• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
Blood cytokines differentiate bipolar disorder and major depressive disorder during a major depressive episode: Initial discovery and independent sample replication.血液细胞因子在重度抑郁发作期间可区分双相情感障碍和重度抑郁症:初步发现及独立样本复制
Brain Behav Immun Health. 2021 Mar 10;13:100232. doi: 10.1016/j.bbih.2021.100232. eCollection 2021 May.
2
Explainable machine-learning algorithms to differentiate bipolar disorder from major depressive disorder using self-reported symptoms, vital signs, and blood-based markers.利用自我报告的症状、生命体征和基于血液的标志物,解释性机器学习算法来区分双相情感障碍和重度抑郁症。
Comput Methods Programs Biomed. 2023 Oct;240:107723. doi: 10.1016/j.cmpb.2023.107723. Epub 2023 Jul 17.
3
Mood spectrum symptoms during a major depressive episode: Differences between 145 patients with bipolar disorder and 155 patients with major depressive disorder. Arguments for a dimensional approach.重性抑郁发作时的心境谱症状:双相障碍 145 例与重性抑郁障碍 155 例患者的差异。对维度方法的论证。
Bipolar Disord. 2020 Jun;22(4):385-391. doi: 10.1111/bdi.12855. Epub 2019 Oct 31.
4
A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach.外周炎症特征可区分双相和单相抑郁:一种机器学习方法。
Prog Neuropsychopharmacol Biol Psychiatry. 2021 Mar 8;105:110136. doi: 10.1016/j.pnpbp.2020.110136. Epub 2020 Oct 9.
5
The prevalence and illness characteristics of DSM-5-defined "mixed feature specifier" in adults with major depressive disorder and bipolar disorder: Results from the International Mood Disorders Collaborative Project.《精神疾病诊断与统计手册》第5版定义的“混合特征说明符”在重度抑郁症和双相情感障碍成人患者中的患病率及疾病特征:国际心境障碍协作项目的结果
J Affect Disord. 2015 Feb 1;172:259-64. doi: 10.1016/j.jad.2014.09.026. Epub 2014 Oct 12.
6
Differences in the immune-inflammatory profiles of unipolar and bipolar depression.单相和双相抑郁症免疫炎症特征的差异。
J Affect Disord. 2020 Feb 1;262:8-15. doi: 10.1016/j.jad.2019.10.037. Epub 2019 Oct 30.
7
AI algorithm combined with RNA editing-based blood biomarkers to discriminate bipolar from major depressive disorders in an external validation multicentric cohort.人工智能算法结合基于 RNA 编辑的血液生物标志物在外部分型多中心队列中鉴别双相情感障碍与重度抑郁症。
J Affect Disord. 2024 Jul 1;356:385-393. doi: 10.1016/j.jad.2024.04.022. Epub 2024 Apr 13.
8
Sociodemographic and clinical features of bipolar disorder patients misdiagnosed with major depressive disorder in China.中国被误诊为重度抑郁症的双相情感障碍患者的人口统计学和临床特征。
Bipolar Disord. 2013 Mar;15(2):199-205. doi: 10.1111/bdi.12052.
9
Symptom networks in acute depression across bipolar and major depressive disorders: A network analysis on a large, international, observational study.双相和单相抑郁障碍急性抑郁中的症状网络:一项大型国际观察性研究的网络分析。
Eur Neuropsychopharmacol. 2020 Jun;35:49-60. doi: 10.1016/j.euroneuro.2020.03.017. Epub 2020 May 12.
10
Interleukin-8 and tumor necrosis factor-alpha in youth with mood disorders-A longitudinal study.青少年情绪障碍患者体内白细胞介素-8和肿瘤坏死因子-α的纵向研究
Front Psychiatry. 2022 Aug 11;13:964538. doi: 10.3389/fpsyt.2022.964538. eCollection 2022.

