Evidence map›Paper›PMID 40382337›Full record

ArticleNature communications2025

Plasmonic coffee-ring biosensing for AI-assisted point-of-care diagnostics.

Kamyar Behrouzi, Zahra Khodabakhshi Fard, Chun-Ming Chen, Peisheng He, Megan Teng, Liwei Lin

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
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

6 authors.

Kamyar BehrouziDepartment of Mechanical Engineering, University of California, Berkeley, CA, USA. kbehrouzi@berkeley.edu.ORCID http://orcid.org/0000-0002-3800-2197
Zahra Khodabakhshi FardDepartment of Applied Science and Technology, University of California, Berkeley, CA, USA.
Chun-Ming ChenDepartment of Mechanical Engineering, University of California, Berkeley, CA, USA.
Peisheng HeDepartment of Mechanical Engineering, University of California, Berkeley, CA, USA.
Megan TengDepartment of Mechanical Engineering, University of California, Berkeley, CA, USA.
Liwei LinDepartment of Mechanical Engineering, University of California, Berkeley, CA, USA. lwlin@berkeley.edu.ORCID http://orcid.org/0000-0001-7083-624X

Funding

GPU Computing Cluster for Transformative Public Health driven Molecular ScienceS10OD034382 · OD · UNIVERSITY OF CALIFORNIA BERKELEY · PI DURKIN, KATHLEEN A · 2023 to 2023
$491k
U.S. Department of Health & Human Services | National Institutes of Health (NIH) S10OD034382
6 · The paper itself

Abstract

A major challenge in addressing global health issues is developing simple, affordable biosensors with high sensitivity and specificity. Significant progress has been made in at-home medical detection kits, especially during the COVID-19 pandemic. Here, we demonstrated a coffee-ring biosensor with ultrahigh sensitivity, utilizing the evaporation of two sessile droplets and the formation of coffee-rings with asymmetric nanoplasmonic patterns to detect disease-relevant proteins as low as 3 pg/ml, under 12 min. Experimentally, a protein-laden droplet dries on a nanofibrous membrane, pre-concentrating biomarkers at the coffee ring. A second plasmonic droplet with functionalized gold nanoshells is then deposited at an overlapping spot and dried, forming a visible asymmetric plasmonic pattern due to distinct aggregation mechanisms. To enhance detection sensitivity, a deep neural model integrating generative and convolutional networks was used to enable quantitative biomarker diagnosis from smartphone photos. We tested four different proteins, Procalcitonin (PCT) for sepsis, SARS-CoV-2 Nucleocapsid (N) protein for COVID-19, Carcinoembryonic antigen (CEA) and Prostate-specific antigen (PSA) for cancer diagnosis, showing a working concentration range over five orders of magnitude. Sensitivities surpass equivalent lateral flow immunoassays by over two orders of magnitude using human saliva samples. The detection principle, along with the device, and materials can be further advanced for early disease diagnostics.

Indexed as

Biosensing TechniquesCOVID-19Point-of-Care SystemsBiomarkersGoldHumansProcalcitoninSARS-CoV-2Sensitivity and SpecificitySmartphoneBiomarkersGoldProcalcitonin

Identifiers

PMID40382337
PMCPMC12085679

What OpenQuestion holds

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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.