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Attenuated Single Neuron and Network Hyperexcitability Following MicroRNA-134 Inhibition in Mice with Drug-Resistant Temporal Lobe Epilepsy

JNeurosci: Findings from Quintana-Sarti et al. help explain how targeting microRNA-134 in mice can reduce seizure activity and support the continued development of this novel RNA-based approach for the treatment of epilepsy.

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The multifactorial pathophysiology of acquired epilepsies lends itself to a multitargeting therapeutic approach. MicroRNAs (miRNA) are short noncoding RNAs that individually can negatively regulate dozens of protein-coding transcripts. Previously, we reported that central injection of antisense oligonucleotides targeting microRNA-134 (Ant-134) shortly after status epilepticus potently suppressed the development of recurrent spontaneous seizures in rodent models of temporal lobe epilepsy. The mechanism(s) of these antiseizure effects remain, however, incompletely understood. Here we show that intracerebroventricular microinjection of Ant-134 in male mice with preexisting epilepsy caused by intra-amygdala kainic acid-induced status epilepticus potently reduces the occurrence of spontaneous seizures.

Oxidative Stress and Neuroinflammation in Parkinson’s Disease: The Role of Dopamine Oxidation Products

Parkinson’s disease (PD) is a chronic neurodegenerative condition affecting more than 1% of people over 65 years old. It is characterized by the preferential degeneration of nigrostriatal dopaminergic neurons, which is responsible for the motor symptoms of PD patients. The pathogenesis of this multifactorial disorder is still elusive, hampering the discovery of therapeutic strategies able to suppress the disease’s progression. While redox alterations, mitochondrial dysfunctions, and neuroinflammation are clearly involved in PD pathology, how these processes lead to the preferential degeneration of dopaminergic neurons is still an unanswered question. In this context, the presence of dopamine itself within this neuronal population could represent a crucial determinant.

Scientists find evidence some Alzheimer’s symptoms may begin outside the brain

Researchers used a microscopic model of human nerves and muscles to show that Alzheimer’s disease directly damages peripheral nerves. This physical damage happens independently of cognitive decline and does not improve with standard medications for the illness.

Transparent cooling film cuts car cabin temperature by 6.1°C without electricity

A transparent radiative cooling film technology that dissipates heat directly to the outside without consuming electricity has been developed to reduce vehicle overheating during summer. The technology was validated through real-vehicle experiments conducted under diverse conditions—including different countries, seasons, and both parking and driving scenarios—and demonstrated the ability to lower cabin temperatures by up to 6.1°C and reduce cooling energy consumption by more than 20%.

Seoul National University College of Engineering announced that a research team led by Prof. Seung Hwan Ko (Department of Mechanical Engineering, SNU), in collaboration with Prof. Gang Chen at MIT and research teams from Hyundai Motor Company and Kia (Materials Research & Engineering Center and Thermal Energy Total Development Group), has designed and fabricated a large-area Scalable Transparent Radiative Cooling (STRC) film applicable to vehicle windows. Through real-vehicle evaluations conducted under various climatic and driving conditions, the team demonstrated both energy-saving and carbon reduction effects.

This research was published online on February 4 in the journal Energy & Environmental Science.

Q&A: Will agentic AI replace human scientists?

An emerging type of artificial intelligence, known as “agentic” AI, seems to do everything that biomedical scientists do—and often, does it faster. This next-generation technology can interpret experimental data, report the results and make decisions on its own. But is agentic AI smart enough to replace actual scientists?

Jason Moore, Ph.D., chair of the Department of Computational Biomedicine at Cedars-Sinai, discusses the pluses and minuses of agentic AI. Moore is corresponding author of a new paper, published in Nature Biotechnology, that examines where agentic AI is today and where it is headed.

An Extracellular Matrix Aging Clock Based on Circulating Matrisome Proteins Predicts Biological Aging and Disease

A 14-protein extracellular matrix aging clock derived from circulating matrisome proteins predicts chronological and biological age across cohorts and biofluids, distinguishes health from disease, an…

Education Gap Tied to Higher Risk for Young-Onset CRC Death

In a cross-sectional study of adults aged 25–49 years in the US, colorectal cancer (CRC) mortality rose from 1994 to 2023, primarily among those with 15 or fewer years of education, and educational disparities in mortality widened over time. More on the study.

Researchers analyze trends in colorectal cancer mortality among adults aged 25–49 years in a study spanning about three decades.

Radio Blips in the Ice Are Promising Sign for Neutrino Hunt

A South Pole neutrino experiment has measured radio waves induced by cosmic rays—thus demonstrating that its detection method works.

Detection of high-energy neutrinos, elusive particles produced in supernovae and other astrophysical events, is opening up a new window on the Universe. One way to spot them is to search for signals of neutrino collisions with molecules in large sheets of polar ice. An international collaboration working in Antarctica has now reported the detection of ice-traversing radio waves that originate from cosmic-ray-induced particle showers [1]. Even though the radio waves were generated by cosmic rays rather than neutrinos, the result establishes a proof of principle that the technique should work for neutrinos too.

Ice-sheet-based detection of cosmic neutrinos has been reported previously from the IceCube Neutrino Observatory at the South Pole [2]. In that experiment, neutrino collisions with water molecules produce flashes of visible light, called Cherenkov radiation, generated by fast-moving collision by-products. This method becomes challenging for neutrinos of extremely high energy (around 1018 electron volts, or 1 exa-electron-volt) because these neutrinos are expected to be exceedingly rare. Researchers would need detectors spread over hundreds of cubic kilometers of ice to have a chance of seeing Cherenkov radiation from such a rare exa-electron-volt event.

Platinum-free catalyst splits hydrogen from water for energy, running 1,000 hours at industry standards

Using a renewable energy source has multiple benefits, including reducing harmful emissions and dependence on fossil fuels while increasing efficiency. But many renewable energy sources have a higher cost than fossil fuels due to the materials needed to make them usable, such as platinum group metals (PGMs), and the high cost of storage.

A team of researchers led by Gang Wu, a professor of energy, environmental and chemical engineering at the McKelvey School of Engineering at Washington University in St. Louis is working to change that. The team is creating a heterostructure catalyst for an anion-exchange membrane water electrolyzer (AEMWE) that splits water into hydrogen and oxygen using electricity from renewable sources. They created the catalyst with two phosphides that gave them an efficient method to extract hydrogen, a valuable yet low-cost source of zero-emissions fuel. The study is published in the Journal of the American Chemical Society.

Wu’s team has been looking for alternatives to catalysts that use expensive platinum group metals. In this research, their idea began with using sunlight, wind or water to create electricity that they could then use to separate hydrogen from water.

Quantum-informed AI improves long-term turbulence forecasts while using far less memory

An AI model informed by calculations from a quantum computer can better predict the behavior of a complex physical system over the long term than current best models that use only conventional computers, according to a new study led by UCL (University College London) researchers. The findings, published in the journal Science Advances, could improve models predicting how liquids and gases move and interact (fluid dynamics), used in areas ranging from climate science to transport, medicine and energy generation.

The researchers say the improved performance is linked to a quantum device’s ability to hold a large amount of information more efficiently. That is because instead of bits that are switched on or off, 1 or 0, as in a classical computer, the quantum computer’s qubits can be 1, 0, or any state in between, and each qubit can affect any of the other qubits—meaning a few qubits can generate a vast number of possible states.

Senior author Professor Peter Coveney, based in UCL Chemistry and the Advanced Research Computing Center at UCL, said, To make predictions about complex systems, we can either run a full simulation, which might take weeks—often too long to be useful—or we can use an AI model, which is quicker but more unreliable over longer time scales.

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