The IBIS platform is a validated system that allows researchers to quickly bring the results of their work into the operating room for evaluation. It is the first open-source navigation system to provide a complete solution for AR visualization.

It has been reported consistently that many female chronic pain sufferers have an attenuation of symptoms during pregnancy. Rats display increased pain tolerance during pregnancy due to an increase in opioid receptors in the spinal cord. Past studies did not consider the role of non-neuronal cells, which are now known to play an important role in chronic pain processing. Using an inflammatory (complete Freund's adjuvant) or neuropathic (spared nerve injury) model of persistent pain, we observed that young adult female mice in early pregnancy switch from a microglia-independent to a microglia-dependent pain hypersensitivity mechanism. During late pregnancy, female mice show no evidence of chronic pain whatsoever. This pregnancy-related analgesia is reversible by intrathecal administration of naloxone, suggesting an opioid-mediated mechanism; pharmacological and genetic data suggest the importance of δ-opioid receptors. We also observe that T-cell-deficient ( and -null mutant) pregnant mice do not exhibit pregnancy analgesia, which can be rescued with the adoptive transfer of CD4 or CD8 T cells from late-pregnant wild-type mice. These results suggest that T cells are a mediator of the opioid analgesia exhibited during pregnancy. Chronic pain symptoms often subside during pregnancy. This pregnancy-related analgesia has been demonstrated for acute pain in rats. Here, we show that pregnancy analgesia can produce a complete cessation of chronic pain behaviors in mice. We show that the phenomenon is dependent on pregnancy hormones (estrogen and progesterone), δ-opioid receptors, and T cells of the adaptive immune system. These findings add to the recent but growing evidence of sex-specific T-cell involvement in chronic pain processing.

Augmented reality is a promising technology for neurovascular surgery. However, for more complex anomalies such as AVMs and AVFs, better visualization techniques that allow one to distinguish between arteries and veins and determine the absolute depth of a vessel of interest are needed.

Image-guided surgery (IGS) has allowed for more minimally invasive procedures, leading to better patient outcomes, reduced risk of infection, less pain, shorter hospital stays and faster recoveries. One drawback that has emerged with IGS is that the surgeon must shift their attention from the patient to the monitor for guidance. Yet both cognitive and motor tasks are negatively affected with attention shifts. Augmented reality (AR), which merges the realworld surgical scene with preoperative virtual patient images and plans, has been proposed as a solution to this drawback. In this work, we studied the impact of two different types of AR IGS set-ups (mobile AR and desktop AR) and traditional navigation on attention shifts for the specific task of craniotomy planning. We found a significant difference in terms of the time taken to perform the task and attention shifts between traditional navigation, but no significant difference between the different AR set-ups. With mobile AR, however, users felt that the system was easier to use and that their performance was better. These results suggest that regardless of where the AR visualisation is shown to the surgeon, AR may reduce attention shifts, leading to more streamlined and focused procedures.

The components and functionality of a new prototype system are described and its precision and accuracy evaluated. It was found to have an accuracy similar to other comparable systems in the literature.

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Cardiovascular disease is a significant cause of morbidity and mortality in the developed world. 3D imaging of the heart's structure is critical to the understanding and treatment of cardiovascular disease. However, open-source tools for image analysis of cardiac images, particularly 3D echocardiographic (3DE) data, are limited. We describe the rationale, development, implementation, and application of SlicerHeart, a cardiac-focused toolkit for image analysis built upon 3D Slicer, an open-source image computing platform. We designed and implemented multiple Python scripted modules within 3D Slicer to import, register, and view 3DE data, including new code to volume render and crop 3DE. In addition, we developed dedicated workflows for the modeling and quantitative analysis of multi-modality image-derived heart models, including heart valves. Finally, we created and integrated new functionality to facilitate the planning of cardiac interventions and surgery. We demonstrate application of SlicerHeart to a diverse range of cardiovascular modeling and simulation including volume rendering of 3DE images, mitral valve modeling, transcatheter device modeling, and planning of complex surgical intervention such as cardiac baffle creation. SlicerHeart is an evolving open-source image processing platform based on 3D Slicer initiated to support the investigation and treatment of congenital heart disease. The technology in SlicerHeart provides a robust foundation for 3D image-based investigation in cardiovascular medicine.

Introduction Two established subjective memory decline facets (SMD; complaints, concerns) are early indicators of memory decline and Alzheimer's disease. We report (1) a four‐facet SMD inventory (memory complaints, concerns, compensation, self‐efficacy) and (2) prediction of memory change and moderation by sex. Methods The longitudinal design featured 40 years (53 to 97) of non‐demented aging ( n = 580) from the Victoria Longitudinal Study. Statistical analyses included confirmatory factor analyses and conditional latent growth modeling. Results The four‐facet SMD Inventory was psychometrically confirmed. Longitudinal analyses revealed significant variability in level and change for SMD and memory. Prediction analyses showed complaints and concerns predicted lower level and steeper memory decline; however, follow‐up moderation analyses revealed selective predictions for females. Memory compensation predicted decline overall. Lower memory self‐efficacy predicted steeper decline selectively for males. Discussion Although traditional and novel SMD facets predicted memory decline, differential sex moderation was observed. SMD research benefits from conceptual complementarity and precision prediction.