Evidence map›Paper›PMID 42419079›Full record

ArticleBiosensors & bioelectronics2026

Multiplexed plasmon-enhanced lateral flow assay for early diagnosis of acute kidney injury.

Heng Guo, Yuxiong Liu, Ravi Jada, Ying Liu, Carissa Ng, Aquiles Payte, Jiaying Xu, Connor Malone, Jie Zhao, Kedhareswara Sairam Pasupuleti and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biosensors & bioelectronics, 2026. 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

15 authors.

Heng GuoDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX, 77843, USA.
Yuxiong LiuDepartment of Mechanical Engineering and Materials Science, Institute of Materials Science and Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA.
Ravi JadaDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX, 77843, USA.
Ying LiuDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX, 77843, USA.
Carissa NgDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX, 77843, USA.
Aquiles PayteDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX, 77843, USA.
Jiaying XuDepartment of Mechanical Engineering and Materials Science, Institute of Materials Science and Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA.
Connor MaloneDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX, 77843, USA.
Jie ZhaoDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX, 77843, USA.
Kedhareswara Sairam PasupuletiDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX, 77843, USA.
Arda Aidan ArikanDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX, 77843, USA.
Sameer ThadaniDivisions of Critical Care Medicine and Nephrology, Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, 77030, USA.
Srikanth SingamaneniDepartment of Mechanical Engineering and Materials Science, Institute of Materials Science and Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA.
Ayse Akcan ArikanDivisions of Critical Care Medicine and Nephrology, Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, 77030, USA.
Limei TianDepartment of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX, 77843, USA. Electronic address: ltian@tamu.edu.

Funding

Continuous metabolite and protein profiling for immune monitoringR35GM147568 · NIGMS · TEXAS ENGINEERING EXPERIMENT STATION · PI Limei Tian · 2022 to 2026
$1.8M
Refreshable Biosensors for Continuous Renal Function MonitoringR21EB029064 · NIBIB · TEXAS ENGINEERING EXPERIMENT STATION · PI TIAN, LIMEI · 2020 to 2020
$589k
Ultrasensitive Point of Care Antigen Test for Detection of Neisseria Gonorrhoeae Using Plasmonic FloursR21AI178217 · NIAID · WASHINGTON UNIVERSITY · PI GANDRA, SUMANTH, SINGAMANENI, SRIKANTH · 2023 to 2024
$405k
NIAID NIH HHS R21 AI178217NIBIB NIH HHS R21 EB029064NIGMS NIH HHS R35 GM147568
6 · The paper itself

Abstract

Early diagnosis of acute kidney injury (AKI) remains a major clinical challenge due to the delayed and insensitive nature of conventional markers such as serum creatinine. Urinary protein biomarkers, including neutrophil gelatinase-associated lipocalin (NGAL) and cystatin C (CysC), provide earlier and complementary information on kidney injury, but their clinical translation is limited by the lack of rapid, quantitative, and multiplexed point-of-care (POC) diagnostic tests. Here, we report a multiplexed plasmonic-fluor-based lateral flow assay (p-LFA) that enables sensitive, quantitative, and simultaneous detection of NGAL and CysC. By harnessing ultrabright plasmonic-fluor nanolabels and ratiometric fluorescence analysis, the p-LFA achieves picogram-per-milliliter analytical sensitivity comparable to that of the enzyme-linked immunosorbent assay (ELISA). In addition, the simplified assay format enables sample-to-answer times as short as 10 min, while maintaining sensitivity sufficient for accurate AKI detection. Clinical validation using pediatric urine samples demonstrates a strong correlation with ELISA measurements for both biomarkers. Combined analysis of NGAL and CysC significantly improves AKI diagnostic performance, yielding an area under the receiver operating characteristic curve of 0.973 with a clinical sensitivity of 90.9% and specificity of 91.2% in the studied cohort. Furthermore, a low-cost, portable fluorescence reader enables quantitative performance equivalent to benchtop imaging while dramatically reducing instrument cost and complexity. Collectively, this work establishes a scalable and adaptable p-LFA platform that bridges laboratory-grade immunoassays and practical POC diagnostics, offering a promising solution for early AKI detection and multiplexed biomarker analysis in decentralized healthcare settings.

Indexed as

Acute Kidney InjuryBiosensing TechniquesCystatin CLipocalin-2BiomarkersEarly DiagnosisHumansRapid Diagnostic TestsBiomarkersCystatin CLCN2 protein, humanLipocalin-2Acute kidney injuryCystatin CLateral flow assayMultiplexed detectionNeutrophil gelatinase-associated lipocalinPlasmonic-fluor nanolabels

Identifiers

PMID42419079
PMCPMC13489507

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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.