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To explore trends in intraoperative procoagulant factor concentrate use in patients undergoing heart transplantation (HTx) in Virginia. Secondarily, to evaluate their association with postoperative thrombosis. Patients who underwent HTx were identified using a statewide database. Trends in off-label recombinant activated factor VII (rFVIIa) use and on-label and off-label prothrombin complex concentrate (PCC) use were tested using the Mantel-Haenszel test. Multivariate logistic regression was used to test for an association between procoagulant factor concentrate administration and thrombosis. Virginia hospitals performing HTx. Adults undergoing HTx between 2012 and 2022. None. Among 899 patients who required HTx, 100 (11.1%) received off-label rFVIIa, 69 (7.7%) received on-label PCC, and 80 (8.9%) received off-label PCC. There was a downward trend in the use of rFVIIa over the 10-year period (p = 0.04). There was no trend in on-label PCC use (p = 0.12); however, there was an increase in off-label PCC use (p < 0.001). Patients who received rFVIIa were transfused more and had longer cardiopulmonary bypass time (p < 0.001). Receipt of rFVIIa was associated with increased thrombotic risk (odds ratio [OR] 1.92; 95% CI 1.12-3.29; p = 0.02), whereas on-label and off-label PCC use had no association with thrombosis (OR 0.98, 95% CI 0.49-1.96, p = 0.96 for on-label use; and OR 0.61, 95% CI 0.29-1.30, p = 0.20 for off-label use). Use of rFVIIa in HTx decreased over the past decade, whereas off-label PCC use increased. Receipt of rFVIIa was associated with thrombosis; however, patients who received rFVIIa were more severely ill, and risk adjustment may have been incomplete. [ABSTRACT FROM AUTHOR]

Drug-coated balloons (DCB) represent 1 of the most promising innovations in interventional cardiology and may represent a valid alternative to drug-eluting stents. Currently, some sirolimus-coated balloons (SCB) are being investigated for several coronary artery disease applications. This study sought to understand the role of a novel SCB for the treatment of coronary artery disease. EASTBOURNE (All-Comers Sirolimus-Coated Balloon European Registry) is a prospective, multicenter, investigator-driven clinical study that enrolled real-world patients treated with SCB. Primary endpoint was target lesion revascularization (TLR) at 12 months. Secondary endpoints were procedural success, myocardial infarction (MI), all-cause death, and major adverse clinical events (a composite of death, MI, and TLR). All adverse events were censored and adjudicated by an independent clinical events committee. A total population of 2,123 patients (2,440 lesions) was enrolled at 38 study centers in Europe and Asia. The average age was 66.6 ± 11.3 years, and diabetic patients were 41.5%. De novo lesions (small vessels) were 56%, in-stent restenosis (ISR) 44%, and bailout stenting occurred in 7.7% of the patients. After 12 months, TLR occurred in 5.9% of the lesions, major adverse clinical events in 9.9%, and spontaneous MI in 2.4% of the patients. The rates of cardiac/all-cause death were 1.5% and 2.5%, respectively. The primary outcome occurred more frequently in the ISR cohort (10.5% vs 2.0%; risk ratio: 1.90; 95% CI: 1.13-3.19). After multivariate Cox regression model, the main determinant for occurrence of the primary endpoint was ISR (OR: 5.5; 95% CI: 3.382-8.881). EASTBOURNE, the largest DCB study in the coronary field, shows the safety and efficacy of a novel SCB in a broad population of coronary artery disease including small vessels and ISR patients at mid-term follow-up. (The All-Comers Sirolimus-Coated Balloon European Registry [EASTBOURNE]; NCT03085823) [Display omitted] [ABSTRACT FROM AUTHOR]

The repair and remanufacturing industry is a key contributor to the green technological revolution in manufacturing. Laser Directed Energy Deposition (LDED) is one of the most suited techniques for remanufacturing complex 3-D parts. The localized repair of a free-form component requires the nozzle to be inclined, which can affect the deposition process and the bonding strength. Once repaired, the functional performance of the restored part strongly depends upon the quality of the bond between deposition and substrate. Typically, the determination of bonding strength involves using epoxy for carrying out the tensile test or machining the deposition to carry out shear tests, which are not very effective in determining the bonding strength of ultra-high strength depositions like CPM 9V steels used in remanufacturing. Hence, a novel 'tapered key in a slot' specimen is specially designed to measure the bonding strength of high-strength depositions. The effect of various LDED parameters on the bonding strength of the deposits has been characterized via a designed experiment. Detailed fractography of the failed specimens exhibited three distinct failure patterns: melt interface (MI), deposit-substrate interface (CSI), and deposit cross-section (CC). MI failures occur at low deposition heights and low melt depths. CSI failures are observed at higher deposition heights, and low melt depths and CC failures occur above a critical combination of deposition height and melt depth. These specimens were analyzed for residual stresses, material composition, and porosity. The most prominent failure pattern MI (∼63%) could be attributed to a synergistic effect of peak tensile residual stresses, a sharp transition in the concentration of hard vanadium carbide particles, and intergranular porosity. Based on this study, a set of process recommendations has been developed for sustainable remanufacturing. [Display omitted] • Developed a novel 'tapered key in a slot' technique for determining bonding strength of ultra-hard deposits via DED. • Fractography studies of failed keys identified three primary bonding failure modes: MI, CC and CSI. • Intergranular porosity, residual stresses, and VC concentration were investigated in the dpeosition and substrate. • MI mode occurs most frequently due to a synergistic interaction of residual stresses, VC diffusion, and micro-cracks. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive experimental and statistical approach for the analysis of vibration amplitudes in a high-speed rotating shaft employing a squeeze film damper (SFD). The research combines a comprehensive analysis that connects input parameters and response parameters, with a special emphasis on vibration amplitudes along the X and Z axes. This research utilizes the rigorous procedures of Response Surface Methodology (RSM) together with a Box-Behnken design and harnesses the capabilities of Artificial Neural Network (ANN) optimization techniques. The variables under scrutiny encompass critical factors such as shaft rotational speed, extending up to 8000 rpm, oil pressure, with a range extending up to 100 bar, and various oil mix ratios, spanning from 10 % to 50 %. Various statistical measures are computed to assess the errors and coefficients of determination of the projected models. The artificial neural network (ANN) model has shown somewhat reduced prediction errors and a greater coefficient of determination compared to the Response Surface Methodology (RSM) for both the x and z axes of vibration amplitude. The values of mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R-squared) are found for both x and z axes from RSM (3.50, 3.77), (4.50, 4.72), and (0.81, 0.79), (1.88, 1.70), (2.41, 2.12), and (0.94, 0.95), respectively, using the ANN model. The overall mean absolute percentage error (MAPE) from the ANN model of both the x and z axes (9.73 %, 9.38 %) is found to be lower compared to the RSM model (18.18 %–20.50 %). The conclusive research reveals that the artificial neural network (ANN) prediction model outperforms the regression model based on Response Surface Methodology (RSM), exhibiting superior accuracy in predicting vibration amplitudes. The ANN approach is an excellent option for calculating vibration amplitudes in high-speed rotating shafts. Additionally, it provides significant benefits in terms of effectiveness and time economy. This research provides valuable new insights into the most efficient modeling approaches for vibration management and highlights the benefits of using Artificial Neural Networks (ANN) for predictive assessments in this context. Particle Swarm Optimization is used to minimize the vibration amplitude of the shaft along the x and z axes using experimental data. [ABSTRACT FROM AUTHOR]