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The aim of this study was to characterize the pathological and functional consequences of the G1961E mutant allele in the Stargardt disease gene ABCA4. Data from 15 patients were retrospectively reviewed and all the patients had at least one G1961E mutation. Comprehensive ophthalmic examination, full-field and pattern electroretinograms, and fundus autofluorescence (FAF) imaging were performed on all patients. Microperimetry, spectral-domain optical coherence tomography (OCT), and fluorescein angiography were performed in selected cases. Genetic screening was performed using the ABCR400 micro-array that currently detects 496 disctinct ABCA4 variants. All patients had normal full-field scotopic and photopic electroretinograms (ERGs) and abnormal pattern electroretinograms (PERGs) performed on both eyes, and all the fundi had bull's eye maculopathy without retinal flecks on FAF. On OCT, one patient had disorganization of photoreceptor outer segment, two had outer nuclear layer (ONL) thinning likely due to photoreceptor atrophy proximal to the foveal center, and three had additional retinal pigment epithelium (RPE) atrophy. On microperimetry, six patients had eccentric superior fixation and amongst this group, five had an absolute scotoma in the foveal area. DNA analysis revealed that three patients were homozygous G1961E/G1961E and the rest were compound heterozygotes for G1961E and other ABCA4 mutations. The G1961E allele in either homozygosity or heterozygosity is associated with anatomical and functional pathologies limited to the parafoveal region and a trend to delayed onset of symptoms, relative to other manifestations of ABCA4 mutations. Our observations support the hypothesis that the G1961E allele contributes to localized macular changes rather than generalized retinal dysfunction, and is a cause of bull's eye maculopathy in either the homozygosity or heterozygosity state. In addition, genetic testing provides precise diagnosis of the underlying maculopathy, and current non-invasive imaging techniques could be used to detect photoreceptor damage at the earliest clinical onset of the disease.Corresponding authors: 1 Dr.

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COPD patients are burdened with a daily risk of acute exacerbation and loss of control, which could be mitigated by effective, on-demand decision support tools. In this study, we present a machine learning-based strategy for early detection of exacerbations and subsequent triage. Our application uses physician opinion in a statistically and clinically comprehensive set of patient cases to train a supervised prediction algorithm. The accuracy of the model is assessed against a panel of physicians each triaging identical cases in a representative patient validation set. Our results show that algorithm accuracy and safety indicators surpass all individual pulmonologists in both identifying exacerbations and predicting the consensus triage in a 101 case validation set. The algorithm is also the top performer in sensitivity, specificity, and ppv when predicting a patient’s need for emergency care.

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