Biology subjects

Unlu, M. B.

Publications and source records attributed to Unlu, M. B..

7 recordsLinked to original sources

bioRxiv2025

3D-Printed Magnetic Levitation Device with Deep Learning-Assisted Particle Tracking and Analysis for High-Throughput Sorting

Intrinsic density-based particle separation is fundamental to biomedical research and materials science. Magnetic levitation offers an accessible and label-free approach; however, current platforms are limited by throughput, complex fabrication requirements, and manual analysis methods. Here, we demonstrate a high-throughput magnetic levitation-based microfluidic device fabricated using commercial 3D printing, integrated with dual automated analysis systems. The device features optimized magnet configurations and wide channel design (1 mm x 1 mm) that enables gentle separation (<1 PSI) at throughputs of 66 mL/hr-- a ten-fold improvement over existing levitation platforms. We developed two complementary analysis tools: "Phase" for static levitation height measurements, and a deep learning pipeline combining CNN-based particle classification (>95% accuracy) with SORT (Simple Online and Realtime Tracking) algorithm for dynamic analysis. The automated system showed excellent correlation with manual counting (Pearson coefficients: 0.91-0.99, p<0.001). Through systematic optimization of magnet spacing and paramagnetic medium concentration (150 mM Gd), the platform achieved robust continuous-flow sorting while maintaining exceptional purity (>90%) and resolving density differences as small as 0.03 g/mL. This work establishes a versatile platform for particle sorting, enabling sophisticated analysis without specialized facilities or extensive operator training, with broad applications in biomedical research and diagnostics.

bioengineering↗

bioRxiv2023

Label-free differentiation of functional zones in mature mouse placenta using micro-Raman imaging

In histopathology, it is highly crucial to have chemical and structural information about tissues. Additionally, the segmentation of zones within a tissue plays an important role in investigating the functions of these regions for better diagnosis and treatment. The placenta plays an important role in embryonic and fetal development and in the diagnosis of some diseases associated with its dysfunction. This study provides a label-free approach to obtain the images of mature mouse placenta together with the chemical differences between the tissue compartments using Raman spectroscopy. To generate the Raman images, spectra of placental tissue were collected using a custom-built optical setup. The pre-processed spectra were analyzed using statistical and machine learning methods to acquire the Raman maps. We found that the placental regions called decidua and the labyrinth zone are biochemically distinct from the junctional zone. Comparison and evaluation of the Raman maps with histological images of the placental tissue were performed by a histologist and they are found to be in agreement. The results of this study show that Raman spectroscopy offers the possibility of label-free monitoring of the placental tissue from mature mice while revealing important structural information about the zones at the same time.

biophysics↗

bioRxiv2022

Comparison of the human's and camel's erythrocyte deformability by optical tweezers and Raman spectroscopy

The evolution of red blood cells (RBCs) or erythrocytes has led to variation in morphological and mechanical properties of these cells among many species today. Camelids have the most different RBC characteristics among the vertebrates. As a result of adaptation to the desert environment, camelid RBCs can expand twice as much of their total volume in the case of rapid hydration yet are almost undeformable under mechanical stress. In this work, the difference between cell features of the human and the camelid species was explored both mechanically and chemically with optical tweezers and Raman spectroscopy, respectively. We measured the deformability of camel RBCs relative to the human RBCs at the single-cell level using optical tweezers. We found that the deformability index (DI) of the camel and the human RBCs were 0.024{+/-}0.0188 and 0.215{+/-}0.061, respectively. Raman spectral analysis of the whole blood of these two species indicated that some of the Raman peaks observed on the camels blood spectrum were absent on the human bloods spectrum, which further points to the difference in chemical contents of these two species.

biophysics↗

bioRxiv2022

Quantification of salt stress in wheat leaves by Raman spectroscopy and machine learning

The salinity level of the growing medium has diverse effects on the development of plants, including both physical and biochemical changes. To determine the salt stress level of a plant endures, one can measure these structural and chemical changes. Raman spectroscopy and biochemical analysis are some of the most common techniques in the literature. Here, we present a combination of machine learning and Raman spectroscopy with which we can both find out the biochemical change that occurs while the medium salt concentration changes and predict the level of salt stress a wheat sample experiences accurately using our trained regression models. In addition, by applying different machine learning algorithms, we compare the level of success for different algorithms and determine the best method to use in this application. Production units can take actions based on the quantitative information they get from the trained machine learning models related to salt stress, which can potentially increase efficiency and avoid the loss of crops.

