PDMS Nano-Modified Scaffolds for Improvement of Stem Cells Proliferation and Differentiation in Microfluidic Platform

Hashemzadeh, Hadi; Allahverdi, Abdollah; Sedghi, Mosslim; Vaezi, Zahra; Moghadam, Tahereh Tohidi; Rothbauer, Mario; Fischer, Michael B.; Ertl, Peter; Naderi-Manesh, Hossein

doi:10.3390/nano10040668

Cited by 41 publications

(24 citation statements)

References 37 publications

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“…[1][2][3] Recently, there is accumulating evidence that nanomaterials can modulate fate of cells and interactions between nanomaterials and biological cells have attracted considerable attention in facilitating stem cell therapy and bone tissue engineering. [4][5][6] Nanomaterials can flexibly and controllably manipulate biological process of cells to differentiate toward desired lineages. Particularly, nanomaterials with various composition, size, shape or surface chemistry have been widely used as promising osteogenic agents in accelerating osteogenic differentiation of biological cells and bone tissue regeneration.…”

Section: Introductionmentioning

confidence: 99%

Citrate-Stabilized Gold Nanorods-Directed Osteogenic Differentiation of Multiple Cells

Zhang¹,

Li²,

Liao³

et al. 2021

IJN

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Gold nanorods (AuNRs) show great potential for versatile biomedical applications, such as stem cell therapy and bone tissue engineering. However, as an indispensable shape-directing agent for the growth of AuNRs, cetyltrimethylammonium bromide (CTAB) is not optimal for biological studies because it forms a cytotoxic bilayer on the AuNR surface, which interferes with the interactions with biological cells. Methods: Citrate-stabilized AuNRs with various aspect-ratios (Cit-NRI, Cit-NRII, and Cit-NRIII) were prepared by the combination of end-selective etching and poly(sodium 4-styrenesulfonate)-assisted ligand exchange method. Their effects on osteogenic differentiation of the pre-osteoblastic cell line (MC3T3-E1), rat bone marrow mesenchymal stem cells (rBMSCs), and human periodontal ligament progenitor cells (PDLPs) have been investigated. Potential signaling pathway of citrate-stabilized AuNRs-induced osteogenic effects was also investigated. Results: The experimental results showed that citrate-stabilized AuNRs have superior biocompatibility and undergo aspect-ratio-dependent osteogenic differentiation via expression of osteogenic marker genes, alkaline phosphatase (ALP) activity and formation of mineralized nodule. Furthermore, Wnt/β-catenin signaling pathway might provide a potential explanation for the citrate-stabilized AuNRs-mediated osteogenic differentiation. Conclusion:These findings revealed that citrate-stabilized AuNRs with great biocompatibility could regulate the osteogenic differentiation of multiple cell types through Wnt/βcatenin signaling pathway, which promote innovative AuNRs in the field of tissue engineering and other biomedical applications.

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Section: Introductionmentioning

confidence: 99%

Citrate-Stabilized Gold Nanorods-Directed Osteogenic Differentiation of Multiple Cells

Zhang¹,

Li²,

Liao³

et al. 2021

IJN

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“…The lung normal cell and non-small lung cancer cells (PC-9, SK-LU-1, H-1975, A-427, and A-549) were received from research Institute of Molecular Pathology (IMP), Technical University of Vienna (TU Wien), and Ludwig Boltzmann Institute for cancer research, Vienna, Austria. Based on our previous microfluidic cell-based assays works 4 , 41 the microfluidic device was used for culturing the cancer cells. Briefly, the microfluidic template was designed by AutoCAD 2016 software (Autodesk, San Rafael, CA, USA) and Polydimethylsiloxane (PDMS) sheet was cut using a CAM-1 GS-24 cutter (Roland DGA Corporation, Irvin, CA, USA).…”

Section: Methodsmentioning

confidence: 99%

“…Based on cell morphology, there are two types of lung cancer available: Small Cell Lung Cancer (SCLC) which is responsible for 15–20% of the cancer cases and occurs almost in heavy smokers. Non-Small Lung Cancer (NSCLC) is observed in 80–85% of lung cancers and mainly sub-classified in Adenocarcinoma (ADC), Squamous Cell Carcinoma (SCC), and Large Cell Carcinoma (LCC) 4 . Lung cancer has similar symptoms with other brunch disorders such as chest pain, coughing up blood, and etc.…”

Section: Introductionmentioning

confidence: 99%

A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications

Hashemzadeh

Shojaeilangari

Allahverdi

et al. 2021

Sci Rep

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Lung cancer is a leading cause of cancer death in both men and women worldwide. The high mortality rate in lung cancer is in part due to late-stage diagnostics as well as spread of cancer-cells to organs and tissues by metastasis. Automated lung cancer detection and its sub-types classification from cell’s images play a crucial role toward an early-stage cancer prognosis and more individualized therapy. The rapid development of machine learning techniques, especially deep learning algorithms, has attracted much interest in its application to medical image problems. In this study, to develop a reliable Computer-Aided Diagnosis (CAD) system for accurately distinguishing between cancer and healthy cells, we grew popular Non-Small Lung Cancer lines in a microfluidic chip followed by staining with Phalloidin and images were obtained by using an IX-81 inverted Olympus fluorescence microscope. We designed and tested a deep learning image analysis workflow for classification of lung cancer cell-line images into six classes, including five different cancer cell-lines (P-C9, SK-LU-1, H-1975, A-427, and A-549) and normal cell-line (16-HBE). Our results demonstrate that ResNet18, a residual learning convolutional neural network, is an efficient and promising method for lung cancer cell-lines categorization with a classification accuracy of 98.37% and F1-score of 97.29%. Our proposed workflow is also able to successfully distinguish normal versus cancerous cell-lines with a remarkable average accuracy of 99.77% and F1-score of 99.87%. The proposed CAD system completely eliminates the need for extensive user intervention, enabling the processing of large amounts of image data with robust and highly accurate results.

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“…This modification resulted in a change in surface nanotopography and promoted osteogenesis of hAMSCs on the PDMS microfluidic device. [ 95 ]…”

Section: Topographymentioning

confidence: 99%

The Effect of Physical Cues of Biomaterial Scaffolds on Stem Cell Behavior

Wang

Hashemi

Stratton

et al. 2020

Adv Healthcare Materials

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Stem cells have been sought as a promising cell source in the tissue engineering field due to their proliferative capacity as well as differentiation potential. Biomaterials have been utilized to facilitate the delivery of stem cells in order to improve their engraftment and long-term viability upon implantation. Biomaterials also have been developed as scaffolds to promote stem cell induced tissue regeneration. This review focuses on the latter where the biomaterial scaffold is designed to provide physical cues to stem cells in order to promote their behavior for tissue formation. Recent work that explores the effect of scaffold physical properties, topography, mechanical properties and electrical properties, is discussed. Although still being elucidated, the biological mechanisms, including cell shape, focal adhesion distribution, and nuclear shape, are presented. This review also discusses emerging areas and challenges in clinical translation.

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PDMS Nano-Modified Scaffolds for Improvement of Stem Cells Proliferation and Differentiation in Microfluidic Platform

Cited by 41 publications

References 37 publications

Citrate-Stabilized Gold Nanorods-Directed Osteogenic Differentiation of Multiple Cells

Citrate-Stabilized Gold Nanorods-Directed Osteogenic Differentiation of Multiple Cells

A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications

The Effect of Physical Cues of Biomaterial Scaffolds on Stem Cell Behavior

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