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3D Printing Becomes Stronger and More Economical with Light and AI
<(Front) Ph.D. candidate Jisoo Nam, (Back row, from left) Ph.D. candidate Boxin Chen, Professor Miso Kim>
Photocurable 3D printing, widely used for everything from dental treatments to complex prototype manufacturing, is fast and precise but has the limitation of being fragile and easily broken by impact. A KAIST research team has developed a new technology to overcome this weakness, paving the way for the more robust and economical production of everything from medical implants to precision machine parts.
KAIST (President Kwang Hyung Lee) announced on the 29th that Professor Miso Kim's research team in the Department of Mechanical Engineering has developed a new technology that fundamentally resolves the durability limitations of photocurable 3D printing.
Digital Light Processing (DLP)-based 3D printing is a technique that uses light to solidify liquid resin (polymer) to rapidly manufacture precise structures, used in various fields such as dentistry and precision machinery. While traditional injection molding offers excellent durability, it requires significant time and cost for mold fabrication. In contrast, photocurable 3D printing allows for flexible shape realization but has a durability drawback.
Professor Kim's team solved this problem by combining two key elements:
A new photocurable resin material that absorbs shock and vibration while allowing for a wide range of properties from rubber to plastic.
A machine learning-based design technology that automatically assigns optimal strength to each part of the structure.
<Figure 1. Schematic of a new manufacturing technology for high-durability photocurable 3D printing using light-controlled gradient structures. This approach integrates the development of stiffness-controllable viscoelastic polyurethane acrylate (PUA) materials, machine learning-based property gradient optimization, and grayscale DLP 3D printing. The technology enhances damping performance and alleviates stress concentration, providing an integrated solution for high reliability, durability, and customized manufacturing. It demonstrates potential applications in structural components subjected to repetitive loads such as joints, automotive interior parts, and precision machinery components>
The research team developed a Polyurethane Acrylate (PUA) material incorporating dynamic bonds, which significantly increases shock and vibration absorption capability compared to existing materials. Furthermore, they successfully applied 'grayscale DLP' technology, which controls the light intensity to achieve different strengths from a single resin composition, thereby assigning customized strength to specific areas within the structure. This concept is inspired by the harmonious and different roles played by bone and cartilage in the human body.
A machine learning algorithm automatically proposes the optimal strength distribution by analyzing the structure and load conditions. This organically connects material development and structural design, enabling customized strength distribution.
The economic efficiency is also noteworthy. Previously, expensive 'multi-material printing' technology was required to achieve diverse material properties, but this new technology yields the same effect with a single material and a single process, significantly reducing production costs. It eliminates the need for complex equipment or material management, and the AI-based structural optimization shortens research and development time and product design costs.
Professor Miso Kim explained, "This technology simultaneously expands the degrees of freedom in material properties and structural design. Patient-specific implants will become more durable and comfortable, and precision machine parts can be manufactured more robustly." She added, "The fact that it secures economic viability by realizing various strengths with a single material and single process is highly significant," and "We anticipate its utilization across various industrial fields such as biomedical, aerospace, and robotics."
The research was spearheaded by Professor Miso Kim's team at the KAIST Department of Mechanical Engineering, with Ph.D. candidate Jisoo Nam as the first author. Boxin Chen, a student from Sungkyunkwan University, also contributed to the collaborative research. The findings were published online on July 16 in the world-renowned journal in materials science, Advanced Materials (IF 26.8). Recognizing the research's excellence, it was also selected for the journal's Frontispiece.
Paper Title: Machine Learning-Driven Grayscale Digital Light Processing for Mechanically Robust 3D-Printed Gradient Materials
DOI: 10.1002/adma.202504075
The achievements of this research have brought Professor Miso Kim significant international attention, as she simultaneously received the 'Wiley Rising Star Award' and the 'Wiley Women in Materials Science Award' in July 2025, hosted by the international academic publisher Wiley.
The Wiley Rising Star Award is given to emerging researchers with the potential for academic leadership, and the Wiley Women in Materials Science Award is a prestigious honor established to celebrate outstanding female scientists in the field of materials science.
<Figure 2. Frontispiece image (scheduled for Issue 42). Multi-property structure fabricated using a photocurable 3D printer. By varying the projector light intensity by location, stronger light creates rigid regions while weaker light forms flexible ones. AI designs an optimized pattern for the structural shape to prevent fracture and reinforce the overall strength.>
This research was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (Nos. NRF-2021R1A2C2095767, RS-2023-00254689, and RS-2024-00433654).
2025.09.30 View 1008 -
KAIST Develops Semiconductor Neuron that Remembers and Responds Like the Brain
<(From left, clockwise) Professor Kyung Min Kim, Min-Gu Lee, Dae-Hee Kim, Dr. Han-Chan Song, Tae-Uk Ko, Moon-Gu Choi, and Eun-Young Kim>
The human brain does more than simply regulate synapses that exchange signals; individual neurons also process information through “intrinsic plasticity,” the adaptive ability to become more sensitive or less sensitive depending on context. Existing artificial intelligence semiconductors, however, have struggled to mimic this flexibility of the brain. A KAIST research team has now developed next-generation, ultra-low-power semiconductor technology that implements this ability as well, drawing significant attention.
KAIST (President Kwang Hyung Lee) announced on September 28 that a research team led by Professor Kyung Min Kim of the Department of Materials Science and Engineering developed a “Frequency Switching Neuristor” that mimics “intrinsic plasticity,” a property that allows neurons to remember past activity and autonomously adjust their response characteristics.
“Intrinsic plasticity” refers to the brain’s adaptive ability- for example, becoming less startled when hearing the same sound repeatedly, or responding more quickly to a specific stimulus after repeated training. The “Frequency Switching Neuristor” is an artificial neuron device that autonomously adjusts the frequency of its signals, much like how the brain becomes less startled by repeated stimuli or, conversely, increasingly sensitive through training.
The research team combined a “volatile Mott memristor,” which reacts momentarily before returning to its original state, with a “non-volatile memristor,” which remembers input signals for long periods of time. This enabled the implementation of a device that can freely control how often a neuron fires (its spiking frequency).
<Figure 1. Conceptual comparison between a neuron and a frequency-tunable neuristor. The intrinsic plasticity of brain neurons regulates excitability through ion channels. Similarly, the frequency-tunable neuristor uses a volatile Mott device to generate current spikes, while a non-volatile VCM device adjusts resistance states to realize comparable frequency modulation characteristics>
In this device, neuronal spike signals and memristor resistance changes influence each other, automatically adjusting responses. Put simply, it reproduces within a single semiconductor device how the brain becomes less startled by repeated sounds or more sensitive to repeated stimuli.
