https://scholar.dgist.ac.kr/handle/20.500.11750/371 2026-11-05T02:36:51Z https://scholar.dgist.ac.kr/handle/20.500.11750/61095 Title: A Novel Approach Using Serious Game Data to Predict the WISC-V Processing Speed Index in Children With Attention-Deficit/Hyperactivity Disorder: Machine Learning Study Author(s): Kim, Jun-su; Jeong, Yoo-joo; Kim, Seung-jae; Jin Jun, Su; Park, Jinyeop; Hoe, Hyang Sook; Song, Jeong-heon Abstract: Background: The processing speed index (PSI) of the Korean Wechsler Intelligence Scale for Children-Fifth Edition (K-WISC-V) is highly correlated with symptoms of attention-deficit/hyperactivity disorder (ADHD) and is an important indicator of cognitive function. However, restrictions on the frequency of testing prevent short-term PSI assessments. An accessible, objective technique for predicting PSI scores would enable better short-term monitoring and intervention for children with ADHD. Objective: To enable objective and accessible monitoring of cognitive function beyond traditional clinical assessments, this study aimed to develop a machine learning model that predicts the PSI scores of children with ADHD using behavioral data from serious games. Methods: Sixty-eight children (6-13 y of age) with ADHD were recruited, and after excluding incomplete data, 59 participants were included in the final analysis. The participants completed an initial PSI assessment using the K-WISC-V followed by 25 minutes of engagement with serious game content. Data from the game sessions were used to train machine learning models, and the models’ performance in predicting PSI scores was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE), with K-fold cross-validation (k=4) applied to ensure robustness. Results: Among the individual machine learning models, support vector regression (SVR) had the best performance, with the lowest RMSE of 11.288, MAE of 7.874, and MAPE of 7.375%. The best overall performance was achieved by the ensemble integrating AdaBoost, Elastic Net, and SVR, which recorded the lowest RMSE of 10.072, MAE of 6.798, and MAPE of 6.611%. The predictive accuracy of this ensemble model was highest for PSI scores near the mean value of 100, demonstrating its reliability for clinical applications. Conclusions: The developed PSI prediction model has the potential to serve as an objective and accessible tool for monitoring cognitive function in children with ADHD. As a complement to traditional assessments, this approach allows continuous tracking of symptom changes and can support more personalized treatment planning in both clinical and everyday settings, which may improve accessibility and adherence. However, the findings need to be validated in larger, more diverse populations, and the long-term feasibility of using serious games in clinical and educational settings must be further examined. 2025-09-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/61094 Title: The cardiac pacemakers: A paradigm of robustness in evolutionary biology Author(s): Noble, Denis Abstract: The cardiac pacemaker activity is formed from multiple interlocking physiological networks, any one of which can generate rhythm. The interlocking is reciprocal so that they automatically replace each other. In such interlocking control systems, the association scores for individual components are necessarily low, even though causation, measured by the electric current carried by the relevant ion channels, is large. This kind of reciprocally based robustness is widespread in living organisms, which explains why most association scores in genome-wide association studies are low, or even zero. It also explains why the polygenic scores do not reliably predict disease states. Genomics alone is therefore a poor method for the discovery of treatments for polygenic diseases. Reliance on genomics has led to a gene-centric impasse in healthcare, which requires a shift in favour of physiological studies that can reveal genuine causation rather than just association. The case for such a shift includes understanding that DNA is not a good self-replicator in very large genomes. Self-replication of DNA and RNA in purely chemical environments confirms that fact. The error rate would amount to hundreds of thousands in a genome of three billion base pairs. Living cells can orchestrate the enzymes necessary for nearly perfect replication before cell division by correcting those errors. These cellular proof-reading processes also open the way to control processes that are used to generate new DNA sequences when the immune and other systems need to do so. 2026-06-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/61088 Title: Functionalized Silica-Coated Metallic Replicas of Intracellular Protein Assemblies Author(s): Song, Chang Woo; Kang, Chan; Lee, Jihun; Kim, Nakhyun; Jung, Hoeyun; Yoon, Young Bin; Kim, Eunwoo; Park, Sukho; Chang, Jae-Byum Abstract: To replicate hierarchical biological architectures, biotemplating leverages diverse biological structures as templates for inorganic materials. Among the various inorganic materials explored in biotemplating, silica is an attractive material because of its stability and biocompatibility. However, existing biotemplating methods are largely nonselective, yielding silica replicas of bulk biological specimens. Here, we report an approach for selective materialization of targeted protein-associated architectures from cells and tissue slices, enabling recovery of the resulting silicified structures after enzymatic removal of surrounding non-target biomolecular components. To this end, we (i) immunolabeled target proteins with fluorophore-conjugated antibodies, (ii) embedded specimens in a hydrogel, (iii) removed endogenous proteins via enzymatic digestion, and (iv) converted the retained fluorophore patterns into Au nanoparticle assemblies through fluorophore-directed nanogold labeling and electroless Au growth, followed by silica coating. Using this strategy, we fabricated microtubule-derived silica bioreplicas from a human immune-lineage cell line and tuned their overall size by adjusting the expansion of the surrounding hydrogel matrix. Furthermore, functionalization of the silica surface with magnetic nanoparticles enables controlled motion of the bioreplicas under electromagnetic actuation. Finally, we extended the method to mouse brain slices and produced thermally stable silica bioreplicas of astrocytic and oligodendrocytic structures. Overall, this work expands the structural diversity and selectivity of silica-based biomimetic materials and broadens the palette of biotemplating templates. 2026-09-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/61084 Title: Repeated Exposure to Electroconvulsive Seizures Induces Autistic-like Pathology in Mice Author(s): Kang, Ri Jin; Kim, Yujeong; Shin, Dongpil; Hoe, Hyang-Sook; Hyun, Bae Ji; Kim, Myoung Ok Abstract: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impaired social interactions, communication deficits, and excessive repetitive behaviors. While ASD has a strong genetic basis, growing evidence suggests that epileptic seizures may serve as environmental risk factors for ASD development. The high comorbidity between epilepsy and ASD (20–30%) suggests potential shared neurobiological mechanisms, yet the causal relationship remains poorly understood. To investigate the causal role of seizures in the development of ASD-like pathology, we exposed adolescent mice (3 weeks old) to electroconvulsive seizures (ECS) for 10 consecutive days. This repeated ECS exposure led to the emergence of autistic-like behaviors including significantly decreased sociability, increased repetitive self-grooming, enhanced marble burying behavior, and anxiety-like behaviors, without affecting general locomotor activity. Additionally, repeated exposure to ECS induced significant changes in glutamatergic neurotransmission in the mice’s prefrontal cortex and hippocampus, brain regions critically involved in social cognition and behavioral regulation. Interestingly, these changes occurred without alterations in other excitatory/inhibitory neuronal markers, suggesting a specific impact on glutamate receptor expression rather than a general disruption of excitatory/inhibitory balance. These findings suggest that repeated seizures may contribute to ASD-like symptoms by specifically affecting key glutamatergic neurotransmitter systems, providing insights into the neurobiological mechanisms underlying the comorbidity between epilepsy and autism. In addition, repeated ECS differentially regulated histone deacetylase (HDAC) transcripts in a region-specific manner and produced seizure-intensity-dependent transcriptomic signatures. 2026-08-31T15:00:00Z