引用本文的文献

1
Peripheral biomarkers to differentiate bipolar depression from major depressive disorder: a real-world retrospective study.用于区分双相抑郁与重度抑郁障碍的外周生物标志物:一项真实世界回顾性研究
BMC Psychiatry. 2024 Jul 31;24(1):543. doi: 10.1186/s12888-024-05979-7.
2
Inflammatory mediators in major depression and bipolar disorder.在重度抑郁症和双相情感障碍中的炎症介质。
Transl Psychiatry. 2024 Jun 8;14(1):247. doi: 10.1038/s41398-024-02921-z.
3
Systemic Inflammatory Response Index (SIRI) at Baseline Predicts Clinical Response for a Subset of Treatment-Resistant Bipolar Depressed Patients.基线时的全身炎症反应指数(SIRI)可预测一部分难治性双相抑郁患者的临床反应。
J Pers Med. 2023 Sep 20;13(9):1408. doi: 10.3390/jpm13091408.
4
Brain Inflammatory Marker Abnormalities in Major Psychiatric Diseases: a Systematic Review of Postmortem Brain Studies.主要精神疾病中的脑炎性标志物异常:死后脑研究的系统评价
Mol Neurobiol. 2023 Apr;60(4):2116-2134. doi: 10.1007/s12035-022-03199-2. Epub 2023 Jan 5.
5
Involvement of inflammatory responses in the brain to the onset of major depressive disorder due to stress exposure.大脑中的炎症反应与因应激暴露导致的重度抑郁症发病有关。
Front Aging Neurosci. 2022 Jul 22;14:934346. doi: 10.3389/fnagi.2022.934346. eCollection 2022.
6
False dogmas in mood disorders research: Towards a nomothetic network approach.情绪障碍研究中的错误教条:迈向一种通用网络方法。
World J Psychiatry. 2022 May 19;12(5):651-667. doi: 10.5498/wjp.v12.i5.651.
7
Reduced Serum Levels of Soluble Interleukin-15 Receptor α in Schizophrenia and Its Relationship to the Excited Phenotype.精神分裂症患者血清可溶性白细胞介素-15受体α水平降低及其与兴奋表型的关系
Front Psychiatry. 2022 Mar 9;13:842003. doi: 10.3389/fpsyt.2022.842003. eCollection 2022.

本文引用的文献

1
A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach.外周炎症特征可区分双相和单相抑郁:一种机器学习方法。
Prog Neuropsychopharmacol Biol Psychiatry. 2021 Mar 8;105:110136. doi: 10.1016/j.pnpbp.2020.110136. Epub 2020 Oct 9.
2
Anti-inflammatory IL-10 administration rescues depression-associated learning and memory deficits in mice.抗炎细胞因子 IL-10 给药可挽救抑郁相关的学习和记忆缺陷。
J Neuroinflammation. 2020 Aug 22;17(1):246. doi: 10.1186/s12974-020-01922-1.
3
Differential biomarker signatures in unipolar and bipolar depression: A machine learning approach.单相和双相抑郁症中的差异生物标志物特征:一种机器学习方法。
Aust N Z J Psychiatry. 2020 Apr;54(4):393-401. doi: 10.1177/0004867419888027. Epub 2019 Dec 2.
4
Differences in the immune-inflammatory profiles of unipolar and bipolar depression.单相和双相抑郁症免疫炎症特征的差异。
J Affect Disord. 2020 Feb 1;262:8-15. doi: 10.1016/j.jad.2019.10.037. Epub 2019 Oct 30.
5
Inflammation-related biomarkers in major psychiatric disorders: a cross-disorder assessment of reproducibility and specificity in 43 meta-analyses.主要精神疾病相关炎症生物标志物:43 项荟萃分析中跨疾病的可重复性和特异性评估。
Transl Psychiatry. 2019 Sep 18;9(1):233. doi: 10.1038/s41398-019-0570-y.
6
Changes in the serum levels of inflammatory cytokines in antidepressant drug-naïve patients with major depression.抗抑郁药初治的抑郁症患者血清中炎症细胞因子水平的变化。
PLoS One. 2018 Jun 1;13(6):e0197267. doi: 10.1371/journal.pone.0197267. eCollection 2018.
7
The effect of excess weight on circulating inflammatory cytokines in drug-naïve first-episode psychosis individuals.超重对初发未用药精神分裂症个体循环炎症细胞因子的影响。
J Neuroinflammation. 2018 Feb 28;15(1):63. doi: 10.1186/s12974-018-1096-6.
8
Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis.21 种抗抑郁药治疗成人重度抑郁症的急性治疗的疗效和可接受性比较:系统评价和网络荟萃分析。
Lancet. 2018 Apr 7;391(10128):1357-1366. doi: 10.1016/S0140-6736(17)32802-7. Epub 2018 Feb 21.
9
Serotonin transporter gene expression predicts the worsening of suicidal ideation and suicide attempts along a long-term follow-up of a Major Depressive Episode.5-羟色胺转运体基因表达预测了重性抑郁发作的长期随访中自杀意念和自杀企图的恶化。
Eur Neuropsychopharmacol. 2018 Mar;28(3):401-414. doi: 10.1016/j.euroneuro.2017.12.015. Epub 2017 Dec 27.
10
Rate and predictors of conversion from unipolar to bipolar disorder: A systematic review and meta-analysis.单相障碍转换为双相障碍的发生率及预测因素:一项系统综述与荟萃分析。
Bipolar Disord. 2017 Aug;19(5):324-335. doi: 10.1111/bdi.12513. Epub 2017 Jul 17.

