Connectional-style-guided contextual representation learning for brain disease diagnosis.

Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, and better aggregates features, is easier to optimize, and is more robust to noise, which explains its superiority in theory.

Treatment for middle cerebral artery bifurcation aneurysms: in silico comparison of the novel Contour device and conventional flow-diverters.

Endovascular treatment has become the standard therapy for cerebral aneurysms, while the effective treatment for middle cerebral artery (MCA) bifurcation aneurysms remains a challenge. Current flow-diverting techniques with endovascular coils cover the aneurysm orifice as well as adjacent vessel branches, which may lead to branch occlusion. Novel endovascular flow disruptors, such as the Contour device (Cerus Endovascular), are of great potential to eliminate the risk of branch occlusion. However, there is a lack of valid comparison between novel flow disruptors and conventional (intraluminal) flow-diverters. In this study, two in silico MCA bifurcation aneurysm models were treated by specific Contour devices and flow-diverters using fast-deployment algorithms. Computational fluid dynamic simulations were used to examine the performance and efficiency of deployed devices. Hemodynamic parameters, including aneurysm inflow and wall shear stress, were compared among each Contour device, conventional flow-diverter, and untreated condition. Our results show that the placement of devices can effectively reduce the risk of aneurysm rupture, while the deployment of a Contour device causes more flow reduction than using flow-diverters (e.g. Silk Vista Baby). Besides, the Contour device presents the flow diversion capability of targeting the aneurysm neck without occluding the daughter vessel. In summary, the in silico aneurysm models presented in this study can serve as a powerful pre-planning tool for testing new treatment techniques, optimising device deployment, and predicting the performance in patient-specific aneurysm cases. Contour device is proved to be an effective treatment of MCA bifurcation aneurysms with less daughter vessel occlusion.

The Evaluation of Outcomes after Thoracic Endovascular Aortic Repair for Type B Aortic Dissection in Mainland China.

BACKGROUND: To assess the mortality and outcomes after thoracic endovascular aortic repair (TEVAR) in patients with type B aortic dissection (TBAD) in mainland China, and to compare these outcomes with data from Western countries, while analyzing the potential reasons for differences among different countries. METHODS: An extensive literature search spanning from January 1999 to October 2023 was conducted using PubMed, Cochrane Library, and Embase databases for studies on endovascular treatment for TBAD. This systematic review and meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Data extraction and analysis followed the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Primary outcomes were in-hospital mortality and mid-term (< 5 years) mortality. RESULTS: Based on 25 publications (3,080 patients), pooled estimate for in-hospital mortality was 2.2% (95% confidence interval, 1.6%-2.9%). Major perioperative complications included stroke (2.4% [1.8%-3.3%]), spinal cord ischemia (1.4% [1.0%-2.2%]), retrograde type A aortic dissection (1.2% [0.8%-1.8%]), type I endoleak (5.6% [3.6%-8.6%]), visceral ischemia (1.0% [0.5%-2.1%]), and acute renal failure (2.8% [2.0%-3.8%]). Mid-term mortality was 5.1% (3.6%-7.3%), and secondary intervention rate was 4.9% (4.0%-6.0%) with 1.7% (1.0%-2.9%) conversion rate to open surgery. In subgroup analysis based on uncomplicated TBAD, in-hospital and mid-term mortality was 0.5% (0.2%-1.5%) and 0.6% (0.2-1.7%), respectively. Compared with data from Western countries, mainland Chinese patients had a lower mortality. CONCLUSIONS: In mainland China, the outcomes of endovascular treatment for TBAD are comparable to those of Western countries. The large number of patients undergoing TEVAR in mainland China and its good performance support the use of TEVAR in uncomplicated TBAD.

Using unsupervised capsule neural network reveal spatial representations in the human brain.

Humans can extract high-level spatial features from visual signals, but spatial representations in the brain are complex and remain unclear. The unsupervised capsule neural network (U-CapsNet) is sensitive to the spatial location and relationship of the object, contains a special recurrent mechanism and uses a self-supervised generation strategy to represent images, which is similar to the computational principle in the human brain. Therefore, we hypothesized that U-CapsNet can help us understand how the human brain processes spatial information. First, brain activities were studied using functional magnetic resonance imaging during spatial working memory in which participants had to remember the locations of circles for a short time. Then, U-CapsNet served as a computational model of the brain to perform tasks that are identical to those performed by humans. Finally, the representational models were used to compare the U-CapsNet with the brain. The results showed that some human-defined spatial features naturally emerged in the latent space of U-CapsNet. Moreover, representations in U-CapsNet captured the response structure of two types of brain regions during different activity patterns, as well as important factors associated with human behavior. Together, our study not only provides a computationally feasible framework for modeling how the human brain encodes spatial features but also provides insights into the representational format and goals of the human brain.

