Showing papers by "Savas Tasoglu published in 2023"

Microneedle arrays integrated with microfluidic systems: Emerging applications and fluid flow modeling.

Abstract: Microneedle arrays are patches of needles at micro- and nano-scale, which are competent and versatile technologies that have been merged with microfluidic systems to construct more capable devices for biomedical applications, such as drug delivery, wound healing, biosensing, and sampling body fluids. In this paper, several designs and applications are reviewed. In addition, modeling approaches used in microneedle designs for fluid flow and mass transfer are discussed, and the challenges are highlighted.

Electro-conductive silica nanoparticles-incorporated hydrogel based on alginate as a biomimetic scaffold for bone tissue engineering application

Abstract: An innovative electrically conductive hydrogel was fabricated through the incorporation of silica nanoparticles (SiO2 NPs) and poly(aniline-co-dopamine) (PANI-co-PDA) into oxidized alginate (OAlg) as a biomimetic scaffold for bone tissue engineering application. The developed self-healing chemical hydrogel was characterized by FTIR, SEM, TEM, XRD, and TGA. The electrical conductivity and swelling ratio of the hydrogel were obtained as 1.7 × 10−3 S cm−1 and 130%, respectively. Cytocompatibility and cell proliferation potential of the developed scaffold were approved by MTT assay using MG-63 cells. FE-SEM imaging approved the potential of the fabricated scaffold for hydroxyapatite (HA) formation and bioactivity induction through immersing in SBF solution.

Bioprinting in Microgravity

Abstract: Bioprinting as an extension of 3D printing offers capabilities for printing tissues and organs for application in biomedical engineering. Conducting bioprinting in space, where the gravity is zero, can enable new frontiers in tissue engineering. Fabrication of soft tissues, which usually collapse under their own weight, can be accelerated in microgravity conditions as the external forces are eliminated. Furthermore, human colonization in space can be supported by providing critical needs of life and ecosystems by 3D bioprinting without relying on cargos from Earth, e.g., by development and long-term employment of living engineered filters (such as sea sponges–known as critical for initiating and maintaining an ecosystem). This review covers bioprinting methods in microgravity along with providing an analysis on the process of shipping bioprinters to space and presenting a perspective on the prospects of zero-gravity bioprinting.

Smartphone and wearable diagnostics

Abstract: Department of Mechanical Engineering, Koç University, Sariyer, Turkiye; Division of Optometry, School of Med Services & Techniques, Dogus University, Istanbul, Turkiye; Department of Chemical Engineering, Imperial College London, London, UK; Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Sariyer Turkiye; Koc University Is Bank Artificial Intelligence Lab (KUIS AILab), Koç University, Sariyer, Turkiye; Boğaziçi Institute of Biomedical Engineering, Boğaziçi University, Çengelköy, Turkiye

Machine Learning-Enabled Optimization of Interstitial Fluid Collection via a Sweeping Microneedle Design

Abstract: Microneedles (MNs) allow for biological fluid sampling and drug delivery toward the development of minimally invasive diagnostics and treatment in medicine. MNs have been fabricated based on empirical data such as mechanical testing, and their physical parameters have been optimized through the trial-and-error method. While these methods showed adequate results, the performance of MNs can be enhanced by analyzing a large data set of parameters and their respective performance using artificial intelligence. In this study, finite element methods (FEMs) and machine learning (ML) models were integrated to determine the optimal physical parameters for a MN design in order to maximize the amount of collected fluid. The fluid behavior in a MN patch is simulated with several different physical and geometrical parameters using FEM, and the resulting data set is used as the input for ML algorithms including multiple linear regression, random forest regression, support vector regression, and neural networks. Decision tree regression (DTR) yielded the best prediction of optimal parameters. ML modeling methods can be utilized to optimize the geometrical design parameters of MNs in wearable devices for application in point-of-care diagnostics and targeted drug delivery.

