Embryo stem cells created from skin cells

Yossi Buganim from The Hebrew University of Jerusalem has discovered a set of genes that can transform murine skin cells into all three of the cell types that comprise the early embryo: the embryo itself, the placenta and the extra-embryonic tissues, such as the umbilical cord.

Buganim and colleagues discovered a combination of five genes that, when inserted into skin cells, reprogram the cells into the three early embryonic cell types–iPS cells which create fetuses, placental stem cells, and stem cells that develop into other extra-embryonic tissues. The transformations take about one month.

To uncover the molecular mechanisms that are activated during the formation of these cell types, the researchers analyzed changes to the genome structure and function inside the cells when the five genes are introduced. They discovered that during the first stage, skin cells lose their cellular identity and then slowly acquire a new identity of one of the three early embryonic cell types, and that this process is governed by the levels of two of the five genes.

This discovery may enable creation of entire human embryos out of human skin cells, without the need for sperm or eggs. It will also impact the modeling of embryonic defects and the understanding of placental dysfunctions.  It could address fertility problems by creating human embryos in a petri dish.


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Thought generated speech

Edward Chang and UCSF colleagues are developing technology that will translate signals from the brain into synthetic speech.  The research team believes that the sounds would be nearly as sharp and normal as a real person’s voice. Sounds made by the human lips, jaw, tongue and larynx would be simulated.

The goal is a communication method for those with disease and paralysis.

According to Chang: “For the first time, this study demonstrates that we can generate entire spoken sentences based on an individual’s brain activity.”

Berkeley’s Bob Knight has developed related technology, using HFB activity to decode imagined speech to develop a BCI for treatment of disabling language deficits.  He described this work at the 2018 ApplySci conference at Stanford.


Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School and the 13th Wearable Tech + Neurotech + Digital Health Silicon Valley conference on February 11-12, 2020 at Stanford University

Voice-detected PTSD

Charles Marmar, Adam Brown, and NYU colleagues are using AI-based voice analysis to detect PTSD with 89 per cent accuracy, according to a recent study.

PTSD is typically determined by bias-prone clinical interviews or self-reports.

The team recorded standard diagnostic interviews of 53 Iraq and Afghanistan veterans with military-service-related PTSD, as well as 78 veterans without the disease. The recordings were then fed into voice software to yield 40,526 speech-based features captured in short spurts of talk, which were then sifted  for patterns.

The  program linked less clear speech and a lifeless metallic tone with PTSD., While the study did not explore  disease mechanisms behind PTSD, the team believes that traumatic events change brain circuits that process emotion and muscle tone, affecting a person’s voice.


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Trigeminal nerve stimulation to treat ADHD

NeuroSigma has received FDA clearance for its forehead patch which stimulates the trigeminal nerve during sleep to treat ADHD. The device won CE Mark approval in Europe in  2015.

The  approval was based on study of  62 subjects. Over four weeks, those who received the treatment showed  a decrease in ADHD-RS by -31.4%. The control group showed a  -18.4% decrease.

The FDA’s Carlos Pena said: “This new device offers a safe, non-drug option for treatment of ADHD in pediatric patients through the use of mild nerve stimulation, a first of its kind.”

Trigeminal nerve stimulation is also being studied in Epilepsy and PTSD.


Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School and the 13th Wearable Tech + Neurotech + Digital Health Silicon Valley conference on February 11-12, 2020 at Stanford University

3D printed, vascularized heart, using patient’s cell, biological materials

Tel Aviv University professor Tal Dvir has printed a 3D vascularized engineered heart, including cells, blood vessels, ventricles and chambers,  using a patient’s own cell and biological materials.

A biopsy of fatty tissue was taken from patients. Cellular and a-cellular materials were separated. While the cells were reprogrammed to become pluripotent stem cells, the extracellular matrix were processed into a personalized hydrogel that served as printing “ink.” After being mixed with the hydrogel, the cells were efficiently differentiated to cardiac or endothelial cells to create patient-specific, immune-compatible cardiac patches with blood vessels and, subsequently, an entire heart.

Dvir believes that this “3D-printed thick, vascularized and perfusable cardiac tissues that completely match the immunological, cellular, biochemical and anatomical properties of the patient” reduces the risk of implant rejection.

The team now plans on culturing the printed hearts and “teaching them to behave” like hearts, then transplanting them in animal models.


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MRI detected intracellular calcium signaling

Alan Jasanoff and MIT colleagues are using MRI to monitor calcium activity at a much deeper level in the brain than previously possible, to show how neurons communicate with each other.  The research team believes that this enables neural activity to be linked with specific behaviors.

