Browsing by Subject "Stenosis"

Now showing 1 - 4 of 4
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    Activation of the Hedgehog signaling pathway leads to fibrosis in aortic valves
    (BMC, 2023-03-02) Gu, Dongsheng; Soepriatna, Arvin H.; Zhang, Wenjun; Li, Jun; Zhao, Jenny; Zhang, Xiaoli; Shu, Xianhong; Wang, Yongshi; Landis, Benjamin J.; Goergen, Craig J.; Xie, Jingwu; Pediatrics, School of Medicine
    Background: Fibrosis is a pathological wound healing process characterized by excessive extracellular matrix deposition, which interferes with normal organ function and contributes to ~ 45% of human mortality. Fibrosis develops in response to chronic injury in nearly all organs, but the a cascade of events leading to fibrosis remains unclear. While hedgehog (Hh) signaling activation has been associated with fibrosis in the lung, kidney, and skin, it is unknown whether hedgehog signaling activation is the cause or the consequence of fibrosis. We hypothesize that activation of hedgehog signaling is sufficient to drive fibrosis in mouse models. Results: In this study, we provide direct evidence to show that activation of Hh signaling via expression of activated smoothened, SmoM2, is sufficient to induce fibrosis in the vasculature and aortic valves. We showed that activated SmoM2 -induced fibrosis is associated with abnormal function of aortic valves and heart. The relevance of this mouse model to human health is reflected in our findings that elevated GLI expression is detected in 6 out of 11 aortic valves from patients with fibrotic aortic valves. Conclusions: Our data show that activating hedgehog signaling is sufficient to drive fibrosis in mice, and this mouse model is relevant to human aortic valve stenosis.
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    Image Segmentation, Parametric Study, and Supervised Surrogate Modeling of Image-based Computational Fluid Dynamics
    (2022-05) Islam, Md Mahfuzul; Yu, Huidan (Whitney); Du, Xiaoping; Wagner, Diane
    With the recent advancement of computation and imaging technology, Image-based computational fluid dynamics (ICFD) has emerged as a great non-invasive capability to study biomedical flows. These modern technologies increase the potential of computation-aided diagnostics and therapeutics in a patient-specific environment. I studied three components of this image-based computational fluid dynamics process in this work. To ensure accurate medical assessment, realistic computational analysis is needed, for which patient-specific image segmentation of the diseased vessel is of paramount importance. In this work, image segmentation of several human arteries, veins, capillaries, and organs was conducted to use them for further hemodynamic simulations. To accomplish these, several open-source and commercial software packages were implemented. This study incorporates a new computational platform, called InVascular, to quantify the 4D velocity field in image-based pulsatile flows using the Volumetric Lattice Boltzmann Method (VLBM). We also conducted several parametric studies on an idealized case of a 3-D pipe with the dimensions of a human renal artery. We investigated the relationship between stenosis severity and Resistive index (RI). We also explored how pulsatile parameters like heart rate or pulsatile pressure gradient affect RI. As the process of ICFD analysis is based on imaging and other hemodynamic data, it is often time-consuming due to the extensive data processing time. For clinicians to make fast medical decisions regarding their patients, we need rapid and accurate ICFD results. To achieve that, we also developed surrogate models to show the potential of supervised machine learning methods in constructing efficient and precise surrogate models for Hagen-Poiseuille and Womersley flows.