To verify the effectiveness of this technology, the researchers conducted simulations with a “sparse neural network.” They found that, through the neuron’s built-in memory function, the system achieved the same performance with 27.7% less energy consumption compared to conventional neural networks.
They also demonstrated excellent resilience: even if some neurons were damaged, intrinsic plasticity allowed the network to reorganize itself and restore performance. In other words, artificial intelligence using this technology consumes less electricity while maintaining performance, and it can compensate for partial circuit failures to resume normal operation.
Professor Kyung Min Kim, who led the research, stated, “This study implemented intrinsic plasticity, a core function of the brain, in a single semiconductor device, thereby advancing the energy efficiency and stability of AI hardware to a new level. This technology, which enables devices to remember their own state and adapt or recover even from damage, can serve as a key component in systems requiring long-term stability, such as edge computing and autonomous driving.”
This research was carried out with Dr. Woojoon Park (now at Forschungszentrum Jülich, Germany) and Dr. Hanchan Song (now at ETRI) as co-first authors, and the results were published online on August 18 in Advanced Materials (IF 26.8), a leading international journal in materials science.
※ Paper title: “Frequency Switching Neuristor for Realizing Intrinsic Plasticity and Enabling Robust Neuromorphic Computing,” DOI: 10.1002/adma.202502255
This research was supported by the National Research Foundation of Korea and Samsung Electronics.
2025.09.30 View 816 -
KAIST, Cancer Cell Nuclear Hypertrophy May Suppress Spread
<(From Left) Ph.D candidate Saemyeong Hong, Dr. Changgon Kim, Professor Joon Kim, Professor Ji Hun Kim>
In tissue biopsies, cancer cells are frequently observed to have nuclei (the cell's genetic information storage) larger than normal. Until now, this was considered a sign that the cancer was worsening, but the exact cause and effect had not been elucidated. In this study, the KAIST research team found that cancer cell nuclear hypertrophy is not a cause of malignancy but a temporary response to replication stress, and that it can, in fact, suppress metastasis. This discovery is expected to lead to the development of new diagnostic and therapeutic strategies for cancer and metastasis inhibition.
KAIST (President Kwang Hyung Lee) announced on the September 26th that a research team led by Professor Joon Kim of the Graduate School of Medical Science and Engineering, in collaboration with the research teams of Professor Ji Hun Kim and Professor You-Me Kim, discovered the molecular reason why the nucleus enlarges in cancer cells. This achievement provides an important clue for understanding nuclear hypertrophy, a phenomenon frequently observed in pathological examinations but whose direct cause and relationship with cancer development were unclear.
The research team confirmed that DNA replication stress (the burden and error signal that occurs when a cell copies its DNA), which is common in cancer cells, causes the 'actin' protein inside the nucleus to aggregate (polymerize), which is the direct cause of the nuclear enlargement.
<Mechanisms Inducing Nuclear Enlargement in Cancer Cells and Its Impact on Cellular Physiology>
This result suggests that the change in cancer cell nuclear size may not simply be a "trait evolved by the cancer cell for its benefit." Rather, it suggests that it is a temporary, makeshift response to stress, and that it may impose constraints on the cancer cell's potential for metastasis.
Therefore, future research needs to explore whether changes in nuclear size can become a target for cancer treatment or a clue related to the suppression of metastasis. That is, nuclear hypertrophy may be a temporary response to replication stress and should not necessarily be seen as indicating the malignancy of the cancer.
This conclusion was substantiated through: (1) Gene Function Screening (inhibiting thousands of genes sequentially to find the key genes involved in nuclear size regulation); (2) Transcriptome Analysis (confirming which gene programs are activated when the nucleus enlarges); (3) 3D Genome Structure Analysis (Hi-C), which revealed that nuclear hypertrophy is not just a size change but is connected to changes in DNA folding and gene arrangement; and (4) Mouse Xenograft Models (confirming that cancer cells with enlarged nuclei actually have reduced motility and metastatic ability).
Professor Joon Kim of the Graduate School of Medical Science and Engineering said, "We confirmed that DNA replication stress disrupts the nuclear size balance, explaining the underlying mechanism of long-standing pathological observations," adding, "The possibility of utilizing nuclear structural changes as a new indicator for cancer diagnosis and metastasis prediction has now opened up."
Dr. Changgon Kim (currently a Hematology and Oncology specialist at Korea University Anam Hospital) and Saemyeong Hong, a PhD candidate from the KAIST Graduate School of Medical Science and Engineering, participated as co-first authors in this study. The results were published online in the international journal PNAS (Proceedings of the National Academy of Sciences of the United States of America) on September 9th.
※ Paper Title: Replication stress-induced nuclear hypertrophy alters chromatin topology and impacts cancer cell fitness ※ DOI: https://doi.org/10.1073/pnas.2424709122
Meanwhile, this research was supported by the Mid-career Researcher Program and the Engineering Research Center (ERC) program of the National Research Foundation of Korea.
2025.09.29 View 791 -
Thinking outside the box to Fabricate Customized 3D Neural Chips
<(From Left) Professor Yoonkey Nam, Dr. Dongjo Yoon from the Department of Bio and Brain Engineering>
Cultured neural tissues have been widely used as a simplified experimental model for brain research. However, existing devices for growing and recording neural tissues, which are manufactured using semiconductor processes, have limitations in terms of shape modification and the implementation of three-dimensional (3D) structures.
By "thinking outside the box," a KAIST research team has successfully created a customized 3D neural chip. They first used a 3D printer to fabricate a hollow channel structure, then used capillary action to automatically fill the channels with conductive ink, creating the electrodes and wiring. This achievement is expected to significantly increase the design freedom and versatility of brain science and brain engineering research platforms.
On the 25th, KAIST announced that a research team led by Professor Yoonkey Nam from the Department of Bio and Brain Engineering has successfully developed a platform technology that overcomes the limitations of traditional semiconductor-based manufacturing. This technology allows for the precise fabrication of "3D microelectrode array" (neural interfaces with multiple microelectrodes arranged in a 3D space to measure and stimulate the electrophysiological signal of neurons) in various customized forms for in vitro culture chips.
