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In December 2022, founder Elon Musk gave an update on his other, other company, the brain implant startup Neuralink. As early as 2020, the company had been saying it was close to starting clinical trials of the implants, but the December update suggested those were still six months away. This time, it seems that the company was correct, as it now claims that the Food and Drug Administration (FDA) has given its approval for the start of human testing.

Neuralink is not ready to start recruiting test subjects, and there are no details about what the trials will entail. Searching the ClinicalTrials.gov database for “Neuralink” also turns up nothing. Typically, the initial trials are small and focused entirely on safety rather than effectiveness. Given that Neuralink is developing both brain implants and a surgical robot to do the implanting, there will be a lot that needs testing.

It’s likely that these will focus on the implants first, given that other implants have already been tested in humans, whereas an equivalent surgical robot has not.

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Glioblastoma Multiforme (GBM) is the most aggressive and most common primary malignant brain tumor. The age of GBM patients is considered as one of the disease’s negative prognostic factors and the mean age of diagnosis is 62 years. A promising approach to preventing both GBM and aging is to identify new potential therapeutic targets that are associated with both conditions as concurrent drivers. In this work, we present a multi-angled approach of identifying targets, which takes into account not only the disease-related genes but also the ones important in aging. For this purpose, we developed three strategies of target identification using the results of correlation analysis augmented with survival data, differences in expression levels and previously published information of aging-related genes.

A team of engineers at the University of Colorado Boulder has designed a new class of tiny, self-propelled robots that can zip through liquid at incredible speeds—and may one day even deliver prescription drugs to hard-to-reach places inside the human body.

The researchers describe their mini healthcare providers in a paper published last month in the journal Small.

“Imagine if microrobots could perform certain tasks in the body, such as non-invasive surgeries,” said Jin Lee, lead author of the study and a postdoctoral researcher in the Department of Chemical and Biological Engineering. “Instead of cutting into the patient, we can simply introduce the robots to the body through a pill or an injection, and they would perform the procedure themselves.”

Israeli-based health tech company Cordio has developed machine learning software that can be downloaded to a smartphone and help keeps cardiac patients out of the hospital.

One day in the future.

It’s a simple daily habit that could save their life, because one day after repeating their daily refrain, their doctor might be notified that a patient is at risk of heart failure without immediate care.

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The first protein-based nano-computing agent that functions as a circuit has been created by Penn State researchers. The milestone puts them one step closer to developing next-generation cell-based therapies to treat diseases like diabetes and cancer.

Traditional synthetic biology approaches for cell-based therapies, such as ones that destroy cancer cells or encourage tissue regeneration after injury, rely on the expression or suppression of proteins that produce a desired action within a cell. This approach can take time (for proteins to be expressed and degrade) and cost cellular energy in the process. A team of Penn State College of Medicine and Huck Institutes of the Life Sciences researchers are taking a different approach.

“We’re engineering proteins that directly produce a desired action,” said Nikolay Dokholyan, G. Thomas Passananti Professor and vice chair for research in the Department of Pharmacology. “Our protein-based devices or nano-computing agents respond directly to stimuli (inputs) and then produce a desired action (outputs).”

Miniaturization is progressing rapidly in many fields, and the trend toward the creation of ever smaller units is also prevalent in the world of robot technology. In the future, minuscule robots used in medical and pharmaceutical applications might be able to transport medication to targeted sites in the body. Statistical physics can contribute to the foundations for the development of such technologies.

A team of researchers at Johannes Gutenberg University Mainz (JGU) has now taken a new approach to the issue by analyzing a group of robots and how they behave as collectives of motile units based on the model of active Brownian particles. The team’s findings demonstrating that there may be an alternative route to realize programmable active matter have been published in Science Advances.

Researchers are looking for new ways to perform tasks on the micro-and nanoscale that are otherwise difficult to realize, particularly as the miniaturization of devices and components is beginning to reach physical limits. One new option being considered is the use of collectives of robotic units in place of a single robot to complete a task.

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The ability to acquire gut stem cells via biopsy and have a significant proliferative capacity in culture make them an invaluable resource for autologous cell treatments. In the mouse gut, insulin-producing cells can be produced. Still, human gut tissues have not been able to produce an abundance or durability of insulin-secreting cells to assess their potential as a cell treatment for diabetes.

In a new study, scientists from Weill Cornell Medicine showed that stem cells from human stomach can be converted into insulin-secreting cells. Scientists demonstrated that they could obtain the stem cells from the human stomach and reprogram them directly—with strikingly high efficiency—into cells that closely resemble pancreatic insulin-secreting cells known as beta cells.

In experiments on a mouse model, this approach reversed disease signs. According to scientists, the study offers a promising approach, based on patient’s cells, for type 1 diabetes and severe type 2 diabetes.