Wound healing strategies based on nanoparticles incorporated in hydrogel wound patches

Abstract: The intricate, tightly controlled mechanism of wound healing that is a vital physiological mechanism is essential to maintaining the skin's natural barrier function. Numerous studies have focused on wound healing as it is a massive burden on the healthcare system. Wound repair is a complicated process with various cell types and microenvironment conditions. In wound healing studies, novel therapeutic approaches have been proposed to deliver an effective treatment. Nanoparticle-based materials are preferred due to their antibacterial activity, biocompatibility, and increased mechanical strength in wound healing. They can be divided into six main groups: metal NPs, ceramic NPs, polymer NPs, self-assembled NPs, composite NPs, and nanoparticle-loaded hydrogels. Each group shows several advantages and disadvantages, and which material will be used depends on the type, depth, and area of the wound. Better wound care/healing techniques are now possible, thanks to the development of wound healing strategies based on these materials, which mimic the extracellular matrix (ECM) microenvironment of the wound. Bearing this in mind, here we reviewed current studies on which NPs have been used in wound healing and how this strategy has become a key biotechnological procedure to treat skin infections and wounds.

Piezoelectric Metamaterial Blood Pressure Sensor

Abstract: Continuous blood pressure monitoring allows for detecting the early onset of cardiovascular disease and assessing personal health status. Conventional piezoelectric blood pressure monitoring techniques have the ability to sense biosignals due to their good dynamic responses but have significant drawbacks in terms of power consumption, which limits the operation of blood pressure sensors. Although piezoelectric materials can be used to enhance the self-powered blood pressure sensor responses, the structure of the piezoelectric element can be modified to achieve a higher output voltage. Here, a structural study on piezoelectric metamaterials in blood pressure sensors is demonstrated, and output voltages are computed and compared to other architectures. Next, a Bayesian optimization framework is defined to get the optimal design according to the metamaterial design space. Machine learning algorithms were used for applying regression models to a simulated dataset, and a 2D map was visualized for key parameters. Finally, a time-dependent blood pressure was applied to the inner surface of an artery vessel inside a 3D tissue skin model to compare the output voltage for different metamaterials. Results revealed that all types of metamaterials can generate a higher electric potential in comparison to normal square-shaped piezoelectric elements. Bayesian optimization showed that honeycomb metamaterials had the optimal performance in generating output voltage, which was validated according to regression model analysis resulting from machine learning algorithms. The simulation of time-dependent blood pressure in a 3D skin tissue model revealed that the design suggested by the Bayesian optimization process can generate an electric potential more than two times greater than that of a conventional square-shaped piezoelectric element.

3D-Printed Microrobots: Translational Challenges

Abstract: The science of microrobots is accelerating towards the creation of new functionalities for biomedical applications such as targeted delivery of agents, surgical procedures, tracking and imaging, and sensing. Using magnetic properties to control the motion of microrobots for these applications is emerging. Here, 3D printing methods are introduced for the fabrication of microrobots and their future perspectives are discussed to elucidate the path for enabling their clinical translation.

Machine learning-augmented fluid dynamics simulations for micromixer educational module.

Abstract: Micromixers play an imperative role in chemical and biomedical systems. Designing compact micromixers for laminar flows owning a low Reynolds number is more challenging than flows with higher turbulence. Machine learning models can enable the optimization of the designs and capabilities of microfluidic systems by receiving input from a training library and producing algorithms that can predict the outcomes prior to the fabrication process to minimize development cost and time. Here, an educational interactive microfluidic module is developed to enable the design of compact and efficient micromixers at low Reynolds regimes for Newtonian and non-Newtonian fluids. The optimization of Newtonian fluids designs was based on a machine learning model, which was trained by simulating and calculating the mixing index of 1890 different micromixer designs. This approach utilized a combination of six design parameters and the results as an input data set to a two-layer deep neural network with 100 nodes in each hidden layer. A trained model was achieved with R2 = 0.9543 that can be used to predict the mixing index and find the optimal parameters needed to design micromixers. Non-Newtonian fluid cases were also optimized using 56700 simulated designs with eight varying input parameters, reduced to 1890 designs, and then trained using the same deep neural network used for Newtonian fluids to obtain R2 = 0.9063. The framework was subsequently used as an interactive educational module, demonstrating a well-structured integration of technology-based modules such as using artificial intelligence in the engineering curriculum, which can highly contribute to engineering education.