Biology subjects

Parlatan, U.

Publications and source records attributed to Parlatan, U..

4 recordsLinked to original sources

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↗