Yang M, Bair JA, Hodges RR, Serhan CN, Dartt DA.
Resolvin E1 Reduces Leukotriene B4-Induced Intracellular Calcium Increase and Mucin Secretion in Rat Conjunctival Goblet Cells. Am J Pathol 2020;190(9):1823-1832.
AbstractLeukotriene B4 (LTB4) is a major proinflammatory mediator important in host defense, whereas resolvins (Rvs) are produced during the resolution phase of inflammation. The authors determined the actions of both RvE1 and RvD1 on LTB4-induced responses of goblet cells cultured from rat conjunctiva. The responses measured were an increase in the intracellular [Ca] ([Ca]) and high-molecular-weight glycoprotein secretion. Treatment with RvE1 or RvD1 for 30 minutes significantly blocked the LTB4-induced [Ca] increase. The actions of RvE1 on LTB4-induced [Ca] increase were reversed by siRNA for the RvE1 receptor, and the actions of RvD1 were reversed by an RvD1 receptor inhibitor. The RvE1 and RvD1 block of LTB4-stimulated increase in [Ca] was also reversed by an inhibitory peptide to β-adrenergic receptor kinase. LTB4 and block of the LTB4-stimulated increase in [Ca] by RvE1 and RvD1 were partially mediated by the depletion of intracellular Ca stores. RvE1, but not RvD1, counterregulated the LTB4-induced high-molecular-weight glycoprotein secretion. Thus, both RvE1 and RvD1 receptors directly inhibit LTB4 by phosphorylating the LTB4 receptor using β adrenergic receptor kinase. RvE1 receptor counterregulates the LTB4-induced increase in [Ca] and secretion, whereas RvD1 receptor only counterregulates LTB4-induced [Ca] increase.
Yousefi S, Elze T, Pasquale LR, Saeedi O, Wang M, Shen LQ, Wellik SR, De Moraes CG, Myers JS, Boland MV.
Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard. Ophthalmology 2020;127(9):1170-1178.
AbstractPURPOSE: To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss. DESIGN: Retrospective, cross-sectional, longitudinal cohort study. PARTICIPANTS: Of 31 591 visual fields (VFs) on 8077 subjects, 13 231 VFs from the most recent visit of each patient were included to develop the AI dashboard. Longitudinal VFs from 287 eyes with glaucoma were used to validate the models. METHOD: We entered VF data from the most recent visit of glaucomatous and nonglaucomatous patients into a "pipeline" that included principal component analysis (PCA), manifold learning, and unsupervised clustering to identify eyes with similar global, hemifield, and local patterns of VF loss. We visualized the results on a map, which we refer to as an "AI-enabled glaucoma dashboard." We used density-based clustering and the VF decomposition method called "archetypal analysis" to annotate the dashboard. Finally, we used 2 separate benchmark datasets-one representing "likely nonprogression" and the other representing "likely progression"-to validate the dashboard and assess its ability to portray functional change over time in glaucoma. MAIN OUTCOME MEASURES: The severity and extent of functional loss and characteristic patterns of VF loss in patients with glaucoma. RESULTS: After building the dashboard, we identified 32 nonoverlapping clusters. Each cluster on the dashboard corresponded to a particular global functional severity, an extent of VF loss into different hemifields, and characteristic local patterns of VF loss. By using 2 independent benchmark datasets and a definition of stability as trajectories not passing through over 2 clusters in a left or downward direction, the specificity for detecting "likely nonprogression" was 94% and the sensitivity for detecting "likely progression" was 77%. CONCLUSIONS: The AI-enabled glaucoma dashboard, developed using a large VF dataset containing a broad spectrum of visual deficit types, has the potential to provide clinicians with a user-friendly tool for determination of the severity of glaucomatous vision deficit, the spatial extent of the damage, and a means for monitoring the disease progression.