Existing 3D microelectrode array fabrication, based on semiconductor processes, has limited 3D design freedom and is expensive. While 3D printing-based fabrication techniques have recently been proposed to overcome these issues, they still have limitations in terms of 3D design freedom for various in vitro neural network structures because they follow the traditional sequence of "conductive material patterning → insulator coating → electrode opening."
The KAIST research team leveraged the excellent 3D design freedom provided by 3D printing technology and its ability to use printed materials as insulators. By reversing the traditional process, they established an innovative method that allows for more flexible design and functional measurement of 3D neuronal network models for in vitro culture.
<Schematic Diagram of an Integrated Cell Culture Substrate-Microelectrode Array Platform for In Vitro Cultured 3D Neural Network Models>
First, they used a 3D printer to print a hollow 3D insulator with micro-tunnels. This structure was designed to serve as a stable scaffold for conductive materials in 3D space while also supporting the creation of various 3D neuronal networks. They then demonstrated that by using capillary action to fill these internal micro-tunnels with conductive ink, they could create a 3D scaffold-microelectrode array with more freely arranged microelectrodes within a complex 3D culture support structure.
The new platform can be used to create various chip shapes, such as probe-type, cube-type, and modular-type, and supports the fabrication of electrodes using different materials like graphite, conductive polymers, and silver nanoparticles. This allows for the simultaneous measurement of multichannel neural signals from both inside and outside the 3D neuronal network, enabling precise analysis of the dynamic interactions and connectivity between neurons.
Professor Nam stated, "This research, which combines 3D printing and capillary action, is an achievement that significantly expands the freedom of neural chip fabrication." He added that it will contribute to the advancement of fundamental brain science research using neural tissue, as well as applied fields like cell-based biosensors and biocomputing.
Dr. Dongjo Yoon from KAIST's Department of Bio and Brain Engineering participated as the first author of the study. The research findings were published online in the international academic journal Advanced Functional Materials (June 25th issue).
※Paper Title: Highly Customizable Scaffold-Type 3D Microelectrode Array Platform for Design and Analysis of the 3D Neuronal Network In Vitro
This research was supported by the Consolidator Grants Program and the Global Basic Research Laboratory Program of the National Research Foundation of Korea.
2025.09.26 View 704 -
KAIST team links early life epigenetic memory to adult brain inflammation
<(From left) Professor Won-Suk Chung, Ph.D. Ph.D candidate Hyeonji Park Dr. Seongwan Park, Professor Inkyung Jung>
Why do some people remain healthy through childhood yet become more vulnerable to brain disorders such as dementia later in life? A KAIST (President Kwang Hyung Lee) -led team has uncovered a key part of the answer: a developmental ‘switch’ in astrocytes—the brain’s most abundant support cells that shapes how strongly the brain’s immune system reacts in adulthood. The study identifies a gene, NR3C1 (encoding the glucocorticoid receptor), as a master regulator of this switch and shows how early-life epigenetic ‘memory’ can predispose the adult brain to excessive inflammation.
The work was carried out by a joint team led by Professor Inkyung Jung (Department of Biological Sciences, KAIST) and Associate Director Won-Suk Chung (Center for Vascular Research, Institute for Basic Science; Professor, KAIST Biological Sciences). Using mouse models, the researchers mapped gene-regulatory programs across multiple stages of astrocyte development and found that NR3C1 acts during a brief early-postnatal window to enforce long-term immune restraint.
<The schematic illustrates how the NR3C1 gene (glucocorticoid receptor) suppresses the immune response of astrocytes. In normal (control) astrocytes, NR3C1 binds to specific regulatory regions of DNA (nGRE) to inhibit the expression of immune-related genes, thereby maintaining brain homeostasis even under immune stimulation. In contrast, in NR3C1-deficient astrocytes (KO), this suppression is lost, leading to excessive activation of inflammation-related genes such as Gfap, Il6st, Stat2, and Cxcl10. As a result, in an autoimmune encephalomyelitis (EAE) model, pronounced neuroinflammation and clinical symptoms (paralysis and severe debilitation) are observed>
To build this map, the team combined state-of-the-art 3D epigenome profiling with RNA sequencing and chromatin accessibility analyses, capturing how DNA folds and which regulatory elements contact target genes. They identified 55 stage-specific transcription factors that guide astrocyte maturation; among them, NR3C1 emerged as the critical ‘switch’ in early life. Notably, deleting NR3C1 in astrocytes did not disrupt normal development. However, when the adult mice were challenged with an autoimmune model of multiple sclerosis, animals lacking astrocytic NR3C1 mounted exaggerated inflammatory responses and developed more severe disease.
Mechanistically, the study shows that early loss of NR3C1 epigenetically primes immune genes - keeping their regulatory elements open and ready - so that later in life these genes respond too strongly to inflammatory cues. In effect, NR3C1 serves as an early ‘brake’ that prevents over-activation of astrocyte immune programs in adulthood.
“This is the first demonstration that astrocyte immune functions are governed by epigenetic memory,” said Professor Won-Suk Chung. “Our findings offer new clues to the origins of degenerative brain disorders, including Alzheimer’s disease.”
“We reveal a temporal regulatory window in astrocyte development that can set the stage for disease vulnerability in adulthood,” added Professor Inkyung Jung. “Understanding the 3D genome logic behind these programs could open paths to therapies for immune-related brain disorders such as multiple sclerosis.”
<The figure shows the three-dimensional genome structure of astrocytes at specific gene loci, illustrating how NR3C1 regulates their expression. In normal cells, NR3C1 binds to DNA and maintains the chromatin in a closed state, thereby preventing unnecessary activation between distal regulatory elements (enhancers) and gene promoters. In contrast, when NR3C1 is absent, the chromatin becomes open, creating a state in which enhancers and genes can be more easily activated. As a result, genes such as Mxi1 are overexpressed, triggering inflammatory responses. This clearly demonstrates that NR3C1 plays an essential role in maintaining immune homeostasis by stabilizing three-dimensional gene regulatory mechanisms.>
The results of this study were published online on September 22 in the international journal Nature Communications (IF 15.7), with Dr. Seongwan Park and PhD student Hyeonji Park of KAIST’s Department of Biological Sciences as co-first authors.
※ Paper title: “NR3C1-mediated epigenetic regulation suppresses astrocytic immune responses in mice,” DOI: https://www.nature.com/articles/s41467-025-64088-5
In addition, on September 17, the journal published a commentary article introducing this research: https://www.nature.com/articles/s41467-025-64102-w
This research was supported by the Suh Kyungbae Science Foundation, the Ministry of Health and Welfare, the Ministry of Science and ICT, and IBS.