Surgical Treatment for Type A Aortic Dissection after Endovascular Aortic Repair: A 12-year, Single-Center Study.

OBJECTIVE: This study aims to investigate the clinical manifestations, operative techniques, and outcomes of patients who undergo open repair after thoracic endovascular aortic repair (TEVAR). METHODS: From January 2010 to June 2022, 113 consecutive type A aortic dissection (TAAD) patients underwent secondary open operation after TEVAR at our institution, and the median interval from primary intervention to open surgery was 12 (1.9-48.0) months. We divided the patients into two groups (RTAD (retrograde type A dissection) group, N = 56; PNAD (proximal new aortic dissection) group, N = 57) according to their anatomical features. Survival analysis during the follow-up was evaluated using a Kaplan-Meier survival curve and a log-rank test. RESULTS: The 30-day mortality was 6.2% (7/113), the median follow-up period was 31.7 (IQR 14.7-65.6) months, and the overall survival at 1 year, 5 years, and 10 years was 88.5%, 88.5%, and 87.6%, respectively. Fourteen deaths occurred during the follow-up, but there were no late aorta-related deaths. Three patients underwent total thoracoabdominal aortic replacement 1 year after a second open operation. The RTAD group had a smaller ascending aorta size (42.5 ± 7.7 mm vs 48.4 ± 11.4 mm; P < .01) and a closer proximal landing zone (P < .01) compared to the PNAD group. However, there were no differences in survival between the two groups. CONCLUSIONS: TAAD can present as an early or a late complication after TEVAR due to stent-grafting-related issues or disease progression. Open operation can be performed to treat TAAD, and this has acceptable early and mid-term outcomes. Follow-up should become mandatory for patients after TEVAR because these patients are at increased risk for TAAD.

Deep Learning For Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study.

PURPOSE: To develop and externally validate an automatic artificial intelligence (AI) tool for delineating gross tumor volume (GTV) in patients with esophageal squamous cell carcinoma (ESCC), which can assist in neo-adjuvant or radical radiation therapy treatment planning. METHODS AND MATERIALS: In this multi-institutional study, contrast-enhanced CT images from 580 eligible ESCC patients were retrospectively collected. The GTV contours delineated by 2 experts via consensus were used as ground truth. A 3-dimensional deep learning model was developed for GTV contouring in the training cohort and internally and externally validated in 3 validation cohorts. The AI tool was compared against 12 board-certified experts in 25 patients randomly selected from the external validation cohort to evaluate its assistance in improving contouring performance and reducing variation. Contouring performance was measured using dice similarity coefficient (DSC) and average surface distance. Additionally, our previously established radiomics model for predicting pathologic complete response was used to compare AI-generated and ground truth contours, to assess the potential of the AI contouring tool in radiomics analysis. RESULTS: The AI tool demonstrated good GTV contouring performance in multicenter validation cohorts, with median DSC values of 0.865, 0.876, and 0.866 and median average surface distance values of 0.939, 0.789, and 0.875 mm, respectively. Furthermore, the AI tool significantly improved contouring performance for half of 12 board-certified experts (DSC values, 0.794-0.835 vs 0.856-0.881, P = .003-0.048), reduced the intra- and interobserver variations by 37.4% and 55.2%, respectively, and saved contouring time by 77.6%. In the radiomics analysis, 88.7% of radiomic features from ground truth and AI-generated contours demonstrated stable reproducibility, and similar pathologic complete response prediction performance for these contours (P = .430) was observed. CONCLUSIONS: Our AI contouring tool can improve GTV contouring performance and facilitate radiomics analysis in ESCC patients, which indicates its potential for GTV contouring during radiation therapy treatment planning and radiomics studies.

Design and hierarchical analysis of magnetic actuated robot: A governing equation based approach.

