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The present study focuses on the weldability of hot-rolled Fe–Mn–Al–Ni shape memory alloy sheets by vacuum electron beam welding. Tailored process-specific welding parameters, such as preheating with electron beam or beam oscillation during welding, allowed defect-free joining with very thin weld seams and heat-affected zones. By applying a post-weld cyclic heat treatment, abnormal grain growth can be promoted across the weld seams. However, regardless of the selected welding parameters, some specimens are characterized by the formation of smaller grains within the former fusion zone. In situ incremental strain tests reveal that the former fusion zone has only a minor influence on the functional properties and is not responsible for structural failure. Thus, electron beam welding is a promising welding technology for joining Fe–Mn–Al–Ni shape memory alloys.

Background The ability of the patient specific 3D printed neurovascular phantoms to accurately replicate the anatomy and hemodynamics of the chronic neurovascular diseases has been demonstrated by many studies. Acute occurrences, however, may still require further development and investigation and therefore we studied acute ischemic stroke (AIS). The efficacy of endovascular procedures such as mechanical thrombectomy (MT) for the treatment of large vessel occlusion (LVO), can be improved by testing the performance of thrombectomy devices and techniques using patient specific 3D printed neurovascular models. Methods 3D printed phantoms were connected to a flow loop with physiologically relevant flow conditions, including input flow rate and fluid temperature. A simulated blood clot was introduced into the model and placed in the proximal Middle Cerebral Artery (MCA) region. Clot location, composition, length, and arterial angulation were varied and MTs were simulated using stent retrievers. Device placement relative to the clot and the outcome of the thrombectomy were recorded for each situation. Digital subtraction angiograms (DSA) were captured before and after LVO simulation. Recanalization outcome was evaluated using DSA as either ‘no recanalization’ or ‘recanalization’. Forty-two 3DP neurovascular phantom benchtop experiments were performed. Results Clot angulation within the MCA region had the most significant impact on the MT outcome, with a p-value of 0.016. Other factors such as clot location, clot composition, and clot length correlated weakly with the MT outcome. Conclusions This project allowed us to gain knowledge of how such characteristics influence thrombectomy success and can be used in making clinical decisions when planning the procedure and selecting specific thrombectomy tools and approaches.

Introduction Body fluid status can be determined by plasma volume status and extracellular water to total body water (ECW/TBW) ratio and is a well-known parameter associated with survival in patients with chronic heart failure (1–3). However, its prognostic impact in patients with pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) is not fully understood. Methods 409 patients with PAH and CTEPH who entered the Giessen PH registry were included in this study (4). Plasma volume status was estimated using Duarte formula (ePVS = (1 − hematocrit) / hemoglobin × 100). ECW was calculated via Peters formula ((−2.47 × 0.842 + 8.76 × body surface area) for men and (−1.96 × 0.572 + 8.05 × body surface area) for women) and TBW via Watson formula ((2.447 − [0.09516 × age] + [0.1073 × height] + [0.3362 × body weight]) for men and (−2.097 + [0.1069 × height] + [0.2466 × body weight]) for women). Statistical analyzes were performed with R (The R Foundation). Results Median age of the included patients was 65 [53, 74] years. Median ePVS and ECW/TBW ratio were 4.5 [3.9, 5.5] and 0.39 [0.37, 0.40]. Correlation analyses revealed that ePVS correlates with pulmonary artery wedge pressure (PAWP), pulmonary vascular resistance (PVR), cardiac index, and mixed venous oxygen saturation (SvO2), whereas ECW/TBW ratio did not correlate with pulmonary hemodynamics. Accordingly, univariate Cox regression revealed that ePVS but not ECW/TBW ratio was associated with mortality (HR: 1.18 [1.02, 1.37]). ePVS remained as an independent prognostic parameter in multivariate Cox regression analysis. Patients with high ePVS showed significantly decreased survival rates (log-rank p Conclusion ePVS but not ECW/TBW ratio is associated with prognostic parameters such as PVR, cardiac index and SvO2. Thus, ePVS may be an independent prognostic parameter in patients with PAH and CTEPH. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)

Purpose Postoperative Ileus (POI) remains an important complication for patients after abdominal surgery with an incidence of 10–27% representing an everyday issue for abdominal surgeons. It accounts for patients’ discomfort, increased morbidity, prolonged hospital stays, and a high economic burden. This review outlines the current understanding of POI pathophysiology and focuses on preventive treatments that have proven to be effective or at least show promising effects. Methods Pathophysiology and recommendations for POI treatment are summarized on the basis of a selective literature review. Results While a lot of therapies have been researched over the past decades, many of them failed to prove successful in meta-analyses. To date, there is no evidence-based treatment once POI has manifested. In the era of enhanced recovery after surgery or fast track regimes, a few approaches show a beneficial effect in preventing POI: multimodal, opioid-sparing analgesia with placement of epidural catheters or transverse abdominis plane block; μ-opioid-receptor antagonists; and goal-directed fluid therapy and in general the use of minimally invasive surgery. Conclusion The results of different studies are often contradictory, as a concise definition of POI and reliable surrogate endpoints are still absent. These will be needed to advance POI research and provide clinicians with consistent data to improve the treatment strategies.

