Astrocytes identified as hidden culprit behind PTSD

Excessive astrocytic GABA impairs fear extinction in PTSD, new drug target offers hope for treatment

Institute for Basic Science

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Astrocyte-Derived GABA and Therapeutic Effects of KDS2010 in PTSD. Brain imaging of PTSD patients revealed unusually high levels of GABA and reduced cerebral blood flow in the prefrontal cortex, showing that changes strongly correlated with symptom severity. In animal models, this excess GABA was traced to reactive astrocytes producing it abnormally due to increased MAOB and reduced levels of the GABA-degrading enzyme ABAT. This disrupted normal brain function and impaired the ability to extinguish fear. Treatment with KDS2010, a selective MAOB inhibitor, successfully lowered astrocytic GABA, restored brain activity, and rescued fear extinction, highlighting its potential as a therapeutic option.

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Credit: Institute for Basic Science

Did you know that patients with post traumatic stress disorder (PTSD) often struggle to forget traumatic memories, even long after the danger has passed? This failure to extinguish fear memories has long puzzled scientists and posed a major hurdle for treatment, especially since current medications targeting serotonin receptors offer limited relief for only a subset of patients.

In a new discovery, scientists at the Institute for Basic Science (IBS) and Ewha Womans University have uncovered a new brain mechanism driving PTSD — and a promising drug that may counteract its effects.

Led by Dr. C. Justin LEE at the IBS Center for Cognition and Sociality and Professor LYOO In Kyoon at Ewha Womans University, the team has shown that excessive GABA (gamma-aminobutyric acid) produced by astrocytes, which are star-shaped support cells in the brain, impairs the brain’s ability to extinguish fear memories. This deficit is a core feature of PTSD and helps explain why traumatic memories can persist long after the threat has passed.

Crucially, the researchers found that a brain-permeable drug called KDS2010, which selectively blocks the monoamine oxidase B enzyme responsible for this abnormal GABA production, can reverse PTSD-like symptoms in mice. The drug has already passed Phase 1 safety trials in humans, making it a strong candidate for future PTSD treatments.

PTSD remains difficult to treat, with current medications targeting serotonin pathways providing limited relief for many patients. The new study focused on the medial prefrontal cortex (mPFC), a region of the brain critical for regulating fear, and found that PTSD patients had unusually high levels of GABA and reduced cerebral blood flow in this area. These findings emerged from brain imaging studies of more than 380 participants. Importantly, GABA levels decreased in patients who showed clinical improvement, pointing to the chemical’s central role in recovery.

To uncover the origin of this excess GABA, the researchers examined postmortem human brain tissue and used PTSD-like mouse models. They discovered that astrocytes, not neurons, were producing abnormal amounts of GABA via the enzyme monoamine oxidase B (MAOB). This astrocyte-derived GABA impaired neural activity, blocking the brain’s ability to forget traumatic memories.

When the researchers administered KDS2010, a highly selective, reversible MAOB inhibitor developed at IBS, the mice showed normalized brain activity and were able to extinguish fear responses. The drug reduced GABA levels, restored blood flow in the mPFC, and re-enabled memory extinction mechanisms. The study thus confirms astrocytic MAOB as a central driver of PTSD symptoms, and MAOB inhibition as a viable therapeutic path.

A major challenge of the study was linking clinical findings in humans with cellular mechanisms in the lab. The researchers addressed this by applying a “reverse translational” strategy: they began with clinical brain scans and moved backward to identify the cellular source of dysfunction, then confirmed the mechanism and tested drug effects in animal models. This approach led to a new understanding of how glial cells — long thought to be passive — actively shape psychiatric symptoms.

“This study is the first to identify astrocyte-derived GABA as a key pathological driver of fear extinction deficit in PTSD,” said Dr. WON Woojin, a postdoctoral researcher and co-first author of the study. “Our findings not only uncover a novel astrocyte-based mechanism underlying PTSD, but also provide preclinical evidence for a new therapeutic approach using an MAOB inhibitor.”

Director C. Justin LEE, who led the study, emphasized that “This work represents a successful example of reverse translational research, where clinical findings in human guided the discovery of underlying mechanisms in animal models. By identifying astrocytic GABA as a pathological driver in PTSD and targeting it via MAOB inhibition, the study opens a completely new therapeutic paradigm not only for PTSD but also for other neuropsychiatric disorders such as panic disorder, depression, and schizophrenia.”