As the alternative solution to the conventional guidewire, the magnetic robot can help interventionists perform percutaneous coronary intervention (PCI) because magnetic fields are transparent and safe for biological tissues. Despite extensive research on magnetic robots, the exploration of their deflection control for practical applications still requires further research. In this paper, a hierarchical analysis framework (HAF) is proposed to control the magnetic robot's deflection. Six deflection subpatterns are analyzed through HAF, incorporating statistical and regression analyses to establish governing equations of magnetic robots. The performance of the control equations is validated through precise control of the magnetic continuum robot (MCR) and magnet-tipped robot (MTR) in both uniform and gradient magnetic fields. Experimental results show that under the uniform magnetic field, the average root mean square error (RMSE) of governing equation of MCR is 0.08±0.05°, 0.41±0.34°, 1.47±0.49° and 1.07±0.66° for four-types horizontal deflection, 0.19±0.07mm and 0.16±0.10mm for two-types vertical deflection, respectively. Based on the governing equations, the MTR is able to precisely navigate to coronary arteries with various degrees of stenosis (30%, 52%, and 60%), and successfully pass through a series of rings, with an average error of 1.05 mm. The research successfully demonstrates the potential of HAF in creating robust and reliable governing equations for magnetic actuation in medical robotics, with significant implications for enhancing the precision and safety of PCI procedures.

Validation of biomechanical assessment of coronary plaque vulnerability based on intravascular optical coherence tomography and digital subtraction angiography.

Background: It has been suggested that biomechanical factors may influence plaque development. However, key determinants for assessing plaque vulnerability remain speculative. Methods: In this study, a two-dimensional (2D) structural mechanical analysis and a three-dimensional (3D) fluid-structure interaction (FSI) analysis were conducted based on intravascular optical coherence tomography (IV-OCT) and digital subtraction angiography (DSA) data sets. In the 2D study, 103 IV-OCT slices were analyzed. An in-depth morpho-mechanic analysis and a weighted least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to identify the crucial features related to plaque vulnerability via the tuning parameter (λ). In the 3D study, the coronary model was reconstructed by fusing the IV-OCT and DSA data, and a FSI analysis was subsequently performed. The relationship between vulnerable plaque and wall shear stress (WSS) was investigated. Results: The influential factors were selected using the minimum criteria (λ-min) and one-standard error criteria (λ-1se). In addition to the common vulnerable factor of the minimum fibrous cap thickness (FCTmin), four biomechanical factors were selected by λ-min, including the average/maximal displacements and average/maximal stress, and two biomechanical factors were selected by λ-1se, including the average/maximal displacements. Additionally, the positions of the vulnerable plaques were consistent with the sites of high WSS. Conclusions: Functional indices are crucial for plaque status assessment. An evaluation based on biomechanical simulations might provide insights into risk identification and guide therapeutic decisions.

Shape-margin knowledge augmented network for thyroid nodule segmentation and diagnosis.

BACKGROUND AND OBJECTIVE: Thyroid nodule segmentation is a crucial step in the diagnostic procedure of physicians and computer-aided diagnosis systems. However, prevailing studies often treat segmentation and diagnosis as independent tasks, overlooking the intrinsic relationship between these processes. The sequencial steps of these independent tasks in computer-aided diagnosis systems may lead to the accumulation of errors. Therefore, it is worth combining them as a whole by exploring the relationship between thyroid nodule segmentation and diagnosis. According to the diagnostic procedure of thyroid imaging reporting and data system (TI-RADS), the assessment of shape and margin characteristics is the prerequisite for radiologists to discriminate benign and malignant thyroid nodules. Inspired by TI-RADS, this study aims to integrate these tasks into a cohesive process, leveraging the insights from TI-RADS, thereby enhancing the accuracy and interpretability of thyroid nodule analysis. METHODS: Specifically, this paper proposes a shape-margin knowledge augmented network (SkaNet) for simultaneous thyroid nodule segmentation and diagnosis. Due to the visual feature similarities between segmentation and diagnosis, SkaNet shares visual features in the feature extraction stage and then utilizes a dual-branch architecture to perform thyroid nodule segmentation and diagnosis tasks respectively. In the shared feature extraction, the combination of convolutional feature maps and self-attention maps allows to exploitation of both local information and global patterns in thyroid nodule images. To enhance effective discriminative features, an exponential mixture module is introduced, combining convolutional feature maps and self-attention maps through exponential weighting. Then, SkaNet is jointly optimized by a knowledge augmented multi-task loss function with a constraint penalty term. The constraint penalty term embeds shape and margin characteristics through numerical computations, establishing a vital relationship between thyroid nodule diagnosis results and segmentation masks. RESULTS: We evaluate the proposed approach on a public thyroid ultrasound dataset (DDTI) and a locally collected thyroid ultrasound dataset. The experimental results reveal the value of our contributions and demonstrate that our approach can yield significant improvements compared with state-of-the-art counterparts. CONCLUSIONS: SkaNet highlights the potential of combining thyroid nodule segmentation and diagnosis with knowledge augmented learning into a unified framework, which captures the key shape and margin characteristics for discriminating benign and malignant thyroid nodules. Our findings suggest promising insights for advancing computer-aided diagnosis joint with segmentation.

