Evidence map›Paper›PMID 42174191›Full record

Articlenpj biomedical innovations2026

A "turn-off" photoacoustic contrast for urokinase-type plasminogen activator activity.

Ananya Sharma, Suvam Kumar Panda, Thuria Hasan, Sneha Ravanan, Sanhita Sinharay

Abstract read
In one paragraph

Article in npj biomedical innovations, 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

5 authors.

Ananya SharmaDepartment of Bioengineering, Indian Institute of Science, Bangalore, India.
Suvam Kumar PandaDepartment of Bioengineering, Indian Institute of Science, Bangalore, India.
Thuria HasanDepartment of Bioengineering, Indian Institute of Science, Bangalore, India.
Sneha RavananDepartment of Bioengineering, Indian Institute of Science, Bangalore, India.
Sanhita SinharayDepartment of Bioengineering, Indian Institute of Science, Bangalore, India. sanhitas@iisc.ac.in.

Funding

Ministry of Education, India MoE-STARS/STARS-2/2023-0855
6 · The paper itself

Abstract

The ability to distinguish cancerous lesions based on aggressiveness using noninvasive molecular imaging techniques enables more precise and accurate diagnosis. In colorectal cancer screening, current approaches such as blood or fecal tests and endoscopic examination are widely used. However, reliably differentiating malignant adenomatous lesions from benign lesions, particularly those ≤5 mm in size, remains a significant clinical challenge. We synthesized, optimized, and validated a small-molecule near-infrared (NIR) activated photoacoustic dye conjugated to a tripeptide substrate specific for urokinase plasminogen activator (uPA), a protease that is upregulated in colorectal cancer tissues. The probe was designed to produce an "ON-OFF" photoacoustic signal upon activation by uPA. Specificity of the probe towards aggressiveness was evaluated using two colorectal cancer cell lines with differential uPA and cathepsin B expression. The uPA-responsive photoacoustic probe demonstrated high sensitivity and a clear "ON-OFF" activation signal in response to uPA activity. It showed strong specificity between colorectal cancer cell lines with different levels of uPA expression, confirming its selective activation. Noninvasive monitoring of extracellular uPA activity using photoacoustic imaging shows promise as a predictive screening approach for distinguishing malignant and premalignant colorectal lesions, particularly those that are small and difficult to classify using current screening methods.

Identifiers

PMID42174191
PMCPMC13197411

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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.