Background Three-dimensional printing (3DP) offers a unique opportunity to build flexible vascular patient-specific coronary models for device testing, treatment planning, and physiological simulations. By optimizing the 3DP design to replicate the geometrical and mechanical properties of healthy and diseased arteries, we may improve the relevance of using such models to simulate the hemodynamics of coronary disease. We developed a method to build 3DP patient specific coronary phantoms, which maintain a significant part of the coronary tree, while preserving geometrical accuracy of the atherosclerotic plaques and allows for an adjustable hydraulic resistance. Methods Coronary computed tomography angiography (CCTA) data was used within Vitrea (Vital Images, Minnetonka, MN) cardiac analysis application for automatic segmentation of the aortic root, Left Anterior Descending (LAD), Left Circumflex (LCX), Right Coronary Artery (RCA), and calcifications. Stereolithographic (STL) files of the vasculature and calcium were imported into Autodesk Meshmixer for 3D model optimization. A base with three chambers was built and interfaced with the phantom to allow fluid collection and independent distal resistance adjustment of the RCA, LAD and LCX and branching arteries. For the 3DP we used Agilus for the arterial wall, VeroClear for the base and a Vero blend for the calcifications, respectively. Each chamber outlet allowed interface with catheters of varying lengths and diameters for simulation of hydraulic resistance of both normal and hyperemic coronary flow conditions. To demonstrate the manufacturing approach appropriateness, models were tested in flow experiments. Results Models were used successfully in flow experiments to simulate normal and hyperemic flow conditions. The inherent mean resistance of the chamber for the LAD, LCX, and RCA, were 1671, 1820, and 591 (dynes ∙ sec/ cm5), respectively. This was negligible when compared with estimates in humans, with the chamber resistance equating to 0.65–5.86%, 1.23–6.86%, and 0.05–1.67% of the coronary resistance for the LAD, LCX, and RCA, respectively at varying flow rates and activity states. Therefore, the chamber served as a means to simulate the compliance of the distal coronary trees and to allow facile coupling with a set of known resistance catheters to simulate various physical activity levels. Conclusions We have developed a method to create complex 3D printed patient specific coronary models derived from CCTA, which allow adjustable distal capillary bed resistances. This manufacturing approach permits comprehensive coronary model development which may be used for physiologically relevant flow simulations.

Purpose Coronary computed tomography angiography (CTA) has one of the highest diagnostic sensitivities for detection of the significance of coronary artery disease (CAD); however, sensitivity is moderate and may result in increased catheterization rates. We performed an efficacy study to determine whether a trained machine learning algorithm that uses coronary CTA data may improve CAD diagnosis accuracy. Methods Sixty-four-patient image datasets based on coronary CTA were retrospectively collected to generate eight views considering 45° increments around the coronary artery centerline. The dataset was randomly split into training and testing cohorts. Invasive FFR measurements were used as ground truth labels. A convolutional neural network (CNN) was trained and the model's capacity to predict severity of CAD was assessed on the testing cohort. Classification accuracy and area under the receiver operating characteristic curve (AUROC) analysis were performed. Similar CAD severity classification accuracy and AUROC analyses were performed using only percent diameter stenosis (%DS) and CT-derived FFR performed by 13 operators with various levels of expertise. Results Classification accuracy over the test cohort was 80.9% using the trained network and 72.4% using the user-operated CT-derived FFR software. AUROC over the test cohort was 0.862 using the trained network, 0.807 using %DS, and 0.758 using the human-operated CT-derived FFR software. Conclusions A trained neural network compared noninferiorly in-terms of classification accuracy and AUROC with human operators of a CT-derived FFR software, and in-terms of AUROC with clinical decision-making using %DS.