Glossary - Epigenetic priming: preparing genes for rapid future activation by altering chromatin without changing DNA sequence
2025.09.25 View 1263 -
Simultaneous On and Off Gene Control with Gene Scissors
<(From left to right) Dr. Soo Young Moon, KAIST Institute of Life Science,Professor Ju Young Lee, Graduate School of Engineering Biology (Adjunct Professor of Biological Sciences),Dr. Myung Hyun Noh, Korea Research Institute of Chemical Technology (KRICT),Researcher Nan-Yeong An, Department of Biological Sciences>
Turning genes on and off is like flipping a light switch, controlling whether genes in a cell are active. When a gene is turned on, the production of proteins or other substances is promoted; when it's turned off, production is suppressed. Korean researchers have gone beyond the limitations of existing CRISPR technology, which focused primarily on "off" functions, by developing the world's first innovative system that can simultaneously turn genes on and off, opening a new paradigm for the synthetic biology-based bio-industry.
A joint research team led by Professor Ju Young Lee of KAIST Graduate School of Biological Engineering (Adjunct Professor of Biological Sciences) and Dr. Myung Hyun Noh of the Korea Research Institute of Chemical Technology (KRICT), an organization under the National Research Council of Science & Technology (NST) , announced on the 21st that they have developed a new dual-mode CRISPR gene editing system that can simultaneously turn on and off desired genes in E. coli.
E. coli is a representative microorganism that is easy to experiment with and can be directly applied to industrial uses. Meanwhile, CRISPR technology is considered one of the most innovative tools in 21st-century biotechnology.
In particular, bacteria, which are the foundation of synthetic biology, have a simple structure and multiply rapidly, while also being able to produce a variety of useful substances. Therefore, gene activation in bacteria is a key technology for designing "microbial factories," and its industrial value is very high.
The core of synthetic biology is to design the genetic circuits of living organisms like programming a circuit board to perform a desired function. Just as switches are turned on and off in an electronic circuit, a technology is needed to optimize metabolic pathways by activating certain genes while suppressing others. The dual-mode gene scissors developed by the research team are the key tool that enables this precise gene regulation.
Existing CRISPR gene scissors were primarily specialized for the "off" function (repression) and were excellent at blocking gene expression, but their ability to turn genes on was very limited.
Furthermore, for CRISPR to work, a specific DNA recognition sequence (PAM, protospacer adjacent motif) is required, and the narrow range of PAM recognition in existing systems limited the scope of genes that could be controlled.
In addition, while CRISPR-based activation (CRISPRa) has been somewhat developed in eukaryotic cells (human, plant, and animal cells), there were limitations in bacteria where the "on" function did not work properly due to differences in their internal transcription regulation mechanisms.
To overcome these limitations, the research team expanded the target range to access more genes and significantly improved gene activation performance by utilizing E. coli proteins. As a result, the gene scissors, which were previously "mainly for turning off," have evolved into a system that can simultaneously control both "on" and "off."
The performance verification results of the developed system were very impressive. In gene activation experiments, expression levels increased by up to 4.9 times, and in repression experiments, they could be suppressed by up to 83%.
Even more astonishing was the ability to control two different genes simultaneously. The team successfully activated one gene by 8.6 times while simultaneously repressing another by 90%.
< (Left) The principle of the dual-mode CRISPR gene scissors. When the guide RNA (gRNA) binds to the target sequence, dxCas9-CRP either promotes (CRISPRa) or inhibits (CRISPRi) the binding of RNA polymerase near the transcription start site, precisely controlling gene expression. (Center) A large-scale screening of the entire E. coli genome is conducted to identify key regulatory targets for optimizing target substance production. The metabolic pathway for producing the target substance is then re-engineered by simultaneously regulating gene expression through activation and repression. (Right) The dual-mode CRISPR gene scissors system enables systematic redesign of cell metabolism, precise reconfiguration of gene expression, and the construction of microbial strains that can perform various functions, ultimately leading to a significant increase in target substance productivity. In this study, the dual-mode CRISPR system was applied to E. coli to demonstrate the enhanced production of 'violacein,' a purple functional biopigment with anticancer effects, and its potential for expansion to other bacterial species was also confirmed. >
To demonstrate the practicality of this technology, the research team challenged themselves to increase the production of 'violacein,' a purple pigment with anticancer properties. Through large-scale experiments on all genes of E. coli, they identified genes that help in violacein production.
As a result, production increased by 2.9 times when the 'rluC' gene, which helps protein production, was turned on, and by 3.0 times when the 'ftsA' gene, which helps cell division, was turned off. When both genes were controlled simultaneously, a greater synergistic effect was observed, achieving a remarkable 3.7-fold increase in production.
Dr. Myung Hyun Noh of KRICT stated, "Precise gene activation is now possible in bacteria," and "This will greatly contribute to the development of the synthetic biology-based bio-industry."
Professor Ju Young Lee said, "This research is a successful outcome of combining gene scissors with synthetic biology to significantly enhance the efficiency of microbial production platforms," and "The ability to control a complex genetic network with a single system presents a new research paradigm." He added, "This technology has also been confirmed to work in other bacterial species and can be utilized in various fields such as the production of biopharmaceuticals, chemicals, and fuels."
< (A) A diagram of the violacein biosynthesis pathway, a functional biopigment produced from the starting material L-tryptophan through several enzymatic reactions. Violacein is a functional substance with broad applications in various industries and research fields, including medicine, healthcare, dyes, textiles, food and beverage, and cosmetics. (B) The results of a large-scale screening of gRNAs for gene activation and repression using the dual-mode CRISPR gene scissors system confirmed a 2.9-fold increase in violacein production (mg/L) upon rluC activation and a 3.0-fold increase upon ftsA repression compared to the control group. >
The results of this research, with Dr. Soo Young Moon, a postdoctoral researcher at our university's Institute of Life Science, as the first author, were published online in 'Nucleic Acids Research,' a top-tier journal in the field of molecular biology, on August 21st.
Paper Title: Dual-mode CRISPRa/i for genome-scale metabolic rewiring in Escherichia coli
Author Information: Soo Young Moon (KAIST, First Author), Mi Ri Kim (KRICT), Nan-Yeong An (KAIST), Myung Hyun Noh (KRICT, Corresponding Author), Ju Young Lee (KAIST, Corresponding Author) (Total of 5 authors)
DOI: 10.1093/nar/gkad818
This research was supported by the joint research and development program of the Ministry of Science and ICT, the National Research Foundation of Korea, and Boston Korea.
