Taipei City Hospital

Psychotherapy is vital for identifying and managing craving-triggering cues. In this study, we paired virtual reality (VR) drug-cue scenarios with physiological sensors to capture participants’ real-time responses. Qualitative interviews with nineteen patients and six therapists showed that contextual elements resembling past drug-use experiences reliably provoked cravings, while biofeedback data confirmed that VR effectively elicits measurable physical responses. Our findings further suggest that adjusting the completeness and fidelity of VR scenarios to match a patient’s recovery stage can manage craving intensity and prevent urges from persisting beyond each session. Therapists also see opportunities to integrate virtual reality into clinical practice to address challenges from prior sessions and enhance therapeutic outcomes. This paper offers concrete recommendations for developing clinically deployable VR scenarios and outlines implications for future research and therapeutic applications in drug treatment.

Bladder urothelial carcinoma (BLCA) remains a clinical challenge because of its high recurrence rate and the persistence of cancer stem cells (CSCs) within the tumor microenvironment (TME). Neuropilin-1 (NRP1) is recognized as a key orchestrator of epithelial–mesenchymal transition (EMT) and stemness in several malignancies; however, its specific mechanism in driving BLCA aggressiveness and CSC maintenance has not been fully elucidated. This study aimed to characterize the regulatory role of NRP1 in bladder cancer progression and identify potential therapeutic options. The clinical significance of NRP1 was first assessed using TCGA patient datasets and tissue microarrays. The functional roles of NRP1 were determined through CRISPR/Cas9-mediated knockout, shRNA-mediated knockdown, and overexpression of NRP1 in the T24 and 5637 cell lines. Bulk RNA sequencing and GSEA were utilized to identify downstream pathways, and ELISA and Western blotting were used to validate signaling interactions. Finally, a synergistic therapeutic strategy was tested using a subcutaneous xenograft nude mouse model. High NRP1 expression was significantly associated with advanced tumor stage, chemoresistance, and poor overall survival in patients with BLCA. Mechanistically, NRP1 regulated the IL-6–STAT3 signaling axis; NRP1 depletion significantly reduced IL-6 secretion and phosphorylated STAT3 levels, thereby impairing sphere formation and EMT features. Conversely, exogenous IL-6 treatment rescued the CSC phenotype in NRP1-deficient cells. Clinically, NRP1 expression strongly correlated with IL-6 and STAT3 levels, and patients with high expression of NRP1 and IL-6 had significantly worse survival outcomes, identifying NRP1–IL-6 coexpression as a novel prognostic biomarker in BLCA. Based on these findings, we demonstrated that dual targeting with the NRP1 inhibitor EG00229 and the IL-6-neutralizing antibody siltuximab synergistically suppressed CSC self-renewal and inhibited tumor growth in vivo. This study provides a comprehensive characterization of the NRP1–IL-6–STAT3 axis as a fundamental driver of bladder cancer stemness. Our findings establish a foundational mechanistic proof-of-concept for a dual-targeting approach. By simultaneously inhibiting NRP1 and neutralizing IL-6, we demonstrated profound suppression of tumor-initiating capacity and CSC self-renewal. This strategy offers significant translational potential for improving clinical outcomes and overcoming therapeutic resistance in patients with aggressive BLCA.

Type 2 diabetes (T2D) is associated with cognitive decline and neurodegenerative disorders. Changes in the connections between brain regions responsible for emotions and memory might play a role in the reduced cognitive function observed in individuals with diabetes. Utilizing machine learning approaches on neuroimaging data shows potential for exploring these intricate associations. A research study was carried out with 40 individuals diagnosed with T2D and 30 control participants, all of whom were middle-aged and right-handed. The participants underwent neuropsychological assessments and fMRI scans while engaging in an emotional Stroop task. Our analysis concentrated on the functional connectivity of specific brain regions associated with cognitive control. We utilized a fully connected network (FCN)-based machine learning approach to predict cognitive decline using neural connectivity patterns. The FCN accurately forecasted Montreal Cognitive Assessment scores in patients with Type 2 Diabetes, showing a robust relationship between anticipated and observed scores in both the training and testing sets. It revealed important patterns of connectivity in the anterior cingulate cortex and other regions responsible for cognitive control, which played a vital role in predicting cognitive deterioration. Our results indicate that machine learning approach, when using functional connectivity information, have the capability to forecast cognitive deterioration in T2D patients. This method may aid in early identification and intervention plans, potentially reducing the effects of cognitive deficits in this group. Further studies should confirm these results with larger and more varied samples to improve their applicability and relevance for clinical practice.