Relationship between Anxiety and Problematic Smartphone Use in First-Year Junior High School Students: Moderated Mediation Effects of Physical Activity and School Adjustment.

BACKGROUND AND AIMS: Despite previous research identifying anxiety as a risk factor for problematic smartphone use among students, the mediating and moderating mechanisms underlying the relationship between the two aforementioned variables are poorly understood. This study aims to explore the relationship between anxiety and problematic smartphone use among first-year junior high school students, together with the mediating effects of school adjustment and the moderating effects of physical activity on the mentioned relationship. METHOD: This study was conducted using a Web-based self-report questionnaire survey with data collected from 445 first-year junior high school students in Jinan City, Shandong Province. Mediation and moderation analyses were performed using the PROCESS macro in SPSS. RESULTS: The results showed that anxiety predicted problematic smartphone use not only directly but also indirectly via school adjustment. School adjustment played a partial mediating role in the relationship between anxiety and problematic smartphone use. Physical activity also played a moderating role in the relationship between anxiety and school adjustment. CONCLUSION: school adjustment and physical activity may be important variables in the relationship between anxiety and problematic smartphone use.

Investigating the heterogeneity within the somatosensory-motor network and its relationship with the attention and default systems.

The somatosensory-motor network (SMN) not only plays an important role in primary somatosensory and motor processing but is also central to many disorders. However, the SMN heterogeneity related to higher-order systems still remains unclear. Here, we investigated SMN heterogeneity from multiple perspectives. To characterize the SMN substructures in more detail, we used ultra-high-field functional MRI to delineate a finer-grained cortical parcellation containing 430 parcels that is more homogenous than the state-of-the-art parcellation. We personalized the new parcellation to account for individual differences and identified multiscale individual-specific brain structures. We found that the SMN subnetworks showed distinct resting-state functional connectivity (RSFC) patterns. The Hand subnetwork was central within the SMN and exhibited stronger RSFC with the attention systems than the other subnetworks, whereas the Tongue subnetwork exhibited stronger RSFC with the default systems. This two-fold differentiation was observed in the temporal ordering patterns within the SMN. Furthermore, we characterized how the distinct attention and default streams were carried forward into the functions of the SMN using dynamic causal modeling and identified two behavioral domains associated with this SMN fractionation using meta-analytic tools. Overall, our findings provided important insights into the heterogeneous SMN organization at the system level and suggested that the Hand subnetwork may be preferentially involved in exogenous processes, whereas the Tongue subnetwork may be more important in endogenous processes.

Deep learning-based multi-stage postoperative type-b aortic dissection segmentation using global-local fusion learning.