2025.09.22 View 678 -
A Breakthrough in Parkinson's Research: Precision Diagnosis and Treatment with AI and Optogenetics
<Research team photo (from top left) Dr. Bobae Hyeon, Professor Daesoo Kim, Director Chang-joon Lee, (right) Professor Won Do Heo>
Globally recognized figures like Muhammad Ali and Michael J. Fox have long suffered from Parkinson's disease. The disease presents a complex set of motor symptoms, including tremors, rigidity, bradykinesia, and postural instability. However, traditional diagnostic methods have struggled to sensitively detect changes in the early stages, and drugs targeting brain signal regulation have had limited clinical effectiveness.
Recently, Korean researchers successfully demonstrated the potential of a technology that integrates AI and optogenetics as a tool for precise diagnosis and therapeutic evaluation of Parkinson's disease in mice. They have also proposed a strategy for developing next-generation personalized treatments.
KAIST (President Kwang Hyung Lee) announced on the 22nd of September that a collaborative research team—comprising Professor Won Do Heo's team from the Department of Biological Sciences, Professor Daesoo Kim's team from the Department of Brain and Cognitive Sciences, and Director Chang-Jun Lee's team from the Institute for Basic Science (IBS) Center for Cognition and Sociality—achieved a preclinical research breakthrough by combining AI analysis with optogenetics. Their work simultaneously demonstrated the possibility of early and precise diagnosis and treatment in an animal model of Parkinson's disease.
The research team created a Parkinson's disease mouse model with two stages of severity. These were male mice with alpha-synuclein protein abnormalities, a standard model used to simulate human Parkinson's disease for diagnostic and therapeutic research.
In collaboration with Professor Kim's team at KAIST, they introduced AI-based 3D pose estimation for behavioral analysis. The team analyzed over 340 behavioral features—such as gait, limb movements, and tremors—from the Parkinson's mice and condensed them into a single metric: the AI-predicted Parkinson's disease score (APS).
The analysis showed that the APS exhibited a significant difference from the control group as early as two weeks after the disease was induced. It also proved more sensitive in assessing the disease's severity than traditional motor function tests. The study identified key diagnostic features, including changes in stride, asymmetrical limb movements, and chest tremors. The top 20 behavioral features included hand/foot asymmetry, changes in stride and posture, and an increase in high-frequency chest movement.
To confirm that these behavioral indicators were not just general motor decline but specific to Parkinson's, the team applied the same analysis to a mouse model of Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig's disease, in collaboration with Director Lee's team at IBS. Since both Parkinson's and ALS cause motor function problems, if the APS simply reflected poor motor skills, a high score should have appeared in both diseases.
However, the analysis of the ALS animal model showed that despite a decline in motor function, the mice did not exhibit the high APS seen in the Parkinson's model. Instead, their scores remained low, and their behavioral changes were distinctly different. This demonstrates that APS is directly related to specific, characteristic changes that only appear in Parkinson's disease.
For treatment, the research team used optoRET, an optogenetics technology that precisely controls neurotrophic signals with light. This technique proved effective in the animal model, leading to smoother gait and limb movements and a reduction in tremors.
Specifically, a regimen of shining light on alternate days was found to be the most effective, and it also showed a tendency to protect dopamine-producing neurons in the brain.
Professor Won Do Heo of KAIST stated, "This is the first time in the world that a preclinical framework has been implemented that connects early diagnosis, treatment evaluation, and mechanism verification of Parkinson's disease by combining AI-based behavioral analysis with optogenetics." He added, "This lays a crucial foundation for future personalized medicine and customized treatments for patients."
The study, with Dr. Bobae Hyeon, a postdoctoral researcher at the KAIST Institute for Biological Science, as the first author, was published online in the international journal Nature Communications on August 21st. Dr. Hyeon is conducting follow-up research to advance Parkinson's cell therapy at McLean Hospital, Harvard Medical School, supported by the "Global Physician-Scientist Training Program" of the Korea Health Industry Development Institute.
This research was supported by the KAIST Global Singularity Project, the Ministry of Science and ICT/National Research Foundation of Korea, the IBS Center for Cognition and Sociality, and the Ministry of Health and Welfare/Korea Health Industry Development Institute.
Paper Title: Integrating artificial intelligence and optogenetics for Parkinson's disease diagnosis and therapeutics in male mice
DOI: https://doi.org/10.1038/s41467-025-63025-w
2025.09.22 View 578 -
Next-Generation Humanoid Robot Capable of Moonwalk Developed
<From the middle of the back row, clockwise: Professor Hae-Won Park, Dongyun Kang (Ph.D. candidate), Hajun Kim (Ph.D. candidate), JongHun Choe (Ph.D. candidate), Min-su Kim (Research Professor)>
KAIST research team's independently developed humanoid robot boasts world-class driving performance, reaching speeds of 12km/h, along with excellent stability, maintaining balance even with its eyes closed or on rough terrain. Furthermore, it can perform complex human-specific movements such as duck walk and moonwalk, drawing attention as a next-generation robot platform that can be utilized in actual industrial settings. Professor Park Hae-won's research team at the Humanoid Robot Research Center (HuboLab) of KAIST's Department of Mechanical Engineering announced on the 19th that they have independently developed the lower body platform for a next-generation humanoid robot. The developed humanoid is characterized by its design tailored for human-centric environments, targeting a height (165cm) and weight (75kg) similar to that of a human. The significance of the newly developed lower body platform is immense as the research team directly designed and manufactured all core components, including motors, reducers, and motor drivers. By securing key components that determine the performance of humanoid robots with their own technology, they have achieved technological independence in terms of hardware. In addition, the research team trained an AI controller through a self-developed reinforcement learning algorithm in a virtual environment, successfully applied it to real-world environments by overcoming the Sim-to-Real Gap, thereby securing technological independence in terms of algorithms as well.
<Developed 'KAIST Humanoid' Lower Body Platform>
Currently, the developed humanoid can run at a maximum speed of 3.25m/s (approximately 12km/h) on flat ground and has a step-climbing capability of over 30cm (a performance indicator showing how high a curb, stairs, or obstacle can be overcome). The team plans to further enhance its performance, aiming for a driving speed of 4.0m/s (approximately 14km/h), ladder climbing, and over 40cm step-climbing capability.
