Accurate localization of seizure onset zones (SOZs) in patients with drug-resistant epilepsy is essential for improving prognostic outcomes. This process can be significantly enhanced through effective network representation and analysis of functional dependencies among brain regions. However, traditional network construction methods often lack generalizability due to individual variability. Furthermore, the independent design of the network construction and analysis modules restricts the overall optimization of localization frameworks. In this study, we propose a novel deep learning framework that integrates graph building and analysis modules for seizure localization. The graph building block adaptively generates customized network representations from Stereo-Electroencephalography (SEEG) data of individual patients by extracting feature vectors of each channel and calculating functional connectivity weights among channels with these vectors. While the GCN-and-LSTM-based graph analysis block identifies abnormal nodes corresponding to SOZs by aggregating spatial and temporal information in the network representations. The graph analysis block is trained alongside the graph building block via the seizure prediction task. Attention weights assigned to each channel are utilized to characterize epileptogenicity, facilitating precise localization of the SOZ. Our method demonstrates superior performance, surpassing baseline and state-of-the-art approaches in 9 of 13 patients from a public dataset and 11 of 14 patients from a clinical dataset. Visualization of the identified brain regions aligns well with labeled SOZs. Furthermore, the adaptive functional brain network reveals that the connectivity density among SOZ channels is greater than that of other brain regions, corroborating existing clinical findings and further confirming the model’s reliability and interpretability.

AbstractObjectiveIsolated REM sleep behavior disorder (iRBD) is considered as the strongest predictor of Parkinson's disease (PD). Reliable and accurate biomarkers for iRBD detection and the prediction of phenoconversion are in urgent need. This study aimed to investigate whether α‐Synuclein (α‐Syn) species in plasma neuron‐derived extracellular vesicles (NDEVs) could differentiate between iRBD patients and healthy controls (HCs).MethodsNanoscale flow cytometry was used to detect α‐Syn‐containing NDEVs in plasma.ResultsA total of 54 iRBD patients and 53 HCs were recruited. The concentrations of total α‐Syn, α‐Syn aggregates, and phosphorylated α‐Syn at Ser129 (pS129)‐containing NDEVs in plasma of iRBD individuals were significantly higher than those in HCs (p < 0.0001 for all). In distinguishing between iRBD and HCs, the area under the receiver operating characteristic (ROC) curve (AUC) for an integrative model incorporating the levels of α‐Syn, pS129, and α‐Syn aggregate‐containing NDEVs in plasma was 0.965. This model achieved a sensitivity of 94.3% and a specificity of 88.9%. In iRBD group, the concentrations of α‐Syn aggregate‐containing NDEVs exhibited a negative correlation with Sniffin’ Sticks olfactory scores (r = −0.351, p = 0.039). Smokers with iRBD exhibited lower levels of α‐Syn aggregates and pS129‐containing NDEVs in plasma compared to nonsmokers (pα‐Syn aggregates = 0.014; ppS129 = 0.003).InterpretationThe current study demonstrated that the levels of total α‐Syn, α‐Syn aggregates, and pS129‐containing NDEVs in the plasma of individuals with iRBD were significantly higher compared to HCs. The levels of α‐Syn species‐containing NDEVs in plasma may serve as biomarkers for iRBD.

