Evidence map›Paper›PMID 34714639›Full record

ArticleACS nano2021

Machine-Learning-Assisted Microfluidic Nanoplasmonic Digital Immunoassay for Cytokine Storm Profiling in COVID-19 Patients.

Zhuangqiang Gao, Yujing Song, Te Yi Hsiao, Jiacheng He, Chuanyu Wang, Jialiang Shen, Alana MacLachlan, Siyuan Dai, Benjamin H Singer, Katsuo Kurabayashi and 1 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACS nano, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
4.5field-weighted citation impact, top 4% 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

38 citing papers in PubMed, 79 citations in OpenAlex.

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  16. [Therapeutic effects of inulin-type oligosaccharides ofNan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025
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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

11 authors at 2 institutions in 1 country.

Zhuangqiang GaoMaterials Research and Education Center, Materials Engineering, Department of Mechanical Engineering, Auburn University, Auburn, Alabama 36849, United States.ORCID 0000-0001-9097-1799
Yujing SongDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States.
Te Yi HsiaoMaterials Research and Education Center, Materials Engineering, Department of Mechanical Engineering, Auburn University, Auburn, Alabama 36849, United States.
Jiacheng HeMaterials Research and Education Center, Materials Engineering, Department of Mechanical Engineering, Auburn University, Auburn, Alabama 36849, United States.
Chuanyu WangMaterials Research and Education Center, Materials Engineering, Department of Mechanical Engineering, Auburn University, Auburn, Alabama 36849, United States.
Jialiang ShenMaterials Research and Education Center, Materials Engineering, Department of Mechanical Engineering, Auburn University, Auburn, Alabama 36849, United States.
Alana MacLachlanMaterials Research and Education Center, Materials Engineering, Department of Mechanical Engineering, Auburn University, Auburn, Alabama 36849, United States.
Siyuan DaiMaterials Research and Education Center, Materials Engineering, Department of Mechanical Engineering, Auburn University, Auburn, Alabama 36849, United States.ORCID 0000-0001-7259-7182
Benjamin H SingerDepartment of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan 48109, United States.ORCID 0000-0002-4721-6920
Katsuo KurabayashiDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States.ORCID 0000-0002-9613-3590
Pengyu ChenMaterials Research and Education Center, Materials Engineering, Department of Mechanical Engineering, Auburn University, Auburn, Alabama 36849, United States.ORCID 0000-0003-3380-872X
Auburn University · USUniversity of Michigan · US

Funding

Purchase of a modularized confocal microscopeR35GM133795 · NIGMS · AUBURN UNIVERSITY AT AUBURN · PI Pengyu Chen · 2019 to 2026
$3.3M
β-Amyloid Potentiates Neuroinflammation and Cognitive Decline After Sepsis SurvivalK08NS101054 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SINGER, BENJAMIN HERSCHEL · 2017 to 2021
$1.0M
NIGMS NIH HHS R35 GM133795NINDS NIH HHS K08 NS101054
6 · The paper itself

Abstract

Cytokine storm, known as an exaggerated hyperactive immune response characterized by elevated release of cytokines, has been described as a feature associated with life-threatening complications in COVID-19 patients. A critical evaluation of a cytokine storm and its mechanistic linkage to COVID-19 requires innovative immunoassay technology capable of rapid, sensitive, selective detection of multiple cytokines across a wide dynamic range at high-throughput. In this study, we report a machine-learning-assisted microfluidic nanoplasmonic digital immunoassay to meet the rising demand for cytokine storm monitoring in COVID-19 patients. Specifically, the assay was carried out using a facile one-step sandwich immunoassay format with three notable features:

Indexed as

COVID-19MicrofluidicsCytokine Release SyndromeCytokinesHumansImmunoassayMachine LearningCytokinescoronavirus disease 2019cytokine stormdigital/single-molecule detectionmachine learningmicrofluidic immunoassaynanoplasmonics

Identifiers

PMID34714639
PMCPMC8577373
OpenAlexW3208708655

What OpenQuestion holds

Textmetadata
LicenceTDM
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.