Circulating tumor cell isolation, culture, and downstream molecular analysis.

According to the World Health Organization (WHO), hundreds of millions of people of all ages and in all countries suffer from chronic respiratory diseases, with particular negative consequences such as poor health-related quality of life, impaired work productivity, and limitations in the activities of daily living. Chronic obstructive pulmonary disease, asthma, occupational lung diseases (such as silicosis), cystic fibrosis, and pulmonary arterial hypertension are the most common of these diseases, and none of them are curable with current therapies. The advent of nanotechnology holds great therapeutic promise for respiratory conditions, because non-viral vectors are able to overcome the mucus and lung remodeling barriers, increasing pharmacologic and therapeutic potency. It has been demonstrated that the extent of pulmonary nanoparticle uptake depends not only on the physical and chemical features of nanoparticles themselves, but also on the health status of the organism; thus, the huge diversity in nanotechnology could revolutionize medicine, but safety assessment is a challenging task. Within this context, the present review discusses some of the major new perspectives in nanotherapeutics for lung disease and highlights some of the most recent studies in the field.

/pdf/new-perspectives-in-nanotherapeutics-for-chronic-respiratory-t7oawkk18z.pdf

New perspectives in nanotherapeutics for chronic respiratory diseases.

In this work, a novel approach is presented to design a metamaterial-based sensitive sensor for biomedical applications. The metamaterial-based sensor objective is to differentiate between different types of cancer cell lines (biological cells) based on their electrical properties. The sensor is designed by incorporating a single circular split ring resonator (SRR) and a Hilbert fractal curve. The SRR is designed based on its capacitive and inductive resonance properties, thereby making the SRR a favorable candidate for sensing applications. Moreover, the Hilbert fractal curve is introduced as a defected ground structure to move the resonance frequency of the SSR to lower band, and to increase sensor sensitivity. Different Hilbert curve orders are investigated. The fourth-order Hilbert curve is used in the final design as it showed the optimal performance among other orders. To verify the sensor functionality and selectivity, the proposed sensor is tested on three types of breast cancer cell lines, these are MCF7, MCF-7, and HS578T. Measurement results of the fabricated sensor compared well with simulated results obtained using high-frequency structure simulator (HFSS18).

/pdf/metamaterial-based-sensor-design-using-split-ring-resonator-576npgn3er.pdf

Metamaterial-based sensor design using split ring resonator and Hilbert fractal for biomedical application

Cancer treatment, care, and outcomes are much more effective if started at early stages of the disease. The presence of malignant cancer cells in human samples such as blood or biopsied tissue can be used to reduce overtreatment and underdiagnosis as well as for prognosis monitoring. Reliable quantification of metastatic tumor cells (MTCs) and non-metastatic tumor cells (NMTCs) from human samples can help in cancer staging as well. We report a simple, fast, and reliable approach to identify and quantify metastatic and non-metastatic cancer cells from whole biological samples in a point-of-care manner. The metastatic (MDA MB-231) and non-metastatic (MCF7) breast cancer cells were pushed through a solid-state micropore made in a 200 nm thin SiO2 membrane while measuring current across the micropore. The cells generated very distinctive translocation profiles. The translocation differences stemmed from their peculiar mechanophysical properties. The detection efficiency of the device for each type of tumor cells was ∼75%. MTCs showed faster translocation (36%) and 34% less pore blockage than NMTCs. The micropore approach is simple, exact, and quantitative for metastatic cell detection in a lab-on-a chip setting, without the need for any preprocessing of the sample.

Differentiating Metastatic and Non-metastatic Tumor Cells from Their Translocation Profile through Solid-State Micropores

Characterization of circulating tumor cells with quantitative phase imaging could provide a unique approach for identifying early biomarkers for cancer. However, current QPI cytometers have limitations which can prevent their use on heterogeneous tumor cell populations. Here we present our holographic cytometry approach applied to identifying carcinogen-exposed cells, namely, Cadmium-treated breast epithelial cells, in contrast to normal and cancerous cells. Based on single-cell phase images collected in high throughput by the holographic cytometer, we analyze the biophysical properties of different cell lines to discriminate by phenotype. Both phenotypic profiling and deep learning show comparable high accuracy levels and reveal key defining biophysical cellular features.

Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry

Tumor cells are malignant derivatives of normal cells. There are characteristic differences in the mechanophysical properties of normal and tumor cells, and these differences stem from the changes that occur in the cell cytoskeleton during cancer progression. There is a need for viable whole blood processing techniques for rapid and reliable tumor cell detection that do not require tagging. Micropore biosensors have previously been used to differentiate tumor cells from normal cells and we have used a micropore-based electromechanical transducer to differentiate one type of tumor cells from the other types. This device generated electrical signals that were characteristic of the cell properties. Three non-small cell lung cancer (NSCLC) cell lines, NCl-H1155, A549 and NCI-H460, were successfully differentiated. NCI-H1155, due to their comparatively smaller size, were found to be the quickest in translocating through the micropore. Their translocation through a 15 μm micropore caused electrical pulses with an average translocation time of 101 ± 9.4 μs and an average peak amplitude of 3.71 ± 0.42 μA, whereas translocation of A549 and NCI-H460 caused pulses with average translocation times of 126 ± 17.9 μs and 148 ± 13.7 μs and average peak amplitudes of 4.58 ± 0.61 μA and 5.27 ± 0.66 μA, respectively. This transformation of the differences in cell properties into differences in the electrical profiles (i.e. the differences in peak amplitudes and translocation times) with this electromechanical transducer is a quantitative way to differentiate these lung cancer cells. The solid-state micropore device processed whole biological samples without any pre-processing requirements and is thus ideal for point-of-care applications.

https://iopscience.iop.org/article/10.1088/0957-4484/27/19/195101/pdf

Electromechanical transducer for rapid detection, discrimination and quantification of lung cancer cells

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Bailey Sayles

Author Tools

Chat about Author

Papers

Differentiating Metastatic and Non-metastatic Tumor Cells from Their Translocation Profile through Solid-State Micropores

Electromechanical transducer for rapid detection, discrimination and quantification of lung cancer cells