Objective.Type-b aortic dissection (AD) is a life-threatening cardiovascular disease and the primary treatment is thoracic endovascular aortic repair (TEVAR). Due to the lack of a rapid and accurate segmentation technique, the patient-specific postoperative AD model is unavailable in clinical practice, resulting in impracticable 3D morphological and hemodynamic analyses during TEVAR assessment. This work aims to construct a deep learning-based segmentation framework for postoperative type-b AD.Approach.The segmentation is performed in a two-stage manner. A multi-class segmentation of the contrast-enhanced aorta, thrombus (TH), and branch vessels (BV) is achieved in the first stage based on the cropped image patches. True lumen (TL) and false lumen (FL) are extracted from a straightened image containing the entire aorta in the second stage. A global-local fusion learning mechanism is designed to improve the segmentation of TH and BR by compensating for the missing contextual features of the cropped images in the first stage.Results.The experiments are conducted on a multi-center dataset comprising 133 patients with 306 follow-up images. Our framework achieves the state-of-the-art dice similarity coefficient (DSC) of 0.962, 0.921, 0.811, and 0.884 for TL, FL, TH, and BV, respectively. The global-local fusion learning mechanism increases the DSC of TH and BV by 2.3% (p< 0.05) and 1.4% (p< 0.05), respectively, based on the baseline. Segmenting TH in stage 1 can achieve significantly better DSC for FL (0.921 ± 0.055 versus 0.857 ± 0.220,p< 0.01) and TH (0.811 ± 0.137 versus 0.797 ± 0.146,p< 0.05) than in stage 2. Our framework supports more accurate vascular volume quantifications compared with previous segmentation model, especially for the patients with enlarged TH+FL after TEVAR, and shows good generalizability to different hospital settings.Significance.Our framework can quickly provide accurate patient-specific AD models, supporting the clinical practice of 3D morphological and hemodynamic analyses for quantitative and more comprehensive patient-specific TEVAR assessments.

A computational study of fluid transport characteristics in the brain parenchyma of dementia subtypes.

The cerebral environment is a complex system consisting of parenchymal tissue and multiple fluids. Dementia is a common class of neurodegenerative diseases, caused by structural damages and functional deficits in the cerebral environment. In order to better understand the pathology of dementia from a cerebral fluid transport angle and provide clearer evidence that could help differentiate between dementia subtypes, such as Alzheimer's disease and vascular dementia, we conducted fluid-structure interaction modelling of the brain using a multiple-network poroelasticity model, which considers both neuropathological and cerebrovascular factors. The parenchyma was further subdivided and labelled into parcellations to obtain more localised and detailed data. The numerical results were converted to computed functional images by an in-house workflow. Different cerebral blood flow (CBF) and cerebrospinal fluid (CSF) clearance abnormalities were identified in the modelling results, when comparing Alzheimer's disease and vascular dementia. This paper presents our preliminary results as a proof of concept for a novel clinical diagnostic tool, and paves the way for a larger clinical study.

The Role of Location, Length, and Thickness of the Intimal Flap in the Propagation of Stanford Type B Aortic Dissection Based on Ex Vivo Porcine Aorta Models.

PURPOSE: To explore the role of location, length, and thickness of the intimal flap in the propagation of Stanford type B aortic dissection (TBAD) based on ex vivo porcine aorta models based on ex vivo porcine aorta models. MATERIALS AND METHODS: The porcine aortas were harvested and randomly divided into 6 groups to create various TBAD aortic models. We constructed intimal flaps for different locations (group A [entry tear on outer curvature] and group B [entry tear on inner curvature]), lengths (group C [long] and group D [short]), and thicknesses (group E [thick] and group F [thin]). For the ex vivo perfusion experiments conducted on model aortas, an experimental circulation loop (ECL) was employed. The pressure in false lumen (FL) was constantly monitored. A comparison was made between the morphological data collected before and after the experiment to quantify the changes in the FL after the experiment. RESULTS: Compared the results with group B, the mean peak pressures of the FL in group A were lower (106.87±15.55 vs. 124.01±22.75 mm Hg, p=0.028). The mean axial propagation length in group A was shown to be shorter than that of group B (88.14±33.38 vs. 197.43±41.65 mm, p<0.001). The mean peak pressure was higher in group C than in group D (144.04±19.37 vs. 92.51±26.70 mm Hg, p<0.001). The mean peak pressure of group E was higher than that of group F (160.83±32.83 vs. 109.33±15.62 mm Hg, p<0.001), as was the mean axial propagation length of group E (143.11±39.73 vs. 100.45±35.44 mm, p=0.021). According to the results of multivariable linear regression, axial propagation length=45.873-0.703×length of initial FL+0.863× peak pressure (p<0.001). CONCLUSION: There was a relationship between FL propagation and the location, length, and thickness of the intimal flap. The axial propagation length was related to the length of the intimal flap and the peak pressure of propagation. It may be helpful to evaluate the risk of propagation in patients with TBAD. CLINICAL IMPACT: This study found that the locations, lengths, and thickness of the intimal flap significantly contributed to propagation pressure of FL. Using dissection flap characteristics, a physician can predict FL development in a patient and formulate a treatment plan.The purpose was to investigate the relationship between the dissection flap characteristics (location, length, and thickness) and the propagation of the FL, which is not clear at present. This study employed porcine models to create an experimental circulation loop. The perfusion experiment was conducted using a FL without distal re-entry and a non-pulsating flow.