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    Outcomes in patients with aortic stenosis and severely reduced ejection fraction following surgical aortic valve replacement and transcatheter aortic valve replacement
    (Springer Nature, 2024-04-20) Bain, Eric R.; George, Bistees; Jafri, Syed H.; Rao, Roopa A.; Sinha, Anjan K.; Guglin, Maya E.; Medicine, School of Medicine
    Background: Patients with severe aortic stenosis (AS) and left ventricular (LV) dysfunction demonstrate improvement in left ventricular injection fraction (LVEF) after aortic valve replacement (AVR). The timing and magnitude of recovery in patients with very low LVEF (≤ 25%) in surgical or transcatheter AVR is not well studied. Objective: Determine clinical outcomes following transcatheter aortic valve replacement (TAVR) and surgical aortic valve repair (SAVR) in the subset of patients with severely reduced EF ≤ 25%. Methods: Single-center, retrospective study with primary endpoint of LVEF 1-week following either procedure. Secondary outcomes included 30-day mortality and delayed postprocedural LVEF. T-test was used to compare variables and linear regression was used to adjust differences among baseline variables. Results: 83 patients were enrolled (TAVR = 56 and SAVR = 27). TAVR patients were older at the time of procedure (TAVR 77.29 ± 8.69 vs. SAVR 65.41 ± 10.05, p < 0.001). One week post procedure, all patients had improved LVEF after both procedures (p < 0.001). There was no significant difference in LVEF between either group (TAVR 33.5 ± 11.77 vs. SAVR 35.3 ± 13.57, p = 0.60). Average LVEF continued to rise and increased by 101% at final follow-up (41.26 ± 13.70). 30-day mortality rates in SAVR and TAVR were similar (7.4% vs. 7.1%, p = 0.91). Conclusion: Patients with severe AS and LVEF ≤ 25% have a significant recovery in post-procedural EF following AVR regardless of method. LVEF doubled at two years post-procedure. There was no significant difference in 30-day mortality or mean EF recovery between TAVR and SAVR.
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    Predicting future occlusion or stenosis of lower extremity bypass grafts using artificial intelligence to simultaneously analyze all flow velocities collected in current and previous ultrasound examinations
    (Elsevier, 2024-02-05) Luo, Xiao; Tahabi, Fattah Muhammad; Rollins, Dave M.; Sawchuk, Alan P.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Objective: Routine surveillance with duplex ultrasound (DUS) examination is recommended after femoral-popliteal and femoral-tibial-pedal vein bypass grafts with various intervals postoperatively. The presently used methodology to analyze bypass graft DUS examination does not use all the available data and has been shown to have a significant rate for missing impending bypass graft failure. The objective of this research is to investigate recurrent neural networks (RNNs) to predict future bypass graft occlusion or stenosis. Methods: This study includes DUS examinations of 663 patients who had bypass graft operations done between January 2009 and June 2022. Only examinations without missing values were included. We developed two RNNs (a bidirectional long short-term memory unit and a bidirectional gated recurrent unit) to predict bypass graft occlusion and stenosis based on peak systolic velocities collected in the 2 to 5 previous DUS examinations. We excluded the examinations with missing values and split our data into training and test sets. Then, we applied 10-fold cross-validation on training to optimize the hyperparameters and compared models using the test data. Results: The bidirectional long short-term memory unit model can gain an overall sensitivity of 0.939, specificity of 0.963, and area under the curve of 0.950 on the prediction of bypass graft occlusion, and an overall sensitivity of 0.915, specificity of 0.909, and area under the curve of 0.912 predicting the development of a future critical stenosis. The results on different bypass types show that the system performs differently on different types. The results on subcohorts based on gender, smoking status, and comorbidities show that the performance on current smokers is lower than the never smoker. Conclusions: We found that RNNs can gain good sensitivity, specificity, and accuracy for the detection of impending bypass graft occlusion or the future development of a critical bypass graft stenosis using all the available peak systolic velocity data in the present and previous bypass graft DUS examinations. Integrating clinical data, including demographics, social determinants, medication, and other risk factors, together with the DUS examination may result in further improvements. Clinical relevance: Detecting bypass graft failure before it occurs is important clinically to prevent amputations, salvage limbs, and save lives. Current methods evaluating screening duplex ultrasound examinations have a significant failure rate for detecting a bypass graft at risk for failure. Artificial intelligence using recurrent neural networks has the potential to improve the detection of at-risk bypass graft before they fail. Additionally, artificial intelligence is in the news and is being applied to many fields. Vascular surgeons need to know its potential to improve vascular outcomes.