biophysics↗

bioRxiv2022

Raman Signal Denoising Using Fully Convolutional Encoder Decoder Network

Raman spectroscopy is a vibrational method that gives molecular information rapidly and non-invasively. Despite its advantages, the weak intensity of Raman spectroscopy leads to low-quality signals, particularly with tissue samples. The requirement of high exposure times makes Raman a time-consuming process and diminishes its non-invasive property while studying living tissues. Novel denoising techniques using convolutional neural networks (CNN) have achieved remarkable results in image processing. Here, we propose a similar approach for noise reduction for the Raman spectra acquired with 10x lower exposure times. In this work, we developed fully convolutional encoder-decoder architecture (FCED) and trained them with noisy Raman signals. The results demonstrate that our model is superior (p-value < 0.0001) to the conventional denoising techniques such as the Savitzky-Golay filter and wavelet denoising. Improvement in the signal-to-noise ratio values ranges from 20% to 80%, depending on the initial signal-to-noise ratio. Thus, we proved that tissue analysis could be done in a shorter time without any need for instrumental enhancement.

biochemistry↗

bioRxiv2021

Simulations on the efficacy of radiotherapy with different time schemes of antiangiogenic therapy

The combination of radiotherapy and antiangiogenic agents has been suggested to be potent in tumor growth control compared to the application of antiangiogenic therapy or radiotherapy alone. Since radiotherapy is highly dependent on the oxygen level of the tumor area, antiangiogenic agents are utilized for the reoxygenation of tumor vasculature. We present a mathematical framework to investigate the efficacy of radiotherapy combined with antiangiogenic treatment. The framework consists of tumor cells, vasculature, and oxygenation levels evolving with time to mimic a tumor microenvironment. Non-linear partial differential equations (PDEs) are employed to simulate each component of the framework. Different treatment schemes are investigated to see the changes in tumor growth and oxygenation. To test combination schedules, radiation monotherapy, neoadjuvant, adjuvant, and concurrent cases are simulated. The efficiency of each therapy scheme on tumor growth control, the changes in tumor cell density, and oxygen levels shared by tumor cells are represented. The simulation results indicate that the application of radiotherapy after antiangiogenic treatment is more efficient in tumor growth control compared to other therapy schemes. The present study gives an insight into the possible interaction and timing of the combination of radiotherapy and antiangiogenic drug treatment.

cancer biology↗

bioRxiv2020

Chemotherapy response prediction with diffuser elapser network

In solid tumors, elevated fluid pressure and inadequate blood perfusion resulting from unbalanced angiogenesis are the prominent reasons for the ineffective drug delivery inside tumors. To normalize the heterogeneous and tortuous tumor vessel structure, antiangiogenic treatment is an effective approach. Additionally, the combined therapy of antiangiogenic agents and chemotherapy drugs has shown promising effects on enhanced drug delivery. However, the need to find the appropriate scheduling and dosages of the combination therapy is one of the main problems in anticancer therapy. Our study aims to generate a realistic response to the treatment schedule, making it possible for future works to use these patient-specific responses to decide on the optimal starting time and dosages of cytotoxic drug treatment. Our dataset is based on our previous in-silico model with a framework for the tumor microenvironment, consisting of a tumor layer, vasculature network, interstitial fluid pressure, and drug diffusion maps. In this regard, the chemotherapy response prediction problem is discussed in the study, putting forth a proof-of-concept for deep learning models to capture the tumor growth and drug response behaviors simultaneously. The proposed model utilizes multiple convolutional neural network submodels to predict future tumor microenvironment maps considering the effects of ongoing treatment. Since the model has the task of predicting future tumor microenvironment maps, we use two image quality evaluation metrics, which are structural similarity and peak signal-to-noise ratio, to evaluate model performance. We track tumor cell density values of ground truth and predicted tumor microenvironments. The model predicts tumor microenvironment maps seven days ahead with the average structural similarity score of 0.973 and the average peak signal ratio of 35.41 in the test set. It also predicts tumor cell density at the end day of 7 with the mean absolute percentage error of 2.292 {+/-} 1.820. Author summaryThe disorganized structure and leakiness of tumor vessels induce the inadequate blood supply and high fluid pressure within tumors. These features of the tumor microenvironment, identified as delivery barriers, lead to an insufficient amount of drugs to reach the interior parts of tumors. It is observed that the use of anti-vascular drugs makes the structure and function of the tumor vascular system more normal. Moreover, the combination of these drugs with cytotoxic agents provides favorable results with increased treatment response. But, it is also important to adjust the treatment schedule properly. In this regard, we build a deep learning model, designed to examine the tumor response with the ongoing treatment schedule. Our study suggests that deep learning models can be used to predict tumor growth and drug response in the scheduling of cytotoxic drugs.

systems biology↗