Showing papers by "Savas Tasoglu published in 2022"

3D-printed microrobots from design to translation

Abstract: Abstract Microrobots have attracted the attention of scientists owing to their unique features to accomplish tasks in hard-to-reach sites in the human body. Microrobots can be precisely actuated and maneuvered individually or in a swarm for cargo delivery, sampling, surgery, and imaging applications. In addition, microrobots have found applications in the environmental sector (e.g., water treatment). Besides, recent advancements of three-dimensional (3D) printers have enabled the high-resolution fabrication of microrobots with a faster design-production turnaround time for users with limited micromanufacturing skills. Here, the latest end applications of 3D printed microrobots are reviewed (ranging from environmental to biomedical applications) along with a brief discussion over the feasible actuation methods (e.g., on- and off-board), and practical 3D printing technologies for microrobot fabrication. In addition, as a future perspective, we discussed the potential advantages of integration of microrobots with smart materials, and conceivable benefits of implementation of artificial intelligence (AI), as well as physical intelligence (PI). Moreover, in order to facilitate bench-to-bedside translation of microrobots, current challenges impeding clinical translation of microrobots are elaborated, including entry obstacles (e.g., immune system attacks) and cumbersome standard test procedures to ensure biocompatibility.

Toilet-based continuous health monitoring using urine

Abstract: Regular health monitoring can result in early detection of disease, accelerate the delivery of medical care and, therefore, considerably improve patient outcomes for countless medical conditions that affect public health. A substantial unmet need remains for technologies that can transform the status quo of reactive health care to preventive, evidence-based, person-centred care. With this goal in mind, platforms that can be easily integrated into people's daily lives and identify a range of biomarkers for health and disease are desirable. However, urine - a biological fluid that is produced in large volumes every day and can be obtained with zero pain, without affecting the daily routine of individuals, and has the most biologically rich content - is discarded into sewers on a regular basis without being processed or monitored. Toilet-based health-monitoring tools in the form of smart toilets could offer preventive home-based continuous health monitoring for early diagnosis of diseases while being connected to data servers (using the Internet of Things) to enable collection of the health status of users. In addition, machine learning methods can assist clinicians to classify, quantify and interpret collected data more rapidly and accurately than they were able to previously. Meanwhile, challenges associated with user acceptance, privacy and test frequency optimization should be considered to facilitate the acceptance of smart toilets in society.

3D-Printed Microneedles for Point-of-Care Biosensing Applications

Abstract: Microneedles (MNs) are an emerging technology for user-friendly and minimally invasive injection, offering less pain and lower tissue damage in comparison to conventional needles. With their ability to extract body fluids, MNs are among the convenient candidates for developing biosensing setups, where target molecules/biomarkers are detected by the biosensor using the sample collected with the MNs. Herein, we discuss the 3D printing of microneedle arrays (MNAs) toward enabling point-of-care (POC) biosensing applications.

3D bioprinted glioma models

Abstract: Glioma is one of the most malignant types of cancer and most gliomas remain incurable. One of the hallmarks of glioma is its invasiveness. Furthermore, glioma cells tend to readily detach from the primary tumor and travel through the brain tissue, making complete tumor resection impossible in many cases. To expand the knowledge regarding the invasive behavior of glioma, evaluate drug resistance, and recapitulate the tumor microenvironment, various modeling strategies were proposed in the last decade, including three-dimensional (3D) biomimetic scaffold-free cultures, organ-on-chip microfluidics chips, and 3D bioprinting platforms, which allow for the investigation on patient-specific treatments. The emerging method of 3D bioprinting technology has introduced a time- and cost-efficient approach to create in vitro models that possess the structural and functional characteristics of human organs and tissues by spatially positioning cells and bioink. Here, we review emerging 3D bioprinted models developed for recapitulating the brain environment and glioma tumors, with the purpose of probing glioma cell invasion and gliomagenesis and discuss the potential use of 4D printing and machine learning applications in glioma modelling.

Three-Dimensional-Bioprinted Liver Chips and Challenges

Abstract: Drug testing, either on animals or on 2D cell cultures, has its limitations due to inaccurate mimicking of human pathophysiology. The liver, as one of the key organs that filters and detoxifies the blood, is susceptible to drug-induced injuries. Integrating 3D bioprinting with microfluidic chips to fabricate organ-on-chip platforms for 3D liver cell cultures with continuous perfusion can offer a more physiologically relevant liver-mimetic platform for screening drugs and studying liver function. The development of organ-on-chip platforms may ultimately contribute to personalized medicine as well as body-on-chip technology that can test drug responses and organ–organ interactions on a single or linked chip model.