Stent retriever (SR) thrombectomy is commonly used for the treatment of emergent large vessel occlusion (ELVO) in acute ischemic stroke. Clot imaging parameters such as clot length, diameter, distance to the internal carotid artery terminus, and vessel angle where the SR is deployed may predict the likelihood of achieving first pass effect (FPE). Most of the proposed factors that seem to affect recanalization success have been studied individually, and conflicting data derived from clinical versus in vitro studies using 3-dimensional printed models of intracranial circulation currently exist.Using patient-specific 3-dimensional phantoms of the cervical and intracranial circulation, we simulated middle cerebral arteries (MCA) M1 and M2 occlusions treated with SR thrombectomy using Solitaire (Medtronic) or Trevo (Styker). Our primary outcome was FPE, defined as Thrombolysis in Cerebral Infarction score of 2c-3 achieved after a single thrombectomy attempt. We also performed retrospective analysis of same clot imaging characteristics of consecutive cases of MCA occlusion and its association with FPE matching the 3-dimensional in vitro experiments. Analysis was conducted using IBM SPSS Statistics Version 25 (IBM Corp., Armonk, NY). Chi-square tests and bivariate logistic regressions were the main statistical tests used in analysis. A p-value of less than .05 was considered to indicate statistical significance. Ninety-five confidence intervals (95% CI) were generated.We compared 41 thrombectomy experiments performed using patient-specific 3-dimensional in vitro models with a retrospective cohort of 41 patients treated with SR thrombectomy. We found that in the in vitro cohort, higher MCA angulation was associated with a lower likelihood of FPE (odds ratio [OR] = 0.967, 95% CI = 0.944-0.991, p = .008). Meanwhile in the in vivo cohort, higher MCA angulation was associated with a higher likelihood of FPE (OR = 1.039, 95% CI = 1.003-1.077, p = .033). Neither clot length nor location of clot (M1 vs. M2) was associated with a difference in FPE rates in either cohort.Comparison of SR thrombectomy performed during actual MCA occlusion cases versus patient-specific 3-dimensional replicas revealed MCA angulation as an independent predictor of procedure success or failure. However, the opposite direction of effect was observed between the two studied environments, indicating potential limitations of studying SR thrombectomy using 3-dimensional models of LVO.

To assess the value of a T1-3D black-blood turbo spin echo (TSE) sequence for the diagnosis of abdominal large vessel vasculitis (LVV). The study included 20 patients with abdominal LVV and 17 controls, who underwent a 3T-MRI scan using a modified T1-3D volumetric isotropic TSE acquisition and a segmented T1-3D turbo field echo sequence (T1-mVISTA/T1-eTHRIVE). Two radiologists independently analyzed the aorta for concentric contrast enhancement, concentric wall thickening, image quality, and flow artifact intensity (CCE/CWT/IQ/FAI; 4-point scales). The mean aortic wall thickness (MAWT) in post-contrast T1-mVISTA was compared between patients and controls. IQ of T1-mVISTA was rated good to excellent in 91.5% of 282 evaluated vessel segments with no or minor FAI present in 85.5%. The inter-observer reproducibility for the identification of CCE/CWT on T1-mVISTA was 0.92 and 0.93 (p

Background Stent retriever (SR) thrombectomy is commonly used for the treatment of emergent large vessel occlusion (ELVO) in acute ischemic stroke (AIS). Clot imaging parameters such as clot length, diameter, distance to the internal carotid artery (ICA) terminus and vessel angle where the SR is deployed may predict the likelihood of achieving first-pass effect (FPE). Most of the proposed factors that seem to affect recanalization success have been studied individually, and conflicting data derived from clinical versus in vitro studies using 3D printed models of intracranial circulation currently exist. Methods Using patient-specific 3D phantoms of the cervical and intracranial circulation, we simulated MCA M1 and M2 occlusions treated with SR thrombectomy using Solitaire (Medtronic) or Trevo (Styker). Our primary outcome was first-pass effect, defined as Thrombolysis in Cerebral Infarction score of 2c-3 achieved after a single thrombectomy attempt. We also performed retrospective analysis of same clot imaging characteristics of consecutive cases of MCA occlusion and its association with FPE matching the 3D in vitro experiments. Analysis was conducted using IBM SPSS Statistics Version 25 (IBM Corp., Armonk, NY). Chi-square tests, and bivariate logistic regressions were the main statistical tests used in analysis. A p-value of less than 0.05 was considered to indicate statistical significance. 95% confidence intervals (95CI) were generated. Results We compared 42 thrombectomy experiments performed using patient-specific 3D in vitro models with a retrospective cohort of 42 patients treated with SR thrombectomy. We found that in the in vitro cohort, higher MCA angulation was associated with a lower likelihood of FPE (Odds ratio (OR) = 0.967 95CI = 0.944-0.991, p = 0.008). Meanwhile in the in vivo cohort, higher MCA angulation was associated with a higher likelihood of FPE (OR = 1.039 95CI = 1.003-1.077, p = 0.033). Neither clot length nor location of clot (M1 vs M2) was associated with a difference in FPE rates in either cohort. Discussion Comparison of SR thrombectomy performed during actual MCA occlusion cases versus patient specific 3D replicas revealed MCA angulation as an independent predictor of procedure success or failure. However, the opposite direction of effect was observed between the two studied environments, indicating potential limitations of studying SR thrombectomy using 3D models of LVO. Disclosures M. Mokin: 1; C; NIH R21NS109575. E. Pressman: None. K. Sommer: None. C. Ionita: None.

