I-Shou University

The association between the risk of dementia and different classes of antidepressants, as well as the potential influence of treatment resistance or response, remains unexplored in individuals with major depressive disorder (MDD).

Using the Taiwan National Health Insurance Research Database, 30,390 patients with MDD and 24,312 well-matched controls were enrolled between 2002 and 2004. We stratified the antidepressant-resistant depression (ART, n = 6,078) and antidepressant-responsive depression (ARP, n = 24,312) groups. Selective serotonin reuptake inhibitors (SSRIs) versus non-SSRIs, was included as a stratification factor. The risks of dementia were examined using a Cox regression model (reported as hazard ratio with 95% confidence interval).

Both the ART and ARP groups had a 5-24 fold higher risk of developing dementia than the controls. ART appeared to be associated with a higher risk of developing dementia than ARP, including any dementia (13.02 [11.08-15.31] versus 7.70 [6.63-8.94] and unspecified dementia 12.81 [10.52-15.59] versus 7.33 [6.11-8.79]). Subsequent analysis demonstrated that in the ART group, individuals who were resistant to SSRIs only (12.01 [10.02-14.40]) or both SSRIs and non-SSRIs (13.87 [11.67-16.47]) appeared to have a higher risk of developing any dementia than the ARP group (7.70 [6.63-8.94]). With the ARP as reference group, patients who were only resistant to SSRIs and those who were resistant to both SSRIs and non-SSRIs had a higher risk of developing any dementia.

In elderly patients with MDD, the risk of dementia is higher among those who are resistant to any type of antidepressant medication.

Varroa destructor is a major ectoparasite threatening apiculture worldwide, especially in Taiwan. We retrofit conventional hives with an entrance imaging chamber and deploy a YOLOv5s-based detector for real-time mite detection on Apis mellifera. Trained on 1,600 annotated images supplemented with mite close-ups, the model achieved a mean average precision (mAP@0.5) of 97.4 %. Video tests at hive entrances further confirmed robust performance under motion and illumination variability. We retrofitted conventional hives with an entrance imaging chamber and implemented a YOLOv5s-based detector for real-time mite detection. To facilitate adoption, we present a per-hive bill of materials and a five-year annualized cost model, demonstrating a low annual per-hive cost suitable for apiary-scale deployment. The proposed system reduces labor-intensive inspections and enables early mite detection, contributing to sustainable and data-driven beekeeping practices.

The strong association between alcohol use disorder (AUD) and driving under the influence of alcohol (DUIA) suggests substantial overlaps across the behavioral, cognitive, and neurobiological domains. Taking advantage of the unsupervised machine learning approach in uncovering hidden patterns, this study incorporated diverse clinical, neuropsychological, and electroencephalographic (EEG) features to identify distinctive patterns among twenty-seven AUD adults with and without DUIA (16 DUIA vs. 11 non-DUIA) recruited from a single tertiary referral center. Following Principal Component Analysis (PCA), K-means clustering was applied to the PCA-transformed feature space based on 457 characteristics, including demographic (e.g., gender and marriage), clinical data (i.e., drinking frequency, depression severity, and emotion), and performance in neuropsychological tests (i.e., the traffic-themed version of the stop signal task, delay discounting task, Iowa gambling test, and community mental status examination), as well as EEG data obtained in resting state and virtual traffic scenarios. The silhouette analysis revealed a peak of clustering performance at a score of 0.479 when k = 9, indicating that the nine-cluster solution provided the optimal balance between compactness and separation. PCA identified beta-band synchronization in the fronto-parietal region as the primary pattern of EEG coherence. Based on the nine-cluster solution, the top five discriminative features within each cluster were predominantly associated with performance indices from the Iowa gambling task and patterns of EEG coherence. The findings highlighted the combined contribution of behavioral decision-making and neural synchronization to DUIA in AUD individuals, reflecting the interplay between cognitive control and fronto-parietal connectivity, consistent with previous empirical evidence.