BackgroundStereoelectroencephalography (SEEG), as a minimally invasive method that can stably collect intracranial electroencephalographic information over long periods, has increasingly been applied in the diagnosis and treatment of intractable epilepsy in recent years. Over the past 20 years, with the advancement of materials science and computer science, the application scenarios of SEEG have greatly expanded. Bibliometrics, as a method of scientifically analyzing published literature, can summarize the evolutionary process in the SEEG field and offer insights into its future development prospects.MethodsThis article selected all the literature records retrieved on November 4, 2024, from the Web of Science Core Collection (WoSCC). The search terms were as follows: “Stereo-electroencephalography” or “Stereo electroencephalography” or “Stereo-EEG” or “Stereo EEG” or “SEEG.” The document types included were research articles and reviews. For analysis, VOSviewer, CiteSpace, and the R package “bibliometrix” were employed to analyze various aspects of the SEEG field, including authors, institutions, countries and regions, and research hotspots.ResultsWe reviewed a total of 1,383 non-duplicate literature records from 2002 to 2023, including 1,241 research articles, 116 review articles and 26 letters. Observing the annual publication trends, there has been an overall increase since 2002. The most influential journal in this field is Epilepsia. Other journals with considerable impact include Clinical Neurophysiology, Epileptic Disorders, Epilepsy Research, NeuroImage, and Epilepsy & Behavior. The top 5 most influential scholars are Bartolomei F, Tassi L, Nobili L, Russo GL, and Mc Gonigal A. As for the analysis of countries and regions, France occupies a leading position in this field with its early start, while China and the United States have also emerged as focal points since 2020. Research on SEEG has expanded beyond its initial use for localizing epileptic foci and thermo-coagulation treatments and have been employed as a medium to facilitate real-time prediction of epileptic seizures and enabling the exploration of brain network connectivity.ConclusionAs a minimally invasive tool for collecting intracranial electroencephalographic signals, SEEG continues to offer vast potential for development and application. Advances in electrode materials and robotic-assisted stereotactic techniques, have enabled SEEG to simultaneously sample multiple brain regions, acquire electrical signals from deep brain structures. These advantages significantly enhance the precision of epileptic focus localization in diagnosis and treatment, addressing the limitations of subdural electrodes. Through bibliometric analysis, this paper traces the developmental trajectory of SEEG and identifying key technological milestones, thereby providing a reference for scholarly research directions.

BACKGROUND:Levodopa could induce orthostatic hypotension (OH) in Parkinson's disease (PD) patients. Accurate prediction of acute OH post levodopa (AOHPL) is important for rational drug use in PD patients. Here, we develop and validate a prediction model of AOHPL to facilitate physicians in identifying patients at higher probability of developing AOHPL.METHODS:The study involved 497 PD inpatients who underwent a levodopa challenge test (LCT) and the supine-to-standing test (STS) four times during LCT. Patients were divided into two groups based on whether OH occurred during levodopa effectiveness (AOHPL) or not (non-AOHPL). The dataset was randomly split into training (80%) and independent test data (20%). Several models were trained and compared for discrimination between AOHPL and non-AOHPL. Final model was evaluated on independent test data. Shapley additive explanations (SHAP) values were employed to reveal how variables explain specific predictions for given observations in the independent test data.RESULTS:We included 180 PD patients without AOHPL and 194 PD patients with AOHPL to develop and validate predictive models. Random Forest was selected as our final model as its leave-one-out cross validation performance [AUC_ROC 0.776, accuracy 73.6%, sensitivity 71.6%, specificity 75.7%] outperformed other models. The most crucial features in this predictive model were the maximal SBP drop and DBP drop of STS before medication (ΔSBP/ΔDBP). We achieved a prediction accuracy of 72% on independent test data. ΔSBP, ΔDBP, and standing mean artery pressure were the top three variables that contributed most to the predictions across all individual observations in the independent test data.CONCLUSIONS:The validated classifier could serve as a valuable tool for clinicians, offering the probability of a patient developing AOHPL at an early stage. This supports clinical decision-making, potentially enhancing the quality of life for PD patients.

Parkinson's disease (PD) is known to impact both sexes, yet women exhibit unique clinical profiles and require tailored disease management strategies. This study sought to delineate the differences in sex and thyroid hormone levels, along with menstrual factors, in postmenopausal women with PD with motor complications and to evaluate their correlation with motoric issues. A cohort of 95 postmenopausal women with PD provided data encompassing menarche and menopause timing, menstrual cycle characteristics, and thyroid and gynecological histories. Hormonal and thyroid function assessments were conducted, correlating with PD patients’ clinical features and disease severity. Key findings include lower serum prolactin in women with PD and motor complications, a negative correlation between estradiol levels and HAMA scores, and no significant differences in menstrual characteristics between those with and without motor complications. Menarche age negatively correlated with cognitive scores, while the menstrual cycle and its duration showed associations with motor symptom severity. Women with motor complications demonstrated specific correlations between menopause timing, menstrual cycle, and psychological scores and presented with lower T3 and higher thyroid-stimulating hormone levels. T3 and FT3 levels were negatively linked to motor symptom severity and H-Y staging in this group. Motor complications in female PD patients are potentially linked to prolactin and T3 levels, underscoring the need for vigilant thyroid function monitoring. Advanced age at PD onset appears protective against motor complications, contrasting with the risks of extended disease duration and elevated NMSS scores.

