Aggregated A beta 1-42 is selectively toxic for neurons, whereas glial cells produce mature fibrils with low toxicity in Drosophila

The basis for selective vulnerability of certain cell types for misfolded proteins (MPs) in neurodegenerative diseases is largely unknown. This knowledge is crucial for understanding disease progression in relation to MPs spreading in the CNS. We assessed this issue in Drosophila by cell-specific expression of human A beta 1-42 associated with Alzheimer's disease. Expression of A beta 1-42 in various neurons resulted in concentration-dependent severe neurodegenerative phenotypes, and intraneuronal ringtangle-like aggregates with immature fibril properties when analyzed by aggregate-specific ligands. Unexpectedly, expression of A beta 1-42 from a pan-glial driver produced a mild phenotype despite massive brain load of A beta 1-42 aggregates, even higher than in the strongest neuronal driver. Glial cells formed more mature fibrous aggregates, morphologically distinct from aggregates found in neurons, and was mainly extracellular. Our findings implicate that A beta 1-42 cytotoxicity is both cell and aggregate morphotype dependent

A randomized and controlled clinical trial of two different compositions of deproteinized bovine bone and autogenous bone used for lateral ridge augmentation

OBJECTIVE: The aim of the study was to radiologically and histologically evaluate the graft healing and volumetric changes after lateral augmentation with two different compositions of deproteinized bovine bone (DPBB) and autogenous bone (AB). MATERIAL AND METHODS: Thirteen patients with a mean age of 59.6 ± 12.1 years (six men and seven women) were included in this randomized and controlled trial, designed as a split-mouth study. Ten edentulous and four partially edentulous jaws with an alveolar ridge width of ≤4 mm were laterally augmented with a graft composition of 60 : 40 (DPBB/AB) on one side and 90 : 10 (DPBB/AB) on the contralateral side. Cone beam computed tomography (CB/CT) was obtained immediately postoperatively and after a healing period of 7.5 months. Width changes were measured on CB/CT scans. After a mean healing period of 8.1 months (range, 7.9-8.3), biopsies were retrieved perpendicular to the crest from each graft by means of a trephine bur. Histomorphometry was performed, and the following variables were recorded: Ingrowth of new bone (percentage of total graft width), percentage of DPBB, bone and soft tissue, and percentage of DPBB particles in contact with bone. RESULTS: The mean gained width of the alveolar crest after 7.5 months was significantly more for the 60 : 40 mixture compared with the 90 : 10 mixture, 3.5 (±1.3) mm and 2.9 (±1.3) mm, respectively. There was a significant difference in graft width reduction between 60 : 40 and 90 : 10 after 7.5 months, 37 (±19.9)% and 46.9 (±23.5)%, respectively. New bone ingrowth had occurred in 82.1 (±23.3)% and 82.3 (±26.6)% of the graft, respectively. There were no statistical differences between fractions of different tissues between the 90 : 10 and 60 : 40 compositions. However, there were significantly more soft tissue and less new bone formation closer to the periosteum compared with the graft portion closer to the residual bone in both 60 : 40 and 90 : 10 compositions. CONCLUSIONS: There was significantly less graft width reduction with a mixture of 60 : 40 (DPBB/AB) compared with a mixture of 90 : 10 composition, but the results from the histomorphometry showed no statistical differences comparing the groups

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures