AI News, Artificial Intelligence Helps Find Cancer Cells

Artificial Intelligence Helps Find Cancer Cells

A microscope, invented by a professor at the University of California, uses artificial intelligence in order to locate cancer cells more efficiently than ever before.

Other techniques currently in practice do not label cells, but identify cancer cells based on physical characteristics that can oftentimes falsely identify regular cells as damaged.

The photonic time stretch microscope images cells without causing them harm and can identify over two dozen physical characteristics, including: biomass, granularity and size.

Couple that with a deep learning computer program that locates cancer cells correctly 95% of the time, and you have a much better chance of pinpointing cancer cells early on, allowing for quicker treatment to stop the spread.

With the help of optics that boost clarity within images while at the same time slowing them down just enough to detect and capture at a rate of 36 million images each second, the new microscope can track information not possible in the past.

Chen states the photonic time stretch approach allows researchers to identify rogue cells in a very short period of time even with low levels of illumination.

New Technologies for Human Cancer Imaging

There are only six imaging modalities available to clinicians who diagnose, stage, and treat human cancer: x-ray (plain film and computed tomography [CT]), ultrasound (US), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and optical imaging.

However, solid tumors typically display Gompertzian kinetics,1 with a first lag phase starting from the single cell stage, a log phase heralded by angiogenesis and an escape from diffusion-limited nutrition at approximately the 105 cell stage, and a second lag phase culminating in death of the patient at approximately 1012 cell (1 kg) stage (Fig 1).

The goal of cancer imaging should be to detect and/or image the smallest possible number of tumor cells, ideally before the angiogenic switch.2,3 The distinction between what we call detection and imaging is rather arbitrary and is based on the volume element (ie, voxel) size of the particular imaging modality being used.

Microscope uses artificial intelligence to find cancer cells more efficiently

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.

It combines two components that were invented at UCLA: a photonic time stretch microscope, which is capable of quickly imaging cells in blood samples, and a deep learning computer program that identifies cancer cells with over 95 percent accuracy.

Deep learning is a form of artificial intelligence that uses complex algorithms to extract meaning from data with the goal of achieving accurate decision making.

The new microscope overcomes those challenges usingspecially designed optics that boost the clarity of the images and simultaneously slow them enough to be detected and digitized at a rate of 36 million images per second.

physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.

Real-time Detection of Gene Expression in Cancer Cells Using Molecular Beacon Imaging: New Strategies for Cancer Research

We found that a combination of survivin and cyclin D1 molecular beacons detected the expression of both survivin and cyclin D1 genes simultaneously and generated fluorescent signals corresponding to either survivin (green) or cyclin D1 (red) mRNA in the cancer cells ( Fig.

Importantly, the fluorescent signal was very low for both molecular beacons in a normal immortalized human mammary epithelial cell line (MCF-10A), indicating that survivin and/or cyclin D1 molecular beacons can be used as fluorescence probes for the detection of breast cancer cells ( Fig.

The results of examination of fluorescence intensity and the level of survivin or cyclin D1 gene expression in tumor and normal cell lines further showed that the fluorescent signals detected by the molecular beacons correlated very well with the levels of survivin or cyclin D1 gene expression, both in mRNA and protein levels ( Fig.

Our previous study showed that survivin is expressed in 72% of breast cancer tissues, including 34 invasive breast ductal carcinoma and 2 lymph node metastases, using Western blot analysis of tissue lysates obtained from frozen tissue samples of the patients with cancer (19).

A high level of survivin gene expression was consistently detected in the breast cancer cells in nine of nine invasive ductal carcinoma tissues and one lymph node with metastastic lesions that were previously found positive for survivin protein by Western blot analysis.

We further examined frozen tissue sections from two DCIS tissues and found that breast cancer cells in those DCIS tissues were positive for survivin molecular beacon, suggesting that survivin gene expression is an early event in the tumorigenesis of breast cancer ( Fig.

We used three model systems to determine whether survivin molecular beacon was able to detect changes of survivin gene expression in viable cells, including EGF or docetaxel induced up-regulation and tumor suppressor gene p53–induced down-regulation of survivin gene expression (21, 22) .

The ability of molecular beacons to detect a decreased level of gene expression suggests that the fluorescent signals detected intracellularly after molecular beacon transfection are not from nonspecific degradation of the molecular beacons because the same amount of survivin and GAPDH molecular beacons were delivered into Adp53 and control vector–transduced cells.

Although detection of the level of gene expression by FACScan could accurately measure the fluorescence intensity in individual cells as well as in cell populations, the procedure for FACScan is time-consuming and does not easily detect changes of gene expression in real time in the same cell population.

To develop a high-throughput method for monitoring the changes of gene expression in real time in viable cells, we examined the feasibility of detecting levels of gene expression in cells cultured in 96-well plates using the molecular beacon-transfection approach.

For real-time detection of the level of gene expression in viable cells, it is important to determine how long the molecular beacon probes will stay in the cells and still be able to produce fluorescent signals that reflect the relative level of the gene expression.

One of the important issues to be addressed in developing an oligo-based approach for detecting gene expression in viable cells is whether the binding of the molecular beacon probes to their target RNA leads to degradation of the mRNA by RNase H, which may affect the level of target mRNA (23).

More than Moles: When Melanoma Doesn't Look Like You Think It Should

a lesion that, at a given moment in time, looks or feels different than the patient’s other moles, or that over time, changes differently than the patient’s other moles.

if a spot doesn’t look like the images of skin cancer you’ve seen?

These variations are caused by changes in the melanin, the normally dark brown to black pigment that occurs in hair, skin and the iris of the eye to give them their color.

14 early warning signs that cancer is growing in your body

DISCLAIMER ON COMMENTS & ADVICE GIVEN Please note that the below information is designed to provide general information on the topics presented.

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