A CT-based predictive model for stent-induced vessel damage: application to type B aortic dissection.

OBJECTIVES: The distal stent-induced new entry (distal SINE) is a life-threatening device-related complication after thoracic endovascular aortic repair (TEVAR). However, risk factors for distal SINE are not fully determined, and prediction models are lacking. This study aimed to establish a predictive model for distal SINE based on the preoperative dataset. METHODS: Two hundred and six patients with Stanford type B aortic dissection (TBAD) that experienced TEVAR were involved in this study. Among them, thirty patients developed distal SINE. Pre-TEVAR morphological parameters were measured based on the CT-reconstructed configurations. Virtual post-TEVAR morphological and mechanical parameters were computed via the virtual stenting algorithm (VSA). Two predictive models (PM-1 and PM-2) were developed and presented as nomograms to help risk evaluation of distal SINE. The performance of the proposed predictive models was evaluated and internal validation was conducted. RESULTS: Machine-selected variables for PM-1 included key pre-TEVAR parameters, and those for PM-2 included key virtual post-TEVAR parameters. Both models showed good calibration in both development and validation subsamples, while PM-2 outperformed PM-1. The discrimination of PM-2 was better than PM-1 in the development subsample, with an optimism-corrected area under the curve (AUC) of 0.95 and 0.77, respectively. Application of PM-2 in the validation subsample presented good discrimination with an AUC of 0.9727. The decision curve demonstrated that PM-2 was clinically useful. CONCLUSION: This study proposed a predictive model for distal SINE incorporating the CT-based VSA. This predictive model could efficiently predict the risk of distal SINE and thus might contribute to personalized intervention planning. CLINICAL RELEVANCE STATEMENT: This study established a predictive model to evaluate the risk of distal SINE based on the pre-stenting CT dataset and planned device information. With an accurate VSA tool, the predictive model could help to improve the safety of the endovascular repair procedure. KEY POINTS: • Clinically useful prediction models for distal stent-induced new entry are still lacking, and the safety of the stent implantation is hard to guarantee. • Our proposed predictive tool based on a virtual stenting algorithm supports different stenting planning rehearsals and real-time risk evaluation, guiding clinicians to optimize the presurgical plan when necessary. • The established prediction model provides accurate risk evaluation for vessel damage, improving the safety of the intervention procedure.

Qualitative and Quantitative Assessment of Invasive Surgery Skill Based on a Custom Training Simulator.

PURPOSE: Qualitative and quantitative assessment of interventional performance is a vital component in the evaluation of endovascular surgery skill training. We established a custom simulator with qualitative and quantitative metrics for endovascular performance training. METHODS: The simulator included an in vitro silicone phantom, mock circulation loop, visual module, force-sensing module, and custom software for image and force data postprocessing. Two tasks to deliver the guidewire to the target location of the carotid artery were conducted by the expert (n=4), novice (n=6), and test (n=4) groups. Seven features with significant differences extracted from the expert and novice groups were applied for qualitative assessment using the support vector machine (SVM) and quantitative assessment using the Mahalanobis distance (MD). RESULTS: Significant differences were observed in kinematic and force data between experts and novices during the intervention procedure. The median value of finished time for task 1 was 26.88 seconds for experts and 63.36 seconds for novices. The maximum speed for experts and novices was 32.79 and 7.43 cm/s, respectively. Moreover, the classified results depicted that the accuracy of qualitative assessment for task 1 and task 2 was 96.67% and 90%, respectively. As for the quantitative data, the residents had higher scores than individuals majored in biomedical engineering at 2 tasks (70.06±5.30 vs 41.81±6.58 for task 1, p=0.001). CONCLUSIONS: The proposed endovascular intervention skill training simulator provides qualitative and quantitative metrics on intervention performance skills and may be a useful tool in future interventional surgical training. CLINICAL IMPACT: This simulator comprised an in-vitro silicone phantom, mock circulation loop, visual module, force-sensing module, and custom software for image and force data post-processing. Seven interventional performance features were used for qualitative assessment using the support vector machine and quantitative assessment using the Mahalanobis Distance. From the observations, we conclude that this endovascular intervention skill training simulator provides qualitative and quantitative metrics on intervention performance and may be a useful tool in future surgical training.