Background Patients with type 2 diabetes mellitus (T2DM) are commonly at high risk for developing cognitive dysfunction. Antidiabetic agents might be repurposed for targeting cognitive dysfunction in addition to modulation on glucose homeostasis. This study aimed to evaluate the impact of dipeptidyl peptidase-4 inhibitors (DPP-4i) on cognitive function in T2DM. Methods PubMed, Embase, Cochrane Library and Web of Science were systematically searched from inception to September 30, 2023. Weighted mean differences were calculated using the Mantel-Haenszel (M-H) fixed or random effects model based on the degree of heterogeneity among studies. Heterogeneity was evaluated using a Chi-squared test and quantified with Higgins I 2 . Sensitivity analysis was performed with the leave-one-out method, and publication bias was evaluated according to Begg’s and Egger’s tests. Results Six clinical trials involving 5,178 participants were included in the pooled analysis. Administration of DPP-4i generally correlated with an increase of Mini-Mental State Examination (MMSE) scores (1.09, 95% CI: 0.22 to 1.96). DPP-4i alleviated cognitive impairment in the copying skill subdomain of MMSE (0.26, 95% CI: 0.12 to 0.40). Treatment with DPP-4i also resulted in an increase of Instrumental Activities of Daily Living (IADL) scores (0.82, 95% CI: 0.30 to 1.34). However, DPP-4i produced no significant effects on Barthel Activities of Daily Living (BADL) scores (0.37, 95% CI: -1.26 to 1.99) or other test scores. Conclusions DPP-4i treatment favourably improved cognitive function in patients with T2DM. Further trials with larger samples should be performed to confirm these estimates and investigate the association of different DPP-4i with cognitive function among diabetic patients. Trial registration in PROSPERO CRD42023430873.

Background Parkinson's disease (PD) is a neurodegenerative disease with a broad spectrum of motor and non-motor symptoms. The great heterogeneity of clinical symptoms, biomarkers, and neuroimaging and lack of reliable progression markers present a significant challenge in predicting disease progression and prognoses. Methods We propose a new approach to disease progression analysis based on the mapper algorithm, a tool from topological data analysis. In this paper, we apply this method to the data from the Parkinson's Progression Markers Initiative (PPMI). We then construct a Markov chain on the mapper output graphs. Results The resulting progression model yields a quantitative comparison of patients' disease progression under different usage of medications. We also obtain an algorithm to predict patients' UPDRS III scores. Conclusions By using mapper algorithm and routinely gathered clinical assessments, we developed a new dynamic models to predict the following year's motor progression in the early stage of PD. The use of this model can predict motor evaluations at the individual level, assisting clinicians to adjust intervention strategy for each patient and identifying at-risk patients for future disease-modifying therapy clinical trials.

BackgroundDegeneration of the substantia nigra (SN) may contribute to levodopa-induced dyskinesia (LID) in Parkinson's disease (PD), but the exact characteristics of SN in LID remain unclear. ObjectiveTo further understand the pathogenesis of patients with PD with LID (PD-LID), we explored the structural and functional characteristics of SN in PD-LID using multimodal magnetic resonance imaging (MRI). MethodsTwenty-nine patients with PD-LID, 37 patients with PD without LID (PD-nLID), and 28 healthy control subjects underwent T1-weighted MRI, quantitative susceptibility mapping, neuromelanin-sensitive MRI, multishell diffusion MRI, and resting-state functional MRI. Different measures characterizing the SN were obtained using a region of interest-based approach. ResultsCompared with patients with PD-nLID and healthy control subjects, the quantitative susceptibility mapping values of SN pars compacta (SNpc) were significantly higher (P = 0.049 and P = 0.00002), and the neuromelanin contrast-to-noise ratio values in SNpc were significantly lower (P = 0.012 and P = 0.000002) in PD-LID. The intracellular volume fraction of the posterior SN in PD-LID was significantly higher compared with PD-nLID (P = 0.037). Resting-state fMRI indicated that PD-LID in the medication off state showed higher functional connectivity between the SNpc and putamen compared with PD-nLID (P = 0.031), and the functional connectivity changes in PD-LID were positively correlated with Unified Dyskinesia Rating Scale total scores (R = 0.427, P = 0.042). ConclusionsOur multimodal imaging findings highlight greater neurodegeneration in SN and the altered nigrostriatal connectivity in PD-LID. These characteristics provide a new perspective into the role of SN in the pathophysiological mechanisms underlying PD-LID. (c) 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

