Evidence map›Paper›PMID 39165194›Full record

ArticleElectrophoresis2024

Nanopore-based detection of periodontitis biomarker miR31 in saliva samples.

Pearl Arora, Haiyan Zheng, Sathishkumar Munusamy, Rana Jahani, Xiyun Guan

Abstract read
In one paragraph

Article in Electrophoresis, 2024. 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
–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

2 citing papers in PubMed.

  1. Article
  2. Regulation of Protein Transport in Functionalized PET Nanopores.The journal of physical chemistry. B · 2025
    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

5 authors.

Pearl AroraDepartment of Chemistry, Illinois Institute of Technology, Chicago, Illinois, USA.
Haiyan ZhengDepartment of Chemistry, University of Missouri, Columbia, Missouri, USA.
Sathishkumar MunusamyDepartment of Chemistry, University of Missouri, Columbia, Missouri, USA.
Rana JahaniDepartment of Chemistry, University of Missouri, Columbia, Missouri, USA.
Xiyun GuanDepartment of Chemistry, University of Missouri, Columbia, Missouri, USA.

Funding

Solid-state nanopore detection of protein biomarkers for early sepsisdiagnosisR01GM147247 · NIGMS · UNIVERSITY OF MISSOURI-COLUMBIA · PI GUAN, XIYUN · 2022 to 2025
$1.2M
National Science Foundation 2203763National Science Foundation 2345813NIGMS NIH HHS R01 GM147247NIH HHS R01GM147247
6 · The paper itself

Abstract

MicroRNAs (miRNAs) play important roles in posttranscriptional gene regulation. Aberrations in the miRNA levels have been the cause behind various diseases, including periodontitis. Therefore, sensitive, specific, and accurate detection of disease-associated miRNAs is vital to early diagnosis and can facilitate inhibitor screening and drug design. In this study, we developed a label-free, real-time sensing method for the detection of miR31, which has been frequently linked to periodontitis, using an engineered protein nanopore and in the presence of a complementary ssDNA as a molecular probe. Our method is rapid and highly sensitive with nanomolar concentration of miR31 that could be determined in minutes. Furthermore, our sensor showed high selectivity toward the target miR31 sequence even in the presence of interfering nucleic acids. In addition, artificial saliva and human saliva samples were successfully analyzed. Our developed nanopore sensing platform could be used to detect other miRNAs and offers a potential application for the clinical diagnosis of disease biomarkers.

Indexed as

BiomarkersMicroRNAsNanoporesPeriodontitisSalivaBiosensing TechniquesHumansLimit of DetectionBiomarkersMicroRNAsDisease biomarkerlabel‐freemicroRNAsnanoporesaliva sample analysis

Identifiers

PMID39165194
PMCPMC11663126

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Registered trials

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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.