Kirchhoff rod-based three-dimensional dynamical model and real-time simulation for medical-magnetic guidewires.

BACKGROUND AND OBJECTIVE: Magnetic guidewire, fabricated from hard-magnetic soft composites, has recently emerged as an appropriate candidate for magnetic actuation systems to perform intravascular surgical navigation, owing to its elastic, magnetically steerable properties and good interphase with biological tissues. A suitable and efficient mathematical model for the magnetic guidewire is essential in the system to execute remote manipulation and active control. METHODS: This paper presents a real-time Kirchhoff rod-based dynamical modeling approach, the magneto-elastic rod model, to simulate magnetic guidewire, which provides accurate simulations for two- and three-dimensional dynamic deflections induced by external magnetic fields and obtains deformed guidewire shapes in quasi-static status. RESULTS: The proposed model is capable of describing the intrinsic principles of elastic body actuation by torques generated from the hard-magnetic soft matrix. The effectiveness of the developed model is validated, and the real-time simulation application is conducted via the semi-implicit numerical integration method. CONCLUSIONS: It has been shown that the presented dynamical model captures large nonlinear deformations and transient responses of the magnetic guidewire in an imitated human blood environment, which could offer robust support for the construction of a simulated magnetically driven surgical system.

Ascending aortic volume: A feasible indicator for ascending aortic aneurysm elective surgery?

Diameter-based criterion have been widely adopted for preventive surgery of ascending thoracic aortic aneurysm (ATAA). However, recent and growing evidence has shown that diameter-based methods may not be sufficient for identifying patients who are at risk of an ATAA. In this study, fluid-structure interaction (FSI) analysis was performed on one-hundred ATAA geometries reconstructed from clinical data to examine the relationship between hemodynamic conditions, ascending aortic volume (AAV), ascending aortic curvature, and aortic ratios measured from the reconstructed 3D models. The simulated hemodynamic and biomechanical parameters were compared among different groups of ATAA geometries classified based on AAV. The ATAAs with enlarged AAV showed significantly compromised hemodynamic conditions and higher mechanical wall stress. The maximum oscillatory shear index (OSI), particle residence time (PRT) and wall stress (WS) were significantly higher in enlarged ATAAs compared with controls (0.498 [0.497, 0.499] vs 0.499 [0.498, 0.499], p = 0.002, 312.847 [207.445, 519.391] vs 996.047 [640.644, 1573.140], p < 0.001, 769.680 [668.745, 879.795] vs 1072.000 [873.060, 1280.000] kPa, p < 0.001, respectively). Values were reported as median with interquartile range (IQR). AAV was also found to be more strongly correlated with these parameters compared to maximum diameter. The correlation coefficient between AAV and average WS was as high as 0.92 (p < 0.004), suggesting that AAV might be a feasible risk identifier for ATAAs. STATEMENT OF SIGNIFICANCE: Ascending thoracic aortic aneurysm is associated with the risk of dissection or rupture, creating life-threatening conditions. Current surgical intervention guidelines are mostly diameter based. Recently, many studies proposed to incorporate other morphological parameters into the current clinical guidelines to better prevent severe adverse aortic events like rupture or dissection. The purpose of this study is to gain a better understanding of the relationship between morphological parameters and hemodynamic parameters in ascending aortic aneurysms using fluid-solid-interaction analysis on patient-specific geometries. Our results suggest that ascending aortic volume may be a better indicator for surgical intervention as it shows a stronger association with pathogenic hemodynamic conditions.

TRIM29 (Tripartite Motif Containing 29) Alleviates NLRC4 (NLR Family CARD Domain Containing Protein 4) Inflammasome Related Cerebral Injury via Promoting Proteasomal Degradation of NLRC4 in Ischemic Stroke.