Human granulocytic anaplasmosis, a tickborne disease caused by the bacterium Anaplasma phagocytophilum, was first identified during 1994 and is now an emerging public health threat in the United States. New York state (NYS) has experienced a recent increase in the incidence of anaplasmosis. We analyzed human case surveillance and tick surveillance data collected by the NYS Department of Health for spatiotemporal patterns of disease emergence. We describe the epidemiology and growing incidence of anaplasmosis cases reported during 2010–2018. Spatial analysis showed an expanding hot spot of anaplasmosis in the Capital Region, where incidence increased >8-fold. The prevalence of A. phagocytophilum increased greatly within tick populations in the Capital Region over the same period, and entomologic risk factors were correlated with disease incidence at a local level. These results indicate that anaplasmosis is rapidly emerging in a geographically focused area of NYS, likely driven by localized changes in exposure risk.

Digital subtraction angiography (DSA) is the main imaging modality used to assess reperfusion during mechanical thrombectomy (MT) when treating large vessel occlusion (LVO) ischemic strokes. To improve this visual and subjective assessment, hybrid models combining angiographic parametric imaging (API) with deep learning tools have been proposed. These models use convolutional neural networks (CNN) with single view individual API maps, thus restricting use of complementary information from multiple views and maps resulting in loss of relevant clinical information. This study investigates use of ensemble networks to combine hemodynamic information from multiple bi-plane API maps to assess level of reperfusion. Three-hundred-eighty-three anteroposterior (AP) and lateral view DSAs were retrospectively collected from patients who underwent MTs of anterior circulation LVOs. API peak height (PH) and area under time density curve (AUC) maps were generated. CNNs were developed to classify maps as adequate/inadequate reperfusion as labeled by two neuro-interventionalists. Outputs from individual networks were combined by weighting each output, using a grid search algorithm. Ensembled, AP-AUC, AP-PH, lateral-AUC, and lateral-PH networks achieved accuracies of 83.0% (95% confidence-interval: 81.2%-84.8%), 74.4% (72.0%-76.7%), 74.2% (72.8%-75.7%), 74.9% (72.2%-77.7%), and 76.9% (74.4%-79.5%); area under receiver operating characteristic curves of 0.86 (0.84-0.88), 0.81 (0.79-0.83), 0.83 (0.81-0.84), 0.82 (0.8-0.84), and 0.84 (0.82-0.87); and Matthews correlation coefficients of 0.66 (0.63-0.70), 0.48 (0.43-0.53), 0.49 (0.46-0.52), 0.51 (0.45-0.56), and 0.54 (0.49-0.59) respectively. Ensembled network performance was significantly better than individual networks (McNemar's p-value

Purpose In recent years, endovascular treatment has become the dominant approach to treat intracranial aneurysms (IAs). Despite tremendous improvement in surgical devices and techniques, 10-30% of these surgeries require retreatment. Previously, we developed a method which combines quantitative angiography with data-driven modeling to predict aneurysm occlusion within a fraction of a second. This is the first report on a semi-autonomous system, which can predict the surgical outcome of an IA immediately following device placement, allowing for therapy adjustment. Additionally, we previously reported various algorithms which can segment IAs, extract hemodynamic parameters via angiographic parametric imaging, and perform occlusion predictions. Methods We integrated these features into an Aneurysm Occlusion Assistant (AnOA) utilizing the Kivy library's graphical instructions and unique language properties for interface development, while the machine learning algorithms were entirely developed within Keras, Tensorflow and skLearn. The interface requires pre- and post-device placement angiographic data. The next steps for aneurysm segmentation, angiographic analysis and prediction have been integrated allowing either autonomous or interactive use. Results The interface allows for segmentation of IAs and cranial vasculature with a dice index of ~0.78 and prediction of aneurysm occlusion at six months with an accuracy 0.84, in 6.88 seconds. Conclusion This is the first report on the AnOA to guide endovascular treatment of IAs. While this initial report is on a stand-alone platform, the software can be integrated in the angiographic suite allowing direct communication with the angiographic system for a completely autonomous surgical guidance solution.