Retinal vascular fractals predict long-term microvascular complications in type 1 diabetes mellitus:the Danish Cohort of Pediatric Diabetes 1987 (DCPD1987)

Diabetic neuropathy, nephropathy, and retinopathy cause significant morbidity in patients with type 1 diabetes, even though improvements in treatment modalities delay the appearance and reduce the severity of these complications. To prevent or further delay the onset, it is necessary to better understand common underlying pathogenesis and to discover preclinical biomarkers of these complications. Retinal vessel calibers have been associated with the presence of microvascular complications, but their long-term predictive value has only been sparsely investigated. We examined retinal vessel calibers as 16-year predictors of diabetic nephropathy, neuropathy, and proliferative retinopathy in a young population-based Danish cohort with type 1 diabetes. We used semiautomated computer software to analyze vessel diameters on baseline retinal photos. Calibers of all vessels coursing through a zone 0.5–1 disc diameter from the disc margin were measured and summarized as the central artery and vein equivalents. In multiple regression analyses, we found wider venular diameters and smaller arteriolar diameters were both predictive of the 16-year development of nephropathy, neuropathy, and proliferative retinopathy. Early retinal vessel caliber changes are seemingly early markers of microvascular processes, precede the development of microvascular complications, and are a potential noninvasive predictive test on future risk of diabetic retinopathy, neuropathy, and nephropathy.</jats:p

Effects of Topically Administered Neuroprotective Drugs in Early Stages of Diabetic Retinopathy:Results of the EUROCONDOR Clinical Trial

The primary objective of this study was to assess whether the topical administration of two neuroprotective drugs (brimonidine and somatostatin) could prevent or arrest retinal neurodysfunction in patients with type 2 diabetes. For this purpose, adults aged between 45 and 75 years with a diabetes duration ≥5 years and an Early Treatment of Diabetic Retinopathy Study (ETDRS) level of ≤35 were randomly assigned to one of three arms: placebo, somatostatin, or brimonidine. The primary outcome was the change in implicit time (IT) assessed by multifocal electroretinography between baseline and at the end of follow-up (96 weeks). There were 449 eligible patients allocated to brimonidine (n = 152), somatostatin (n = 145), or placebo (n = 152). When the primary end point was evaluated in the whole population, we did not find any neuroprotective effect of brimonidine or somatostatin. However, in the subset of patients (34.7%) with preexisting retinal neurodysfunction, IT worsened in the placebo group (P < 0.001) but remained unchanged in the brimonidine and somatostatin groups. In conclusion, the topical administration of the selected neuroprotective agents appears useful in preventing the worsening of preexisting retinal neurodysfunction. This finding points to screening retinal neurodysfunction as a critical issue to identify a subset of patients in whom neuroprotective treatment might be of benefit

Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes

publisher: Elsevier articletitle: Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes journaltitle: Cell articlelink: https://doi.org/10.1016/j.cell.2018.05.046 content_type: article copyright: © 2018 Elsevier Inc

Metal Artifact Reduction in Spectral X-ray CT Using Spectral Deep Learning

Spectral X-ray computed tomography (SCT) is an emerging method for non-destructive imaging of the inner structure of materials. Compared with the conventional X-ray CT, this technique provides spectral photon energy resolution in a finite number of energy channels, adding a new dimension to the reconstructed volumes and images. While this mitigates energy-dependent distortions such as beam hardening, metal artifacts due to photon starvation effects are still present, especially for low-energy channels where the attenuation coefficients are higher. We present a correction method for metal artifact reduction in SCT that is based on spectral deep learning. The correction efficiently reduces streaking artifacts in all the energy channels measured. We show that the additional information in the energy domain provides relevance for restoring the quality of low-energy reconstruction affected by metal artifacts. The correction method is parameter free and only takes around 15 ms per energy channel, satisfying near-real time requirement of industrial scanners