Browsing by Author "Avci, M.B."

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Automated Smartphone Based Cell Analysis Platform
(Springer Nature, 2025) Avci, M.B.; Kurul, F.; Turkan, M.; Çetin, A.E.
Cell analysis technologies play a critical role in biomedical research, enabling precise evaluation of essential parameters such as cell viability, density, and confluency. In this article, we introduce Quantella, a smartphone-based platform designed to perform comprehensive cell analysis encompassing these key metrics. Addressing limitations of conventional systems, such as high cost, hardware complexity, and limited adaptability, Quantella integrates low-cost optics, a rinsable flow cell, bluetooth-enabled hardware control, and a cloud-connected mobile application. Its adaptive image-processing pipeline employs multi-exposure fusion, thresholding, and morphological filtering for accurate, morphology-independent segmentation without requiring deep learning or user-defined parameters. System validation studies across diverse cell types showed deviations under 5% from flow cytometry. With the capacity to analyze over 10,000 cells per test, Quantella delivers high-throughput, reproducible results. Its accessible, scalable design makes it a promising tool for biomedical research, diagnostics, and education, particularly in resource-limited settings. © The Author(s) 2025.
Citation - Scopus: 3
Decoding of Palmar Grasp and Hand Open Tasks From Low-Frequency Eeg From People With Spinal Cord Injury Using Machine Learning Algorithms
(Institute of Electrical and Electronics Engineers Inc., 2023) Avci, M.B.; Kucukselbes, H.; Sayılgan, Ebru
Spinal cord injury (SCI) is a chronic disorder that is detrimental to the spinal cord and causes the loss of neuronal function, particularly sensorimotor functions. Brain-computer interface (BCI)-controlled rehabilitation systems have been proposed as a promising treatment component for people with SCI whose treatment is based on a long and tiring rehabilitation process. With respect to this, we presented a novel approach using an electroencephalography (EEG) based BCI rehabilitation system to help SCI patients. For this purpose, low-frequency EEG signals acquired from nine people with SCI were analyzed by considering attempted arm and hand movements. We used both time-domain features based on statistical changes (e.g., mean, variance, skewness, and kurtosis, etc.) and frequency-domain features based on Fast Fourier Transform in the EEG signal to decode the two intentions: hand open and palmar grasp. For binary classification, seven machine learning models (Fine KNearest Neighbour, Ensemble, Logistic Regression Kernel, Support Vector Machines Kernel, Fine Tree, Quadratic Discriminant, and Wide Neural Network) were used to classify the features. Accuracy, Precision, Recall, and F1 score criteria were used to evaluate machine learning models. In conclusion, we achieved successful results like an Accuracy of %91.70, Precision of %93, Recall of %90, and F1 Score of %91 by using frequency domain features combined with the Fine K-Nearest Neighbour model, with a prediction speed of 8848.84 obs/sec, and a training time of only 10.59 seconds. These results indicate that our methodology can decode executed hand open and palmar grasp motions from people with SCI. For this reason, it could be a critical and crucial contribution to the literature regarding the application of BCI. © 2023 IEEE.
Citation - WoS: 2
Citation - Scopus: 2
Handheld Optofluidic Platform Towards Binding Dynamics Applications in Field-Settings
(Elsevier B.V., 2023) Yaman, S.; Avci, M.B.; Kurul, F; Topkaya, S.N.; Cetin, A.E.
We have introduced a lensfree optofluidic platform that incorporates subwavelength nanohole arrays, a compact microfluidics system, and on-chip computational imaging to enable label-free identification of biomolecular interactions. Our platform weighs only 260 g and has dimensions of 16 cm × 10 cm × 11 cm. It utilizes a CMOS imager to capture plasmonic diffraction field images, offering a wide field-of-view of up to 11.5 mm² for refractive index sensing. To illuminate the plasmonic chip, we employ an LED source positioned close to the transmission resonance of the nanohole arrays. This LED source creates diffraction patterns on the imager. The platform ensures the targeted delivery of analytes to the ligand-coated sensing surface using microfluidics. By analyzing real-time variations within the diffraction field images, we could reveal the time-dependent binding dynamics of biomolecules. Our platform has demonstrated an experimentally obtained limit of detection (LOD) as low as 5 ng/mL for protein IgG. Furthermore, based on the real-time diffraction field images, we successfully determine the association and disassociation constants, which account for the binding and detachment between protein A/G and IgG. We have also developed a software that allows full control of the hardware settings of the portable platform, including the camera and pump system. This software also incorporates an image-processing algorithm to calculate the binding parameters for the analytes of interest. Providing high-quality sensing capabilities in a cost-effective infrastructure, we believe that our optofluidic biosensor platform offers significant advantages for surface plasmon resonance (SPR) applications for field-settings. © 2023 Elsevier B.V.
Portable Optofluidic Device for Dynamic Binding Analysis in Field-Settings
(SPIE, 2024) Kurul, F.; Avci, M.B.; Yaman, S.; Topkaya, S.N.; Cetin, A.E.
Compact and portable biosensing technologies play an important role in replacing traditional counterparts that require costly and heavy equipment, as well as complex infrastructure. The integration of these easy-to-use and cheap devices allows for the conducting of biosensing analyses in resource-limited settings. The study produced a portable optofluidic platform that is lightweight (260 g) and compact (16 cm×10 cm×11 cm). It combines subwavelength nanohole arrays, microfluidics technology, and on-chip computational imaging. It records plasmonic diffraction field images with a CMOS imager and an LED light, allowing for a large field of view for refractive index measurement. This LED source generates diffraction patterns on the imager. The microfluidic pump confirms accurate analyte delivery, allowing real-time analysis of diffraction field images to reveal time-dependent binding kinetics of biomolecules. It identifies biomolecular interactions without labelling, allowing for the detection and quantification of biomolecules. Our platform has an outstanding limit-of-detection (LOD) of 5ng/mL for label-free detection of protein IgG. We effectively determined the association and dissociation constants for protein A/G and IgG binding using real-time diffraction field images. The optofluidic biosensor platform is ideal for surface plasmon resonance (SPR) in field applications. It can monitor interactions in real-time, making it useful for studying the way various biological and chemical compounds bind in many areas. © 2024 SPIE.