<‘KAIST Humanoid’ Lower Body Platform running>
Professor Hae-Won Park's team is collaborating with Professor Jae-min Hwangbo's team (arms) from KAIST's Department of Mechanical Engineering, Professor Sangbae Kim's team (hands) from MIT, Professor Hyun Myung's team (localization and navigation) from KAIST's Department of Electrical Engineering, and Professor Jae-hwan Lim's team (vision-based manipulation intelligence) from KAIST's Kim Jaechul AI Graduate School to implement a complete humanoid hardware with an upper body and AI. Through this, they are developing technology to enable the robot to perform complex tasks such as carrying heavy objects, operating valves, cranks, and door handles, and simultaneously walking and manipulating when pushing carts or climbing ladders. The ultimate goal is to secure versatile physical abilities to respond to the complex demands of actual industrial sites.
<An Intermediate Result: A Single-Leg Hopping Robot Has Been Developed>
During this process, the research team also developed a single-leg 'Hopping' robot. This robot demonstrated high-level movements, maintaining balance on one leg and repeatedly hopping, and even exhibited extreme athletic abilities such as a 360-degree somersault. Especially in a situation where imitation learning was impossible due to the absence of a biological reference model, the research team achieved significant results by implementing an AI controller through reinforcement learning that optimizes the center of mass velocity while reducing landing impact. Professor Park Hae-won stated, "This achievement is an important milestone that has achieved independence in both hardware and software aspects of humanoid research by securing core components and AI controllers with our own technology," and added, "We will further develop it into a complete humanoid including an upper body to solve the complex demands of actual industrial sites and furthermore, foster it as a next-generation robot that can work alongside humans."
<Key Components of the Directly Developed Robot: (a) Reducer, (b) Motor Stator, (c) Motor Driver, (d) EtherCAT-CAN convert board>
The results of this research will be presented by JongHun Choe, a Ph.D. candidate in Mechanical Engineering, as the first author, on hardware development at 'Humanoids 2025', an international humanoid robot specialized conference held on October 1st. Additionally, Ph.D. candidates Dongyun Kang, Gijeong Kim, and JongHun Choe from Mechanical Engineering will present the AI algorithm achievements as co-first authors at 'CoRL 2025', the top conference in robot intelligence, held on September 29th. ※Paper Titles and Papers: Learning Impact-Rich Rotational Maneuvers via Centroidal Velocity Rewards and Sim-to-Real Techniques: A One-Leg Hopper Flip Case Study, Conference on Robot Learning (CoRL), Seoul, Korea 2025, Dongyun Kang, Gijeong Kim, JongHun Choe, Hajun Kim, Hae-Won Park, arxiv version: https://arxiv.org/abs/2505.12222 Design of a 3-DOF Hopping Robot with an Optimized Gearbox: An Intermediate Platform Toward Bipedal Robots, IEEE-RAS, International Conference on Humanoid Robots, Seoul, Korea, 2025, JongHun Choe, Gijeong Kim, Hajun Kim, Dongyun Kang, Min-Su Kim, Hae-Won Park, arxiv version: https://arxiv.org/abs/2505.12231 This research was supported by research funding from the Ministry of Trade, Industry and Energy and the Korea Institute of Industrial Technology Planning and Evaluation (KEIT) (RS-2024-00427719). ※ Related Video: https://youtu.be/ytWO7lldN4c
2025.09.19 View 969 -
Accurate Real time ECG Measurement While Comfortably Lying Down at Home
< (From left) Professor Chul Kim of the Department of Bio and Brain Engineering, Ph.D. candidate Minjae Kim, researcher Premravee Teeravichayangoon >
KAIST's research team has developed a technology that can measure electrocardiogram (ECG) and heart rate variability (HRV) in real time by simply lying on a bed with clothes on, without having to go to the hospital. This technology is expected to evolve into a daily heart health monitoring platform in conjunction with remote healthcare, and further expand into various bio-healthcare fields such as sleep and stress analysis, contributing to personalized prevention and early diagnosis for patients.
KAIST announced on the 19th that Professor Chul Kim's research team from the Department of Bio and Brain Engineering has developed an 'in-bed cardiac monitoring on-device system'.
The research team manufactured a flexible substrate sensor that integrates the electronic circuit and electrodes into one to increase precision, and implemented an integrated system that can perform signal-noise separation, heart beat signal (R-peak) detection, and heart rate variability analysis in real time through on-device signal processing.
Existing ECG measurement had the inconvenience of visiting a hospital, taking off clothes, and attaching wet electrodes to the skin. Because of this, long-term monitoring was difficult, and it was not easy for the elderly or patients with chronic diseases to use it daily. Non-contact methods also had a technical limitation of being vulnerable to external noise.
To solve these problems, the research team applied a circuit that blocks external noise (active shielding) and a circuit that stably captures minute current changes in the human body (right-leg drive circuit). In addition, they implemented a mathematical transformation technique (wavelet transform) that extracts only the important parts from the heart beat signal and a calculation method (peak detection algorithm) that accurately identifies the moment of the heart's electrical beat (R-peak) as on-device signal processing techniques, allowing for precise real-time analysis of the signal.
As a result, users can obtain stable and accurate ECG signals even when lying on their backs with clothes on.
< Figure. Overall structural diagram of the developed non-contact in-bed cardiac monitoring on-device system, schematic diagram of the R-peak detection algorithm, real-time ECG and HRV measurement screen >
This research presents new possibilities for managing chronic cardiovascular diseases and supporting the health of the elderly, as it can be easily used not only in hospitals but also at home.
Professor Chul Kim said, "This system, which can extract signals in real time even in a noisy environment, can be used to easily check heart health in daily life," and added, "In the future, it will become the foundation of sleep health management by adding the measurement of various bio-signals."
This paper, in which Ph.D. candidate Minjae Kim and researcher Premravee Teeravichayangoon from the Department of Bio and Brain Engineering participated as co-first authors, was published online in the international journal 'Biosensors and Bioelectronics' on August 9, 2025.
※ Paper title: A homecare in-bed hardware system for precise real-time ECG and HRV monitoring with layered clothing. DOI: https://doi.org/10.1016/j.bios.2025.117838
※ Author information: Minjae Kim (KAIST Department of Bio and Brain Engineering, First Author), Premravee Teeravichayangoon (KAIST Department of Bio and Brain Engineering, First Author), Chul Kim (KAIST Department of Bio and Brain Engineering, Corresponding Author)
Meanwhile, this research was carried out with the support of the National Research Foundation of Korea's Basic Research Lab and Bio-medical Technology Development Project, and the KAIST-Ceragem Future Healthcare Research Center.
