Evidence map›Paper›PMID 36831959›Full record

ArticleBiosensors2023

Highly Stable InSe-FET Biosensor for Ultra-Sensitive Detection of Breast Cancer Biomarker CA125.

Hao Ji, Zhenhua Wang, Shun Wang, Chao Wang, Kai Zhang, Yu Zhang, Lin Han

Abstract 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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

7 authors.

Hao JiInstitute of Marine Science and Technology, Shandong University, Qingdao 266237, China.ORCID 0000-0002-7052-8797
Zhenhua WangInstitute of Marine Science and Technology, Shandong University, Qingdao 266237, China.
Shun WangInstitute of Marine Science and Technology, Shandong University, Qingdao 266237, China.
Chao WangInstitute of Marine Science and Technology, Shandong University, Qingdao 266237, China.
Kai ZhangInstitute of Marine Science and Technology, Shandong University, Qingdao 266237, China.
Yu ZhangInstitute of Marine Science and Technology, Shandong University, Qingdao 266237, China.ORCID 0000-0002-0666-2537
Lin HanInstitute of Marine Science and Technology, Shandong University, Qingdao 266237, China.ORCID 0000-0003-0461-2031

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Two-dimensional materials-based field-effect transistors (FETs) are promising biosensors because of their outstanding electrical properties, tunable band gap, high specific surface area, label-free detection, and potential miniaturization for portable diagnostic products. However, it is crucial for FET biosensors to have a high electrical performance and stability degradation in liquid environments for their practical application. Here, a high-performance InSe-FET biosensor is developed and demonstrated for the detection of the CA125 biomarker in clinical samples. The InSe-FET is integrated with a homemade microfluidic channel, exhibiting good electrical stability during the liquid channel process because of the passivation effect on the InSe channel. The InSe-FET biosensor is capable of the quantitative detection of the CA125 biomarker in breast cancer in the range of 0.01-1000 U/mL, with a detection time of 20 min. This work provides a universal detection tool for protein biomarker sensing. The detection results of the clinical samples demonstrate its promising application in early screenings of major diseases.

Indexed as

Biosensing TechniquesBreast NeoplasmsBiomarkers, TumorElectricityFemaleHumansTransistors, ElectronicBiomarkers, Tumorbiomarker detectionbiosensorCA125field-effect transistorInSeliquid gate

Identifiers

PMID36831959
PMCPMC9954013

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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.