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Abbreviations:: AF, atrial fibrillation; CAD, coronary artery disease; CI, confidence interval; HDL-C, high-density lipoprotein cholesterol; HF, heart failure; HR, hazard ratio; ICD, International Classification of Diseases; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride Provenance: Commissioned; not externally peer reviewed. Despite the association between high lipoprotein levels and the increased risk of atherosclerosis and coronary artery disease (CAD), which, in turn, may lead to an increased risk of AF [3], several studies have suggested that high levels of low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C) are associated with a lower risk of AF [4]. Higher levels of TC and LDL-C were associated with lower risk of AF within the first 5 years (hazard ratios [HRs]: 0.61, 95% confidence intervals [CIs]: 0.41 to –0.99; HR: 0.64, 95% CI: 0.45 to 0.92), but the effect was attenuated after 5 years of follow-up. Since HDL-C and high TG levels are important components of metabolic syndrome, this finding may demonstrate a role of metabolic syndrome and its components in the risk of AF.

To promote environmental sustainability, this research investigated the potential of utilizing recycled polymethylmethacrylate (PMMA) as raw material in material extrusion (MEX) additive manufacturing (AM). To enhance its mechanical response, carbon black (CB) was employed as the filler in nanocomposite formation. Filament extrusion of the mixture at different concentrations produced printable feedstocks for MEX AM. Rheological analysis (viscosity and material flow rate) showed that the CB introduction to the matrix was beneficial for consistent layer deposition, while differential scanning calorimetry and thermogravimetric analyses verified the thermal stability of the nanocomposites during processing. Mechanical properties were optimized, with increases in modulus (27.8% and 25.8%, respectively, in tensile and bending loadings) and tensile strength at optimal CB loadings. Dynamic mechanical analysis revealed the viscoelastic response of the nanocomposites. Raman and energy dispersive spectroscopy provided element-related insights. Surface morphology and parts structure were observed employing scanning electron microscopy and micro-computed tomography, respectively, revealing a positive impact on the AM parts due to the CB presence in the nanocomposites. The 4 wt.% in CB content nanocomposite was the optimum one. This research pioneers the development of new sustainable nanocomposite filaments and highlights the potential of next-generation MEX-based AM.

Both energy efficiency and robustness are popular demands for 3D-printed components nowadays. These opposing factors require compromises. This study examines the effects of seven general control variables on the energy demands and the compressive responses of polyamide (PA6) material extrusion (MEX) 3D printed samples. Nozzle Temperature, Layer Thickness, Orientation Angle, Raster Deposition Angle, Printing Speed, Bed Temperature, and Infill Density were studied. An L27 orthogonal array was compiled with five replicas. A total of 135 trials were conducted, following the ASTM D695-02a specifications. The stopwatch method was used to assess the construction time and energy usage. The compressive strength, toughness, and elasticity modulus were experimentally determined. The Taguchi technique ranks each control parameter’s impact on each response measure. The control parameter that had the greatest impact on both energy use and printing time was layer thickness. Additionally, the infill density had the greatest influence on the compressive strength. Quadratic regression model equations were formed for each of the response measures. The ideal compromise between mechanical strength and energy efficiency is now reported, with merit related to technological and economic benefits.

Sustainability and energy efficiency of additive manufacturing (AM) is an up-to-date industrial request. Likewise, the claim for 3D-printed parts with capable mechanical strength remains robust, especially for polymers that are considered high-performance ones, such as polycarbonates in material extrusion (MEX). This paper explains the impact of seven generic control parameters (raster deposition angle; orientation angle; layer thickness; infill density; nozzle temperature; bed temperature; and printing speed) on the energy consumption and compressive performance of PC in MEX AM. To meet this goal, a three-level L27 Taguchi experimental design was exploited. Each experimental run included five replicas (compressive specimens after the ASTM D695-02a standard), summating 135 experiments. The printing time and the power consumption were stopwatch-derived, whereas the compressive metrics were obtained by compressive tests. Layer thickness and infill density were ranked the first and second most significant factors in energy consumption. Additionally, the infill density and the orientation angle were proved as the most influential factors on the compressive strength. Lastly, quadratic regression model (QRM) equations for each response metric versus the seven control parameters were determined and evaluated. Hereby, the optimum compromise between energy efficiency and compressive strength is attainable, a tool holding excessive scientific and engineering worth.

