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Category: biotech/medical – Page 211

The overlooked astrocyte: Star-shaped brain cells may form specialized networks for reward learning

Most neuroscience research carried out up to date has primarily focused on neurons, the most renowned type of cell in the human brain. As a result, the unique functions of other brain cell types are less understood and have often been entirely overlooked.

Researchers at Instituto Cajal (CSIC), the Autonomous University of Madrid and Institute de Salud Carlos III recently carried out a study aimed at better understanding the contributions of astrocytes, a class of star-shaped glial cells found in the brain and spinal cord, to key mental functions. Their findings, published in Nature Neuroscience, unveiled the existence of astrocytic ensembles, specialized subsets that appear to be active during reward-driven behaviors.

“It is known that astrocytes are a heterogeneous cell type in their molecular and gene expression signatures, morphology and origin,” Marta Navarrete, senior author of the paper, told Medical Xpress.

Atomic traffic control—researchers develop novel technology for more precise quantum sensors

Quantum sensors can be significantly more precise than conventional sensors and are used for Earth observation, navigation, material testing, and chemical or biomedical analysis, for example. TU Darmstadt researchers have now developed and tested a technique that makes quantum sensors even more precise.

What is behind this technology? Quantum sensors, based on the wave nature of , use quantum interference to measure accelerations and rotations with extremely high precision. This technology requires optimized beam splitters and mirrors for atoms. However, atoms that are reflected in unintentional ways can significantly impair such measurements.

The scientists therefore use specially designed as velocity-selective atom , which reflect the desired atoms and allow parasitic atoms to pass through. This approach reduces the noise in the signal, making the measurements much more precise. The research is published in the journal Physical Review Research.

Scientists Just Made a Breakthrough in Nanocrystals That Could Supercharge Solar Power

Researchers are breaking new ground with halide perovskites, promising a revolution in energy-efficient technologies.

By exploring these materials at the nanoscale.

The term “nanoscale” refers to dimensions that are measured in nanometers (nm), with one nanometer equaling one-billionth of a meter. This scale encompasses sizes from approximately 1 to 100 nanometers, where unique physical, chemical, and biological properties emerge that are not present in bulk materials. At the nanoscale, materials exhibit phenomena such as quantum effects and increased surface area to volume ratios, which can significantly alter their optical, electrical, and magnetic behaviors. These characteristics make nanoscale materials highly valuable for a wide range of applications, including electronics, medicine, and materials science.

The Role of AI in medical learning: How chatbots and digital assistants are reshaping education

Artificial Intelligence (AI) is revolutionizing industries globally, and medical education is no exception. For a nation like India, where the healthcare system faces immense pressure, AI integration in medical learning is more than a convenience, it’s a necessity. AI-powered tools offer medical students transformative benefits: personalized learning pathways that adapt to individual knowledge gaps, advanced clinical simulation platforms for risk-free practice, intelligent tutoring systems that provide immediate feedback, and sophisticated diagnostic training algorithms that enhance clinical reasoning skills. From offering personalized guidance to transforming clinical training, chatbots and digital assistants are redefining how future healthcare professionals prepare for their complex and demanding roles, enabling more efficient, interactive, and comprehensive medical education.

Personalized learning One of AI’s greatest contributions to medical education is its ability to create and extend personalized learning experiences. Conventional methods, on the other hand, often utilize a one-size-fits-all approach, leaving students to fend for themselves when they struggle. AI has the power to change this by analyzing a student’s performance and crafting study plans tailored to their strengths and weaknesses. This means students can focus on areas where they need the most help, saving time and effort.

Replacing trial and error: Molecular methods clear the way for faster and more cost-effective separations

The process of separating useful molecules from mixtures of other substances accounts for 15% of the nation’s energy, emits 100 million tons of carbon dioxide and costs $4 billion annually.

Commercial manufacturers produce columns of porous materials to separate potential new drugs developed by the pharmaceutical industry, for example, and also for energy and chemical production, environmental science and making foods and beverages.

But in a new study, researchers at Case Western Reserve University have found these manufactured separation materials don’t function as intended because the pores are so packed with polymer they become blocked. That means the separations are inefficient and unnecessarily expensive.

Mouse model unveils dynamics through which SYNGAP1 gene supports cognitive function

The SYNGAP1 gene, which supports the production of a protein called SynGAP (Synaptic Ras GTPase-Activating Protein), is known to play a key role in supporting the development of synapses and neural circuits (i.e., connections between neurons). Mutations in this gene have been linked to various learning disabilities, including intellectual disabilities, speech and language delays, autism spectrum disorder (ASD), and epilepsy.

Researchers at the Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology recently carried out a study aimed at better understanding the via which the SYNGAP1 gene contributes to healthy cognitive function. Their findings, published in Nature Communications, suggest that the autonomous expression of this gene in the cortical excitatory neurons of mice promotes the animals’ cognitive abilities via the assembly of long-range integrating sensory and motor information.

“Our paper builds on our ongoing research into how major risk genes for mental health disorders, including autism, regulate brain organization and function,” Gavin Rumbaugh, senior author of the paper, told Medical Xpress. “The field knows the major risk genes that directly contribute to cognitive and behavioral impairments that lead to diagnosable forms of autism and related neuropsychiatric disorders in humans.

New findings on the power of enzymes could reshape biochemistry

Using a series of more than 1,000 X-ray snapshots of the shapeshifting of enzymes in action, researchers at Stanford University have illuminated one of the great mysteries of life—how enzymes are able to speed up life-sustaining biochemical reactions so dramatically. Their findings could impact fields ranging from basic science to drug discovery, and provoke a rethinking of how science is taught in the classroom.

“When I say enzymes speed up reactions, I mean as in a trillion-trillion times faster for some reactions,” noted senior author of the study, Dan Herschlag, professor of biochemistry in the School of Medicine. “Enzymes are really remarkable little machines, but our understanding of exactly how they work has been lacking.”

There are lots of ideas and theories that make sense, Herschlag said, but biochemists have not been able to translate those ideas into a specific understanding of the chemical and physical interactions responsible for enzymes’ enormous reaction rates. As a result, biochemists don’t have a basic understanding and, therefore, have been unable to predict rates or design new enzymes as well as nature does, an ability that would be impactful across industry and medicine.

AI model generates antimicrobial peptide structures for screening against treatment-resistant microbes

A team of microbiologists, chemists and pharmaceutical specialists at Shandong University, Guangzhou Medical University, Second Military Medical University and Qingdao University, all in China, has developed an AI model that generates antimicrobial peptide structures for screening against treatment-resistant microbes.

In their study published in the journal Science Advances, the group developed a compression method to reduce the number of elements needed in training data for an AI system, which helped to reduce diversification issues with current AI models.

Prior research has suggested that drug-resistant microbes are one of the most pressing problems in medical science. Researchers around the world have been looking for new ways to treat people infected with such microbes—one approach involves developing , which work by targeting bacterial membranes.