Evidence map›Paper›PMID 42131365›Full record

ArticleRSC advances2026

Integrated triplex LFIA platform for decentralized molecular subtyping of breast cancer.

Wenting Gao, Iftak Hussain, Theopisti A Nikolaou, Samantha Symons, Ganga Dripaul, Monalisa Almeida, Yunting Guo, Sally Adebamowo, Clement Adebamowo, David Erickson

Abstract read
In one paragraph

Article in RSC advances, 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

10 authors.

Wenting GaoMeinig School of Biomedical Engineering, Cornell University Ithaca NY USA.ORCID https://orcid.org/0009-0005-2356-5244
Iftak HussainSibley School of Mechanical and Aerospace Engineering, Cornell University Ithaca NY USA de54@cornell.edu.
Theopisti A NikolaouMeinig School of Biomedical Engineering, Cornell University Ithaca NY USA.
Samantha SymonsMeinig School of Biomedical Engineering, Cornell University Ithaca NY USA.
Ganga DripaulMeinig School of Biomedical Engineering, Cornell University Ithaca NY USA.
Monalisa AlmeidaMeinig School of Biomedical Engineering, Cornell University Ithaca NY USA.
Yunting GuoSibley School of Mechanical and Aerospace Engineering, Cornell University Ithaca NY USA de54@cornell.edu.ORCID https://orcid.org/0009-0006-0661-0899
Sally AdebamowoGreenebaum Comprehensive Cancer Center, Department of Epidemiology & Public Health, University of Maryland School of Medicine Baltimore MD USA cadebamowo@som.umaryland.edu.
Clement AdebamowoGreenebaum Comprehensive Cancer Center, Department of Epidemiology & Public Health, University of Maryland School of Medicine Baltimore MD USA cadebamowo@som.umaryland.edu.ORCID https://orcid.org/0000-0002-6571-2880
David EricksonSibley School of Mechanical and Aerospace Engineering, Cornell University Ithaca NY USA de54@cornell.edu.ORCID https://orcid.org/0000-0002-1624-9711

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is the most frequently diagnosed malignancy among women worldwide, with over 2.3 million new cases annually. Effective treatment relies on precise molecular subtyping, which stratifies tumors into categories such as Luminal A, Luminal B, HER2-enriched, and triple-negative breast cancer based on progesterone receptor (PR), estrogen receptor (ER), and human epidermal growth factor receptor 2 (HER2) expression. Despite its critical importance, subtyping remains largely inaccessible in low- and middle-income countries (LMICs) due to resource limitations, including the high cost and complexity of conventional diagnostic methods. Here, we present a rapid, portable, and cost-effective diagnostic platform that simultaneously quantifies PR, ER, and HER2 with high sensitivity and specificity. The system integrates a modular multiplex LFIA architecture, an incubation-enhanced reaction zone, and europium-based fluorescence detection with an automated, field-deployable imaging reader. These combined engineering features enable clinically relevant detection limits of 32 pM for PR, 76 pM for ER, and 25 pM for HER2, with cross-reactivity below 5%. Validation against enzyme-linked immunosorbent assay (ELISA) revealed strong correlation (

Identifiers

PMID42131365
PMCPMC13162216

What OpenQuestion holds

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LicenceCC BY-NC
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.