Email: [email protected]. Or to: Bo Yang, 1,500 E Medical Center Dr., 5,144 Frankel Cardiovascular Center, Ann Arbor, Michigan, 48,109, USA. Email: [email protected].
Email: [email protected]. Or to: Bo Yang, 1,500 E Medical Center Dr., 5,144 Frankel Cardiovascular Center, Ann Arbor, Michigan, 48,109, USA. Email: [email protected].
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.
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.
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.
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.
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.
Bacterial sensors usually rely on emitting light to transfer information about what they’re sensing, but that method isn’t practical in many settings. That’s why most information transmission is done via electricity. And while electricity-emitting bacteria exist, manipulating them into useful sensors has been quite challenging. Rice University professor Caroline Ajo-Franklin’s group, working in collaboration with researchers from Tufts University and Baylor College of Medicine, recently developed a flexible bioelectrical sensor system called electroactive co-culture sensing system (e-COSENS). The study is published in Nature Biotechnology.
“Bioelectrical sensing is by no means a new concept,” said Ajo-Franklin, the Ralph and Dorothy Looney Professor of Biosciences and corresponding author on this paper. “But e-COSENS is the first system that allows us to easily engineer bioelectronic sensors in a modular manner, like assembling Legos, allowing us to potentially use them to monitor everything from human health to environmental contaminants.”
Bioelectrical sensing requires bacteria that produce electricity and are easy for researchers to manipulate to respond to different substances. Ideally, the bacteria would be able to live in a variety of different places so that the system could be used in environments ranging from rivers to milk.
Infections from antibiotic-resistant bacteria are difficult to treat and are responsible for over 2.8 million infections and more than 35,000 deaths in the U.S. each year. A new study in Nature Communications reports that a drug used to lower blood pressure could also be the basis of a promising new treatment for methicillin-resistant Staphylococcus aureus (MRSA).
“MRSA commonly causes infections in both hospitals and the community. It infects people in different ways and can survive even when antibiotics are used, which makes treatment extremely difficult,” said corresponding author Eleftherios Mylonakis, M.D., Ph.D., chair, Houston Methodist Charles W. Duncan Jr. Department of Medicine.
“Scientists around the world are looking at various ways to provide treatment options outside of established antibiotics. The high cost of developing new drugs, and the time it takes to do so, led our team to explore the possibility of using existing medications, approved for other uses, to treat bacterial infections.”