2025.09.19 View 515 -
KAIST Develops AI Crowd Prediction Technology to Prevent Disasters like the Itaewon Tragedy
<(From Left) Ph.D candidate Youngeun Nam from KAIST, Professor Jae-Gil Lee from KAIST, Ji-Hye Na from KAIST, (Top right, from left) Professor Soo-Sik Yoon from Korea University, Professor HwanJun Song from KAIST>
To prevent crowd crush incidents like the Itaewon tragedy, it's crucial to go beyond simply counting people and to instead have a technology that can detect the real-
inflow and movement patterns of crowds. A KAIST research team has successfully developed new AI crowd prediction technology that can be used not only for managing large-scale events and mitigating urban traffic congestion but also for responding to infectious disease outbreaks.
On the 17th, KAIST (President Kwang Hyung Lee) announced that a research team led by Professor Jae-Gil Lee from the School of Computing has developed a new AI technology that can more accurately predict crowd density.
The dynamics of crowd gathering cannot be explained by a simple increase or decrease in the number of people. Even with the same number of people, the level of risk changes depending on where they are coming from and which direction they are heading.
Professor Lee's team expressed this movement using the concept of a "time-varying graph." This means that accurate prediction is only possible by simultaneously analyzing two types of information: "node information" (how many people are in a specific area) and "edge information" (the flow of people between areas).
In contrast, most previous studies focused on only one of these factors, either concentrating on "how many people are gathered right now" or "which paths are people moving along." However, the research team emphasized that combining both is necessary to truly capture a dangerous situation.
For example, a sudden increase in density in a specific alleyway, such as Alley A, is difficult to predict with just "current population" data. But by also considering the flow of people continuously moving from a nearby area, Area B, towards Area A (edge information), it's possible to pre-emptively identify the signal that "Area A will soon become dangerous."
To achieve this, the team developed a "bi-modal learning" method. This technology simultaneously considers population counts (node information) and population flow (edge information), while also learning spatial relationships (which areas are connected) and temporal changes (when and how movement occurs).
Specifically, the team introduced a 3D contrastive learning technique. This allows the AI to learn not only 2D spatial (geographical) information but also temporal information, creating a 3D relationship. As a result, the AI can understand not just whether the population is "large or small right now," but "what pattern the crowd is developing into over time." This allows for a much more accurate prediction of the time and place where congestion will occur than previous methods.
<Figure 1. Workflow of the bi-modal learning-based crowd congestion risk prediction developed by the research team.
The research team developed a crowd congestion risk prediction model based on bi-modal learning. The vertex-based time series represents indicator changes in a specific area (e.g., increases or decreases in crowd density), while the edge-based time series captures the flow of population movement between areas over time. Although these two types of data are collected from different sources, they are mapped onto the same network structure and provided together as input to the AI model. During training, the model simultaneously leverages both vertex and edge information based on a shared network, allowing it to capture complex movement patterns that might be overlooked when relying on only a single type of data. For example, a sudden increase in crowd density in a particular area may be difficult to predict using vertex information alone, but by additionally considering the steady inflow of people from adjacent areas (edge information), the prediction becomes more accurate. In this way, the model can precisely identify future changes based on past and present information, ultimately predicting high-risk crowd congestion areas in advance.>
The research team built and publicly released six real-world datasets for their study, which were compiled from sources such as Seoul, Busan, and Daegu subway data, New York City transit data, and COVID-19 confirmed case data from South Korea and New York.
The proposed technology achieved up to a 76.1% improvement in prediction accuracy over recent state-of-the-art methods, demonstrating strong perf
Professor Jae-Gil Lee stated, "It is important to develop technologies that can have a significant social impact," adding, "I hope this technology will greatly contribute to protecting public safety in daily life, such as in crowd management for large events, easing urban traffic congestion, and curbing the spread of infectious diseases."
Youngeun Nam, a Ph.D candidate in the KAIST School of Computing, was the first author of the study, and Jihye Na, another Ph.D candidate, was a co-author. The research findings were presented at the Knowledge Discovery and Data Mining (KDD) 2025 conference, a top international conference in the field of data mining, this past August.
※ Paper Title: Bi-Modal Learning for Networked Time Series ※ DOI: https://doi.org/10.1145/3711896.3736856
This technology is the result of research projects including the "Mid-Career Researcher Project" (RS-2023-NR077002, Core Technology Research for Crowd Management Systems Based on AI and Mobility Big Data) and the "Human-Centered AI Core Technology Development Project" (RS-2022-II220157, Robust, Fair, and Scalable Data-Centric Continuous Learning).
2025.09.17 View 758 -
Tracking Atoms during Fuel Cell Cycles: KAIST Team Reveals the Atomic-Scale Secret Behind Fuel Cell Catalyst Durability
<Professor Yongsoo Yang, Professor Eun-Ae Cho, Dr. Chaehwa Jeong, Dr. Joohyuk Lee, Dr. Hyesung Cho, Researcher Kwangho Lee from KAIST>
Hydrogen fuel cell vehicles have long been hailed as the future of clean mobility: cars that emit nothing but water while delivering high efficiency and power density. Yet a stubborn obstacle remains. The heart of the fuel cell, the platinum-based catalyst, is both expensive and prone to degradation. Over time, the catalyst deteriorates during operation, forcing frequent replacements and keeping hydrogen vehicles costly.
Understanding why and how these catalysts degrade at the atomic level is a longstanding challenge in the catalysis research. Without this knowledge, designing truly durable and affordable fuel cells for mass adoption remains out of reach.
Now, a team led by Professor Yongsoo Yang of the Department of Physics at KAIST (Korea Advanced Institute of Science and Technology), in collaboration with Professor Eun-Ae Cho of KAIST’s Department of Materials Science and Engineering, researchers at Stanford University and the Lawrence Berkeley National Laboratory, has successfully tracked the three-dimensional change of individual atoms inside fuel cell catalysts during thousands of operating cycles. The results provide unprecedented insight into the atomic-scale degradation mechanisms of platinum-nickel (PtNi) catalysts, and demonstrate how gallium (Ga) doping dramatically improves both their performance and durability.
A New Atomic “CT Scan” for Catalysts
To achieve this breakthrough, the team utilized a neural network-assisted atomic electron tomography (AET) technique. Much like a CT scan in a hospital reconstructs the inside of the human body from X-ray images, AET determines the positions of thousands of atoms inside nanomaterials from high-resolution electron microscopy images taken at many different angles. By combining these reconstructions with advanced AI-based data correction, the researchers were able to map the exact 3D coordinates and chemical identity of every atom in the nanoparticle catalysts.
