Magnetically Levitated Microrobotic Mixer

Microfluidic systems, when combined with microrobots, offer enhanced precision in chemical synthesis by precisely controlling reaction conditions. These systems, when integrated with analytical tools, allow for real-time monitoring and are cost-efficient due to their minimal volume requirements, thereby reducing risks associated with hazardous chemicals. In our study, we have investigated the mixing efficiency of Thymolphthalein indicator with NaOH solution in a magnetically levitated microrobotic mixer. A PMMA microfluidic chip was used to transfer fluid containing two different solutions and achieve fast and efficient mixing. By adjusting five different flow rates and altering the rotational speeds of the microrobots, the mixing efficiency was observed. The studies were carried out under the laminar regime, with incompressible Newtonian flow rates and varying actuator speeds. The measurement of mixing efficiency was accomplished through the calculation of changes in pixel intensity observed in microscopic images acquired throughout the mixing process. The presence of the microrobots resulted in the best efficiency at 80.37% at 500 rpm and 7 mL/min flow rate. Their potential in advanced reactions, such as nanoparticle synthesis and encapsulation, suggests promising avenues for improving product yields.Comment: 5 pages, 2 figures, 1 tabl

Comparative Analysis of Deep Learning Architectures for Breast Cancer Diagnosis Using the BreaKHis Dataset

Cancer is an extremely difficult and dangerous health problem because it manifests in so many different ways and affects so many different organs and tissues. The primary goal of this research was to evaluate deep learning models' ability to correctly identify breast cancer cases using the BreakHis dataset. The BreakHis dataset covers a wide range of breast cancer subtypes through its huge collection of histopathological pictures. In this study, we use and compare the performance of five well-known deep learning models for cancer classification: VGG, ResNet, Xception, Inception, and InceptionResNet. The results placed the Xception model at the top, with an F1 score of 0.9 and an accuracy of 89%. At the same time, the Inception and InceptionResNet models both hit accuracy of 87% . However, the F1 score for the Inception model was 87, while that for the InceptionResNet model was 86. These results demonstrate the importance of deep learning methods in making correct breast cancer diagnoses. This highlights the potential to provide improved diagnostic services to patients. The findings of this study not only improve current methods of cancer diagnosis, but also make significant contributions to the creation of new and improved cancer treatment strategies. In a nutshell, the results of this study represent a major advancement in the direction of achieving these vital healthcare goals.Comment: 7 pages, 1 figure, 2 table

Recent Advances in Health Biotechnology During Pandemic

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in 2019, cut the epoch that will make profound fluctuates in the history of the world in social, economic, and scientific fields. Urgent needs in public health have brought with them innovative approaches, including diagnosis, prevention, and treatment. To exceed the coronavirus disease 2019 (COVID-19) pandemic, various scientific authorities in the world have procreated advances in real time polymerase chain reaction (RT-PCR) based diagnostic tests, rapid diagnostic kits, the development of vaccines for immunization, and the purposing pharmaceuticals for treatment. Diagnosis, treatment, and immunization approaches put for- ward by scientific communities are cross-fed from the accrued knowledge of multidisciplinary sciences in health biotechnology. So much so that the pandemic, urgently prioritized in the world, is not only viral infections but also has been the pulsion in the development of novel approaches in many fields such as diagnosis, treatment, translational medicine, virology, mi- crobiology, immunology, functional nano- and bio-materials, bioinformatics, molecular biol- ogy, genetics, tissue engineering, biomedical devices, and artificial intelligence technologies. In this review, the effects of the COVID-19 pandemic on the development of various scientific areas of health biotechnology are discussed

Determination of Cell Stiffness Using Polymer Microbeads as Reference

Knowing the mechanical properties of cells is very important in cell detection, analysis of cell activities, diagnosis and drug treatment. The determination of cell stiffness, which used effectively in cell analysis, is carried out with different measurement techniques. In this study, the stiffness of cells is determined by comparison to the displacement of polystyrene microparticles induced by vibration generated by piezoelectric transducers. The difference of stiffness of the cells and polystyrene microparticles is measured using a digital holographic imaging technique