Older adults face increased health risks, especially accidental injuries such as falls, which are one of the top ten causes of death in this age group. To address these changes, recent innovations have focused on developing advanced monitoring technologies to detect and prevent accidents in real time. Among these, fall detection systems have emerged as a critical area of research. This study aimed to evaluate the ability of millimeter-wave (mmWave) sensors to accurately detect multiple falls in the presence of large obstacles in a large, real-world indoor space. The mmWave sensors employed the Doppler effect to capture a human body’s point cloud and track its center point to estimate body position and identify fall events. A 12 $$\times$$ 12 meter indoor test area was established for the trials. The mmWave system’s accuracy was validated with video ground truth. Multiple sensors and azimuth tests were conducted to optimize radar configurations. 10 participants performed multiple human fall detection trials under 10 different scenarios. We have successfully validated the mmWave system with the video ground truth. In fall detection testing, the mmWave system achieved an overall accuracy of 97.9% across 10 multi-person scenarios. The results show that the system’s fall detection false negative rate increases with the number of subjects. This study validated the performance of a mmWave system for fall detection in a large indoor environment, demonstrating a high accuracy of 97.9% in a multi-person scenario. However, performance varied with crowd density, showing a correlation between increased false negative rates and the number of subjects due to occlusion. This study supports that mmWave technology offers good capabilities for fall accident monitoring in large indoor spaces with both privacy protection and convenience. IRB Registry: Institutional Review Board of the Chang Gung Medical Foundation, Approval No.: 202500191B0, Registration date: 3 March 2025.

Subthreshold depression (StD) confers a high risk for major depression and is characterized by substantial individual clinical heterogeneity. However, the neurobiological substrates underlying this heterogeneity remain largely unknown. Using a large multisite resting-state functional MRI dataset including 1203 healthy participants and 197 individuals with StD, we constructed connectome-based normative models to identify individual brain deviations and biotypes in StD. We highlighted remarkable individual variability in the connectome deviations in StD, leading to the identification of two distinct biotypes. Subtype 1 exhibits severe positive deviations primarily in the default mode regions and negative deviations in the sensorimotor and ventral attention areas, while subtype 2 shows a moderate but opposite deviation pattern. The two subtypes differ significantly in depressive symptoms, gene expression profiles, and treatment responses to bright light therapy. These findings highlight the neurobiological underpinnings of the clinical diversity in StD, emphasizing the necessity for developing personalized interventions for this condition.

Recreational ketamine use has increased globally and is associated with psychiatric and cognitive concerns. The hippocampus in preclinical models shows damage and working-memory disruption with repeated dosing. However, whether specific hippocampal subregions may differ in people with chronic ketamine use remains unclear. In Taiwan, ketamine is predominantly consumed by smoking ketamine mixed with tobacco, producing smoking-related behavioral profiles like non-ketamine tobacco use participants (TUs). We therefore examined individuals with urine-confirmed ketamine as the only detected substance who reported predominantly smoking-administered recreational use (KUs) and used TUs as controls. This study aimed to: (1) characterize ketamine-use patterns and psychiatric symptoms; (2) compare working-memory and affective-behavioral measures between KUs and TUs; (3) quantify group differences in hippocampal subregion volumes; and (4) assess group differences in functional connectivity (FC) of identified subregions and relationships with neurotransmitter receptor distributions. Cross-sectional case-control study with cognitive testing and neuroimaging. Community-based recruitment in Taiwan. 58 KUs (44 males; mean age = 21.00 ± 4.57) and 73 TUs (52 males; mean age = 24.34 ± 5.86). Ketamine-use patterns (Addiction Severity Index), psychiatric symptoms [Symptom Checklist-90-Revised (SCL-90-R)], working-memory (N-back), affective-behavioral measures [Barratt Impulsiveness Scale (BIS-11), Buss and Perry Aggression Questionnaire (BPAQ), Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ)], hippocampal subfield volumes (FreeSurfer) and functional connectivity (FC) of identified subregions (seed-based analysis). Spatial correspondence with N-methyl-D-aspartate (NMDA) receptor density was evaluated using JuSpace. Heavier ketamine use was associated with greater psychological distress [Global Severity Index (GSI) r = 0.343, P = 0.011], particularly anxiety (r = 0.457, P < 0.001) and hostility (r = 0.442, P < 0.001). Although self-reported impulsivity, aggression and reward/punishment sensitivity did not differ between groups, KUs showed reduced accuracy under higher working-memory load [2-back: F(1, 124) = 4.16, P = 0.04, partial η2 = 0.03; 1-back: F(1, 124) = 8.10, P = 0.005, η2 = 0.06]. KUs displayed reduced left hippocampal volume [F(1, 119) = 4.23, P = 0.04, η2 = 0.03], most marked in the hippocampal-amygdaloid-transition-area [HATA; F(1, 119) = 10.52, P = 0.002, η2 = 0.08]. KUs also showed increased FC between left HATA and frontal, cingulate, temporal, subcortical, insular and cerebellar regions (P < 0.05, AlphaSim corrected), which correlated with NMDA-receptor distributions (z = 0.30, P = 0.005, false discovery rate corrected). Recreational smoking-administered ketamine use appears to be associated with dose-dependent psychiatric symptoms, load-dependent working memory impairment, selective hippocampal subregion volumetric differences and altered network connectivity aligned with N-methyl-D-aspartate- (NMDA) receptor distribution.