Detection of egg white allergy in children by specific IgE microarray chemiluminescence immunoassay

IF 3.2 3区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY
Ren-Wei-Yang Zhang , Dan-Dan Yuan , Xue Yang , Yong-Bing Yang , Fa-Ping Li , Xu-Yang Chen , Kai Wang , Jie Liu , Li-Na Yu , Zhi-Gang Hu
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引用次数: 0

Abstract

Background

Allergen testing has emerged as a pivotal component in prevention and treatment strategies for allergic diseases among children and the utilization of specific IgE (sIgE) through a fully automated chemiluminescent microarray immunoassay (CLMIA) has emerged as a promising trend in the simultaneous detection of multiple allergenic components of children.

Methods

The accuracy and reliability of CLMIA were verified using children’s serum samples that concentrated on allergens. the allergens. The clinical diagnostic practicability of CLMIA was assessed through comprehensive evaluations including measurements of the limit of detection (LOD), intra-batch, and inter-batch precision, linearity analysis, the cross-contamination rate, and the concordance rate with the Phadia system.

Results

After the optimization process of CLMIA, the LODs for allergens were calculated to be below 0.01 kU/L, demonstrating the high sensitivity of CLMIA. All components exhibited good linearity within the range of 0.1–100.0 kU/L and the coefficient of determinations (R2 > 0.99). The data of intra-batch precision (<10 %) and inter-batch data (<15 %) illustrated the high reproducibility of CLMIA. The cross-contamination rates for allergens (<0.5 %) showed the high accuracy of CLMIA without interfering. The positive concordance rate between CLMIA and the Phadia system exceeds 90 % with a good negative concordance rate (>85 %) and the Kappa coefficients (>0.8), suggesting the close alignment of CLMIA and the Phadia system and showing the satisfactory clinical potential of CLMIA in children’s allergy disease.

Conclusions

The application of CLMIA has been promising in allergen testing, especially for detecting multiple allergenic components in children.

用特异性 IgE 微阵列化学发光免疫测定法检测儿童蛋清过敏症
背景过敏原检测已成为儿童过敏性疾病预防和治疗策略的关键组成部分,而通过全自动化学发光微阵列免疫测定(CLMIA)利用特异性 IgE(sIgE)同时检测儿童的多种过敏原成分已成为一种很有前途的趋势。通过对检测限(LOD)、批内和批间精密度、线性分析、交叉污染率以及与 Phadia 系统的吻合率进行测量等综合评价,评估了 CLMIA 的临床诊断实用性。所有成分在 0.1-100.0 kU/L 范围内线性关系良好,测定系数(R2 > 0.99)。批内精密度(10%)和批间精密度(15%)数据表明 CLMIA 具有很高的重现性。过敏原的交叉污染率(0.5%)表明 CLMIA 在无干扰的情况下具有很高的准确性。CLMIA与Phadia系统的阳性吻合率超过90%,阴性吻合率(85%)和Kappa系数(0.8)良好,表明CLMIA与Phadia系统密切吻合,显示了CLMIA在儿童过敏性疾病方面令人满意的临床潜力。
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来源期刊
Clinica Chimica Acta
Clinica Chimica Acta 医学-医学实验技术
CiteScore
10.10
自引率
2.00%
发文量
1268
审稿时长
23 days
期刊介绍: The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.
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