- Research Thrust #1: Synthesizing Highly Magnetic Nanoparticles as MRI Contrast Agents
- Research Thrust #2: Producing Synthetic Functional Nanoparticles with Unique Properties Using Nanofabrication Approaches
- Research Thrust #3: Metal Nanoparticle Embedded 3D-Bioprinted Constructs Towards Bone Regeneration
- Research Thrust #4: Fabricating Anti-Bacterial Surfaces Using Microfabrication Techniques
- Research Thrust #5: Synthesizing Upconversion Nanoparticles for In Vivo Photothermal Therapy Applications
- Research Thrust #6: Metal Based Nanoparticles for In Vivo Radiotherapy
We have an established cleanroom facility towards microfluidics, lab-on-a-chip, sensor applications.
Installed tools: Photolithography, Maskless Lithography, Thermal Evaporator, Electron-Beam Evaporator, ICP-RIE, SEM
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Magnetic Nanoparticles as MRI Contrast Agents
Magnetic resonance imaging (MRI) is one of the most widely used diagnostic tools in the clinic and affords advantages such as deep tissue visualization, non-invasiveness, no ionizing radiation, good soft tissue contrast, and sub-millimeter spatial resolution. More than 100 million MRI procedures are performed annually worldwide and nearly half of these scans use contrast agents (CAs), which increase the contrast difference between normal and abnormal tissues (such as tumors), resulting in a higher image quality.
We are interested in utilizing micro and nanofabrication techniques to produce synthetic anisotropic magnetic nanostructures which display high r1 and r2 relaxivities, making them ideal candidates for MRI contrast agents. To realize this goal, we carefully control the size, shape, composition, crystallinity and surface effects (ligands) of the nanoconstructs.
Artificial Intelligence for Digital Pathology Image Analysis
Pathology is the study and diagnosis of disease through the examination of body tissue, which is typically fixed on glass slides and viewed under a microscope. Because pathology relies almost solely on glass slides to render a diagnosis, initial diagnoses and subsequent second opinions are often delayed while waiting for the glass slide to be physically delivered to the appropriate pathologist and patient care may be suspended. Diagnostic pathology is entering into an exciting time with the more widespread use of digital imaging in pathology, in particular, with the use of whole slide imaging (WSI) technology which allows the scanning of entire glass slides, with an output of an image file that is a digitized reproduction of the glass slide with images that are of diagnostic quality. With digital pathology, the entire slide can be digitized and analyzed, using artificial intelligence (AI) to automatically select the area of interest. WSI serves as an enabling platform for the application of AI in digital pathology. Images produced by WSI are a great source of information; complexity is higher than in many other imaging modalities because of their large size (a resolution of 100k × 100k is typical), presence of color information (haematoxylin and eosin and immunohistochemistry), no apparent anatomical orientation as in radiology, availability of information at multiple scales, and multiple z-stack levels (each slice contains a finite thickness and, depending on the plane of focus, will generate different images). Clearly, this kind of visual information cannot be extracted as easily by a human reader. AI enables pathologists to identify unique imaging markers associated with disease processes with the goal of improving early detection, determining prognosis, and selecting treatments most likely to be effective. This allows pathologists to serve more patients while maintaining diagnostic and prognostic accuracy. Digital pathology and AI can have immense potential for oncology and precision medicine. Much like the evolution of efficiency and effectiveness in radiology, the pressure on pathologists to reduce turnaround time and develop more efficient workflows is trending towards digitalization. By using AI algorithms, many of the tasks that are manual and subjective can become more automated and standardized. This helps speed up the process of analysis, allowing large volumes of slides to be analyzed in short periods of time. This can not only help to speed up diagnosis, but it can also help speed up drug development, as samples are tested much more quickly than conventional methods would allow, determining the efficacy of a candidate drug in shorter time periods.
Together with our collaborators from Harvard Medical School and Bogazici University, we work on the development and application of machine learning, deep learning and artificial intelligence algorithms and techniques towards cancer diagnosis, prognosis, and biomarker discovery. Several cancer types such as breast cancer, prostate cancer, glioblastoma are investigated. This research requires the collection of pathology images in large volumes at multiple national and international centers. In order to match this high demand, we utilize a digital slide scanner located at Erciyes University Hospital and serve as the main image data supplier in this study.