Electrical characteristics of living cells have been proven to reveal important details about their internal structure, charge distribution and composition changes in the cell membrane, as well as the extracellular context. An impedance flow cytometry is a common approach to determine the electrical properties of a cell, having the advantage of label-free and high throughput. However, the current techniques are complex and costly for the fabrication process. For that reason, we introduce an integrated dual microneedle-microchannel for single-cell detection and electrical properties extraction. The dual microneedles utilized a commercially available tungsten needle coated with parylene. When a single cell flows through the parallel-facing electrode configuration of the dual microneedle, the electrical impedance at multiple frequencies is measured. The impedance measurement demonstrated the differential of normal red blood cells (RBCs) with three different sizes of microbeads at low and high frequencies, 100 kHz and 2 MHz, respectively. An electrical equivalent circuit model (ECM) was used to determine the unique membrane capacitance of individual cells. The proposed technique demonstrated that the specific membrane capacitance of an RBC is 9.42 mF/m-2, with the regression coefficients, ρ at 0.9895. As a result, this device may potentially be used in developing countries for low-cost single-cell screening and detection.
This paper introduces a novel technique to evaluate comfort properties of zinc oxide nanoparticles (ZnO NPs) coated woven fabrics. The proposed technique combines artificial neural network (ANN) and golden eagle optimizer (GEO) to ameliorate the training process of ANN. Neural networks are state-of-the-art machine learning models used for optimal state prediction of complex problems. Recent studies showed that the use of metaheuristic algorithms improve the prediction accuracy of ANN. GEO is the most advanced methaheurstic algorithm inspired by golden eagles and their intelligence for hunting by tuning their speed according to spiral trajectory. From application point of view, this study is a very first attempt where GEO is applied along with ANN to improve the training process of ANN for any textiles and composites application. Furthermore, the proposed algorithm ANN with GEO (ANN-GEO) was applied to map out the complex input-output conditions for optimal results. Coated amount of ZnO NPs, fabric mass and fabric thickness were selected as input variables and comfort properties were evaluated as output results. The obtained results reveal that ANN-GEO model provides high performance accuracy than standard ANN model, ANN models trained with latest metaheuristic algorithms including particle swarm optimizer and crow search optimizer, and conventional multiple linear regression.
- MeSH
- Accipitridae * MeSH
- algoritmy MeSH
- neuronové sítě (počítačové) MeSH
- oxid zinečnatý * MeSH
- propylaminy MeSH
- sulfidy MeSH
- textilie MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
This paper presents a new hybrid approach for the prediction of functional properties i.e., self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor (UPF), of titanium dioxide nanoparticles (TiO2 NPs) coated cotton fabric. The proposed approach is based on feedforward artificial neural network (ANN) model called a multilayer perceptron (MLP), trained by an optimized algorithm known as crow search algorithm (CSA). ANN is an effective and widely used approach for the prediction of extremely complex problems. Various studies have been proposed to improve the weight training of ANN using metaheuristic algorithms. CSA is a latest and an effective metaheuristic method relies on the intelligent behavior of crows. CSA has been never proposed to improve the weight training of ANN. Therefore, CSA is adopted to optimize the initial weights and thresholds of the ANN model, in order to improve the training accuracy and prediction performance of functional properties of TiO2 NPs coated cotton composites. Furthermore, our proposed algorithm i.e., multilayer perceptron with crow search algorithm (MLP-CSA) was applied to map out the complex input-output conditions to predict the optimal results. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and UPF were evaluated as output results. A sensitivity analysis was carried out to assess the performance of CSA in prediction process. MLP-CSA provided excellent result that were statistically significant and highly accurate as compared to standard MLP model and other metaheuristic algorithms used in the training of ANN reported in the literature.
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Introduction: Conventional biopsy, based on extraction from a tumor of a solid tissue specimen requiring needles, endoscopic devices, excision or surgery, is at risk of infection, internal bleeding or prolonged recovery. A non-invasive liquid biopsy is one of the greatest axiomatic consequences of the identification of circulating tumor DNA (ctDNA) as a replaceable surgical tumor bioQpsy technique. Most of the literature studies thus far presented ctDNA detection at almost final stage III or IV of cancer, where the treatment option or cancer management is nearly impossible for diagnosis. Objective: Hence, this paper aims to present a simulation study of extraction and separation of ctDNA from the blood plasma of cancer patients of stage I and II by superparamagnetic (SPM) bead particles in a microfluidic platform for early and effective cancer detection. Method: The extraction of ctDNA is based on microfiltration of particle size to filter some impurities and thrombocytes plasma, while the separation of ctDNA is based on magnetic manipulation to high yield that can be used for the upstream process. Result: Based on the simulation results, an average of 5.7 ng of ctDNA was separated efficiently for every 10 μL blood plasma input and this can be used for early analysis of cancer management. The particle tracing module from COMSOL Multiphysics traced ctDNA with 65.57% of sensitivity and 95.38% of specificity. Conclusion: The findings demonstrate the ease of use and versatility of a microfluidics platform and SPM bead particles in clinical research related to the preparation of biological samples. As a sample preparation stage for early analysis and cancer diagnosis, the extraction and separation of ctDNA is most important, so precision medicine can be administered.
- MeSH
- cirkulující nádorová DNA * MeSH
- lidé MeSH
- magnetické nanočástice oxidů železa MeSH
- mikrofluidika MeSH
- nádory * diagnóza MeSH
- tekutá biopsie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