This allowed them to directly observe—at single-atom resolution—how the catalysts changed in structure, chemical composition, and internal strain as they were cycled thousands of times under fuel cell operating conditions.
Key Findings: Why Gallium Makes a Difference
The researchers compared conventional PtNi catalysts with Ga-doped PtNi catalysts. The results revealed:
a) Shape stability: While undoped PtNi particles gradually lost their advantageous octahedral shape and became more spherical (i.e., the fraction of highly active {111} facets has been reduced), Ga-doped particles retained their octahedral shape even after 12,000 cycles.
b) Chemical stability: In PtNi catalysts, nickel atoms leached from both the surface and subsurface regions, driving structural instability. In Ga-doped catalysts, surface nickel was largely preserved, preventing collapse of the structure.
c) Strain preservation: The compressive strain in PtNi particles, crucial for optimizing oxygen reduction activity, relaxed substantially over time. In contrast, Ga-doped particles maintained near-optimal strain.
d) Catalytic performance: By integrating these factors, the researchers showed that while undoped PtNi catalysts lost ~17% of their oxygen reduction activity after 12,000 cycles, Ga-doped PtNi catalysts lost only ~4% and maintained superior activity throughout.
Dr. Yang, who led the research, explained the significance of the results: “These results represent the first time the true 3D atomic-scale degradation dynamics of fuel cell catalysts have been directly visualized. Our findings not only reveal why gallium doping works, but also establish a powerful framework for rationally designing durable, high-efficiency catalysts.”
Implications for a Hydrogen-Powered Future
The study demonstrates that neural network-assisted AET can reveal how nanomaterials evolve during real operating conditions, overcoming the limitations of traditional 2D imaging and ensemble-averaged methods. Beyond PtNi catalysts, the technique can be applied to a wide range of nanomaterials and catalytic systems, helping to design the next generation of energy materials with atomic precision.
For the hydrogen economy, this means that more durable catalysts could extend the lifetime of fuel cells, lower replacement costs, and accelerate the widespread adoption of hydrogen-powered vehicles and clean energy technologies.
[Figure 1] Three-dimensional atomic structures and catalytic activity of Ga-doped PtNi nanoparticles during potential cycling. The top row shows the 3D atomic structures at different stages (Pristine to 12,000 cycles; blue: platinum, pink: nickel). The bottom row visualizes oxygen reduction reaction (ORR) catalytic activity, where red indicates higher activity. Gallium doping stabilizes the octahedral geometry and preserves highly active {111} facets, enabling sustained catalytic performance even after extensive cycling.
This research, with Chaehwa Jeong, Juhyeok Lee, Hyesung Jo, KwangHo Lee from the KAIST as co-first authors, was published online in Nature Communications on August 28th (Title: Atomic-scale 3D structural dynamics and functional degradation of Pt alloy nanocatalysts during the oxygen reduction reaction).
The study was mainly supported by the National Research Foundation of Korea (NRF) Grants funded by the Korean Government (MSIT).
2025.09.15 View 830 -
Opening the Door to Personalized Bipolar Disorder Treatment
<(From Left) Professor Jinju Han, Dr. Gyu Hyeon Baek, Dr. Dayeon Kim, Dr. Geurim Son, Dr. Hyunsu Do>
Bipolar disorder, also known as 'manic-depressive illness,' a brain disorder known to have afflicted the famous painter Vincent van Gogh, is characterized by recurrent episodes of mania and depression. This disease affects about 1-2% of the world's population, and the risk of suicide is 10 to 30 times higher than in the general population. However, because each patient's response to lithium, the main treatment, varies greatly, there is an urgent need to develop personalized treatments. In response, a research team at KAIST has identified the differences in lithium responsiveness and presented the new possibility of developing personalized treatments and a drug discovery platform based on this finding.
On September 10th, the research team led by Professor Jinju Han from the KAIST Graduate School of Medical Science and Engineering announced they were the first to identify metabolic differences in astrocytes based on lithium responsiveness, thereby suggesting the potential for personalized treatment develogpment for bipolar disorder.
Astrocytes are star-shaped cells in the brain that act as 'helpers to neurons,' providing them with nutrients and maintaining the brain's environment.
Breaking away from the existing neuron-centric research paradigm, Professor Jinju Han's team focused on astrocytes, which make up half of the brain's cells, and discovered that they play a key role in regulating the metabolism of bipolar disorder.
The research team differentiated induced pluripotent stem cells (iPSCs) from patients' cells into astrocytes (a process in which stem cells grow and specialize into cells with specific functions) and observed them. As a result, it was confirmed that the cells' energy metabolism changed significantly depending on whether they responded to lithium.
In cases of no lithium response, distinct metabolic abnormalities were observed, including an excessive accumulation of lipid droplets (tiny fat storage depots) inside the cells, decreased mitochondrial function (the cell's power plant), an over-activation of the glucose breakdown process, and excessive lactate secretion.
<The process of astrocyte-neuron interaction in patients with bipolar disorder>
Specifically, in the astrocytes of lithium-responsive patients, lipid droplets decreased upon lithium treatment, but there was no improvement in non-responsive patients. Furthermore, significant differences were found in the metabolites produced by astrocytes depending on the patient type. This suggests that the cell's energy factory does not function properly depending on the lithium response, and alternative pathways are overused, leading to a buildup of byproducts.
This finding is particularly significant as it proves that astrocytes play a key role in regulating energy metabolism in bipolar disorder, explaining the differences in lithium responsiveness and paving the way for personalized treatment strategies for each patient.
Professor Jinju Han stated, "The development of new treatments targeting astrocytes is now possible, which could provide better treatment strategies for patients who do not respond to existing medications."
This research was published online on August 22 in Molecular Psychiatry, a leading international journal in the field of neuropsychiatric disorders.
※ Paper Title: Differential effects of lithium on metabolic dysfunctions in astrocytes derived from bipolar disorder patients DOI: https://doi.org/10.1038/s41380-025-03176-w
※ Author Information: Gyu Hyeon Baek, Dayeon Kim, Geurim Son, Hyunsu Do (KAIST, co-first authors) and Jinju Han (KAIST, corresponding author).
This research was supported by the National Research Foundation of Korea and the Korea Environmental Industry and Technology Institute, among others.
2025.09.10 View 1055