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Category: biotech/medical – Page 2

Researchers at the Technical University of Darmstadt and the Helmholtz Center Dresden-Rossendorf have developed flexible robot wings that are moved by magnetic fields. Inspired by the efficiency and adaptability of the wings of the monarch butterfly, they enable precise movements without electronics or batteries.

This bio-inspired development could fundamentally change , rescue operations and biomedical applications.

Monarch butterflies are known for their outstanding endurance and adaptability. Every year, they cover thousands of kilometers on their migrations between Canada and Mexico. The key to this feat lies in their unique wings, which allow the insects to fly energy-efficiently through a combination of active movement and passive bending.

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Participants underwent fMRI while completing a monetary incentive delay task. This task is commonly used to assess reward system activation, as it separates the anticipation of a reward from the receipt of the reward. During the task, participants viewed visual cues signaling whether they could win money or not. They were then required to press a button quickly in response to a target, with feedback indicating whether they had succeeded in earning the reward.

The study focused on two key brain regions: the ventral striatum, which is involved in reward anticipation, and the orbitofrontal cortex, which processes reward outcomes. Psychological resilience was measured using the Connor-Davidson Resilience Scale, while PTSD severity was assessed with the Clinician-Administered PTSD Scale. Metabolic syndrome was diagnosed based on established clinical criteria, including elevated blood pressure, abnormal cholesterol or triglyceride levels, elevated blood sugar, and increased waist circumference.

The researchers observed distinct patterns of reward system activation in individuals with PTSD, influenced by the severity of depressive symptoms. Among PTSD participants with lower depression severity, activation in the ventral striatum during reward anticipation was reduced, while activation in the orbitofrontal cortex during reward outcomes was heightened.

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When 3D printing was first introduced in 1985, it marked a major turning point for the manufacturing industry. In addition to being cheaper than traditional manufacturing technologies, it also promised the ability to customize designs and make prototypes on demand. While its technology is still considered relatively new, there has been an accelerating demand for 3D printing methods across sectors in the past decade, ranging from aerospace and defense to medicine.

Yet, Associate Professor Pablo Valdivia y Alvarado from the Singapore University of Technology and Design (SUTD) believes that there are still ways to go before 3D printing can achieve its full potential. In traditional 3D printing, a nozzle is used to print the material layer by layer, and the path that the nozzle takes is known as the toolpath.

However, layer-by-layer printing is incompatible for use with materials like silicone, epoxies, and urethanes that are slow-curing and take more time to harden. These types of materials are often used to create soft mechanical metamaterials which, in turn, are used for lightweight, nature-inspired structures, such as lattices and web structures. Deposition-based processes in 3D printing, such as direct ink writing, would be able to work with these materials to create such structures, but these suffer from non-optimized toolpaths.

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“If you lease it like you lease a car, a $30,000 car, your price point per month is 300 bucks,” says author, futurist, investor, doctor, and engineer Peter Diamandis in a recent TechFirst podcast. “And that translates amazingly to $10 a day and 40 cents an hour. So you’ve got labor that’s waiting for whatever your wish is. You know, clean up the house, go mow the lawn, you know, please change the baby’s diapers.”

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SMC proteins can reverse direction, reshaping DNA

DNA, or deoxyribonucleic acid, is a molecule composed of two long strands of nucleotides that coil around each other to form a double helix. It is the hereditary material in humans and almost all other organisms that carries genetic instructions for development, functioning, growth, and reproduction. Nearly every cell in a person’s body has the same DNA. Most DNA is located in the cell nucleus (where it is called nuclear DNA), but a small amount of DNA can also be found in the mitochondria (where it is called mitochondrial DNA or mtDNA).

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Examples of endosymbiosis are everywhere. Mitochondria, the energy factories in your cells, were once free-living bacteria. Photosynthetic plants owe their sun-spun sugars to the chloroplast, which was also originally an independent organism. Many insects get essential nutrients from bacteria that live inside them. And last year researchers discovered the “nitroplast,” an endosymbiont that helps some algae process nitrogen.

So much of life relies on endosymbiotic relationships, but scientists have struggled to understand how they happen. How does an internalized cell evade digestion? How does it learn to reproduce inside its host? What makes a random merger of two independent organisms into a stable, lasting partnership?

Now, for the first time, researchers have watched the opening choreography of this microscopic dance by inducing endosymbiosis in the lab. After injecting bacteria into a fungus—a process that required creative problem-solving (and a bicycle pump)—the researchers managed to spark cooperation without killing the bacteria or the host. Their observations offer a glimpse into the conditions that make it possible for the same thing to happen in the microbial wild.

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LEV is upon us.

OpenAI chief executive Sam Altman, who provided the initial $180mn to seed the start-up, will put in more money in the series A. The company is in talks with family offices, venture capitalists and sovereign wealth funds, as well as a US “hyperscaler” data centre to provide computing power to run the AI models it uses to create and test its treatments.

In partnership with OpenAI, the start-up has built a bespoke AI model that designs proteins to temporarily turn regular cells into stem cells, which it says can reverse their ageing process.

The San Francisco-based biotech will use the money to fund clinical trials for three drugs, including a potential treatment for Alzheimer’s disease, which will be tested in an early stage study in Australia this year. It is also working on drugs for rejuvenating blood and brain cells.

Continue reading “Sam Altman-backed Retro Biosciences to raise

LEV is upon us.

OpenAI chief executive Sam Altman, who provided the initial $180mn to seed the start-up, will put in more money in the series A. The company is in talks with family offices, venture capitalists and sovereign wealth funds, as well as a US “hyperscaler” data centre to provide computing power to run the AI models it uses to create and test its treatments.

In partnership with OpenAI, the start-up has built a bespoke AI model that designs proteins to temporarily turn regular cells into stem cells, which it says can reverse their ageing process.

The San Francisco-based biotech will use the money to fund clinical trials for three drugs, including a potential treatment for Alzheimer’s disease, which will be tested in an early stage study in Australia this year. It is also working on drugs for rejuvenating blood and brain cells.bn for project to extend human life” | >

Engineered enzymes are poised to have transformative impacts across applications in energy, materials, biotechnology, and medicine. Recently, machine learning has emerged as a useful tool for enzyme engineering. Now, a team of bioengineers and synthetic biologists says they have developed a machine-learning guided platform that can design thousands of new enzymes, predict how they will behave in the real world, and test their performance across multiple chemical reactions.

Their results are published in Nature Communications in an article titled, “Accelerated enzyme engineering by machine-learning guided cell-free expression,” and led by researchers at Stanford University and Northwestern University.

“Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design,” the researchers wrote. “To address this challenge, we develop a machine learning (ML)-guided platform that integrates cell-free DNA assembly, cell-free gene expression, and functional assays to rapidly map fitness landscapes across protein sequence space and optimize enzymes for multiple, distinct chemical reactions.”

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