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Graph neural networks (GNN) rely on graph operations that include neural network training for various graph related tasks. Recently, several attempts have been made to apply the GNNs to functional magnetic resonance image (fMRI) data. Despite recent progresses, a common limitation is its difficulty to explain the classification results in a neuroscientifically explainable way. Here, we develop a framework for analyzing the fMRI data using the Graph Isomorphism Network (GIN), which was recently proposed as a powerful GNN for graph classification. One of the important contributions of this paper is the observation that the GIN is a dual representation of convolutional neural network (CNN) in the graph space where the shift operation is defined using the adjacency matrix. This understanding enables us to exploit CNN-based saliency map techniques for the GNN, which we tailor to the proposed GIN with one-hot encoding, to visualize the important regions of the brain. We validate our proposed framework using large-scale resting-state fMRI (rs-fMRI) data for classifying the sex of the subject based on the graph structure of the brain. The experiment was consistent with our expectation such that the obtained saliency map show high correspondence with previous neuroimaging evidences related to sex differences.

Oral soft tissue damage can lead to hard tissue damage in the oral cavity, such as periodontal lesions, periapical disorders, cysts, and oral tumors. Cold plasma is known to alleviate inflammation and oxidative stress and promote tissue regeneration, yet the effects of helium plasma on human gingival cells remain poorly understood. In this study, we examined whether helium (He) cold atmospheric pressure plasma (CAP) can induce anti-inflammatory and anti-ferroptotic effects in oral soft tissues by ionizing He gas. Erastin treatment followed by He CAP exposure in human gingival fibroblast-1 (HGF-1) cells reduced the mRNA expression of inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), interleukin-1β (IL-1β), tumor necrosis factor-α (TNFα), and interleukin-6 (IL-6), which are linked to inflammatory responses. Additionally, He CAP exposure decreased nuclear receptor coactivator 4 (NCOA4) expression and increased glutathione peroxidase 4 (GPX4) expression. Furthermore, mitochondrial membrane potential was restored by increased voltage-dependent anion channel 1 (VDAC1) expression, and reactive oxygen species (ROS) levels in mitochondria and cytoplasm were reduced. These results suggest that He CAP exposure may be associated with modulation of mitochondrial ROS production and reduction of inflammation and ferroptosis, but whether mitochondrial repair contributes to these effects requires further investigation.

The study presents a novel high focus laser scanning (HFLS) system, which integrates the advantages of conventional equipment, and demonstrates its superiority. The biological functions of biomaterial surfaces modified using HFLS were investigated. The advantages of HFLS, including ease of use, processing speed, and precision, were validated via morphological analyses such as microscopy, and surface characterization techniques such as contact angle measurements. The material surfaces were modified into the ‘Line’ and the ‘Grid’ shapes to facilitate further investigations on cellular response and drug delivery. Cell adhesion, migration, and proliferation were examined to investigate cellular responses to HFLS-modified material surfaces. To evaluate the functionality of HFLS-modified materials as drug carriers, prednisolone (PDS) holding capacity, drug release, platelet adhesion, and western blot analysis for inflammatory cytokines were performed. Compared with conventional methods, HFLS processing proved to be faster and more precise, enabling easy modification of materials into hydrophilic (the Line) or hydrophobic (the Grid) surfaces. The highest contact angle (158.63° ± 1.26) was observed for surfaces processed with a 50 µm wave size. Cell culture medium spread across nearly the entire surface on the Line compared to the control, whereas minimal spread was observed on the Grid. These results align with those of cell adhesion, migration, proliferation, and platelet adhesion assays. Moreover, HFLS-modified materials demonstrated increased PDS retention, with PDS release occurring in a controlled manner rather than disappearance due to rapidly drug eluted. The released PDS maintained an anti-inflammatory effect, reducing the expression of cytokines associated with M1 macrophages. The laser system presented in this study proposes a promising approach for enhancing tissue engineering applications, including surface morphology modification, cytocompatibility improvement, and efficient drug delivery. Additionally, it holds potential for clinical accessibility as an equipment owing to its versatility. Graphical Abstract

