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An Open-Source 3D-Printable Platform for Testing Head-Fixed Cognitive Flexibility in Rodents.
eNeuro
January 2025
Department of Neuroscience, University of Connecticut School of Medicine, Farmington, Connecticut 06030
The study of the neural circuitry underlying complex mammalian decision-making, particularly cognitive flexibility, is critical for understanding psychiatric disorders. To test cognitive flexibility, as well as potentially other decision-making paradigms involving multimodal sensory perception, we developed FlexRig, an open-source, modular behavioral platform for use in head-fixed mice. FlexRig enables the administration of tasks relying upon olfactory, somatosensory, and/or auditory cues and employing left and right licking as a behavior readout and reward delivery mechanism.
View Article and Find Full Text PDFPre-clinical Evaluation of New Generation Bridging Stent for Fenestrated Endovascular Aneurysm Repair using Realistic 3D Printed Aneurysm Models.
Eur J Vasc Endovasc Surg
January 2025
Aortic Center, Hôpital Marie Lannelongue, GHPSJ, Le Plessis-Robinson, France. Electronic address:
Objective: The aim of this study was to perform a comparative pre-clinical evaluation of a new fenestrated endovascular aneurysm repair dedicated stent using perfused 3D printed patient anatomies.
Methods: The test setup included a pulsatile pump set to reproduce human haemodynamics, four 3D printed pararenal aneurysms connected to a bench test, and four corresponding fenestrated grafts (Zenith Fenestrated; Cook, Bloomington, IN, USA). Bridging stents were sized based on analysis of patients computed tomography scans and included either four standard covered stents (BeGraft [BG]; Bentley, Hechingen, Germany) or four similar covered stent grafts mounted on a modified balloon to allow stent implantation and flaring in one step (BeFlared [BF]; Bentley).
Facile Access to Highly Efficient 3D Printing Using Robust Self-Healing CDs/Polymer Hybrids.
ACS Appl Mater Interfaces
January 2025
State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Jiangsu Key Laboratory of Fine Chemicals and Functional Polymer Materials, Nanjing Tech University, No. 5 Xin Mofan Road, Nanjing 210009, P. R. China.
3D printing efficiency, as a key indicator of additive manufacturing technology, directly affects its competitiveness in rapid prototyping, small batch production, and even large-scale industrial applications. Compared with traditional manufacturing methods, the high efficiency of 3D printing is often considered a bottleneck, hindering its application across various fields. Herein, a versatile and efficient strategy is proposed, namely, the dimensional reduction printing (DRP) process, to break the obstacle of high efficiency of 3D printing.
View Article and Find Full Text PDFA Prototype "Smart" 3-Dimensionally Printed Model Showcasing Interactivity: Implementing Voice Command for the Ventricular and Cisternal Systems.
J Comput Assist Tomogr
January 2025
Department of Radiology, George Washington University Hospital, Washington, DC.
The next step in the evolution of static 3-dimensionally (3D) printed models may be the creation of "smart" models, where subcomponents can be seamlessly interacted with through a feedback mechanism, with potential applications in trainee education and patient counseling. Considering the complexity of the ventricular and cisternal systems, they were chosen for segmentation, using Materialize InPrint with outward hollowing using 2.5-mm wall thickness.
View Article and Find Full Text PDFThis paper presents a synthetic holographic stereogram printing approach that integrates neural radiance fields (NeRF) with the effective perspective images segmentation and mosaicking (EPISM) method. Sparse perspectives of a 3D scene are captured through random sampling and used to train a NeRF model with multi-resolution hash encoding, enabling rapid construction of an implicit scene representation. The EPISM method calculates the camera pose parameters needed for parallax images, which are rendered through the trained neural network.
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