WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article BrAIn: A Comprehensive Artificial Intelligence-Based Morphology Analysis System for Brain Organoids and Neuroscience(Wiley, 2026-03-12) Polatli, Elifsu; Guner, Huseyin; Bastanlar, Yalin; Karakulah, Gokhan; Evranos, Ali Eren; Kahveci, Burak; Guven, SinanHuman-induced pluripotent stem cells (iPSCs) offer transformative potential for biomedical research, with iPSC-derived organoids providing more physiologically relevant models than traditional 2D cell cultures. Among these, brain organoids (BO) are particularly valuable for drug screening, disease modeling, and investigations into molecular pathways. Accurate representation of brain morphology is critical, as more complex organoid structures better mimic the human brain. Deep learning (DL) and machine learning (ML) approaches have become integral to analyzing organoid morphology, yet tools for comprehensive, time-resolved assessments are scarce. Here, we introduce BrAIn, a DL-based application for analyzing the developmental progression of BOs. BrAIn tracks their evolution from embryoid bodies (EBs) and quantifies parameters including area, Feret diameter, perimeter, roundness, and circularity. It also classifies budding and abnormal morphologies of 3D organoids and detects monolayer neural rosette structures, key features of neuronal differentiation. Designed with accessibility in mind, BrAIn provides a no-code interface, enabling researchers of all technical backgrounds to conduct advanced morphological analyses with ease. Our study demonstrates the application of BrAIn to evaluate the effects of different growth conditions-static, orbital shaker, and microfluidic chip-based-on BO development. Orbital shaker cultures resulted in the largest organoids, while chip-based systems achieved more homogeneous growth. Both conditions produced organoids with greater morphological complexity compared to static culture. BrAIn emerges as a robust, user-friendly tool to quantify BO development and explore how versatile growth conditions influence their morphology and maturation.Article Citation - WoS: 40Citation - Scopus: 48Molecular Separation by Using Active and Passive Microfluidic Chip Designs: A Comprehensive Review(Wiley, 2023-10-27) Ebrahimi, Aliakbar; Icoz, Kutay; Didarian, Reza; Shih, Chih-Hsin; Tarim, E. Alperay; Nasseri, Behzad; Avci, HuseyinSeparation and identification of molecules and biomolecules such as nucleic acids, proteins, and polysaccharides from complex fluids are known to be important due to unmet needs in various applications. Generally, many different separation techniques, including chromatography, electrophoresis, and magnetophoresis, have been developed to identify the target molecules precisely. However, these techniques are expensive and time consuming. "Lab-on-a-chip" systems with low cost per device, quick analysis capabilities, and minimal sample consumption seem to be ideal candidates for separating particles, cells, blood samples, and molecules. From this perspective, different microfluidic-based techniques have been extensively developed in the past two decades to separate samples with different origins. In this review, "lab-on-a-chip" methods by passive, active, and hybrid approaches for the separation of biomolecules developed in the past decade are comprehensively discussed. Due to the wide variety in the field, it will be impossible to cover every facet of the subject. Therefore, this review paper covers passive and active methods generally used for biomolecule separation. Then, an investigation of the combined sophisticated methods is highlighted. The spotlight also will be shined on the elegance of separation successes in recent years, and the remainder of the article explores how these permit the development of novel techniques. This review is about the microfludic-based methods that have been used in the past two decades for the separation of different biomolecules like protein, DNA, and RNA. In this regard, passive, active, and hybrid microfludic methods that are used for biomolecules separation are disscused and reviewed in this paper.imageArticle Citation - WoS: 11Citation - Scopus: 14Deep Learning Based Semantic Segmentation and Quantification for MRD Biochip Images(Elsevier Sci Ltd, 2022-08) Celebi, Fatma; Tasdemir, Kasim; Icoz, KutayMicrofluidic platforms offer prominent advantages for the early detection of cancer and monitoring the patient response to therapy. Numerous microfluidic platforms have been developed for capturing and quantifying the tumor cells integrating several readout methods. Earlier, we have developed a microfluidic platform (MRD Biochip) to capture and quantify leukemia cells. This is the first study which employs a deep learning-based segmentation to the MRD Biochip images consisting of leukemic cells, immunomagnetic beads and micropads. Implementing deep learning algorithms has two main contributions; firstly, the quantification performance of the readout method is improved for the unbalanced dataset. Secondly, unlike the previous classical computer vision -based method, it does not require any manual tuning of the parameters which resulted in a more generalized model against variations of objects in the image in terms of size, color, and noise. As a result of these benefits, the proposed system is promising for providing real time analysis for microfluidic systems. Moreover, we compare different deep learning based semantic segmentation algorithms on the image dataset which are acquired from the real patient samples using a bright-field microscopy. Without cell staining, hyper-parameter optimized, and modified U-Net semantic segmentation algorithm yields 98.7% global accuracy, 86.1% mean IoU, 92.2% mean precision, 92.2% mean recall and 92.2% mean F-1 score measure on the patient dataset. After segmentation, quantification result yields 89% average precision, 97% average recall on test images. By applying the deep learning algorithms, we are able to improve our previous results that employed conventional computer vision methods.