ASV tables of Myasthenia gravis (MG) and non-Myasthenia gravis

Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and used to establishe a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods with nested leave-one-out cross-validation were trained using ASV taxon–based data and full ASV–based data to identify key ASVs in each data set. Overlapping key features extracted when XGBoost was trained using the full ASV–based and ASV taxon–based data ..., In this prospective study, 19 individuals with MG and 10 individuals without were consecutively recruited from Fu-Jen Catholic University Hospital. Individuals were enrolled in the MG group if they 1) were given a diagnosis of MG on the basis of having the combination of symptoms and signs that are characteristic of muscle weakness with diurnal changes and either 2a) had a positive test result for specific autoantibodies or 2b) had a positive electrophysiological diagnosis obtained using single-fiber electromyography and repetitive nerve stimulation (Rousseff, 2021). None of the participants had received any abdominal chirurgic intervention; consumed antibiotics, probiotics, or antacids during the previous 6 months; or reported gastrointestinal symptoms during the previous year. This study was approved by the Regional Ethics Committee of Fu-Jen Catholic University Hospital and written informed consent was obtained from each participant (No. FJUH109043). All experiments were completed in...,重症肌无力（Myasthenia Gravis, MG）是一类病理生理学机制复杂、临床表现多样的神经肌肉接头疾病，目前尚未发现明确的临床生物标志物。我们提出如下假设：鉴于自身免疫性疾病常伴随肠道微生物组组成发生改变，MG患者的肠道微生物组结构与健康人群存在显著差异，且可基于肠道菌群数据训练监督机器学习（supervised machine learning, ML）分析策略，用于MG的诊断筛查。本研究收集了MG患者与健康对照者的粪便样本基因组DNA，通过构建扩增子序列变异体（amplicon sequence variants, ASVs）并完成各代表性DNA序列的分类学注释，建立测序文库。研究采用四种机器学习方法，结合基于ASV分类单元的数据与全ASV数据，通过嵌套留一交叉验证（nested leave-one-out cross-validation）开展训练，以识别各数据集的关键ASV特征。当使用全ASV数据与ASV分类单元数据训练XGBoost时，提取到的关键特征存在重叠……在本前瞻性研究中，我们从辅仁大学附属医院连续招募了19例MG患者与10例健康对照者。MG组受试者的入组标准为：1）结合具有昼夜波动特征的肌无力典型症状与体征确诊为MG，且满足以下任一条件：2a）特定自身抗体检测结果呈阳性，或2b）通过单纤维肌电图与重复神经刺激检查获得阳性电生理诊断结果（Rousseff等, 2021）。所有受试者均未接受过腹部手术，近6个月内未服用过抗生素、益生菌或抗酸剂，且近1年内无胃肠道症状报告。本研究经辅仁大学附属医院区域伦理委员会批准，所有受试者均签署书面知情同意书（编号：FJUH109043）。所有实验均已完成……