Evidence map›Paper›PMID 41294754›Full record

ArticleBiosensors2025

Handheld Lab-on-a-Chip System for Label-Free Dual-Plex Detection of Biomarkers Through On-Chip Plasma Separation.

Chen-Yuan Chang, Yuan-Pei Lei, Chien Chieh Chiang, Cheng-Sheng Huang

Abstract read
In one paragraph

Article in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Chen-Yuan ChangDepartment of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
Yuan-Pei LeiDepartment of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
Chien Chieh ChiangDepartment of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
Cheng-Sheng HuangDepartment of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.ORCID 0000-0002-9938-5881

Funding

National Science and Technology Council NSTC-113-2221-E-A49-076
6 · The paper itself

Abstract

Rapid and reliable detection of biomarkers in complex fluids such as whole blood is essential for effective disease diagnosis and monitoring, particularly in point-of-care settings. Accordingly, this study developed a handheld lab-on-a-chip (LOC) platform that integrates on-chip plasma separation with label-free optical biosensing for real-time, dual-plex detection of biomarkers. The LOC platform includes a two-stage filtration unit that enables efficient separation of plasma from whole blood. This platform also includes a novel gradient grating period guided-mode resonance sensor array that is capable of simultaneously detecting multiple biomarkers with high sensitivity. A compact handheld reader was developed to acquire and analyze optical signals. By using creatinine and albumin as model biomarkers, we demonstrated that the developed platform could achieve sensitive, specific, and reproducible biomarker detection in both plasma and whole-blood samples. The platform can detect albumin and creatinine at concentrations as low as 0.8 and 1.44 μg/mL, respectively, and it exhibits minimal nonspecific binding. These results highlight the potential of the proposed system as a robust and accessible tool for decentralized diagnostics.

Indexed as

BiomarkersBiosensing TechniquesLab-On-A-Chip DevicesCreatinineHumansBiomarkersCreatinineguided-mode resonanceimmunosensorlabel-free biosensorlab-on-a-chip

Identifiers

PMID41294754
PMCPMC12650282

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.