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通过ARIANNA(用于神经网络分析的人工智能助手)确定生物反馈疗法治疗偏头痛和氧化应激疗效的决定因素。

Identification of Determinants of Biofeedback Treatment's Efficacy in Treating Migraine and Oxidative Stress by ARIANNA (ARtificial Intelligent Assistant for Neural Network Analysis).

作者信息

Ciancarelli Irene, Morone Giovanni, Tozzi Ciancarelli Maria Giuliana, Paolucci Stefano, Tonin Paolo, Cerasa Antonio, Iosa Marco

机构信息

Department of Life, Health and Environmental Sciences, University of L'Aquila, 67100 L'Aquila, Italy.

Santa Lucia Foundation IRCSS, 00179 Roma, Italy.

出版信息

Healthcare (Basel). 2022 May 19;10(5):941. doi: 10.3390/healthcare10050941.

Abstract

Migraines are a public health problem that impose severe socioeconomic burdens and causes related disabilities. Among the non-pharmacological therapeutic approaches, behavioral treatments such as biofeedback have proven effective for both adults and children. Oxidative stress is undoubtedly involved in the pathophysiology of migraines. Evidence shows a complex relationship between nitric oxide (NO) and superoxide anions, and their modification could lead to an effective treatment. Conventional analyses may fail in highlighting the complex, nonlinear relationship among factors and outcomes. The aim of the present study was to verify if an artificial neural network (ANN) named ARIANNA could verify if the serum levels of the decomposition products of NO-nitrite and nitrate (NOx)-the superoxide dismutase (SOD) serum levels, and the Migraine Disability Assessment Scores (MIDAS) could constitute prognostic variables predicting biofeedback's efficacy in migraine treatment. Twenty women affected by chronic migraine were enrolled and underwent an EMG-biofeedback treatment. The results show an accuracy for the ANN of 75% in predicting the post-treatment MIDAS score, highlighting a statistically significant correlation (R = -0.675, = 0.011) between NOx (nitrite and nitrate) and MIDAS only when the peroxide levels in the serum were within a specific range. In conclusion, the ANN was proven to be an innovative methodology for interpreting the complex biological phenomena and biofeedback treatment in migraines.

摘要

偏头痛是一个公共卫生问题,会带来严重的社会经济负担并导致相关残疾。在非药物治疗方法中,诸如生物反馈等行为疗法已被证明对成人和儿童均有效。氧化应激无疑参与了偏头痛的病理生理学过程。有证据表明一氧化氮(NO)和超氧阴离子之间存在复杂关系,对它们的调节可能会带来有效的治疗方法。传统分析可能无法突出因素与结果之间复杂的非线性关系。本研究的目的是验证名为ARIANNA的人工神经网络是否能够验证血清中NO的分解产物亚硝酸盐和硝酸盐(NOx)的水平、超氧化物歧化酶(SOD)的血清水平以及偏头痛残疾评估评分(MIDAS)是否可以构成预测生物反馈治疗偏头痛疗效的预后变量。招募了20名患有慢性偏头痛的女性并对她们进行了肌电图生物反馈治疗。结果显示,人工神经网络预测治疗后MIDAS评分的准确率为75%,仅当血清中的过氧化物水平在特定范围内时,才突出显示了NOx(亚硝酸盐和硝酸盐)与MIDAS之间具有统计学意义的相关性(R = -0.675,P = 0.011)。总之,人工神经网络被证明是一种用于解释偏头痛中复杂生物学现象和生物反馈治疗的创新方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a56/9141187/d54ec4ffd548/healthcare-10-00941-g001.jpg

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