Evidence map›Paper›PMID 36979563›Full record

ArticleBiosensors2023

Polydopamine Nanoparticles-Based Three-Line Lateral Flow Immunoassay for COVID-19 Detection.

Zhe Liu, Chaoyu Cao, Haoyang Tong, Minli You

Open access · goldFull text read
In one paragraph

Article in Biosensors, 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
0.3field-weighted citation impact, top 49% of its field
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, 3 citations in OpenAlex.

  1. Review
  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

4 authors at 2 institutions in 1 country.

Zhe LiuDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
Chaoyu CaoThe Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.
Haoyang TongBioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, China.
Minli YouThe Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.ORCID 0000-0003-3373-8012
Xi'an Jiaotong University · CNFirst Affiliated Hospital of Xi'an Jiaotong University · CN

Funding

Fundamental Research Funds for the Central Universities of China xzy012019067General Financial Grant from the China Postdoctoral Science Foundation 2019M663741Natural Science Foundation of Shaanxi Province 2021JQ-013Natural Science Foundation of Shaanxi Province 2022JQ-985
6 · The paper itself

Abstract

Currently, the global trend of several hundred thousand new confirmed COVID-19 patients per day has not abated significantly. Serological antibody detection has become an important tool for the self-screening of people. While the most commonly used colorimetric lateral flow immunoassay (LFIA) methods for the detection of COVID-19 antibodies are limited by low sensitivity and a lack of quantification ability. This leads to poor accuracy in the screening of early COVID-19 patients. Therefore, it is necessary to develop an accurate and sensitive autonomous antibody detection technique that will effectively reduce the COVID-19 infection rate. Here, we developed a three-line LFIA immunoassay based on polydopamine (PDA) nanoparticles for COVID-19 IgG and IgM antibodies detection to determine the degree of infection. The PDA-based three-line LFIA has a detection limit of 1.51 and 2.34 ng/mL for IgM and IgG, respectively. This assay reveals a good linearity for both IgM and IgG antibodies detection and is also able to achieve quantitative detection by measuring the optical density of test lines. In comparison, the commercial AuNP-based LFIA showed worse quantification results than the developed PDA-based LFIA for low-concentration COVID-19 antibody samples, making it difficult to distinguish between negative and positive samples. Therefore, the developed PDA-based three-line LFIA platform has the accurate quantitative capability and high sensitivity, which could be a powerful tool for the large-scale self-screening of people.

Indexed as

COVID-19ImmunoassayNanoparticlesAnimalsHumansIndolesPolymersSARS-CoV-2IndolespolydopaminePolymersartificial intelligenceautonomous antibodyCOVID-19lateral flow immunoassaypolydopamine

Identifiers

PMID36979563
PMCPMC10046468
OpenAlexW4323361318

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read45
Read underepoch 390

Registered trials

None linked

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.