To create their intracellular calcium sensors, the researchers used manganese as a contrast agent, bound to an organic compound that can penetrate cell membranes, containint a calcium-binding chelator.

Once inside the cell, if calcium levels are low, the calcium chelator binds weakly to the manganese atom, shielding the manganese from MRI detection. When calcium flows into the cell, the chelator binds to the calcium and releases the manganese, which makes the contrast agent appear brighter in an MRI.

The technique could also be used to image calcium as it performs in facilitating the activation of immune cells, or in diagnostic brain or heart imaging.


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Photonics for at-home disease detection

Hatice Altug and EPFL BIOS Laboratory colleagues are developing photonic chips that count individual biomolecules and determine their location.  This could identify trace amounts of undesirable biomarkers in blood or saliva and serve as an early-warning system for disease.

The technology consists of an ultra-thin and miniaturized optical chip that,  coupled with a standard CMOS camera and powered by image analysis. It is based on metasurfaces. At a certain frequency, these elements are able to squeeze light into extremely small volumes, creating ultrasensitive optical ‘hotspots’.

When light shines on the metasurface and hits a molecule at one of these hotspots, the molecule is detected immediately, changing the wavelength of the light that hits it. By using different colored lights on the metasurface and taking a photo with a CMOS camera, the researchers cn count the number of molecules in a sample, and learn exactly what is happening on the sensor chip. First author Filiz Yesilkoy said: “We then use smart data science tools to analyze the millions of CMOS pixels obtained through this process and identify trends. We’ve demonstrated that we can detect and image not just individual biomolecules at the hotspots, but even a single graphene sheet that’s only one atom thick.”

The team also developed a second, simpler, but less precise, version of the system, where the metasurfaces are programmed to resonate at different wavelengths in different regions.

According to Altug: “Light possesses many attributes – such as intensity, phase and polarization – and is capable of traversing space. This means that optical sensors could play a major role in addressing future challenges – particularly in personalized medicine.”


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Starving cancer stem cells as a new approach to glioblastoma

Luis Parada and Sloan Kettering colleagues are focusing on cancer stem cells as a new approach to glioblastoma.

Like normal stem cells, cancer stem cells have the ability to rebuild a tumor, even after most of it has been removed, leading to cancer relapse and metastasis.

According to Parada: “The pharmaceutical industry has traditionally used established cancer cell lines to screen for new drugs, but these cell lines don’t always reflect how cancer behaves in the body. The therapies that are currently in use were designed to target cells that are rapidly dividing. But what we’ve concluded in our studies is that glioblastoma stem cells divide relatively slowly within tumors, leaving them unaffected by these treatments.”

Even if most of the tumor is destroyed, the stem cells allow it to regrow.

The team discovered a drug, which they called Gboxin, that effectively treated glioblastoma in mice, and killed human glioblastoma cells.  They then discovered that Gboxin killed cancer stem cells by starving them of energy – . by preventing cells from making ATP through oxidative phosphorylation in mitochondria.  When Gboxin accumulates within cancer stem cells, it essentially strangles the mitochondria and shuts energy production down.

The next step is to determine that Gboxin will be able to cross the blood-brain barrier, and potential side effects of the drug.


Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School and the 13th Wearable Tech + Neurotech + Digital Health Silicon Valley conference on February 11-12, 2020 at Stanford University

Study: AI accurately predicts childhood disease from health records

Xia Huimin and Guangzhou Women and Children’s Medical Center researchers used AI to read 1.36 million pediatric health records, and diagnosed disease as accurately as doctors, according to a recent study.

Common childhood diseases were detected after processing symptoms, medical history and other clinical data from this massive sample.  The goal is the diagnosis of complex or rare diseases by providing more diagnostic predictions, and to assist triage patients.


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Glutamate sensor could predict migraines, monitor CNS drug effectiveness

Riyi Shi and Purdue colleagues have developed a tiny, spinal cord-implanted, 3D printed sensor that quickly and accurately tracks glutamate in spinal trauma and brain disease. The goal  is to monitor drug effectiveness, and predict migraine headaches in humans, although it has only been tested on animals.

Glutamate spikes are often missed.  Damaged nerve structures allow glutamate to leak into spaces outside of cells, over-exciting and damaging them. Brain diseases, including Alzheimer’s and Parkinson’s, also show elevated levels of glutamate.

Devices to date have not been sensitive, fast, or affordable enough. Measuring levels in vivo would help researchers to study how spinal cord injuries happen, and  how brain diseases develop.

In a recent animal study, the device captured spikes immediately, vs current devices, where researchers must  to wait 30 minutes for data after damaging the spinal cord.

Quick to view Purdue video


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