Evidence map›Paper›PMID 36403494›Full record

ArticleBiosensors & bioelectronics2023

An electrochemical paper-based hydrogel immunosensor to monitor serum cytokine for predicting the severity of COVID-19 patients.

Dongmin Shi, Chiye Zhang, Xiaoyuan Li, Jie Yuan

Open access · greenAbstract read
In one paragraph

Article in Biosensors & bioelectronics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

9 citing papers in PubMed, 33 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Dongmin ShiDepartment of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region of China; Individualized Interdisciplinary Program (Microelectronics), The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region of China. Electronic address: dshiaa@connect.ust.hk.
Chiye ZhangDepartment of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region of China.
Xiaoyuan LiDepartment of Chemistry, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region of China.
Jie YuanDepartment of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region of China.
Hong Kong University of Science and Technology · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Analysis of cytokines levels in human serum is critical as it can be a "symptom diagnostic biomarker" in COVID-19, giving real-time information about human health status. Here, we present the construction and performance of a low-price immunosensor (∼US$0.428 per test) based on microfluidic paper-based system to detect cytokine for predicting the health status of COVID-19 patients. Interleukin-6 (IL-6) was selected as the detection model for the close relationship between IL-6 and COVID-19. The assay, which we integrated into foldable paper system, leverages the magnetic immunoassay, the streptavidin-horseradish peroxidase (HRP) associated with tetramethyl benzidine/hydrogen peroxide (TMB/H

Indexed as

Biosensing TechniquesCOVID-19Metal NanoparticlesCytokinesGoldHumansHydrogelsHydrogen PeroxideImmunoassayInterleukin-6PolymersPyrrolesCytokinesGoldHydrogelsHydrogen PeroxideInterleukin-6PolymersPyrrolesAuNPsCytokineElectrochemical paper-based immunosensorMicrofluidicPPy hydrogel

Identifiers

PMID36403494
PMCPMC9663147
OpenAlexW4308949545

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.