↓ Skip to main content

Optimization of an Anti-NMDA Receptor Autoantibody Diagnostic Bioassay

Overview of attention for article published in Frontiers in Neurology, August 2018
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 X users

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
35 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Optimization of an Anti-NMDA Receptor Autoantibody Diagnostic Bioassay
Published in
Frontiers in Neurology, August 2018
DOI 10.3389/fneur.2018.00661
Pubmed ID
Authors

Nan-Chang Chiu, Yi-Jie Lin, Ruu-Fen Tzang, Ying-Syuan Li, Hui-Ju Lin, Subir Das, Caleb G. Chen, Chiao-Chicy Chen, Kate Hsu

Abstract

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is one of the most frequently encountered autoimmune encephalitis. The pathogenesis of both anti-NMDAR encephalitis and schizophrenia involve down-regulation of NMDA receptors. Whether autoantibody-mediated destruction of neuronal NMDA receptors is associated with schizophrenia or first-episode psychosis (FEP) remains unclear, as the current findings from different groups are inconsistent. The main culprits are likely due to heterogeneity of autoantibodies (autoAbs) in a patient's blood or cerebrospinal fluid (CSF), as well as due to limitation of the current detection methods for anti-NMDAR autoAbs. Here, we optimized the current diagnostic method based on the only commercially-available anti-NMDAR test kit. We first increased detection sensitivity by replacing reporter fluorophore fluorescein isothiocyanate (FITC) in the kit with Alexa Fluor 488, which is superior in resisting photobleaching. We also found that using an advanced imaging system could increase the detection limit, compared to using a simple fluorescence microscope. To improve test accuracy, we implemented secondary labeling with a well-characterized mouse anti-NR1 monoclonal antibody (mAb) after immunostaining with a patient's sample. The degree of colocalization between mouse and human antisera in NMDAR-expressing cells served to validate test results to be truly anti-NMDAR positive or false-positive. We also incorporated DNA-specific DAPI to simultaneously differentiate autoAbs targeting the plasma membrane from those targeting cell nuclei or perinuclear compartments. All the technical implementation could be integrated in a general hospital laboratory setting, without the need of specialized expertise or equipment. By sharing our experience, we hope this may help improve sensitivity and accuracy of the mainstream method for anti-NMDAR detection.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 20%
Student > Master 5 14%
Student > Bachelor 4 11%
Researcher 2 6%
Student > Postgraduate 2 6%
Other 5 14%
Unknown 10 29%
Readers by discipline Count As %
Medicine and Dentistry 7 20%
Neuroscience 6 17%
Agricultural and Biological Sciences 3 9%
Psychology 3 9%
Computer Science 1 3%
Other 2 6%
Unknown 13 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 August 2018.
All research outputs
#15,016,514
of 23,099,576 outputs
Outputs from Frontiers in Neurology
#6,206
of 12,015 outputs
Outputs of similar age
#199,940
of 334,071 outputs
Outputs of similar age from Frontiers in Neurology
#139
of 289 outputs
Altmetric has tracked 23,099,576 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,015 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 334,071 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 289 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.