BACKGROUND: Neuroinflammation plays extremely crucial roles in the neurological damage mediated by ischemic stroke. TRIM29 (tripartite motif containing 29) has previously been proposed to contribute to the regulation of innate immunity, however, the effect of TRIM29 on ischemic stroke induced neurodegenerative processes and neuroinflammation still largely unexplored. In the current article, we aimed to investigate the function and the precise mechanisms of TRIM29 in ischemic stroke. METHODS: Middle cerebral artery occlusion mice model and oxygen-glucose deprivation cell model were established as in vivo and in vitro models of ischemic stroke. Quantitative real-time polymerase chain reaction (PCR), Western blot, and ELSIA were used to detect the expression levels of TRIM29, cytokines, and marker proteins. Immunofluorescence assay was performed to examine the extent of cell death. Different truncations were generated, and coimmunoprecipitation assays were used to confirm the protein interaction. Ubiquitination assay was performed to detect the ubiquitination levels. RESULTS: We found that the cerebral ischemia-reperfusion induced injury was aggravated in TRIM29 knockout mice after middle cerebral artery occlusion operation as well as the increased neurological deficits score. TRIM29 expression was also found to be up-regulated upon middle cerebral artery occlusion or OGD administration, and loss of TRIM29 promoted the apoptosis and pyroptosis of neurons and microglial cells induced by middle cerebral artery occlusion or OGD, consistent with the enhanced proinflammatory mediators production and activation of NLRC4 (NLR [NOD-like receptor] family CARD [caspase recruitment domain] domain containing protein 4) inflammasome. Furthermore, we observed that TRIM29 interacted with NLRC4 directly and promoted the K48-linked polyubiquitination of NLRC4, lead to the proteasomal degradation of NLRC4. CONCLUSIONS: In conclusion, for the first time, we revealed the role of TRIM29 in ischemic stroke and illustrated the direct relationship between TRIM29 and NLRC4.

Necessity and timing of angioplasty in acute large-vessel occlusion strokes due to intracranial atherosclerotic disease: A cohort analysis with data from the angel-ACT registry.

Background: The effects of angioplasty on intracranial atherosclerotic disease (ICAD)-related acute large-vessel occlusion stroke (LVOS) are unknown. We analyzed the efficacy and safety of angioplasty or stenting for ICAD-related LVOS and the optimal treatment duration. Methods: Patients with ICAD-related LVOS from a prospective cohort of the Endovascular Treatment Key Technique and Emergency Work Flow Improvement of Acute Ischemia Stroke registry were classified as follows: the early intraprocedural angioplasty and/or stenting (EAS) group was defined as the strategy using angioplasty or stenting without mechanical thrombectomy (MT) or one attempt of MT; the non-angioplasty and/or stenting (NAS) group, MT procedure without any angioplasty; and the late intraprocedural angioplasty and/or stenting (LAS) group, using same angioplasty techniques following two or more passes of MT. The primary endpoint was the modified Rankin Scale (mRS) score at 90 days. Other efficacy outcomes included mRS scores 0-1, mRS 0-2, and successful recanalization. Death within 90 days, and symptomatic ICH were safety endpoints. We use propensity score method to diminish the effect of treatment-selection bias. The odds ratio of recanalization rate and mRS score among EAS, NAS, and LAS groups were examined by unadjusted and adjusted logistic regression analysis among unweighted samples and inverse probability of treatment weighting (IPTW) samples. Results: We divided 475 cases into three groups. Functional outcomes at 90 days were better in the EAS group than in the NAS and LAS groups. The proportion of mRS 0-1, mRS 0-2, and successful recanalization cases were the highest in the EAS group. However, after IPTW, mortality rate among the three groups were similar (EAS vs. NAS vs. LAS: 19.0 vs. 18.1 vs. 18.7%, p = 0.98) as well as symptomatic intracranial hemorrhage within 24 h however, mortality rate and symptomatic intracranial hemorrhage among the three groups were similar. Logistic regression analysis in unweighted samples and IPTW samples both showed that EAS group had better outcomes. IPTW-adjusted logistic regression analysis demonstrated that the EAS group had better outcomes (mRS 0-1) than the NAS group (adjusted odds ratio [aOR], 0.55; 95% confidence interval [CI]: 0.34-0.88, p = 0.01) and LAS (aOR, 0.39; 95% CI: 0.22-0.68, p = 0.001). Conclusions: Angioplasty and/or stenting should be performed at an early stage for ICAD-related acute LVOS. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03370939.