The machinability in turning mode of three lead-free brass alloys, CuZn42 (CW510L), CuZn38As (CW511L) and CuZn36 (C27450) was evaluated in comparison with a reference free-cutting leaded brass CuZn39Pb3 (CW614N), as far as the quality characteristics, i.e., cutting force and surface roughness, were concerned. A design of experiments (DOE) technique, according to the Taguchi L16 orthogonal array (OA) methodology, as well as analysis of variance (ANOVA) were employed in order to identify the critical-to-machinability parameters and to obtain their optimum values for high-performance machining. The experimental design consisted of four factors (cutting speed, depth of cut, feed rate and alloy) with four levels for each factor using the “smaller-the-better” criterion for quality characteristics’ optimization. The data means and signal-to-noise (S/N) responses indicated that the depth of cut and the feed rate were the most influential factors for the cutting force and surface roughness, respectively. The optimized machining parameters for cutting force (34.59 N) and surface roughness (1.22 μm) minimization were determined. Confirmation experiments (cutting force: 39.37 N and surface roughness: 1.71 μm) seem to show that they are in close agreement to the main conclusions, thereby validating the findings of the statistical evaluation performed.

Catastrophic shear instability is the dominant mechanism during orthogonal cutting of Ti6Al4V. Chip segmentation even at low speeds testifies to the emergence of some kind of instability during plastic deformation of the material. Among the theoretical models, catastrophic thermoplastic slip is proposed as a mechanism to explain the destabilization of homogeneous plastic deformation, which results in localized, band-like adiabatic shear deformation. On the other hand, fracture models which consider machining as a mechanism of ductile or brittle fracture are used to explain the segmented chip formation as a periodic crack generation mechanism. This work aims at elucidating the fundamental mechanisms of the above theoretical models using a coupled thermomechanical rigid-viscoplastic FEM analysis. Introducing an energy criterion for ductile damage, numerical results showed that failure within the adiabatic shear band (ASB) is a post-localization mechanism occurring after intense shear localization. Simulations revealed a void initiation and coalescence mechanism which resembles an array of discontinuous degraded elements of nearly ellipsoidal shapes that grows and progressively coalesces forming a macro crack inside the ASB. Several aspects of ASB formation are addressed, among others, the micro-scale spatial temperature profile, parametric studies of critical damage energies, chip segmentation frequency, etc. Experimental results of ASB formation pertaining to chip morphology and cutting forces are compiled and analyzed to evaluate the FEM model at the low speed regime.

Additive Manufacturing (AM) can provide customized parts that conventional techniques fail to deliver. One important parameter in AM is the quality of the parts, as a result of the material extrusion 3D printing (3D-P) procedure. This can be very important in defense-related applications, where optimum performance needs to be guaranteed. The quality of the Polyetherimide 3D-P specimens was examined by considering six control parameters, namely, infill percentage, layer height, deposition angle, travel speed, nozzle, and bed temperature. The quality indicators were the root mean square (Rq) and average (Ra) roughness, porosity, and the actual to nominal dimensional deviation. The examination was performed with optical profilometry, optical microscopy, and micro-computed tomography scanning. The Taguchi design of experiments was applied, with twenty-five runs, five levels for each control parameter, on five replicas. Two additional confirmation runs were conducted, to ensure reliability. Prediction equations were constructed to express the quality indicators in terms of the control parameters. Three modeling approaches were applied to the experimental data, to compare their efficiency, i.e., Linear Regression Model (LRM), Reduced Quadratic Regression Model, and Quadratic Regression Model (QRM). QRM was the most accurate one, still the differences were not high even considering the simpler LRM model. [Display omitted] •Optimization of critical quality characteristics of Polyetherimide in material extrusion (MEX) 3D printing.•Six 3D printing parameters were investigated.•Taguchi L25 with confirmation runs.•Three modeling approaches, i.e., Linear Regression Model, Reduced Quadratic Regression Model, Quadratic Regression Model.•Prediction models, as functions of the 3D printing parameters for direct industrial use.

