BACKGROUND
Heart rate variability (HRV) is a non-invasive marker of autonomic regulation that may be related to suicide risk, but longitudinal evidence in inpatients is limited. This study examined one-week HRV dynamics and suicide-risk reduction in depressive inpatients.
METHODS
This retrospective one-week longitudinal study included 177 inpatients with major depressive disorder (MDD) or bipolar disorder (BD) during a depressive episode. Suicide risk was assessed using the Mini-International Neuropsychiatric Interview (MINI), and HRV was measured at admission and after one week. Patients were classified as low suicide risk (LSR, n = 96) or moderate-to-high suicide risk (MHSR, n = 81). Linear mixed-effects models and multivariable regression were used.
RESULTS
The MHSR group was younger (FDR-P = 0.003) and had higher Hamilton Depression Rating Scale scores (FDR-P = 0.005). At admission, the MHSR group showed higher average heart rate (AHR) and low-frequency/high-frequency ratio (LF/HF), and lower standard deviation of normal-to-normal intervals (SDNN), root mean square of successive RR interval differences (RMSSD), and high-frequency power (HF) (all FDR-P ≤ 0.05); these differences were no longer significant at week 1. RMSSD showed a significant time × group interaction (FDR-P = 0.026). Greater suicide-risk reduction was associated with decreased AHR (FDR-P = 0.041) and increased SDNN, RMSSD, total power (TP), low-frequency power (LF), and HF (all FDR-P < 0.001).
CONCLUSIONS
Higher suicide risk was associated with altered autonomic regulation at admission. HRV changes over one week were associated with suicide-risk reduction, suggesting that HRV may provide dynamic physiological information for longitudinal inpatient suicide-risk monitoring.
Yi Wang, Chaohua Huang, Bao-Yi Zhong et al.· Journal of Affective Disorde...· 0 citations
Urban water systems are increasingly challenged by climate extremes, aging infrastructure, and rising flood risks. Conventional water management practices remain fragmented across data, operations, and assets, limiting coordinated decision-making and scalable engineering deployment. Digital twins (DT) show great promise to overcome this fragmentation for resilient and efficient management. This review proposes an engineering-practice-oriented framework of digital twins for smart water management (DTSW). Utility demands are first structured through a scenario-oriented decomposition into points of interest (POIs), thereby linking practical engineering problems to digital variables. The review further summarizes a probabilistic graphical model-based scheme as the algorithmic backbone for POI implementation, and examines the key enabling technologies across organized data foundations, models, and real-time control. Particular attention is given to AI-empowered DTSW techniques, including soft sensing and data cleansing, hybrid modeling, and uncertainty-aware model deployment. Future development is discussed from the perspectives of proactive optimization, human-digital collaboration, and scalable engineering deployment. This review thus provides a structured framework for guiding the practical design and deployment of DT in urban water systems, facilitating coordinated, scalable and resilient water management.
Haozheng Wang, Jinkuo Li, Xuhui Dang et al.· Water Research· 0 citations