained using ImageNet, to classify images of the superficial capillary plexus in 347 scans from 134 patients. Images were also manually graded by two independent graders as a ground truth for the supervised learning models. Because requirements for image quality may vary depending on the clinical or research setting, two models were trained—one to identify high-quality images and one to identify low-quality images. Our neural network models demonstrated outstanding area under the curve (AUC) metrics for both low quality image identification (AUC = 0.99, 95%CI 0.98–1.00, κ = 0.90) and high quality image identification (AUC = 0.97, 95%CI 0.96–0.99, κ = 0.81), significantly outperforming machine-reported signal strength (AUC = 0.82, 95%CI 0.77–0.86, κ = 0.52 and AUC = 0.78, 95%CI 0.73–0.83, κ = 0.27 respectively). Our study demonstrates that techniques from machine learning may be used to develop flexible and robust methods for quality control of OCTA images. [ABSTRACT FROM AUTHOR]

The sodium-bicarbonate cotransporter NBC1 is targeted exclusively at the basolateral membrane. Mutagenesis of a dihydrophobic FL motif (residues 1013-1014) in the C-terminal domain disrupts the targeting of NBC1. In the present study, we determined the precise constraints of the FL motif required for basolateral targeting of NBC1 by expressing epitope-tagged wild-type and mutant NBC1 in MDCK cells and RNA-injected Xenopus oocytes and examining their subcellular localization. We assayed the functional activity of the mutants by measuring bicarbonate-induced currents in oocytes. Wild-type NBC1 (containing PFLS) was expressed exclusively on the basolateral membrane in MDCK cells. Reversal of the FL motif (PLFS) had no effect on basolateral targeting or activity. Shifting the FL motif one residue upstream (FLPS) resulted in mistargeting of the apical membrane but the FLPS mutant retained its functional activity in oocytes. Shifting the FL motif one residue downstream resulted in a mutant (PSFL) that did not efficiently translocate to the plasma membrane and was instead colocalized with the ER marker, protein disulfide isomerase (PDI). Analysis of circular dichroism (CD) revealed that a short peptide, 20 amino acid residues, of wild-type NBC1 contained a significant alpha-helical structure, whereas peptides in which the FL motif was reversed or C-terminally shifted were disordered. We therefore propose that the specific orientation and the precise location of the FL motif in the primary sequence of NBC1 are strict requirements for the alpha-helical structure of the C-terminal cytoplasmic domain and for targeting of NBC1 to the basolateral membrane. [ABSTRACT FROM AUTHOR]

Despite the emergence of curated app libraries for mental health apps, personal searches by consumers remain a common method for discovering apps. App store descriptions therefore represent a key channel to inform consumer choice. This study examined the claims invoked through these app store descriptions, the extent to which scientific language is used to support such claims, and the corresponding evidence in the literature. Google Play and iTunes were searched for apps related to depression, self-harm, substance use, anxiety, and schizophrenia. The descriptions of the top-ranking, consumer-focused apps were coded to identify claims of acceptability and effectiveness, and forms of supporting statement. For apps which invoked ostensibly scientific principles, a literature search was conducted to assess their credibility. Seventy-three apps were coded, and the majority (64%) claimed effectiveness at diagnosing a mental health condition, or improving symptoms, mood or self-management. Scientific language was most frequently used to support these effectiveness claims (44%), although this included techniques not validated by literature searches (8/24 = 33%). Two apps described low-quality, primary evidence to support the use of the app. Only one app included a citation to published literature. A minority of apps (14%) described design or development involving lived experience, and none referenced certification or accreditation processes such as app libraries. Scientific language was the most frequently invoked form of support for use of mental health apps; however, high-quality evidence is not commonly described. Improved knowledge translation strategies may improve the adoption of other strategies, such as certification or lived experience co-design. [ABSTRACT FROM AUTHOR]