1 病例介绍 患者女性,74岁,主因"走路不稳、双下肢麻木4年,双上肢麻木疼痛1年"于2021年11月1日入院.患者4年前无明显诱因逐渐出现走路不稳、行走蹒跚、双下肢麻木,伴双手持物时轻微不自主抖动,言语停顿,伴头晕、情绪低落,无头部、肢体静止时抖动,无肢体僵硬、表情僵硬,无肢体无力、言语不清、口角流涎,无精神行为异常、睡眠中行为异常、记忆力减退,无嗅觉减退、便秘、二便失禁,无吞咽困难、饮水呛咳、视物成双.2年前外院考虑干燥综合征相关脑病不除外,予丙种球蛋白、甲泼尼龙、维生素B1、甲钴胺、多巴丝肼片等药物治疗,症状无明显好转.近1年患者逐渐出现双手麻木,伴双手、右肩疼痛,无肢体无力,能做家务,生活尚可自理.目前应用多巴丝肼125?mg 3次/天、甲泼尼龙2?mg 1次/天.为明确诊断、指导治疗来诊.

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disease in the world. The aim of this study was to predict the global, regional, and national prevalence of PD by age, sex, year and Socio-demographic Index (SDI) until 2050 and quantify the factors driving changes in PD prevalence.Methods: A Poisson regression model, incorporating the Socio-demographic Index (SDI) as a predictor and drawing on aggregated data from the Global Burden of Disease (GBD) 2019, was employed to forecast the prevalence of Parkinson's disease (PD) by age, sex, and year for 204 countries and territories up to 2050. Joinpoint regression analysis was applied to examine temporal trends in the prevalence of PD. Decomposition analyses were conducted to assess the impact of population aging, population growth, and changes in prevalence on the growth of global and regional PD cases. The global population attributable fractions (PAF) were calculated for modifiable factors. Findings: In 2050, it is projected that 22.0 million individuals (95% uncertainty interval[UI] : 16.4 to 29.3) will live with PD worldwide, representing a 158.9% increase from 2019, with approximately two-thirds of cases will be found in the top ten countries. The age-standardized prevalence (per 100,000) is predicted to be 122.2 (100 to 153.3) with a slight increase of 15.0% since 2019. The all-age prevalence of PD is expected to increase more rapidly from 2019 to 2050 (average annual percent change [AAPC]: 2.43, 95% confidence interval: 2.42 to 2.45) compared to the period of 1990-2019 (AAPC: 1.98, 1.92 to 2.04). The highest percentage increase in the all-age prevalence of PD from 2019 to 2050 will be in the middle SDI countries (175.5%) and the age group of 40-59 (30.8%), respectively. Population aging (94.3%) is the leading contributor to the growth in cases. An estimated 48.03% (95%CI: 28.94 to 62.67) of PD cases are attributable to combined six risk factors, with the highest PAF observed for PM2.5 (23.08%, 7.23 to 36.36).Interpretation: The prevalence of PD is projected to experience a substantial and accelerated increase by 2050, with the upward trend being more pronounced among men, middle-aged adults and in middle SDI countries. Prevention strategies aimed at healthy aging and minimizing exposure to prevalent risk factors hold great potential in mitigating the global burden of PD.Funding: This work was supported by grants from the National Nature Science Foundation of China (Grant No. 82071422 and 82271459) and the Natural Science Foundation of Beijing Municipality (Grant No. 7212031).Declaration of Interest: No potential conflicts of interest relevant to this article were reported.Ethical Approval: This study does not contain personal or medical information about an identifiable living individual, and animal subjects were not involved in the study. No ethical approval were required.

