Open Access

Background: Disease progression and delayed neurological complications are common after aneurysmal subarachnoid hemorrhage (aSAH). We explored the potential of quantitative blood-brain barrier (BBB) imaging to predict disease progression and neurological outcome. Methods: Data were collected as part of the Co-Operative Studies of Brain Injury Depolarizations (COSBID). We analyzed retrospectively, blinded and semi-automatically magnetic resonance images from 124 aSAH patients scanned at 4 time points (24–48 h, 6–8 days, 12–15 days and 6–12 months) after the initial hemorrhage. Volume of brain with apparent pathology and/or BBB dysfunction (BBBD), subarachnoid space and lateral ventricles were measured. Neurological status on admission was assessed using the World Federation of Neurosurgical Societies and Rosen-Macdonald scores. Outcome at ≥6 months was assessed using the extended Glasgow outcome scale and disease course (progressive or non-progressive based on imaging-detected loss of normal brain tissue in consecutive scans). Logistic regression was used to define biomarkers that best predict outcomes. Receiver operating characteristic analysis was performed to assess accuracy of outcome prediction models. Findings: In the present cohort, 63% of patients had progressive and 37% non-progressive disease course. Progressive course was associated with worse outcome at ≥6 months (sensitivity of 98% and specificity of 97%). Brain volume with BBBD was significantly larger in patients with progressive course already 24–48 h after admission (2.23 (1.23–3.17) folds, median with 95%CI), and persisted at all time points. The highest probability of a BBB-disrupted voxel to become pathological was found at a distance of ≤1 cm from the brain with apparent pathology (0·284 (0·122–0·594), p < 0·001, median with 95%CI). A multivariate logistic regression model revealed power for BBBD in combination with RMS at 24-48 h in predicting outcome (ROC area under the curve = 0·829, p < 0·001). Interpretation: We suggest that early identification of BBBD may serve as a key predictive biomarker for neurological outcome in aSAH. Fund: Dr. Dreier was supported by grants from the Deutsche Forschungsgemeinschaft (DFG) (DFG DR 323/5-1 and DFG DR 323/10–1), the Bundesministerium für Bildung und Forschung (BMBF) Center for Stroke Research Berlin 01 EO 0801 and FP7 no 602150 CENTER-TBI. Dr. Friedman was supported by grants from Israel Science Foundation and Canada Institute for Health Research (CIHR). Dr. Friedman was supported by grants from European Union's Seventh Framework Program (FP7/2007–2013; grant #602102).

Containment strategies and clinical management of coronavirus disease (COVID-19) patients during the current pandemic depend on reliable diagnostic PCR assays for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we compare 11 different RT-PCR test systems used in seven diagnostic laboratories in Germany in March 2020. While most assays performed well, we identified detection problems in a commonly used assay that may have resulted in false-negative test results during the first weeks of the pandemic.

Data on the long-term behavior of computer-aided designed/computer-aided manufactured (CAD-CAM) resin-based composites are sparse. To achieve higher predictability on the mechanical behavior of these materials, the aim of the study was to establish a mathematical relationship between the material thickness of resin-based materials and their fracture load. The tested materials were Lava Ultimate (LU), Cerasmart (GC), Enamic (EN), and Telio CAD (TC). For this purpose, 60 specimens were prepared, each with five different material thicknesses between 0.4 mm and 1.6 mm (N = 60, n = 12). The fracture load of all specimens was determined using the biaxial flexural strength test (DIN EN ISO 6872). Regression curves were fitted to the results and their coefficient of determination (R2) was computed. Cubic regression curves showed the best R2 approximation (LU R2 = 0.947, GC R2 = 0.971, VE R2 = 0.981, TC R2 = 0.971) to the fracture load values. These findings imply that the fracture load of all tested resin-based materials has a cubic relationship to material thickness. By means of a cubic equation and material-specific fracture load coefficients, the fracture load can be calculated when material thickness is given. The approach enables a better predictability for resin-based restorations for the individual patient. Hence, the methodology might be reasonably applied to other restorative materials.