Microfluidic Invasion Chemotaxis Platform for 3D Neurovascular Co-Culture

Abstract: Advances in microfabrication and biomaterials have enabled the development of microfluidic chips for studying tissue and organ models. While these platforms have been developed primarily for modeling human diseases, they are also used to uncover cellular and molecular mechanisms through in vitro studies, especially in the neurovascular system, where physiological mechanisms and three-dimensional (3D) architecture are difficult to reconstruct via conventional assays. An extracellular matrix (ECM) model with a stable structure possessing the ability to mimic the natural extracellular environment of the cell efficiently is useful for tissue engineering applications. Conventionally used techniques for this purpose, for example, Matrigels, have drawbacks of owning complex fabrication procedures, in some cases not efficient enough in terms of functionality and expenses. Here, we proposed a fabrication protocol for a GelMA hydrogel, which has shown structural stability and the ability to imitate the natural environment of the cell accurately, inside a microfluidic chip utilizing co-culturing of two human cell lines. The chemical composition of the synthesized GelMA was identified by Fourier transform infrared spectrophotometry (FTIR), its surface morphology was observed by field emission electron microscopy (FESEM), and the structural properties were analyzed by atomic force microscopy (AFM). The swelling behavior of the hydrogel in the microfluidic chip was imaged, and its porosity was examined for 72 h by tracking cell localization using immunofluorescence. GelMA exhibited the desired biomechanical properties, and the viability of cells in both platforms was more than 80% for seven days. Furthermore, GelMA was a viable platform for 3D cell culture studies and was structurally stable over long periods, even when prepared by photopolymerization in a microfluidic platform. This work demonstrated a viable strategy to conduct co-culturing experiments as well as modeling invasion and migration events. This microfluidic assay may have application in drug delivery and dosage optimization studies.

Deep Learning-Enabled Technologies for Bioimage Analysis

Abstract: Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.

3D engineered neural co-culture model and neurovascular effects of marine fungi-derived citreohybridonol

Abstract: Marine-based biomolecules are emerging metabolites that have gained attention for developing novel biomaterials, drugs, and pharmaceutical in vitro platforms. Here, we developed a 3D engineered neural co-culture model via a 3D prototyped sliding frame-platform for multi-step UV lithography and investigated the neurovascular potential of citreohybridonol in neuroblastoma treatment. Citreohybridonol was isolated from a sponge-derived fungus Penicillium atrovenetum. The model was characterized by Fourier-transform infrared spectroscopy and scanning electron microscopy analysis. Human umbilical cord vein endothelial cells (HUVECs) and neuroblastoma (SH-SY5Y) cell lines were encapsulated in gelatin methacrylate (GelMA) with and without citreohybridonol. The effect of citreohybridonol on the proliferation capacity of cells was assessed via cell viability and immunostaining assays. GelMA and 3D culture characterization indicated that the cells were successfully encapsulated as axenic and mixed with/without citreohybridonol. The cytotoxic test confirmed that the 3D microenvironment was non-toxic for cultural experiments, and it showed the inhibitory effects of citreohybridonol on SH-SY5Y cells and induced the proliferation of HUVECs. Finally, immunohistochemical staining demonstrated that citreohybridonol suppressed SH-SY5Y cells and induced vascularization of HUVECs in mixed 3D cell culture.

Shape Fidelity Evaluation of Alginate-Based Hydrogels through Extrusion-Based Bioprinting

Abstract: Extrusion-based 3D bioprinting is a promising technique for fabricating multi-layered, complex biostructures, as it enables multi-material dispersion of bioinks with a straightforward procedure (particularly for users with limited additive manufacturing skills). Nonetheless, this method faces challenges in retaining the shape fidelity of the 3D-bioprinted structure, i.e., the collapse of filament (bioink) due to gravity and/or spreading of the bioink owing to the low viscosity, ultimately complicating the fabrication of multi-layered designs that can maintain the desired pore structure. While low viscosity is required to ensure a continuous flow of material (without clogging), a bioink should be viscous enough to retain its shape post-printing, highlighting the importance of bioink properties optimization. Here, two quantitative analyses are performed to evaluate shape fidelity. First, the filament collapse deformation is evaluated by printing different concentrations of alginate and its crosslinker (calcium chloride) by a co-axial nozzle over a platform to observe the overhanging deformation over time at two different ambient temperatures. In addition, a mathematical model is developed to estimate Young’s modulus and filament collapse over time. Second, the printability of alginate is improved by optimizing gelatin concentrations and analyzing the pore size area. In addition, the biocompatibility of proposed bioinks is evaluated with a cell viability test. The proposed bioink (3% w/v gelatin in 4% alginate) yielded a 98% normalized pore number (high shape fidelity) while maintaining >90% cell viability five days after being bioprinted. Integration of quantitative analysis/simulations and 3D printing facilitate the determination of the optimum composition and concentration of different elements of a bioink to prevent filament collapse or bioink spreading (post-printing), ultimately resulting in high shape fidelity (i.e., retaining the shape) and printing quality.

