Evidence map›Paper›PMID 40759350›Full record

ReviewJournal of advanced research2026

Biomaterials for biomarker imaging and detection.

Yan Wang, Xinyu Huang, Guiying Wu, Wanping Wu, Shuang Li, Chunyu Su, Li Li, Qizhuang Lv

Abstract readReview
In one paragraph

Review in Journal of advanced research, 2026. 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. Review
  2. Flatland Metasurfaces for Optical Gas Sensing.Sensors (Basel, Switzerland) · 2026
    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

8 authors.

Yan WangCollege of Smart Agriculture, Yulin Normal University, Yulin 537000 Guangxi, China.
Xinyu HuangCollege of Smart Agriculture, Yulin Normal University, Yulin 537000 Guangxi, China.
Guiying WuCollege of Smart Agriculture, Yulin Normal University, Yulin 537000 Guangxi, China.
Wanping WuCollege of Smart Agriculture, Yulin Normal University, Yulin 537000 Guangxi, China.
Shuang LiCollege of Smart Agriculture, Yulin Normal University, Yulin 537000 Guangxi, China.
Chunyu SuCollege of Smart Agriculture, Yulin Normal University, Yulin 537000 Guangxi, China.
Li LiCollege of Smart Agriculture, Yulin Normal University, Yulin 537000 Guangxi, China. Electronic address: lilyylu@163.com.
Qizhuang LvCollege of Smart Agriculture, Yulin Normal University, Yulin 537000 Guangxi, China; Guangxi Key Laboratory of Agricultural Resources Chemistry and Biotechnology, Yulin 537000 Guangxi, China. Electronic address: lvqizhuang062@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn the realm of biomedical research and clinical practice, the identification and accurate detection of biomarkers have become increasingly critical. Biomarkers serve as key indicators for disease diagnosis, prognosis evaluation, and drug efficacy monitoring, playing a pivotal role in advancing personalized medicine. However, the complexity and diversity of biomarkers pose significant challenges to their detection and imaging. Traditional methods, such as immunoassays, nucleic acid detection, and mass spectrometry, often fall short in terms of sensitivity, specificity, and efficiency. Consequently, there is an urgent need for more precise and efficient technologies to enhance the detection of biomarkers. AIM OF REVIEW: This review aims to provide a comprehensive overview of the latest advancements in biomaterials for biomarker imaging and detection. It seeks to highlight the critical role of biomarkers in disease diagnosis and management, while exploring the potential of newly developed biomaterials to overcome the limitations of conventional detection methods. KEY SCIENTIFIC CONCEPTS OF REVIEW: The review delves into the unique physicochemical properties of biomaterials, such as nanoparticles, quantum dots (QDs), and biopolymers, which enable highly sensitive, specific, and high-resolution biomarker detection. This review finds that by integrating their molecular recognition mechanisms with advanced imaging technologies, these biomaterials demonstrate significant advantages in detecting biomarkers for major diseases such as cancer, cardiovascular diseases (CVDs), and neurodegenerative diseases. For instance, nanoparticle-based probes can detect tumor markers at extremely low concentrations, while QD imaging techniques enable high-resolution imaging at the cellular and tissue levels. Additionally, this review provides an in-depth discussion of the numerous challenges confronting biomaterial-based detection technologies during clinical translation and proposes future research directions. We emphasize the necessity of accelerating the development of innovative materials, optimizing imaging and detection technologies, and facilitating clinical application translation.

Indexed as

Biocompatible MaterialsBiomarkersDiagnostic ImagingMolecular ImagingAnimalsHumansNanoparticlesQuantum DotsBiocompatible MaterialsBiomarkersBiomarkersBiomaterialsClinical applicationsDetectionImaging

Identifiers

PMID40759350
PMCPMC13131447

What Socratic 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 Socratic graph.