The researchers plan to further investigate astrocyte-targeted therapies for various neuropsychiatric disorders. With KDS2010 currently undergoing Phase 2 clinical trials, this discovery may soon lead to new options for patients whose symptoms have not responded to conventional treatments.

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Journal

Signal Transduction and Targeted Therapy

DOI

10.1038/s41392-025-02317-5 

Method of Research

Experimental study

Subject of Research

Human tissue samples

Article Title

Astrocytic gamma-aminobutyric acid dysregulation as a therapeutic target for posttraumatic stress disorder

Article Publication Date

28-Jul-2025

 

Virtual care network for rural and First Nations communities

Canadian Medical Association Journal

While the virtual delivery of health services expanded rapidly during the COVID-19 pandemic, many regions have decreased use of these services in recent years. In B.C., however, a unique pan-provincial partnership led by the Rural Coordination Centre of British Columbia (RCCbc), the First Nations Health Authority, the B.C. Ministry of Health, and the University of British Columbia (UBC) Digital Emergency Medicine Unit has worked together to build and grow a network of virtual services to support patients, physicians, and health care providers in rural communities.

Initiated in March 2020, the RTVS network is publicly funded and designed to support existing health services, rather than replace them. The network aims to enhance access to health care and equity for underserved communities through a patient- and community-centred approach.

“Our goal with RTVS is to break down barriers and increase health care access and support for people in partnership with health care providers in rural, remote, and First Nations communities. It’s about meeting people where they are and offering a virtual safety net grounded in kindness, compassion, cultural safety, and responsiveness,” said Dr. Kendall Ho, a professor of emergency medicine at the UBC Faculty of Medicine.

The RTVS program includes peer-to-peer services that connect rural clinicians and nurses with physicians specializing in specific areas; First Nations team-based services delivered with principles of cultural safety, humility, and trauma-informed care; and an 8-1-1 virtual physician telephone and video service to help people determine if they need to get in-person help or can manage their health concern at home.

“RTVS helps providers feel more comfortable and supported in taking on rural work, which means rural patients receive more equitable access to health care,” said Dr. John Pawlovich, the Rural Doctors’ UBC chair in Rural Health and RCCbc’s Virtual Health lead.

Use of services across the program has increased yearly, with evidence showing a range of benefits for people in British Columbia, including improved access to primary, emergency, and specialized care, and the retention and support of rural practitioners. Since its founding, RTVS has had more than

• 20,000 encounters with peer-to-peer services in 129 communities
• 12,000 appointments annually with First Nations primary and specialist services
• 176,000 patient calls to the 8-1-1 virtual physician service

“Long-standing relationships built on trust have enabled these collaborative efforts to be successful,” said Dr. Ray Markham, RCCbc’s executive director.

As virtual care services continue to expand and evolve, the authors note the importance of ensuring programs are inclusive and accessible, including for those with limited digital skills. They also highlight that virtual care cannot replace in-person care but should be complementary to offer optimal patient experience and equity of access through hybrid care.

“Just as banks offer both online and in-person banking options to ensure clients get optimal services, virtual care is not meant to replace in-person care but should be offered in conjunction to optimize the hybrid care experience for patient-centred care,” said Dr. Ho.

“Real-Time Virtual Support: a network of virtual care for rural, remote, Indigenous, and pan-provincial communities in British Columbia” is published July 28, 2025.

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Journal

Canadian Medical Association Journal

DOI

10.1503/cmaj.240908 

Article Title

Real-Time Virtual Support: a network of virtual care for rural, remote, Indigenous, and pan-provincial communities in British Columbia

Article Publication Date

28-Jul-2025

 

Helping Canada lead in health innovation

Canadian Medical Association Journal

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He envisions supporting not only clinicians to innovate, but also health care organizations and the larger health system.

“Ideally, health care systems will evolve into living laboratories that enable clinician- and patient-driven solutions supported by health care organizations as well as business and health care system leaders. This is not a new proposition; in 2015, an advisory panel on health care innovation previously advocated for this very concept in Canada,” writes Dr. Muhammad Mamdani, director of the Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM) at the University of Toronto and vice president of Data Science and Advanced Analytics at Unity Health Toronto, who is also appointed at the Vector Institute and ICES Central, Toronto, Ontario.

