Machine Learning Approaches to Dissect Hybrid and Vaccine-Induced Immunity dataset

What is the “Machine Learning Approaches to Dissect Hybrid and Vaccine-Induced Immunity”dataset? This is the final, standardized dataset that was used for the Machine Learning analysis presented in the publication: “Machine Learning Approaches to Dissect Hybrid and Vaccine-Induced Immunity” authored by G. Montesi, S. Costagli et al. The study uses Machine Learning strategies to discovery individuals unaware of a previous SARS-CoV-2 infection and to dissect hybrid and vaccine-induced immunity using serological data collected upon the third dose with mRNA SARS-CoV-2 vaccines in a cohort of 116 healthy individuals. Individuals were recruited at the Infectious and Tropical Diseases Unit, Azienda Ospedaliera Universitaria Senese (Siena, Italy) in the context of the IMMUNO_COV study. The integrated dataset contains combined data from 18 humoral and cellular immunological variables, as well as data regarding past infection, which were merged using an individual-specific ID. How to use the “Machine Learning Approaches to Dissect Hybrid and Vaccine-Induced Immunity” dataset? Here, you can download the entire database as a xlsx file. Briefly, in the xlsx file, each row represents an individual. For each individual, the following information are reported: Clusters of responders: outcome of the unsupervised GMM clustering analysis on tSNE reduced data (antibody concentrations targeting spike and RBD antigens, along with ACE-2/RBD binding inhibition values, both for wt, Delta, Omicron BA.1, and Omicron BA.2 variants, resulting in 12 variables) Classifiers: outcome of the majority-voting consensus-based approach (applied to antibody concentrations targeting spike and RBD antigens, along with ACE-2/RBD binding inhibition values, both for wt, Delta, Omicron BA.1, and Omicron BA.2 variants, as well as the AUC values for BA.2 N-specific IgG, resulting in 13 variables). Infection (0= no infection; 1= infected before the third dose; 2= infected after the third dose) and Days from infection columns: data regarding history of prior infections and days elapsed between the date of infection and the date of 6 months post-boost blood sample collection. wt-spike specific IgG (ng/ml) - Omicron BA.2-RBD specific IgG (ng/ml): wt, Delta, Omicron BA.1 and Omicron BA.2 spike and RBD-specific IgG concentrations, assessed by ELISA. ACE2/wt RBD binding inhibition (%) - ACE2/BA.2 RBD binding inhibition (%): capacity of plasma antibodies to block the ACE-2/RBD interaction, assessed for the wt strain and the Delta, Omicron BA.1 and Omicron BA.2 variants. BA.2 N-specific IgG (AUC): Omicron BA.2 Nucleocapsid specific IgG, expressed as Area Under the Curve. wt N-specific IgG MBC / 10^6 cell - wt RBD-specific IgG MBC (% IgG MBC): frequency of wt Nucleocapsid-, Spike- and RBD-specific IgG-secreting Memory B cell, assessed by ELISPOT. wt+ RBD MBC (% CD19+): frequency of circulating wt RBD-specific B cells, identified among non-naïve CD19+ B cells, assessed by multiparametric flow cytometry. Contact Information For any further information or detail, please contact us at [e-mail].《机器学习解析混合免疫与疫苗诱导免疫》数据集是什么？ 本数据集为最终标准化数据集，用于发表于论文《机器学习解析混合免疫与疫苗诱导免疫》（作者为G. Montesi、S. Costagli等）中的机器学习分析。本研究采用机器学习策略，通过对116名健康受试者在接种第三剂信使RNA（mRNA）型严重急性呼吸综合征冠状病毒2（SARS-CoV-2）疫苗后采集的血清学数据进行分析，识别此前未被发现的SARS-CoV-2感染者，并解析混合免疫与疫苗诱导免疫机制。受试者均招募自意大利锡耶纳大学综合医院感染与热带病科，相关研究隶属于IMMUNO_COV项目。本整合数据集包含18项体液及细胞免疫学变量数据，以及既往感染史相关数据，所有数据均通过个体专属ID进行整合。 如何使用《机器学习解析混合免疫与疫苗诱导免疫》数据集？ 您可在此处下载完整的XLSX格式数据库文件。简言之，该XLSX文件中每一行代表一名受试者，每名受试者的信息如下： 1. 应答者聚类（Clusters of responders）：基于t分布随机邻域嵌入（t-SNE）降维数据开展无监督高斯混合模型（Gaussian Mixture Model, GMM）聚类分析的结果，所用数据包含针对野生型（wt）、Delta、Omicron BA.1及Omicron BA.2毒株的刺突蛋白（S）、受体结合域（RBD）抗体浓度，以及ACE-2/RBD结合抑制值，共包含12项变量。 2. 分类器结果（Classifiers）：基于多数投票共识法得到的分析结果，所用数据包含针对野生型、Delta、Omicron BA.1及Omicron BA.2毒株的刺突蛋白、RBD抗体浓度，ACE-2/RBD结合抑制值，以及Omicron BA.2核衣壳（N）特异性IgG的曲线下面积（AUC）值，共包含13项变量。 3. 感染状态（Infection）与感染后天数（Days from infection）列：感染状态列取值为0（未感染）、1（第三剂接种前感染）、2（第三剂接种后感染）；感染后天数列记录了从感染日期至加强免疫后6个月采血日期之间的天数。 4. 野生型刺突蛋白特异性IgG（wt-spike specific IgG，单位：ng/ml）至Omicron BA.2-RBD特异性IgG：包含野生型、Delta、Omicron BA.1及Omicron BA.2毒株的刺突蛋白、RBD特异性IgG浓度，均通过酶联免疫吸附试验（Enzyme-Linked Immunosorbent Assay, ELISA）检测得到。 5. ACE2/野生型RBD结合抑制率（ACE2/wt RBD binding inhibition，单位：%）至ACE2/BA.2 RBD结合抑制率：血浆抗体阻断ACE-2与RBD结合的能力，分别针对野生型毒株及Delta、Omicron BA.1、Omicron BA.2变异株进行检测。 6. BA.2核衣壳特异性IgG（BA.2 N-specific IgG，以曲线下面积AUC表示）：Omicron BA.2核衣壳特异性IgG，以曲线下面积（AUC）数值表示。 7. 野生型核衣壳特异性IgG记忆B细胞（MBC）/10^6细胞至野生型RBD特异性IgG记忆B细胞占IgG MBC百分比：野生型核衣壳、刺突蛋白及RBD特异性IgG分泌型记忆B细胞的频率，通过酶联免疫斑点试验（Enzyme-Linked Immunospot Assay, ELISPOT）检测得到。 8. 野生型RBD特异性记忆B细胞占CD19+细胞百分比（wt+ RBD MBC, % CD19+）：循环中野生型RBD特异性B细胞的频率，于非幼稚CD19+ B细胞中鉴定得到，通过多参数流式细胞术检测。 联系方式：若需获取更多信息或细节，请发送邮件至[e-mail]。