Classification of Cells Based on Their Drifting Velocity under Acoustic Radiation Pressure

© 2020 IEEE.This study reports a novel cell classification method based on the observation of trajectories that cells inside a fluidic chamber follow under an externally applied acoustic field. Proposed method is significant both as a cell classification method and as a method for characterising the motion of various cell lines under different surface acoustic wave patterns. The difference is mainly due to the characteristic differences of cells such as mass, surface adhesiveness and cellular volume. We discuss the mechanisms that affect the interaction between cells and surface waves. Classification performance is tested using using support vector machine (SVM), max-likelihood and multilayer perceptron (MLP) methods and accuracy, sensitivity and specificity values are reported for each. The results indicate that the method can be used as a powerful classifier particularly for cells that are hard to distinguish visually. It is observed that for a given frequency, the motion characteristics of different cell lines differ due to the difference between dominant adhesion mechanism for that particular cell line. This observation can be utilized for the development of a frequency based cell manipulation method that is able to target specific cells using their characteristic frequencies. We discuss the potential of the proposed acoustic stimulation method as a cell manipulation technique particularly for uncoupling the motion of different cell lines

Manipulation of Cell Cultures by Means of Holographic Visual Feedback

© 2020 IEEE.In this study we present a novel untethered micro tool that is able to accomplish minimally invasive micro manipulation tasks in 2D and 3D cell cultures. We demonstrate the proposed system for targeted drug delivery, cell translation and mechanical cell stimulation applications. Due to the ability of the proposed system to control the motion of the micro tool in 6 DOF and obtain depth information through holographic visual feedback, we show that the microtool can be manipulated in a way such that the sample cell culture will go negligible mechanical stress. We show that the proposed system can be used for a variety of micro manipulation tasks without effecting the sample in question

Interferometric Investigation of Cell Stiffness and Morphology on Oxidative Stress- Induced Human Umbilical Vein Endothelial Cells (HUVEC)

Cell stiffness that can be measured accordingly elasticity modulus is an important biomechanical feature that plays a one-to-one role on the basic features of the cell, such as migration and proliferation, and this feature is significantly affected by the characteristic of the cytoskeleton. Reactive Oxygen Species (ROS) are side-products formed as a result of the cell's general metabolic activities. Cells have a very effective antioxidant defense to deactivate the toxic effect of ROS however, oxidative stress at abnormal levels significantly damages cellular balance. Many conditions such as inflammation, neurodegenerative and cardiovascular diseases and aging are associated with oxidative stress. Besides, oxidative stress is one of the parameters that affect the biomechanical behavior of the cell, but the mechanism of this effect still remains a mystery. In this study, oxidative stress was mimicked on Human Umbilical Vein Endothelial (HUVEC) cells by using H2O2 and the effect of this situation on cell stiffness and morphological structure was investigated interferometrically for the first time. The changes that occurred in the cell stiffness were determined by calculating the elasticity modules of the cells. Cells were exposed to H2O2 for 24 hours at 0.5 mM and 1 mM concentrations, and as a result, cell stiffness was shown to decrease due to increased H2O2 concentration

Optimization of Different Surface Modifications for Binding of Tumor Cells in a Microfluidic Systems

Objectives:Microfluidic technology is a fast-growing area and provide high-efficient MEMS (Micro-Electro-Mechanical-Systems) sensor integration platform that helps to advance healthcare systems. Due to proper the chemical and mechanical properties of polymers, PDMS (Polydimethylsiloxane) (6) and PMMA (Poly-methyl-methacrylate), they became on the best candidate for health care studies in microfluidic studies (7). Besides, they perform great optical properties for observation of living cell experiments. To increase their performance, surface interactions works with cells, modification techniques are widely used in microfluidic chips. In this paper, our primary purpose is to modify such polymers and glass with matrigel, PDA and APTES so as to increase cell-surface interaction