血液细胞因子在重度抑郁发作期间可区分双相情感障碍和重度抑郁症:初步发现及独立样本复制

Blood cytokines differentiate bipolar disorder and major depressive disorder during a major depressive episode: Initial discovery and independent sample replication.

作者信息

Martinuzzi Emanuela, Barbosa Susana, Courtet Philippe, Olié Emilie, Guillaume Sébastien, Ibrahim El Chérif, Daoudlarian Douglas, Davidovic Laetitia, Glaichenhaus Nicolas, Belzeaux Raoul

机构信息

Université Côte d'Azur, Centre National de la Recherche Scientifique, Institut de Pharmacologie Moléculaire et Cellulaire, Clinical Research Unit, Valbonne, France.

Centre Hospitalier Universitaire de Montpellier, Institut National de la Santé et de la Recherche Médicale, Ho^pital Lapeyronie, Department of Emergency Psychiatry and Acute Care, Montpellier, France.

出版信息

Brain Behav Immun Health. 2021 Mar 10;13:100232. doi: 10.1016/j.bbih.2021.100232. eCollection 2021 May.

DOI:10.1016/j.bbih.2021.100232
PMID:34589747
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8474674/
Abstract

Bipolar disorder (BD) diagnosis currently relies on assessment of clinical symptoms, mainly retrospective and subject to memory bias. BD is often misdiagnosed as Major Depressive Disorder (MDD) resulting in ineffective treatment and worsened clinical outcome. The primary purpose of this study was to identify blood biomarkers that discriminate MDD from BD patients when in a depressed state. We have used clinical data and serum samples from two independent naturalistic cohorts of patients with a Major Depressive Episode (MDE) who fulfilled the criteria of either BD or MDD at inclusion. The discovery and replication cohorts consisted of 462 and 133 patients respectively. Patients were clinically assessed using standard diagnostic interviews, and clinical variables including current treatments were recorded. Blood was collected and serum assessed for levels of 31 cytokines using a sensitive multiplex assay. A penalized logistic regression model combined with nonparametric bootstrap was subsequently used to identify cytokines associated with BD. Interleukin (IL)-6, IL-10, IL-15, IL-27 and C-X-C ligand chemokine (CXCL)-10 were positively associated with BD in the discovery cohort. Of the five cytokines identified as discriminant features in the discovery cohort, IL-10, IL-15 and IL-27 were also positively associated with BD in the replication cohort therefore providing an external validation to our finding. Should our results be validated in a prospective cohort, they could provide new insights into the pathophysiological mechanisms of mood disorders.

摘要

双相情感障碍(BD)的诊断目前依赖于临床症状评估,主要是回顾性的,且存在记忆偏差。BD常被误诊为重度抑郁症(MDD),导致治疗无效和临床结局恶化。本研究的主要目的是确定在抑郁状态下能够区分MDD患者和BD患者的血液生物标志物。我们使用了来自两个独立自然主义队列的临床数据和血清样本,这些队列中的重度抑郁发作(MDE)患者在纳入时符合BD或MDD的标准。发现队列和验证队列分别由462名和133名患者组成。使用标准诊断访谈对患者进行临床评估,并记录包括当前治疗在内的临床变量。采集血液,使用灵敏的多重检测法评估血清中31种细胞因子的水平。随后使用惩罚逻辑回归模型结合非参数自助法来确定与BD相关的细胞因子。在发现队列中,白细胞介素(IL)-6、IL-10、IL-15、IL-27和C-X-C配体趋化因子(CXCL)-10与BD呈正相关。在发现队列中确定为判别特征的五种细胞因子中,IL-10、IL-15和IL-27在验证队列中也与BD呈正相关,从而为我们的发现提供了外部验证。如果我们的结果在前瞻性队列中得到验证,它们可能会为情绪障碍的病理生理机制提供新的见解。