Evidence map›Paper›PMID 37090047›Full record

ReviewAnnals of translational medicine2023

How to improve the sensitivity and specificity of cell-based assays in detecting autoantibodies in neuroimmune diseases.

Feng Gao, Yingna Zhang, Jie Lv, Shumin Wang, Xiaoxiao He, Peipei Liu, Weike Cui

Abstract readReview
In one paragraph

Review in Annals of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Feng GaoDepartment of Neuroimmunology, Henan Institute of Medical and Pharmacological Sciences, Zhengzhou University, Zhengzhou, China.
Yingna ZhangDepartment of Neuroimmunology, Henan Institute of Medical and Pharmacological Sciences, Zhengzhou University, Zhengzhou, China.
Jie LvDepartment of Neuroimmunology, Henan Institute of Medical and Pharmacological Sciences, Zhengzhou University, Zhengzhou, China.
Shumin WangDepartment of Neuroimmunology, Henan Institute of Medical and Pharmacological Sciences, Zhengzhou University, Zhengzhou, China.
Xiaoxiao HeDepartment of Neuroimmunology, Henan Institute of Medical and Pharmacological Sciences, Zhengzhou University, Zhengzhou, China.
Peipei LiuDepartment of Neuroimmunology, Henan Institute of Medical and Pharmacological Sciences, Zhengzhou University, Zhengzhou, China.
Weike CuiDepartment of Neuroimmunology, Henan Institute of Medical and Pharmacological Sciences, Zhengzhou University, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autoantibodies are associated with neuroimmune diseases that affect the central and peripheral nervous systems. There are many methods for detecting autoantibodies, among which cell-based assay (CBA) is a relatively novel and important technology that is widely used. CBAs, as novel indirect immunofluorescence assays with known antigen epitopes, have revolutionized the identification of autoantibodies compared with the traditional immunoassays, such as the radioimmunoprecipitation and enzyme-linked immunosorbent assays, as well as the tissue-based assays (TBAs). However, the results of the same sample might exhibit obvious differences between different laboratories, or among repeated testing in the same laboratory, which influence the sensitivity and specificity in the diagnostic performance for a specific neuroimmune disease. In this paper, we review the establishment of CBA technology, and discuss potential interfering factors in CBA methods on its sensitivity and specificity for the autoantibodies associated with neuroimmune diseases.

Indexed as

antibodycell-based assay (CBA)Neuroimmunologysensitivityspecificity

Identifiers

PMID37090047
PMCPMC10116424

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.