Nanotechnology‐based electrochemical biosensors for monitoring breast cancer biomarkers

Abstract: Breast cancer is categorized as the most widespread cancer type among women globally. On‐time diagnosis can decrease the mortality rate by making the right decision in the therapy procedure. These features lead to a reduction in medication time and socioeconomic burden. The current review article provides a comprehensive assessment for breast cancer diagnosis using nanomaterials and related technologies. Growing use of the nano/biotechnology domain in terms of electrochemical nanobiosensor designing was discussed in detail. In this regard, recent advances in nanomaterial applied for amplified biosensing methodologies were assessed for breast cancer diagnosis by focusing on the advantages and disadvantages of these approaches. We also monitored designing methods, advantages, and the necessity of suitable (nano) materials from a statistical standpoint. The main objective of this review is to classify the applicable biosensors based on breast cancer biomarkers. With numerous nano‐sized platforms published for breast cancer diagnosis, this review tried to collect the most suitable methodologies for detecting biomarkers and certain breast cancer cell types.

Machine Learning-Enabled Classification of Forearm sEMG Signals to Control Robotic Hands Prostheses

Abstract: In this paper, we aim to use and compare different machine learning algorithms, including k-nearest neighbors (k-NN) and support vector machines (SVM), to classify surface electromyography (sEMG) signals that correspond to the flexing of the 4 hand fingers, and recorded through 8 sensor channels. k-NN algorithm was optimized to find the values of k and the best type of distance, while four different kernels were used for SVM to find the optimal one. Moreover, linear discriminant analysis (LDA) was used to reduce the number of dimensions and investigate the effect of reducing the features on classification accuracy. Finally, the best performing ML algorithm was used to classify again using all possible combinations of 2 channels to assess LDA results. Training the algorithms shows that SVM with a Radial basis function (RBF) kernel outperforms k-NN and other SVM kernels, with 100% classification accuracy. Moreover, dimensionality reduction with LDA shows that using only 3 features keeps the accuracy at 100%, suggesting that using less sEMG sensors may not affect the quality of classification, which was confirmed by the result that using only channels 6 and 7 yielded 100% accuracy. Our results can pave the way for implementing strategies to decrease the cost of manufacturing prostheses and accelerate the execution of the classification algorithm since it should be performed in real-time.

Point‐of‐Care Diagnostic Platforms for Loop‐Mediated Isothermal Amplification

Abstract: The loop‐mediated isothermal amplification (LAMP) method is one of the Nucleic acid amplification tests (NAATs) that allows for the amplification of target regions without using a thermal cycle. With its unique primer design, LAMP ensures the rapid replication of the targeted DNA region with high specificity and high efficiency. LAMP technology is used for diagnostic purposes in pathogen detection due to its ease of use, low cost, and simplicity without requiring complex equipment. A wide range of LAMP diagnostic platforms have been developed for applications in bacteria, virus, and parasitic pathogen detection. Herein, the methodology of LAMP technology and its applications in pathogen detection and SNP genotyping and mutation detection are discussed. Point‐of‐care (PoC) LAMP platforms designed with the principles of microfluidic chip technology, including LAMP‐on‐a‐chip, paper‐based LAMP, and smartphone‐based LAMP applications have been elaborated. LAMP technology represents a fast, robust, and reliable diagnostic platform for point‐of‐care testing.

MIT Open Access Articles Sickle cell detection using a smartphone

Abstract: Sickle cell disease affects 25% of people living in Central and West Africa and, if left undiagnosed, can cause life threatening “silent” strokes and lifelong damage. However, ubiquitous testing procedures have yet to be implemented in these areas, necessitating a simple, rapid, and accurate testing platform to diagnose sickle cell disease. Here, we present a label-free, sensitive, and specific testing platform using only a small blood sample ( < 1 μ l) based on the higher density of sickle red blood cells under deoxygenated conditions. Testing is performed with a lightweight and compact 3D-printed attachment installed on a commercial smartphone. This attachment includes an LED to illuminate the sample, an optical lens to magnify the image, and two permanent magnets for magnetic levitation of red blood cells. The sample is suspended in a paramagnetic medium with sodium metabisulfite and loaded in a microcapillary tube that is inserted between the magnets. Red blood cells are levitated in the magnetic field based on equilibrium between the magnetic and buoyancy forces acting on the cells. Using this approach, we were able to distinguish between the levitation patterns of sickle versus control red blood cells based on their degree of confinement.

Microfluidic contact lenses for ocular diagnostics and drug delivery

Abstract: The human eye and tear provide essential physiological information for the detection of ocular dysfunctions and therapy monitoring. The measurements of biomarkers in tear composition are critical for disease diagnosis and early interventions. Hence, significant efforts are dedicated to the development of functional contact lenses that can quantify tear analytes and ocular physiological condition. The combination of microfluidics and contact lens technologies offer real-time monitoring of ocular physiology and timely detection of eye disorders through wireless components. This review discusses the fundamentals of microfluidic contact lenses and their diverse applications in ophthalmic diagnostics and drug delivery. It also elucidates the strategies for the commercialization of microfluidic contact lenses to create clinical and point-of-care products.