Real health care problems should get solved in real health care environments. Unfortunately, many health care solutions are created by the private sector in artificial environments, rather than real-world health care environments. Most health care start-ups fail because they tackle the wrong clinical problem, they lack interaction with clinical environments, and they lack access to real-world data.

Canada needs to reframe its approach to funding health research and commercialization. We invest more than $1 billion in health research, but most initiatives do not focus on clinical uses, and we spend hundreds of millions more on health care incubators and start-ups that are largely disconnected from the health care ecosystem. It’s no wonder more than 90% of health start-ups ultimately fail. Ensuring these initiatives and enterprises are aligned with health care organizations and clinical applications is critical.

“Bringing together relevant key partners to drive high-impact health innovation in a sustainable, cost-effective manner will not be easy but it will be worth the effort to make it happen. Elbows up! It is time Canada realizes its potential to be a leader in health innovation,” concludes Dr. Mamdani.

“Canada’s health innovation imperative” is published July 28, 2025.

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Journal

Canadian Medical Association Journal

DOI

10.1503/cmaj.250888 

Method of Research

Commentary/editorial

Article Title

Canada’s health innovation imperative

Article Publication Date

28-Jul-2025

 

How digital twins can accelerate the global transition from fossil fuels to clean energy

University of Sharjah

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Digital twin of an oil rig.

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Credit: Creative Commons Attribution-Share Alike 4.0 International

As the world grapples with the urgent need to reduce carbon emissions and combat climate change, researchers at the University of Sharjah are turning to a cutting-edge technology that could reshape the future of energy: AI-powered digital twins.

According to the researchers, these digital replicas of the physical world have the potential to transform the generation, management, and optimization of energy across diverse clean energy platforms, accelerating the transition away from fossil fuels, which environmental scientists associate with global warming.

Digital twins’ ability to replicate and interact with complex systems has made them a cornerstone of innovation across industries, driving improvements in efficiency, cost reduction, and the development of novel solutions.

However, the scientists caution that current digital twin models still face notable limitations that restrict their full potential in harnessing energy from sources such as wind, solar, geothermal, hydroelectric, and biomass.

“Digital twins are highly effective in optimizing renewable energy systems,” the researchers write in the journal Energy Nexus. “Yet, each energy source presents unique challenges—ranging from data variability and environmental conditions to system complexity—that can limit the performance of digital twin technologies, despite their considerable promise in improving energy generation and management.”

In their study, the authors conducted an extensive review of existing literature on the application of digital twins in renewable energy systems. They examined various contexts, functions, lifecycles, and architectural frameworks to understand how digital twins are currently being utilized and where gaps remain.

To extract meaningful insights, the researchers employed advanced text mining techniques, leveraging artificial intelligence, machine learning, and natural language processing. This scientifically rigorous approach enabled them to analyze large volumes of raw data and uncover structured patterns, concepts, and emerging trends.

From this in-depth analysis, the authors drew several key conclusions. They identified research gaps, proposed new directions, and outlined the challenges that must be addressed to fully harness the potential of digital twin technology in the renewable energy sector.

Following a detailed discussion on the integration of digital twins across various renewable energy applications, the authors summarized their most significant findings across five major energy sources: wind, solar, geothermal, hydroelectric, and biomass. Each source presents unique opportunities and challenges, and the study offers a comprehensive overview of how digital twins can be tailored to optimize performance in each domain.

The study reveals that digital twins offer significant advantages across various renewable energy systems:

Wind Energy: Digital twins can predict unknown parameters and correct inaccurate measurements, enhancing system reliability and performance.

Solar Energy: They help identify key factors that influence efficiency and output power, enabling better system design and optimization.

Geothermal Energy: Digital twins can simulate the entire operational process—particularly drilling—facilitating cost analysis and reducing both time and expenses.

Hydroelectric Energy: The AI-driven models simulate system dynamics to identify influencing factors. In older hydro plants, they are used to mitigate the impact of worker fatigue on productivity.

Biomass Energy: Digital twins improve performance and management by offering deep insights into operational processes and plant configurations.

But the authors’ contribution to the field stands out in highlighting critical limitations in the application of digital twin technology across these energy sources. Their analysis underscores the need for more robust models that can address specific challenges unique to each renewable energy system.

