Evidence map›Paper›PMID 42427407›Full record

ReviewRSC advances2026

Toward point-of-care cancer diagnostics: gold nanoparticle-based colorimetric sensors.

Evana Sultana, Muhammad Shamim Al Mamun

Abstract readReview
In one paragraph

Review 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

2 authors.

Evana SultanaChemistry Discipline, Khulna University Khulna-9208 Bangladesh s.mamun@chem.ku.ac.bd.
Muhammad Shamim Al MamunChemistry Discipline, Khulna University Khulna-9208 Bangladesh s.mamun@chem.ku.ac.bd.ORCID https://orcid.org/0000-0002-9618-2931

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nowadays, cancer has become one of the major causes of mortality all over the world, irrespective of age and gender. The early detection of cancer can markedly enhance patient survival outcomes. Cancer cells are analyzed by comparing the optimum concentration of different types of biomarkers, including alpha-fetoprotein (AFP), prostate-specific antigen (PSA), microRNA-21, and carcinoembryonic antigen (CEA), in biological samples. These biomarkers play a crucial role in the early detection, screening and treatment of cancer. Novel colorimetric assays based on modified AuNPs are discussed in this review, which have opened a new avenue for the facile, point-of-care diagnosis of cancer. Due to their unique characteristics such as surface plasmon resonance (SPR), AuNPs provide a selective, trustworthy and simple strategy to detect cancerous cells by visualizing a color change when used in colorimetry. The principles of the sensing methods (aggregation, anti-aggregation, etching, and enzyme-mimetic activity) are illustrated in this review. Besides, some portable devices, such as paper-based devices (PADs), barcodes, and lateral flow assays (LFTs), are mentioned, which have made this complicated detection process as convenient as a pregnancy strip test. However, these devices are primarily limited by their low sensitivity and inadequate capability for accurate quantitative analysis. This review highlights the progress of paper-based colorimetric sensing devices for the detection of cancers, such as breast cancer, prostate cancer, pancreatic cancer, and bladder cancer. Furthermore, we outline the key advantages of these devices and emphasize their major limitations. Finally, we have also mentioned some perspectives that may improve the sensitivity of these colorimetric devices as biosensors.

Identifiers

PMID42427407
PMCPMC13347789

What OpenQuestion holds

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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.