Five isoprenoid flavonoids (1–5) were isolated from Sophora davidii. Notably, the absolute configuration of compound 1 and the 13C NMR data of compound 4 are presented herein for the first time. The structures of all compounds were established by spectroscopic data analysis, including HR-ESI-MS, 1D NMR, and 2D NMR. Their absolute configurations were determined via theoretical calculations, including ECD and NMR chemical shift calculations. The cytotoxic effects of the isolated compounds on human HCT-116, MDA-MB-231, and BCPAP cells were evaluated using the MTT assay. Compounds 2, 3, and 5 exhibited cytotoxicity against all three cell lines, with IC50 values ranging from 10.28 μM to 19.34 μM.
BACKGROUND:Depressive and anxiety disorders constitute a major component of the disease burden of mental disorders in China. AIMS:To comprehensively evaluate the disease burden of depressive and anxiety disorders in China. METHOD:The raw data is sourced from the Global Burden of Disease, Injuries, and Risk Factors Study (GBD) 2021. This study presented the disease burden by prevalence and disability-adjusted life years (DALYs) of depressive and anxiety disorders at both the national and provincial levels in China from 1990 to 2021, and by gender (referred to as 'sex' in the GBD 2021) and age. RESULTS:From 1990 to 2021, the number of depressive disorder cases (from 34.4 to 53.1 million) and anxiety disorders (from 40.5 to 53.1 million) increased by 54% (95% uncertainty intervals: 43.9, 65.3) and 31.2% (19.9, 43.8), respectively. The age-standardised prevalence rate of depressive disorders decreased by 6.4% (2.9, 10.4), from 3071.8 to 2875.7 per 100 000 persons, while the prevalence of anxiety disorders remained stable. COVID-19 had a significant adverse impact on both conditions. There was considerable variability in the disease burden across genders, age groups, provinces and temporal trends. DALYs showed similar patterns. CONCLUSION:The burden of depressive and anxiety disorders in China has been rising over the past three decades, with a larger increase during COVID-19. There is notable variability in disease burden across genders, age groups and provinces, which are important factors for the government and policymakers when developing intervention strategies. Additionally, the government and health authorities should consider the potential impact of public health emergencies on the burden of depressive and anxiety disorders in future efforts.
Drug-drug interactions (DDIs) represent a critical challenge in pharmacology, often leading to adverse effects and compromised therapeutic efficacy. Accurate prediction of DDI events, which involve not only identifying interacting drug pairs but also characterizing the specific nature and context of their interactions, is essential for drug safety and personalized medicine. In this study, we propose a novel Multi-view Contrastive Learning framework, namely MCL-DDI, for DDI Event Prediction by leveraging multi-view representations of drugs to enhance predictive performance. MCL-DDI integrates molecular structures and network features, capturing complementary information about drug properties and interactions. By employing contrastive learning, we align and unify drug representations across these diverse views, enabling the framework to distinguish complex interaction patterns. Extensive experiments on benchmark datasets demonstrate that MCL-DDI outperforms state-of-the-art methods in terms of predictive accuracy. Furthermore, case studies highlight the model's ability to identify clinically relevant DDIs, offering practical insights for drug development and risk assessment. Our work establishes a robust and accurate paradigm for DDI event prediction, paving the way for safer and more effective pharmacological interventions.
Microvascular decompression (MVD) is an established treatment for hemifacial spasm (HFS). However, when the vertebral artery (VA) is the offending vessel (OV), the procedure is technically more challenging. Whether MVD yields comparable safety and efficacy in VA-associated versus non-VA-associated cases remains unclear and warrants further investigation. We conducted a retrospective analysis of HFS patients treated at our study between September 2023 to February 2024. We enrolled 51 patients with HFS assigned to the two groups in accordance with the OVs. A total of 51 patients with HFS were divided into the VA-associated (n = 11) and non-VA-associated (n = 40) groups. Spasm-free relief (Park YS grades “excellent” or “good”) was achieved in 81.8 < 0.05). Mean operative times were 147 ± 20.0 min in VA group) and 131.0 ± 30.0 min in non-VA group(p < 0.05) s. No major complications were observed in either group. MVD is effective and safe for both VA- and non-VA-associated HFS. Although VA-associated cases involve greater surgical complexity and a higher incidence of transient postoperative symptoms, long-term outcomes remain comparable when adequate decompression is achieved.
Percutaneous endoscopic interlaminar discectomy (PEID) is a common surgical technique for lumbar disc herniation (LDH), but the risk factors for adverse outcomes remain controversial. This study aims to develop and validate a predictive model based on machine learning algorithms to identify key clinical indicators influencing adverse outcomes after PEID. This retrospective study included 414 LDH patients who underwent single-level PEID between October 2018 and June 2024. Data were divided into training (n = 290) and validation (n = 124) sets. Six machine learning algorithms were used for feature selection, identifying core indicators. Models were constructed based on these indicators and evaluated for predictive performance. Five core indicators were identified: Modic Changes (MC), Basal Width Of The Herniated Disc (BWHD), Body Mass Index (BMI), Ratio Of Disc Herniation (RDH), and Interspinous Ligament Injury (ILI). The XGB model performed best, with an AUC of 0.809 in the training set and 0.718 in the validation set. Risk thresholds for BWHD, BMI, and RDH were 1.7 mm, 23.3 kg/m², and 37.2