Locus coeruleus (LC) is severely affected in Parkinson’s Disease (PD). However, alterations in LC-related resting-state networks (RSNs) in PD remain unclear. We used resting-state functional MRI to investigate the alterations in functional connectivity (FC) of LC-related RSNs and the associations between RSNs changes and clinical features in idiopathic rapid eye movement sleep behavior disorder (iRBD) and PD patients with (PD RBD+ ) and without RBD (PD RBD− ). There was a similarly disrupted FC pattern of LC-related RSNs in iRBD and PD RBD+ patients, whereas LC-related RSNs were less damaged in PD RBD− patients than that in patients with iRBD and PD RBD+ . The FC of LC-related RSNs correlated with cognition and duration in iRBD, depression in PD RBD− , and cognition and severity of RBD in patients with PD RBD+ . Our findings demonstrate that LC-related RSNs are significantly disrupted in the prodromal stage of α-synucleinopathies and proposed body-first PD (PD RBD+ ), but are less affected in brain-first PD (PD RBD− ).

The current study aimed to investigate the clinical characteristics of Parkinson's disease (PD) patients with concomitant periodic limb movements in sleep (PLMS). The clinical data of 36 PD patients who underwent polysomnography (PSG) in Beijing Tiantan Hospital from October 2018 to July 2022 were collected. Unified Parkinson's Disease Rating Scale 3.0 and Hoehn & Yahr (H-Y) stage were used to evaluate the disease severity. Patients were divided into two groups: the PLMS+group periodic limb movements in sleep index [(PLMSI)≥15 times/h] and the PLMS-group (PLMSI<15 times/h), using the PLMSI 15 times/h as the cut-off value. The clinical characteristics between the two groups were compared. There were 15 patients (42%) in the PLMS+group and 21 patients (58%) in the PLMS-group, among which 12 patients (12/15) in the PLMS+group and 9 patients (42.9%) in the PLMS-group had rapid eye movement sleep behavior disorder (RBD). The rate of RBD in PLMS+group was higher than that in PLMS-group (P<0.05). There was statistically significant difference in the blood folate level between the PLMS-group and PLMS+group [6.20 (5.14, 11.70) ng/ml vs 4.41 (3.07, 5.64) ng/ml] (P<0.01). Folate deficiency was more common in the PLMS+group, while no statistically significant differences were found in homocysteine and ferritin levels (both P>0.05). Four patients in the PLMS+group had falling experience, while 14.3% (3/21) patients in the PLMS-group had falling experience. Patients in the PLMS+group were more likely to fall. The PLMS+group had higher arousal index according to PSG [PLMS-group: 11.90 (9.10, 15.80) times/h; PLMS+group: 21.50 (19.35, 29.90) times/h] (P<0.05). No statistically significant differences in other sleep parameters were detected between the two groups (all P>0.05). Meanwhile, the apnea-hypopnea index (AHI) in both groups was higher than normal (<5 times/h), of which the PLMS-group was 9.80 (4.70, 22.20) times/h and the PLMS+group was 8.20 (1.70, 11.15) times/h, indicating that PD patients were more likely to experience sleep apnea and hypopnea. PD patients with PLMS had lower folate level, higher risk for falls, higher sleep arousal index, more sleep fragmentation, and higher prevalence of RBD.

Background:Pathological tau accumulates in the cerebral cortex of Parkinson's disease (PD), resulting in cognitive deterioration. Positron emission tomography (PET) can be used for in vivo imaging of tau protein. Therefore, we conducted a systematic review and meta-analysis of tau protein burden in PD cognitive impairment (PDCI), PD dementia (PDD), and other neurodegenerative diseases and explored the potential of the tau PET tracer as a biomarker for the diagnosis of PDCI.Methods:PubMed, Embase, the Cochrane Library, and Web of Science databases were systematically searched for studies published till 1 June 2022 that used PET imaging to detect tau burden in the brains of PD patients. Standardized mean differences (SMDs) of tau tracer uptake were calculated using random effects models. Subgroup analysis based on the type of tau tracers, meta-regression, and sensitivity analysis was conducted.Results:A total of 15 eligible studies were included in the meta-analysis. PDCI patients (n = 109) had a significantly higher tau tracer uptake in the inferior temporal lobe than healthy controls (HCs) (n = 237) and had a higher tau tracer uptake in the entorhinal region than PD with normal cognition (PDNC) patients (n = 61). Compared with progressive supranuclear palsy (PSP) patients (n = 215), PD patients (n = 178) had decreased tau tracer uptake in the midbrain, subthalamic nucleus, globus pallidus, cerebellar deep white matter, thalamus, striatum, substantia nigra, dentate nucleus, red nucleus, putamen, and frontal lobe. Tau tracer uptake values of PD patients (n = 178) were lower than those of patients with Alzheimer's disease (AD) (n = 122) in the frontal lobe and occipital lobe and lower than those in patients with dementia with Lewy bodies (DLB) (n = 55) in the occipital lobe and infratemporal lobe.Conclusion:In vivo imaging studies with PET could reveal region-specific binding patterns of the tau tracer in PD patients and help in the differential diagnosis of PD from other neurodegenerative diseases.Systematic review registration:https://www.crd.york.ac.uk/PROSPERO/.

