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2
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J Affect Disord. 2020 Mar 15;265:325-332. doi: 10.1016/j.jad.2020.01.045. Epub 2020 Jan 13.
3
Revisiting the link between platelets and depression through genetic epidemiology: new insights from platelet distribution width.通过遗传流行病学重新审视血小板与抑郁症之间的联系:血小板分布宽度的新见解。
Haematologica. 2020 May;105(5):e246-e248. doi: 10.3324/haematol.2019.222513. Epub 2019 Oct 10.
4
Pubertal Status and Age are Differentially Associated with Inflammatory Biomarkers in Female and Male Adolescents.青春期状态和年龄与女性和男性青少年的炎症生物标志物呈不同相关。
J Youth Adolesc. 2020 Jul;49(7):1379-1392. doi: 10.1007/s10964-019-01101-3. Epub 2019 Aug 13.
5
The role of inflammation and the gut microbiome in depression and anxiety.炎症和肠道微生物群在抑郁症和焦虑症中的作用。
J Neurosci Res. 2019 Oct;97(10):1223-1241. doi: 10.1002/jnr.24476. Epub 2019 May 29.
6
Depression in adolescent girls: Relationship to serum vitamins a and E, immune response to heat shock protein 27 and systemic inflammation.少女抑郁症与血清维生素 A 和 E、热休克蛋白 27 免疫反应和全身炎症的关系。
J Affect Disord. 2019 Jun 1;252:68-73. doi: 10.1016/j.jad.2019.04.048. Epub 2019 Apr 8.
7
Child and Adolescent Depression: A Review of Theories, Evaluation Instruments, Prevention Programs, and Treatments.儿童和青少年抑郁症:理论、评估工具、预防项目及治疗方法综述
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8
Cross-sectional study of neutrophil-lymphocyte, platelet-lymphocyte and monocyte-lymphocyte ratios in mood disorders.中性粒细胞-淋巴细胞、血小板-淋巴细胞和单核细胞-淋巴细胞比值在心境障碍中的横断面研究。
Gen Hosp Psychiatry. 2019 May-Jun;58:7-12. doi: 10.1016/j.genhosppsych.2019.02.003. Epub 2019 Feb 15.
9
Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach.从广泛的临床、心理和生物学数据预测抑郁症的自然病程:一种机器学习方法。
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10
High serum levels of tenascin-C are associated with suicide attempts in depressed patients.血清中 tenascin-C 水平高与抑郁患者的自杀企图有关。
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儿科人群中潜在的重度抑郁症生物标志物 - 一项初步研究。

Potential major depressive disorder biomarkers in pediatric population - a pilot study.

机构信息

Department of Pharmacology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic.

出版信息

Physiol Res. 2020 Dec 31;69(Suppl 3):S523-S532. doi: 10.33549/physiolres.934590.

DOI:10.33549/physiolres.934590
PMID:33476174
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8603720/
Abstract

Mental disorders affect 10-20 % of the young population in the world. Major depressive disorder (MDD) is a common mental disease with a multifactorial and not clearly explained pathophysiology. Many cases remain undetected and untreated, which influences patients' physical and mental health and their quality of life also in adulthood. The aim of our pilot study was to assess the prediction value of selected potential biomarkers, including blood cell counts, blood cell ratios, and parameters like peroxiredoxin 1 (PRDX1), tenascin C (TNC) and type IV collagen (COL4) between depressive pediatric patients and healthy peers and to evaluate a short effect of antidepressant treatment. In this study, 27 young depressive patients and 26 non-depressed age-matched controls were included. Blood analyses and immunological assays using commercial kits were performed. Platelet count was the only blood parameter for which the case/control status was statistically significant (p=0.01) in a regression model controlling for the age and gender differences. The results from ELISA analyses showed that the case/control status is a significant predictor of the parameters PRDX1 (p=0.05) and COL4 (p=0.009) in respective regression model considering the age and gender differences between MDD patients and controls. A major finding of this study is that values of platelet count, monocyte to lymphocyte ratio, white blood cell, and monocyte counts were assessed by the Random Forest machine learning algorithm as relevant predictors for discrimination between MDD patients and healthy controls with a power of prediction AUC=0.749.

摘要

精神障碍影响全球 10-20%的年轻人。重度抑郁症(MDD)是一种常见的精神疾病,其病理生理学具有多因素且尚未明确解释的特点。许多病例未被发现和治疗,这会影响患者的身心健康和成年后的生活质量。我们的初步研究旨在评估包括血细胞计数、血细胞比、过氧化物酶 1(PRDX1)、-tenascin C(TNC)和 IV 型胶原(COL4)在内的选定潜在生物标志物对抑郁儿科患者和健康同龄人的预测价值,并评估抗抑郁治疗的短期效果。在这项研究中,我们纳入了 27 名年轻的抑郁患者和 26 名年龄匹配的非抑郁对照组。使用商业试剂盒进行血液分析和免疫测定。血小板计数是唯一在控制年龄和性别差异的回归模型中具有统计学意义的血液参数(p=0.01)。ELISA 分析结果表明,在考虑 MDD 患者和对照组之间的年龄和性别差异的情况下,病例/对照组的状态是 PRDX1(p=0.05)和 COL4(p=0.009)参数的显著预测因子。本研究的一个主要发现是,血小板计数、单核细胞与淋巴细胞比值、白细胞和单核细胞计数的值通过随机森林机器学习算法评估为区分 MDD 患者和健康对照者的相关预测因子,预测 AUC=0.749。