Social anxiety is a symptom widely prevalent among young adults, and when present in excess, can lead to maladaptive patterns of social behavior. Recent approaches that incorporate brain functional radiomic features and machine learning have shown potential for predicting certain phenotypes or disorders from functional magnetic resonance images. In this study, we aimed to predict the level of social anxiety in young adult participants by training machine learning models with resting-state brain functional radiomic features including the regional homogeneity, fractional amplitude of low-frequency fluctuation, fractional resting-state physiological fluctuation amplitude, and degree centrality. Among the machine learning models, the XGBoost model achieved the best performance with balanced accuracy of 77.7% and F1 score of 0.815. Analysis of input feature importance demonstrated that the orbitofrontal cortex and the degree centrality were most relevant to predicting the level of social anxiety among the input brain regions and the input type of radiomic features, respectively. These results suggest potential validity for predicting social anxiety with machine learning of the resting-state brain functional radiomic features and provide further understanding of the neural basis of the symptom.

Background Motivational Interviewing (MI) is a counseling approach that promotes behavior change by eliciting “change talk” and minimizing “sustain talk.” Traditional methods for assessing MI quality, such as manual coding, are labor-intensive, subjective, and difficult to scale. This study introduces an automated framework integrating large language models (LLMs) and Hidden Markov Models (HMMs) for evaluation of MI session quality. Aims This study evaluates the effectiveness of an LLM-HMM framework in predicting MI session quality and examines motivational state transitions in high- and low-quality sessions. Method A dataset of 40 MI sessions was analyzed. Client utterances were classified and numerically scored by an LLM based on their intention toward or away from change. With HMMs, we used these scores to examine the motivational state transitions across each session. Differences between high- and low-quality sessions were quantified by comparing transition matrices using Frobenius norms. Statistical significance was assessed via a permutation test. Predictive performance was evaluated using logistic regression with leave-one-out cross-validation (LOOCV), where transition matrix elements served as independent variables and interview quality as the dependent variable. Results High-quality MI sessions exhibited fluid transitions between motivational states, whereas low-quality sessions showed persistence in resistance-oriented states. A statistically significant difference in transition matrices was observed between session groups ( p  < 0.001). The framework achieved a mean LOOCV accuracy of 0.80, demonstrating strong predictive performance in identifying MI session quality. Conclusions This study presents a scalable, objective alternative to manual MI evaluation. Future applications may include real-time therapist support, training, and prognosis prediction, pending further validation on field-collected data.

Piezoelectric accelerometers using a lead-free (K,Na)NbO3 (KNN) piezoceramic modified by a mixture of two Bi-based perovskites, Bi(Na,K,Li)ZrO3 (BNKLZ) and BiScO3 (BS), were designed, fabricated and characterized. Ring-shaped ceramics were prepared using a conventional solid-state reaction method for integration into a compression-mode accelerometer. A beneficial rhombohedral–tetragonal (R–T) phase boundary structure, especially enriched with T phase, was produced by modifying intrinsic phase transition temperatures, yielding a large piezoelectric charge coefficient d33 (310 pC/N) and a high Curie temperature Tc (331 °C). Using finite element analyses with metamodeling techniques, four optimum accelerometer designs were obtained with high magnitudes of charge sensitivity Sq and resonant frequency fr, as evidenced by two key performance indicators having a trade-off relation. Finally, accelerometer sensor prototypes based on the proposed designs were fabricated using the KNN-BNKLZ-BS ceramic rings, which exhibited high levels of Sq (55.1 to 223.8 pC/g) and mounted fr (14.1 to 28.4 kHz). Perfect charge-to-acceleration linearity as well as broad flat frequency ranges were achieved with excellent reliability. These outstanding sensing performances confirm the potential application of the modified-KNN ceramic in piezoelectric sensors.

Cold atmospheric plasma (CAP) has been extensively investigated in the local treatment of cancer due to its potential of reactive oxygen species (ROS) generation in biological systems. In this study, we examined the synergistic effect of combination of CAP and cisplatin-mediated chemotherapy of oral squamous cell carcinoma (OSCC) in vitro. SCC-15 OSCC cells and human gingival fibroblasts (HGF-1) cells were treated with cisplatin, and then, the cells were irradiated with CAP. Following this, viability and apoptosis behavior of the cells were investigated. The viability of SCC-15 cells was inhibited by cisplatin with a dose-dependent manner and CAP treatment time. HGF-1 cells also showed decreased viability by treatment with cisplatin and CAP. Combination of 1 μM cisplatin plus 3 min of CAP treatment or 3 μM cisplatin plus 1 min of CAP treatment showed a synergistic anticancer effect with appropriate cytotoxicity against normal cells. ROS generation and dead cell staining were also increased by the increase in CAP treatment time. Furthermore, tumor-suppressor proteins and apoptosis-related enzymes also increased according to the treatment time of CAP. We showed the synergistic effect of cisplatin and CAP treatment against SCC-15 cells with low cytotoxicity against normal cells.

Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as for psychiatric well-being. Using questionnaires is an alternative to labor-intensive diagnostic interviews in a large general population, but previous models for predicting suicide attempts suffered from low sensitivity. We developed and validated a deep graph neural network model that increased the prediction sensitivity of suicide risk in young adults (n = 17,482 for training; n = 14,238 for testing) using multi-dimensional questionnaires and suicidal ideation within 2 weeks as the prediction target. The best model achieved a sensitivity of 76.3%, specificity of 83.4%, and an area under curve of 0.878 (95% confidence interval, 0.855–0.899). We demonstrated that multi-dimensional deep features covering depression, anxiety, resilience, self-esteem, and clinico-demographic information contribute to the prediction of suicidal ideation. Our model might be useful for the remote evaluation of suicide risk in the general population of young adults for specific situations such as the COVID-19 pandemic.

O6-methylguanine-DNA methyl transferase (MGMT) methylation prediction models were developed using only small datasets without proper external validation and achieved good diagnostic performance, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the external validation of MGMT prediction models using large-scale multicenter datasets. We tested recent CNN architectures via extensive experiments to investigate whether MGMT methylation in gliomas can be predicted using MR images. Specifically, prediction models were developed and validated with different training datasets: (1) the merged (SNUH + BraTS) (n = 985); (2) SNUH (n = 400); and (3) BraTS datasets (n = 585). A total of 420 training and validation experiments were performed on combinations of datasets, convolutional neural network (CNN) architectures, MRI sequences, and random seed numbers. The first-place solution of the RSNA-MICCAI radiogenomic challenge was also validated using the external test set (SNUH). For model evaluation, the area under the receiver operating characteristic curve (AUROC), accuracy, precision, and recall were obtained. With unexpected negative results, 80.2% (337/420) and 60.0% (252/420) of the 420 developed models showed no significant difference with a chance level of 50% in terms of test accuracy and test AUROC, respectively. The test AUROC and accuracy of the first-place solution of the BraTS 2021 challenge were 56.2% and 54.8%, respectively, as validated on the SNUH dataset. In conclusion, MGMT methylation status of gliomas may not be predictable with preoperative MR images even using deep learning.

In this study, an Integrated Electronics Piezoelectric (IEPE)-type accelerometer based on an environmentally friendly lead-free piezoceramic was fabricated, and its field applicability was verified using a cooling pump owned by the Korea Atomic Energy Research Institute (KAERI). As an environmentally friendly piezoelectric material, 0.96(K,Na)NbO3-0.03(Bi,Na,K,Li)ZrO3-0.01BiScO3 (0.96KNN-0.03BNKLZ-0.01BS) piezoceramic with an optimized piezoelectric charge constant (d33) was introduced. It was manufactured in a ring shape using a solid-state reaction method for application to a compression mode accelerometer. The fabricated ceramic ring has a high piezoelectric constant d33 of ~373 pC/N and a Curie temperature TC of ~330 °C. It was found that the electrical and physical characteristics of the 0.96KNN-0.03BNKLZ-0.01BS piezoceramic were comparable to those of a Pb(Zr,Ti)O3 (PZT) ring ceramic. As a result of a vibration test of the IEPE accelerometer fabricated using the lead-free piezoelectric ceramic, the resonant frequency fr = 20.0 kHz and voltage sensitivity Sv = 101.1 mV/g were confirmed. The fabricated IEPE accelerometer sensor showed an excellent performance equivalent to or superior to that of a commercial IEPE accelerometer sensor based on PZT for general industrial use. A field test was carried out to verify the applicability of the fabricated sensor in an actual industrial environment. The test was conducted by simultaneously installing the developed sensor and a commercial PZT-based sensor in the ball bearing housing location of a centrifugal pump. The centrifugal pump was operated at 1180 RPM, and the generated vibration signals were collected and analyzed. The test results confirmed that the developed eco-friendly lead-free sensor has comparable vibration measurement capability to that of commercial PZT-based sensors.