超高场强磁共振是场强≥7T的磁共振技术,具有高分辨率、高信噪比的特点,有望实现脑内细微病变的可视化.自2001年7T MRI首次用于扫描人脑结构开始,此后的20年中,超高场强磁共振在神经病学方面的应用得到迅速发展.2011年Cho等学者首先将7T MRI应用于帕金森病(Parkinson's disease,PD)研究中发现,PD患者的黑质腹侧缘存在锯齿状改变[1].目前,应用于PD研究的超高场强磁共振主要是7T和9.4T MRI.

帕金森病（PD）的核心病理改变是黑质病变，以黑质小体的多巴胺能神经元退行性改变和铁沉积病变为主要特征。近年研究发现应用神经黑色素序列、磁敏感序列、定量磁化率技术、弥散张量成像等可以显示黑质结构和病变。在高分辨黑质MRI上，神经黑色素敏感的序列及磁敏感的序列可以作为PD患者的影像标志物，区分PD患者及健康人，且对于PD前驱期的识别也有价值，可用于早期PD诊断。但在PD与PD叠加综合征的鉴别诊断方面，黑质MRI的敏感度及特异度均不高。综合考虑黑质及其他区域如中脑、壳核等部位在MRI的变化，是提高PD及PD叠加综合征鉴别率的重要方向。高场强MRI如7.0 T MRI可以更加清晰地观察到黑质结构，区分黑质小体，未来在高场强MRI上进一步研究黑质结构与PD患者病程、分型、治疗等的关系，将有助于医师对PD患者的早期诊断及规范管理。

震颤是指身体特定部位不自主节律性运动,依据病因分为生理性震颤和病理性震颤.病理性震颤常见于原发性震颤(essential tremor,ET)和帕金森病(Parkinson's disease, PD),严重影响患者生活质量[1].目前主要应用药物治疗ET和PD,严重震颤也可以考虑脑深部电刺激为主的中枢性神经调控治疗.部分病理性震颤患者药物治疗效果不理想,而脑深部电刺激疗法虽然属于微创手术治疗,但依然存在手术相关、脑起搏器装置相关的不良反应和风险[2].外周神经电刺激疗法的发展,以 ET、PD 震颤为主的病理性震颤提供了新的治疗选择.外周神经电刺激是指通过放置在外周神经附近的刺激器发放电流脉冲调控神经活动,最初于1967 年被Shelden等、Wall和Sweet两个团队分别报道用于治疗面部和肢体疼痛.Mones和 Weiss于 1969 年首次详细记录了外周神经电刺激对震颤瞬时抑制的肌电图表现.Javidan等于 1992 年首次将外周神经电刺激用于 PD、ET的临床治疗研究,并获得肯定疗效.此后的研究不断调整刺激模式、参数以实现更佳疗效.本研究从外周神经电刺激治疗病理性震颤的研究历史、神经调控机制、治疗的有效性和安全性以及面临的问题等方面进行分析和综述,以期为外周神经调控相关研究及临床应用提供参考.