The authors identify several limitations in the application of digital twins across different renewable energy systems:

 Wind Energy: Digital twins face challenges in accurately modeling and monitoring environmental conditions. They struggle to simulate critical factors such as blade erosion, gearbox degradation, and electrical system performance—particularly in aging turbines.        

Solar Energy: Despite their potential, digital twins still fall short in reliably predicting long-term performance. They have difficulty tracking panel degradation and accounting for environmental influences over time, which affects their accuracy and usefulness.

Geothermal Energy: A major obstacle is the lack of high-quality data, which hampers the ability of digital twins to simulate geological uncertainties and subsurface conditions. The technology also faces complexity in modeling the long-term behavior of geothermal systems, including heat transfer and fluid flow dynamics.

Hydroelectric energy:  Applied to hydroelectric projects, digital twins face challenges in accurately modeling water flow variability and in capturing environmental and ecological constraints. These limitations reduce their effectiveness in optimizing system performance and sustainability.

biomass energy: When used with biomass energy systems, digital twins still struggle to simulate the entire production supply chain. They fall short in providing precise models for biological processes, biomass conversion, and the complex biochemical and thermochemical reactions involved.

The authors emphasize the broader implications of these shortcomings for the renewable energy sector. To address these challenges, they offer a set of guidelines and a research roadmap aimed at helping scientists enhance the reliability and precision of digital twin technologies.

Their recommendations focus on improving data collection methods, advancing modeling techniques, and expanding computational capabilities to ensure digital twins can deliver trustworthy insights for decision-making and system optimization.

The structure of a digital twin. 

Credit

Energy Nexus (2025). DOI: https://doi.org/10.1016/j.nexus.2025.100415

Journal

Energy Nexus

DOI

10.1016/j.nexus.2025.100415 

Method of Research

Literature review

Subject of Research

Not applicable

Article Title

Harnessing the future: Exploring digital twin applications and implications in renewable energy

 

A leading-edge review maps path to better Asian monsoon predictions under global change

Institute of Atmospheric Physics, Chinese Academy of Sciences

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The Asian monsoon is one of the world’s most influential climate systems, directly impacting the weather, water resources, agriculture, and livelihoods of billions of people across Asia.

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Credit: Advances in Atmospheric Sciences

A comprehensive and innovative review, published in Advances in Atmospheric Sciences, offers an in-depth examination of the progress, challenges, and outlook for Asian monsoon climate prediction in the context of global climate change. Led by Professor Bin Wang from the University of Hawaii at Manoa, an international group of scientists synthesizes decades of research to chart a roadmap for more reliable and actionable monsoon seasonal forecasts.

The Asian monsoon is one of the world's most influential climate systems, directly impacting the weather, water resources, agriculture, and livelihoods of billions of people across Asia. Accurate seasonal prediction of the monsoon, especially rainfall, is crucial for disaster prevention, food security, and economic planning in the region. While significant progress has been reached over the past two decades, current climate models still struggle with systematic biases, and the reliability of traditional predictors is changing.

The review systematically summarizes the foundations of monsoon climate prediction, highlighting three key theoretical pillars: El Niño-Southern Oscillation (ENSO), atmospheric teleconnections, and monsoon-ocean interactions. ENSO, in particular, stands out as a major source of monsoon predictability, with different phases and types of ENSO events exerting distinct regional impacts on Asian rainfall patterns. However, the authors emphasize that ENSO is not the only factor at play. Other sources of predictability, such as the Indian Ocean Dipole, land-atmosphere interactions, and remote influences from the Atlantic, North Pacific, and polar regions, also significantly shape monsoon variability. “A comprehensive understanding of these diverse sources of predictability is essential for improving monsoon forecasts,” states Prof. Wang.

The team underscores that external forcings, including greenhouse gases and aerosols, are significantly altering the monsoon system. These factors not only alter shift rainfall patterns but also increase the frequency and intensity of extreme weather events, making the monsoon more variable and more complicated to predict.

In addition, the authors discuss recent advancements in forecasting models and methods, including dynamical models, empirical prediction models, and hybrid dynamic-empirical models. Despite these advancements, significant challenges remain. Current climate models still struggle to accurately simulate key monsoon processes, such as convection and land-sea-air interactions, resulting in systematic biases. Monsoon predictability itself is inherently unstable due to the complex interplay of internal climate variability, remote forcing, and evolving ENSO characteristics.

