Evidence map›Paper›PMID 32587979›Full record

ArticlemedRxiv : the preprint server for health sciences2020

A Digital Protein Microarray for COVID-19 Cytokine Storm Monitoring.

Yujing Song, Yuxuan Ye, Shiuan-Haur Su, Andrew Stephens, Tao Cai, Meng-Ting Chung, Meilan Han, Michael W Newstead, David Frame, Benjamin H Singer and 1 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 1 institution in 1 country.

Yuxuan Ye
Shiuan-Haur Su
Andrew Stephens
Tao Cai
Michael W Newstead
Benjamin H SingerORCID 0000-0002-4721-6920
Katsuo KurabayashiORCID 0000-0002-9613-3590
University of Michigan · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite widespread concern for cytokine storms leading to severe morbidity in COVID-19, rapid cytokine assays are not routinely available for monitoring critically ill patients. We report the clinical application of a machine learning-based digital protein microarray platform for rapid multiplex quantification of cytokines from critically ill COVID-19 patients admitted to the intensive care unit (ICU) at the University of Michigan Hospital. The platform comprises two low-cost modules: (i) a semi-automated fluidic dispensing/mixing module that can be operated inside a biosafety cabinet to minimize the exposure of technician to the virus infection and (ii) a 12-12-15 inch compact fluorescence optical scanner for the potential near-bedside readout. The platform enabled daily cytokine analysis in clinical practice with high sensitivity (<0.4pg/mL), inter-assay repeatability (~10% CV), and near-real-time operation with a 10 min assay incubation. A cytokine profiling test with the platform allowed us to observe clear interleukin-6 (IL-6) elevations after receiving tocilizumab (IL-6 inhibitor) while significant cytokine profile variability exists across all critically ill COVID-19 patients and to discover a weak correlation between IL-6 to clinical biomarkers, such as Ferritin and CRP. Our data revealed large subject-to-subject variability in a patient's response to anti-inflammatory treatment for COVID-19, reaffirming the need for a personalized strategy guided by rapid cytokine assays.

Identifiers

PMID32587979
PMCPMC7310633
OpenAlexW3036282217

What OpenQuestion holds

Textmetadata
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.