目的:探讨帕金森病(PD)同时伴有体位性低血压(OH)及卧位高血压(SH)患者的血流动力学、心脑血管发病率及高危因素的特征,以及对运动症状和非运动症状的影响.方法:入组PD合并OH患者198例,伴有SH 123例(SH组),不伴有SH 75例(无SH组).记录所有入组患者临床信息、实验室检查结果,进行各项运动及非运动症状临床量表的评估.进行卧立位试验及急性左旋多巴冲击试验,记录血压变化.比较2组间的基本临床信息,心脑血管疾病及风险因素,冲击试验服药前后血压的变化及量表评分.结果:伴有OH的PD患者中SH的发生率为62.1％.2组间年龄、性别、病程、左旋多巴等效剂量无明显差异.与无SH组相比,SH组同型半胱氨酸略高(P<0.05),余各项心脑血管疾病高危因素差异无统计学意义(P>0.05).SH组MDS-UPDRSⅢ运动功能总分及姿势步态异常得分更高(P<0.05);SH组在服药前卧立位试验及急性左旋多巴冲击试验后收缩压下降最大差值较高(P<0.05),但出现临床显著OH的发生率较低(P<0.05);无SH的PD-OH患者出现临床显著OH的风险是有SH的PD-OH患者的近3倍(OR=2.991,P=0.002).认知评估中,SH组的MMSE量表回忆能力子项、定向力子项、MoCA量表总分、视空间与执行功能子项、定向力子项的评分均低于无SH组(均P<0.05),但2组间认知障碍的发生率差异无统计学意义(P>0.05).结论:PD患者中合并OH及SH的发生率高,尚未发现PD-OH伴有SH增加心脑血管疾病风险,且SH对显著OH起到一定保护作用.PD-OH伴SH患者需注意跌倒和痴呆风险.

目的:本研究旨在探讨早发型帕金森病(EOPD)患者发生体位性低血压(OH)特征、可能的危险因素,以及OH对运动症状和非运动症状的影响.方法:入组131例EOPD患者.记录患者基本信息,进行各项运动及非运动症状临床量表的评估.测量并记录患者在急性美多巴冲击试验服药前、服药1、2、3h后卧立位血压测试,MDS-UPDRSⅢ运动症状评分,计算左旋多巴改善率.根据是否出现OH分为OH组和非OH组,比较2组的基本临床数据、各量表评分,分析OH的可能危险因素.结果:入组的131例EOPD患者纳入OH组69例,纳入无OH组62例.总OH发生率为52.7％,服药前OH发生率为25.8％,服药后为39.7％(P<0.05).OH组患者病程更长,卧位高血压、剂末现象的发生率更高,左旋多巴最大改善率更高,姿势步态异常得分更高(均P<0.05);OH组患者的冻结步态问卷(FOGQ)、Cleveland便秘评分系统(CCS)和帕金森病日常生活质量问卷调查(PDQ-39)量表评分得分更高(均P<0.05);Logistic回归分析显示卧位高血压(OR=11.057,P=0.000)和便秘(OR=1.170,P=0.019)是EOPD患者发生OH的高危因素.结论:OH是EOPD患者的常见自主神经受损表现,服用左旋多巴药物后更易发生.左旋多巴药物可使EOPD患者卧立位收缩压下降.卧位高血压和便秘是EOPD患者发生OH的高危因素.建议对PD患者进行服用抗PD药物后1～2 h的卧立位试验,明确卧位高血压及OH情况,辅助专科医生制定合适的治疗方案.

目的 总结心脏移植患者手术后行输尿管硬镜和软镜手术的围手术期处理经验.方法 回顾性分析2008年1月-2021年1月首都医科大学附属北京安贞医院10例心脏移植患者术后行输尿管结石治疗的临床资料.结果 10例患者均顺利完成手术,未出现严重并发症,术后随访1个月,结石消失,其他均未见异常,移植心脏功能良好.结论 通过多学科协作和充分的围手术期准备,心脏移植术后输尿管结石患者行输尿管镜治疗是安全和可行的.

In recent years, with the technological advances of magnetic resonance imaging (MRI), especially the development and application of ultra-high field MRI, novel imaging sequences and brain connectome analysis, the unique clinical application prospects and scientific value of MRI in the diagnosis and differential diagnosis, monitoring of disease progression, evaluation of curative effect and study of pathophysiological mechanisms of Parkinson′s disease (PD) have been increasingly recognized. The application of MRI is expected to realize the visualization of PD. The research on imaging biomarkers of PD provides more theoretical basis and implementation methods for precision medicine for PD.