Kwandong University

Oak trees are widely distributed nationwide and account for approximately 24% of the total forest area in South Korea. However, these species are currently threatened by oak wilt disease caused by Dryadomyces quercus-mongolicae, leading to significant economic and ecological losses in the forestry industry. This study evaluated the effectiveness of culture suspension and lyophilized powder formulations of Streptomyces blastmyceticus in controlling oak wilt disease on Mongolian oak (Quercus mongolica). Field experiments were conducted using trunk and root injection methods in Q. mongolica plantations. The non-conductive area (NCA) of sapwood and colonization rate of the oak wilt fungus were analyzed and compared across treatments. In the Chuncheon experiment, Kangwon province, only the root injection of fungicide showed a significant difference compared to the culture suspension treatments. There were no significant differences between culture suspension and lyophilized powder treatments in Uiwang, Gyeonggi Province. Specifically, both preventive and curative treatments using culture suspension and lyophilized powder of S. blastmyceticus resulted in significantly different NCA values compared to the negative control (8.7%) and positive control (88.5%). The NCA for culture suspension ranged from 33.3% to 49.9%, and for lyophilized powder, from 37.3% to 43.9%. The colonization rate of the oak wilt fungus was lowest (9.72%) in the preventive treatment using lyophilized powder via trunk injection. For the culture suspension, the lowest colonization rate (20.83%) was observed in the curative treatment using trunk injection. These findings suggest that the lyophilized powder formulation of S. blastmyceticus efficiently suppresses the progression of oak wilt disease under field conditions.

Objectives: This study aimed to analyze the association between social isolation (SI) and falls in Koreans. Methods: The cross-sectional study of the 2019 Korea Community Health Survey (KCHS) was conducted on 228,143 individuals aged 19 years or older. SI was defined using five indicators as "the presence of a spouse," "social network," "single-person household," "participation in social activities," and "job status," and SI group were defined as SI score >= 3. Multiple logistic regression after adjusting factors (socioeconomic status, health behavior and psychological factors) were used for association SI and falls. Results: Fall injury were significantly higher in the SI group (15.8%) than that in the non-SI group (10.8%) (p < 0.001), and subcomponent scores for nonspouse, single-person household, nonsocial activity, nonsocial network, and no job were significantly higher in the falls group (p < 0.001). The multivariate odds ratio (OR) after adjusting multiple confounding variables showed SI (SI score >= 3) was significantly associated with an increased risk of falls (OR: 1.23, 95% confidence interval [CI]: 1.19-1.27). The presence of falls according to the SI subcomponents was more likely to be associated with nonspouse (OR: 1.40, 95% CI: 1.36-1.44), single-person households (OR: 1.17, 95% CI: 1.13-1.21), non-social network (OR: 1.09, 95% CI: 1.00-1.19) and nonjob (OR: 1.14, 95% CI: 1.11-1.18), respectively. Conclusion: SI was associated with falls.

Parkinson’s disease (PD) is associated with progressive gait deterioration; however, widely used clinical scales such as the Hoehn & Yahr (H&Y) stage are limited in capturing continuous severity changes due to subjectivity and discrete grading. This study proposes a two-stage explainable AI framework using vertical ground reaction force (VGRF) signals to achieve reproducible PD detection and continuous severity estimation. In the first stage, three deep learning models, temporal convolutional network (TCN), BiGRU with attention, and FCNN-Transformer, were trained using windowed VGRF signals under repeated subject-wise data segmentation. All models achieved high discrimination performance (AUC ≥ 0.93), with FCNN-Transformer showing the highest mean AUC (0.940) and statistically superior performance (paired Wilcoxon test, p < 0.05). Stability-based explainable AI using Integrated Gradients consistently identified variability-related VGRF features as the most informative, which were also significantly different between groups at the data level (p < 0.001, FDR-corrected). In the second stage, XGBoost regression was applied to PD subjects to predict continuous H&Y severity, achieving strong correlation with clinical grades (Spearman ρ = 0.921, p < 0.001), low error (MAE = 0.158, RMSE = 0.241), and high determination (R2 = 0.953). This shows that gait-based features are a sensitive enough signal to continuously quantify disease progression. In addition, in the TREND prospective longitudinal cohort (n = 696), wearable walking indicators differed significantly from those of non-patients prior to diagnosis, and a decline in walking pace was observed approximately four years before Parkinson’s disease diagnosis, providing the basis for early screening and monitoring using gait-based digital biomarkers. These results demonstrate that gait-based digital biomarkers can objectively quantify both PD presence and disease progression. The proposed framework provides a reproducible, explainable, and clinically interpretable AI-based decision support approach for PD assessment.