Application of 3D printing to create an in vitro aneurysm rupture model.

Currently available benchtop (in vitro) aneurysm models are inadequate for testing the efficacy of endovascular device treatments. Specifically, current models do not represent the mechanical instability of giant aneurysms (defined as aneurysms with 25 mm in height or width) and do not predictably rupture under simulated physiological conditions. Hence, in vitro aneurysm models with biomechanically relevant material properties and a predictable rupture timeframe are needed to accurately assess the efficacy of new medical device treatment options. Understanding the material properties of an aneurysm (e.g., shear and compression modulus) as it approaches rupture is a crucial step toward creating a pathologically relevant and sophisticated in vitro aneurysm rupture model. We investigated the change in material properties of a blood vessel, via enzymatic treatment, to simulate the degradation of an aneurysm wall and used this information to create a sophisticated aneurysm rupture model using the latest in additive manufacturing technologies (3D printing) with tissue-like materials. Mechanical properties (shear and compression modulus) of swine carotid vessels were evaluated before and after incubation with collagenase D enzyme (30 min at 37°C) to simulate the effect of biochemical activity on aneurysm wall approaching rupture compared to control vessels (untreated). Mechanical strength of a soft and flexible 3D-printed material (VCA-A30: 30 shore A hardness) was tested for comparison to these arterial vessels. This material was then used to create spherical shaped, giant-sized (25-mm diameter) aneurysm phantoms and were run under neurovascular pressures (120/80 ± 5 mmHg), beats per minute (BPM = 70) and flows representing the middle cerebral artery [MCA: 142.67 (±20.13) mL/min] using a blood analog [3.6 (±0.4) cP viscosity] with non-Newtonian shear-thinning properties. The shear modulus of swine carotid vessel before treatment was 12.2 (±2.7) KPa and compression modulus was 663.5 (±111.6) KPa. After enzymatic treatment by collagenase D, shear modulus of animal tissues reduced by 33% (p-value = .039) while compression modulus remained statistically unchanged (p-value = .615). Control group (untreated vessels) showed minimal reduction (13%, p-value = .226) in shear modulus and 78% increase (p-value = .034) in compression modulus. The shear modulus of the 3D-printed material was 228.59 (±24.82) KPa while its compression modulus was 668.90 (±13.16) KPa. This material was used to prototype a sophisticated in vitro giant aneurysm rupture model. When subjected to physiological pressures and flow rates, the untreated models consistently ruptured at ~12 min. These results indicate that aneurysm rupture can be recreated consistently in a benchtop in vitro model, utilizing the latest 3D-printed materials, connected to a physiologically relevant programmable pump. Further studies will investigate the optimization of various aneurysm dome thickness regions within the aneurysm, with tunable rupture times for comparison of aneurysm device deployment and benchtop controls based on the measurable effects of pressure and flow changes within the aneurysm models. These optimized in vitro rupture models could ultimately be used to test the efficacy of device treatment options and rupture risk by quantifying specific device rupture times and aneurysm rupture position.

Estimation of compressive strength of waste concrete utilizing fly ash/slag in concrete with interpretable approaches: optimization and graphical user interface (GUI).

Geo-polymer concrete has a significant influence on the environmental condition and thus its use in the civil industry leads to a decrease in carbon dioxide (CO2) emission. However, problems lie with its mixed design and casting in the field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) to anticipate the mechanical characteristic of fly ash/slag-based geopolymer concrete (FASBGPC) by utilizing AdaBoost and Bagging on MLPNN to make an ensemble model with 156 data points. The data consist of GGBS (kg/m3), Alkaline activator (kg/m3), Fly ash (kg/m3), SP dosage (kg/m3), NaOH Molarity, Aggregate (kg/m3), Temperature (°C) and compressive strength as output parameter. Python programming is utilized in Anaconda Navigator using Spyder version 5.0 to predict the mechanical response. Statistical measures and validation of data are done by splitting the dataset into 80/20 percent and K-Fold CV is employed to check the accurateness of the model by using MAE, RMSE, and R2. Statistical analysis relies on errors, and tests against external indicators help determine how well models function in terms of robustness. The most important factor in compressive strength measurements is examined using permutation characteristics. The result reveals that ANN with AdaBoost is outclassed by giving maximum enhancement with R2 = 0.914 and shows the least error with statistical and external validations. Shapley analysis shows that GGBS, NaOH Molarity, and temperature are the most influential parameter that has significant content in making FASBGPC. Thus, ensemble methods are suitable for constructing prediction models because of their strong and reliable performance. Furthermore, the graphical user interface (GUI) is generated through the process of training a model that forecasts the desired outcome values when the corresponding inputs are provided. It streamlines the process and provides a useful tool for applying the model's abilities in the field of civil engineering.

Examining the benefits, challenges, and drivers of open user innovation in small and medium-sized enterprises operating in low R&D industries.