This study aimed to investigate the potential of antimony-doped tin oxide (ATO) as a reinforcing agent for polyamide 12 (PA12) in 3D printing by examining four mixtures with varying ATO concentrations (2.0 to 8.0 wt.%, with a 2.0 wt.% interval). These mixtures were used to fabricate filaments for the manufacturing of specimens through the material extrusion method. The mechanical properties of the resulting PA12/ATO composites and PA12 pure samples were evaluated through tensile, Charpy impact, flexural, and microhardness tests. Additionally, rheology, structure, morphology, thermal properties, pore size, and consistency in the dimensions of the samples were evaluated. Thermogravimetric analysis, along with differential scanning calorimetry, scanning electron microscopy, energy-dispersive and Raman spectroscopy, and micro-computed tomography, were conducted. The results were correlated and interpreted. The greatest reinforcement was achieved with the PA12/ATO 4.0 wt.% mixture, which exhibited a 19.3% increase in tensile strength and an 18.6% increase in flexural strength compared with pure PA12 (the control samples). The Charpy impact strength and microhardness were also improved by more than 10%. These findings indicate the merit of composites with ATO in additive manufacturing, particularly in the production of components with improved mechanical performance.

Risk stratification of coronavirus disease-19 (COVID-19) patients by simple markers is critical to guide treatment. We studied the predictive value of soluble interleukin-2 receptor (sIL-2R) for the early identification of patients at risk of developing severe clinical outcomes. sIL-2R levels were measured in 197 patients (60.9% males; median age 61 years; moderate disease, n = 65; severe, n = 132, intubated and/or died, n = 42). All patients received combined immunotherapies (anakinra ± corticosteroids ± intravenous immunoglobulin ± tocilizumab) according to our local treatment algorithm. The endpoint was the composite event of intubation due to severe respiratory failure (SRF) or mortality. Median (interquartile range) sIL-2R levels were significantly higher in patients with severe disease, compared with those with moderate disease (6 (6.2) vs. 5.2 (3.4) ng/mL, p = 0.017). sIL-2R was the strongest laboratory predictive factor for intubation/death (hazard ratio 1.749, 95%CI 1.041–2.939, p = 0.035) after adjustment for other known risk factors. Youden’s index revealed optimal sIL-2R cut-off for predicting intubation/death at 9 ng/mL (sensitivity: 67%; specificity: 86%; positive and negative predictive value: 57% and 91%, respectively). Delta sIL-2R between the day of event or discharge minus admission date was higher in patients that intubated/died than in those who did not experience an event (2.91 (10.42) vs. 0.44 (2.88) ng/mL; p = 0.08)). sIL-2R on admission and its dynamic changes during follow-up may reflect disease severity and predict the development of SRF and mortality.

The association of low-density lipoprotein cholesterol lowering with outcomes in embolic stroke of undetermined source (ESUS) patients is unclear. In these patients we aimed to assess the effect of statin on stroke recurrence, major adverse cardiovascular events (MACE) and death rates. Consecutive ESUS patients in the Athens Stroke Registry were prospectively followed-up to 10 years for stroke recurrence, MACE, and death. The Nelson–Aalen estimator was used to estimate the cumulative probability by statin allocation at discharge and cox-regression analyses to investigate whether statin at discharge was a predictor of outcomes. Among 264 ESUS patients who were discharged and followed for 4 years, 89 (33.7%) were treated with statin at discharge. Patients who were discharged on statin had lower rates of stroke recurrence (3.58 vs. 7.23/100 patient-years, HR: 0.48; 95% CI 0.26–0.90), MACE (4.98 vs. 9.89/100 patient-years, HR: 0.49; 95% CI 0.29–0.85), and death (3.93 vs. 8.21/100 patient-years, HR: 0.50; 95% CI: 0.28–0.89). In the multivariate analysis, statin treatment at discharge was an independent predictor of stroke recurrence (adjusted HR: 0.48; 95% CI 0.26–0.91), MACE (adjusted HR: 0.48; 95% CI 0.28–0.82), and death (adjusted HR: 0.50; 95% CI 0.27–0.93). Patients with ESUS discharged on statins have lower rates of stroke recurrence, MACE, and death compared to those not receiving statin therapy.