The effects of a flexible visual acuity-driven ranibizumab treatment regimen in age-related macular degeneration: outcomes of a drug and disease model

Differences in treatment responses to ranibizumab injections observed within trials involving monthly (MARINA and ANCHOR studies) and quarterly (PIER study) treatment suggest that an individualized treatment regimen may be effective in neovascular age-related macular degeneration. In the present study, a drug and disease model was used to evaluate the impact of an individualized, flexible treatment regimen on disease progression.

Long-term effectiveness of canine-to-canine bonded flexible spiral wire lingual retainers

The flexible spiral wire (FSW) canine-to-canine lingual retainer bonded to all 6 anterior teeth is a frequently used type of mandibular fixed retainer. This study aimed to assess the long-term effectiveness of FSW canine-to-canine lingual retainers in maintaining the alignment of the mandibular anterior teeth after orthodontic treatment.

Spy: A Flexible Code Profiling Framework

Code profiling is an essential activity to increase software quality. It is commonly employed in a wide variety of tasks, such as supporting program comprehension, determining execution bottlenecks, and assessing code coverage by unit tests. Spy is an innovative framework to easily build profilers and visualize profiling information. The profiling information is obtained by inserting dedicated code before or after method execution. The gathered profiling information is structured in line with the application structure in terms of packages, classes, and methods. Spy has been instantiated on four occasions so far. We created profilers dedicated to test coverage, time execution, type feedback, and profiling evolution across version. We also integrated Spy in the Pharo IDE. Spy has been implemented in the Pharo Smalltalk programming language and is available under the MIT license.

[The AO/ASIF Flexnail : A flexible intramedullary nail for the treatment of humeral shaft fractures]

BACKGROUND: The use of conventional implants for intramedullary nailing of humeral shaft fractures is associated with specific difficulties. During antegrade implantation structures of the rotator cuff can be affected leading to a reduced functional result of the shoulder. If the nail is implanted in a retrograde manner problems arise due to a relatively large hole close to or within the olecranon fossa, which is necessary for insertion of the nail. Supracondylar fractures as well as persistent elbow pain and loss of function are reported in the literature. To overcome these disadvantages a flexible nail has been developed that can be stiffened and locked after implantation. METHOD: Between October 2000 and February 2002, 34 patients were treated with the flexible nail at our institution; 29 were available for follow-up. Fracture healing was documented on radiographs and clinical outcome was evaluated with use of the Constant as well as the Kwasny score. RESULTS: Median duration until fracture consolidation was 10 weeks. In two patients fracture union was not achieved within the follow-up period. The median outcome measured with the Constant score was 93 points and 2.5 with the Kwasny score. Both values correspond to a very good functional outcome. CONCLUSION: We conclude that the flexible humeral nail is an excellent treatment option for humeral shaft fractures. Damage to the rotator cuff and the distal humerus can be avoided due to its unique flexible construction, improving the functional outcome of intramedullary nailing for the treatment of humeral shaft fractures.

Learning flexible sensori-motor mappings in a complex network

Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex problem.