Recent studies have indicated that many challenges exist in implementing open user innovation in SMEs. As a more advanced paradigm of traditional innovation, open user innovations are developed by users and other stakeholders who share tasks and costs of innovation development and then freely unwrap results. The work presented in this article examines the main factors driving open user innovation in SMEs, operating in industries with low investment in R&D. The work accounts for differences in the economics categorisation of the countries in which the organisations operate in (developing vs developed), and how that impacts various factors related to open user innovation adoption. The findings of this study indicate that differences between Australian and Kuwaiti SMEs exist, in terms of perceptions of success, benefits, challenges, and ease of implementation related to open user innovation. In addition, some differences are observed in the drivers for sensing open user innovation opportunities between the two countries examined.

A state-of-the-art review of the physical and durability characteristics and microstructure behavior of ultra-high-performance geopolymer concrete.

This paper provides a comprehensive review of ultra-high-performance geopolymer concrete (UHPGPC), an innovative, eco-friendly, and cost-effective variant of ultra-high-performance concrete (UHPC), devised to meet the rising request for ultra-high-strength construction materials. Previous research papers have not thoroughly analyzed and compared the rheological, physical, durability, and microstructural properties of UHPGPC with UHPC. Similarly, review articles scarcely investigate UHPGPC's strength properties and microstructural behavior under high temperatures. This paper includes an assessment of the correlation between compressive strength, splitting tensile strength, and modulus of elasticity (MOE). The current study also compares chloride ion penetration test outcomes, elevated temperature, electrical resistivity, and porosity tests to evaluate durability. To analyze the microstructure of UHPGPC, the paper assesses results from Fourier Transform Infrared Spectroscopy (FT-IR), Thermogravimetric Analysis (TGA), Scanning Electron Microscopy (SEM), and Mercury Intrusion Porosimetry (MIP). The findings from the present paper suggest that UHPGPC effectively meets the ideal mechanical property specifications of UHPC. Compared to UHPC, UHPGPC displayed a higher ion passage propensity due to larger pores (>100 nm). Geopolymer technologies present a greener path for producing UHPC by consuming less energy and emitting reduced CO2. Introducing mineral fillers like silica fume impacts the mixture's flowability and increases its water needs. However, adding an optimal ratio of micro-silica as a partial substitute for granulated blast furnace slag further bolsters the strength characteristics of UHPGPC. The strength of UHPC can also be notably improved by adjusting the water-to-binder ratio, with specific ratios yielding considerable enhancements in compression strength. The selection of an alkaline activator plays a pivotal role in UHPC's heat resilience. Among them, a combination of potassium hydroxide and sodium silicate is the prime chemical activator for boosting strength performance, durability behavior, and microstructural attributes, particularly at temperatures beyond 600 °C. Eco-friendly Geopolymer Composites (EGCs) offer lower embodied energy and CO2 emissions than traditional composites, with certain components like polyvinyl alcohol fibers being key contributors to these emissions. Progress in self-healing materials is driving sustainability in construction through innovative techniques, such as bacterial applications and specific chemical reactions. The strength and workability of Engineered Geopolymer Composites are influenced by their fiber content, with certain fibers interacting weaker than others. On a microstructural level, UHPGPC has a relatively weaker structure than UHPC due to differences in pore size, but its durability is improved when reinforced with fibers.

Knowledge and Attitude of Healthcare Practitioners Toward Therapeutic Drug Monitoring Practices in the Najran Region, Kingdom of Saudi Arabia.

BACKGROUND: Therapeutic drug monitoring (TDM) is one of the useful clinical tools that aim to improve and ensure the best therapeutic effects while avoiding drug-related toxicity. We aimed to evaluate the knowledge and attitude toward TDM practices among healthcare practitioners in the Najran region, Saudi Arabia. METHODOLOGY: A cross-sectional study was conducted between May 2021 and October 2021 for assessing the awareness and attitude of licensed doctors and pharmacists working in Saudi Arabia regarding TDM practices. A 31-item questionnaire was distributed to the healthcare professionals via an electronic link. Descriptive and inferential statistics were used to report the data. RESULTS: A total of 392 participants submitted questionnaires. More than half of the professionals (55.9%) had an overall awareness of the TDM, while only 3.1% did not. Only 16% and 23% of those who were surveyed indicated that TDM is used at the beginning of medication administration and when shifting from one drug to another, respectively. The majority of the professionals responded that TDM is revealed with laboratory changes in liver and kidney function (81%), and TDM is specified with suspected therapeutic failure (93%). Only half of the respondents claimed that they had ever requested or suggested a TDM in their practice. More than 90% of the respondents claimed that they were aware of the indications for vancomycin (n = 381), gentamicin (n = 375), lithium (n = 369), digoxin (n = 380), and theophylline (n = 369). CONCLUSION: This study found that the majority of the healthcare professionals in Najran were aware of the TDM, particularly those with more than 10 years of professional experience. In addition, TDM service is not widely available in smaller hospitals in Najran. There is a need to conduct customized training programs for junior healthcare professionals, thus increasing the levels of awareness toward TDM. Future studies in Saudi Arabia can explore the role of clinical pharmacists in the provision of TDM services by assessing the clinical and economic outcomes of pharmacist interventions in TDM.