Research Project: Epilepsinin Erken Tanı ve Izlemi için Crispr-Dcas13 Temelli Label- Free Mirna Biyosensör Sistemi Geliştirilmesi
Loading...
Contributors
Funders
ID
TB.00753
Authors
Taşoğlu, Savaş
Faculty Member
Publications
Mpox diagnosis at POC
(Elsevier, 2025) Yığcı, Defne; Ergönül, Önder; Taşoğlu, Savaş; School of Medicine; Department of Mechanical Engineering; KUAR (KU Arçelik Research Center for Creative Industries); KUTTAM (Koç University Research Center for Translational Medicine); KUISCID (Koç University İşbank Center for Infectious Diseases); Yes; SCHOOL OF MEDICINE; College of Engineering; Research Center; Yığcı, Defne
BAKILACAK
CRISPR-on-chip for point-of-care diagnostics
(American Chemical Society, 2026) Atçeken, Nazente; Yığcı, Defne; Taşoğlu, Savaş; Kahya, Alptekin; KUTTAM (Koç University Research Center for Translational Medicine); KUIS AI (Koç University & İş Bank Artificial Intelligence Center); Department of Mechanical Engineering; School of Medicine; KUAR (KU Arçelik Research Center for Creative Industries); Graduate School of Sciences and Engineering; Yes; Research Center; College of Engineering; SCHOOL OF MEDICINE; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING; Yığcı, Defne
CRISPR-based diagnostic platforms have gained significant momentum in recent years, enabling highly sensitive and specific detection of pathogens and diseases. Due to their practical benefits, these platforms have become widely adopted in point-of-care (PoC) applications. CRISPR-on-chip technology integrates CRISPR-Cas platforms with diverse microfluidic systems, allowing scalability and portable, real-time, and precise biomolecule detection. This approach enhances diagnostic accuracy, reduces processing times, and minimizes the need for complex laboratory infrastructures, unlike in conventional diagnostics. Using CRISPR-Cas enzymes in microfluidic systems, CRISPR-on-chip platforms offer key advantages such as single-molecule sensitivity, multiplex detection, and applicability. However, integration with microfluidics for PoC applications is still poorly understood, despite CRISPR-Cas being widely used. This study reviews recent developments in CRISPR-on-chip-based diagnostics and highlights its potential applications in infectious diseases, biosensors, and personalized medicine. Furthermore, challenges and future perspectives in achieving an ideal diagnostic solution are discussed.
ML-augmented Ti-based microrobotic stents
(Wiley-VCH GmbH, 2025) Choukri, Abdullah Ahmed; Taşoğlu, Savaş; Choukri, Abdullah Ahmed; Department of Mechanical Engineering; KUIS AI (Koç University & İş Bank Artificial Intelligence Center); KUTTAM (Koç University Research Center for Translational Medicine); KUAR (KU Arçelik Research Center for Creative Industries); Yes; College of Engineering; Research Center
The integration of microrobotic stents into biomedical applications has the potential to revolutionize invasive procedures by enabling precise drug delivery, imaging, and vascular interventions. These interventions demand alloys with high radial stiffness for structural integrity and low density for biocompatibility. We developed a machine learning (ML)-finite element analysis (FEA) framework to optimize titanium (Ti)-based and Ti-based high-entropy alloys (Ti-HEAs) compositions using a curated database of 238 alloys. Gaussian process regression (GPR) is trained on FEA-simulated radial stiffness and constrained optimization (interior-point, sequential quadratic programming (SQP), active-set) identified high-performance candidates. The interior-point algorithm yielded the highest stiffness (483.54 kN/m) with balanced composition (Ti: 76.29 at%, Nb: 6.88%, Zr: 7.34%, Ta: 7.31%), outperforming the dataset maximum (TiSn 20, 472.49 kN/m) by 2.32% and Ti-6Al-4 V (368.96 kN/m) by 31%. All algorithms converged to at least 469 kN/m despite compositional diversity, confirming robustness. The framework enables rapid, physics-informed alloy design for next-generation biomedical microrobotics.
Optimizing solid microneedle design: a comprehensive ML-augmented DOE approach
(American Chemical Society, 2024) Ahmadinejad, Erfan; Choukri, Abdullah Ahmed; Taşoğlu, Savaş; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; KUAR (KU Arçelik Research Center for Creative Industries); KUIS AI (Koç University & İş Bank Artificial Intelligence Center); KUTTAM (Koç University Research Center for Translational Medicine); Yes; College of Engineering; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING; Research Center
Microneedles (MNs), that is, a matrix of micrometer-scale needles, have diverse applications in drug delivery, skincare therapy, and health monitoring. MNs offer a minimally invasive alternative to hypodermic needles, characterized by rapid and painless procedures, cost-effective fabrication methods, and reduced tissue damage. This study explores four MN designs, cone-shaped, tapered cone-shaped, pyramidal with a square base, and pyramidal with a triangular-shaped base, and their optimization based on predefined criteria. The workflow encompasses three loading conditions: compressive load during insertion, critical buckling load, and bending loading resulting from incorrect insertion. Geometric parameters such as base radius/width, tip radius/width, height, and tapered angle tip influence the output criteria, namely, total deformation, critical buckling loads, factor of safety (FOS), and bending stress. The comprehensive framework employing a design of experiment approach within the ANSYS workbench toolbox establishes a mathematical model and a response surface fitting model. The resulting regression model, sensitivity chart, and response curve are used to create a multiobjective optimization problem that helps achieve an optimized MN geometrical design across the introduced four shapes, integrating machine learning (ML) techniques. This study contributes valuable insights into a potential ML-augmented optimization framework for MNs via needle designs to stay durable for various physiologically relevant conditions.
