Evidence map›Paper›PMID 40861978›Full record

ReviewRSC advances2025

Electrochemical biosensors based on the 3D immobilization of capture probes for influenza virus detection.

Hyo Won Kim, Ae Sol Lee, Chang Sup Kim

Abstract readReview
In one paragraph

Review in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
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

3 authors.

Hyo Won KimGraduate School of Chemical Engineering, Dongguk University Seoul 04620 Republic of Korea.
Ae Sol LeeGraduate School of Chemical Engineering, Dongguk University Seoul 04620 Republic of Korea.
Chang Sup KimGraduate School of Chemical Engineering, Dongguk University Seoul 04620 Republic of Korea.ORCID https://orcid.org/0000-0001-5850-3450

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Influenza viruses pose a significant global health threat, particularly to vulnerable groups such as young children, the elderly, and individuals with underlying health conditions. Accurate and early detection is vital for effective disease management and the prevention of viral transmission. However, traditional diagnostic methods, including viral cultures, rapid antigen detection, and polymerase chain reaction, often face limitations associated with their sensitivity, turnaround time, cost, and/or accessibility, which hinder their effectiveness in real-world settings. Electrochemical biosensors have recently gained attention as innovative diagnostic tools because they deliver highly sensitive and specific results quickly, making them ideal for point-of-care testing. Incorporating three-dimensional (3D) structured materials can enhance biosensor performance by expanding the binding surface area for biorecognition probes and optimizing signal transduction mechanisms. This review highlights the current understanding of influenza viruses and presents the latest developments in electrochemical biosensing technologies. We emphasize the integration of materials such as metal nanoparticles, carbon-based materials, and metal-organic and covalent-organic framework-based materials that can provide 3D surfaces. These strategies enable the sensitive and selective detection of multiple influenza strains. The development of 3D probe immobilization technologies and biosensor engineering has shown promise for practical clinical implementation and large-scale diagnostic use, potentially contributing to improved influenza surveillance and public health outcomes.

Identifiers

PMID40861978
PMCPMC12376872

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.