Background
Three-dimensional (3D) models have the unique ability to replicate individualized cardiac anatomy and may therefore provide clinical benefit. Transcatheter aortic valve implantation (TAVI) currently relies on preoperative imaging for accurate valve sizing, type of valve used, and avoidance of complications. Three-dimensional (3D) modelling may provide benefit for optimal preoperative TAVI planning. The goal of this study is to assess the utility of 3D modelling in the prediction of paravalvular leak (PVL) post TAVI.
Methods
Retrospective analysis of five patients who underwent TAVI at our center. Pre-operative cardiac gated CT images were utilized to create a 3D printed model with true size aortic root dimensions, including the coronary artery ostium location and left ventricular outflow tract. Deployment of the corresponding model and size TAVI valve into the created 3D model at a similar depth of implantation via fluoroscopy was performed for each patient. Degree of PVL was assessed using a closed system with water infusion under pressure over a duration of 5 s. Correlation was made between the volume obtained in the closed loop model during the pressurized period and the degree of PVL reported on the patients post TAVI placement on transthoracic echocardiogram.
Results
One female, and four males (age in years ranged from 68 to 87) underwent successful TAVI (0% 30-day mortality). PVL on post procedure TTE ranged from none to trivial. Successful deployment of TAVI valves inside the 3D model occurred in all cases. The average volume of water collected on three trials over 5 s ranged between 19.1–24.1 ml A multivariate linear regression showed significant association between the degree of PVL reported on post-operative transthoracic echocardiogram and the amount of volume detected in the 3D model (difference: -3.9657, 95% CI: (− 4.6761,-3.2554),
p
< 0.001).
Conclusions
Our experiments show that replicated 3D models have potential clinical utilization in predicting PVL in the TAVI population. Future research into the role of 3D modelling in the field of TAVI should continue to be explored.
Unfortunately, over 40% of stroke victims have pre-existing diabetes which not only increases their risk of stroke up to 2-6 fold, but also worsens both functional recovery and the severity of cognitive impairment. Our lab has recently linked the chronic inflammation that persists in diabetic animals to their poor functional outcomes and exacerbated cognitive impairment, also known as post-stroke cognitive impairment (PSCI). Although we have shown that the development of PSCI in diabetes is associated with the upregulation and activation of pro-inflammatory microglia, we have not established a direct causation between the two. We tested the hypothesis that microglia depletion in the post-stroke recovery period prevents sustained inflammation and attenuates PSCI in diabetes.
Methods:
Diabetes was induced by a high fat diet (HFD) and low dose streptozotocin (STZ) combination. At 13 weeks of age, diabetic animals received bilateral intracerebroventricular (ICV) injections of short hairpin RNA (shRNA) lentiviral particles targeted at the colony stimulating factor 1 receptor (CSF1R), a key factor for microglia survival. After 14 days, animals were subjected to 60 min middle cerebral artery occlusion (MCAO) or sham surgery. Novel object recognition (NOR), and 2-trial Y-maze were utilized to evaluate cognitive function. Brains were analyzed by flow cytometry (B-D slice containing the prefrontal cortex through the hippocampus) and immunohistochemistry (B slice) 3 weeks post-MCAO.
Results:
CSF1R silencing resulted in a drastic 94% knockdown of residential microglia to relieve inflammation and decrease the macrophage infiltration by 74%. This also led to improved myelination of white matter in the brain and improved cognition in diabetic animals.
Conclusion:
Neuroinflammation, through microglial and macrophage polarization, is largely responsible for the development of PSCI in diabetes.
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