To overcome these challenges, the review outlines a path forward, recommending a multi-pronged approach. “The future of monsoon prediction lies in integrating cutting-edge technologies with fundamental climate science,” Prof. Wang explained. “This includes leveraging artificial intelligence to capture complex non-linear relationships, developing models that can better simulate key physical processes, and improving our sub-seasonal predictions to bridge the gap between weather and climate.” The review emphasizes that strengthening observational networks, enhancing model accuracy, integrating research and operational forecasting, and promoting international collaboration and data sharing are also critical steps forward.

“We hope that this review will inspire new research and innovation to advance monsoon prediction further, ultimately supporting better risk management and adaptation across Asia,” Prof. Wang concluded.

The review is included in a special issue "Global and regional monsoons: State of the art and perspectives" organized by World Climate Research Programme Monsoon Panel.

 

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Journal

Advances in Atmospheric Sciences

Article Title

Advancing Asian Monsoon Climate Prediction under Global Change: Progress, Challenges, and Outlook

Article Publication Date

25-Jul-2025

Engineer's work aims to improve tropical storm predictions

University of Texas at Dallas

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Dr. Kianoosh Yousefi, assistant professor of mechanical engineering at The University of Texas at Dallas, is developing a model based on machine learning to improve hurricane forecasting. Yousefi’s work is supported by an Office of Naval Research 2025 Young Investigator Program award.

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Credit: The University of Texas at Dallas

Tiny droplets of sea spray generated at the ocean surface can affect the intensity and evolution of hurricanes and other tropical storms.

Their impact, however, is not well understood because of the difficulty of measuring spray concentration and the size and velocity of individual droplets under high wind conditions.

At The University of Texas at Dallas, researchers are studying sea spray, particularly spume, or foam, droplets, in the lab to develop a model based on machine learning to improve hurricane forecasting. The model incorporates the effects of the spray generation function, which quantifies the rate at which droplets form.

Dr. Kianoosh Yousefi, assistant professor of mechanical engineering in the Erik Jonsson School of Engineering and Computer Science, received an Office of Naval Research 2025 Young Investigator Program (YIP) award for the project. The program supports academic, tenure-track scientists and engineers who have received their doctorate or equivalent degree in the past seven years and who show exceptional promise for doing creative research. Yousefi’s award provides up to $742,345 over three years.

“Dr. Yousefi’s YIP award will enable him to make important advances in understanding sea spray dynamics and could meaningfully improve weather forecasting models in densely populated coastal regions,” said Dr. Edward White, professor and department head of mechanical engineering and Jonsson School Chair. “This is an exceptionally challenging area for experimental measurements, and his approach to this, combined with high-fidelity numerical simulations, exemplifies the cutting-edge science he and the rest of mechanical engineering continue to pioneer in solving complex challenges.”

The goal of the research is to provide more accurate tropical storm forecasting without the need for expensive, difficult-to-access experimental methods. Yousefi and his team of researchers are using simulations and lab experiments involving a new wind-wave research tunnel. The research tunnel, which has a 40-foot-long water tank, can generate breaking waves so that the researchers can capture high-resolution data on spume droplets, which are as small as 20 micrometers, the width of a strand of human hair.

The researchers use high-speed shadowgraph imaging, a technique involving a high-speed camera to record the motion, size and shape of objects, such as the size and velocity of spume droplets.

“We are working to capture detailed information that will help us estimate the speed and momentum of spume droplets so we can better understand how sea spray is transported under different wind-wave conditions,” Yousefi said. “I am honored and very excited to receive support through the YIP award to advance this research.”

The resulting machine learning model will consider wave profile, wave slope, wind speed and other relevant parameters to improve the prediction of spray generation and its effects on storm intensity.

Yousefi, who joined UTD in 2023, is the principal investigator for the Flow Dynamics and Turbulence Laboratory, which focuses on the study of turbulent air-sea interaction processes, including surface waves and the accompanying generation of turbulence, spray, bubbles, airflow separation and breaking waves.

The new award recognizes Yousefi’s contribution to the fields of physical oceanography and turbulent air-sea interactions and builds on his research supported by a previous National Science Foundation award to study air-sea interactions in collaboration with researchers at Columbia University.