Evidence map›Paper›PMID 40894894›Full record

ArticleiScience2025

Machine learning assisted CRCA multi-color fluorescent droplet imaging for high-sensitivity simultaneous detection of Maillard hazards.

Shikun Zhang, Xianfeng Lin, Zepeng Gu, Lixin Kang, Yingming Zhang, Jun Sun, Nuo Duan, Zhouping Wang, Ruijie Deng, Shijia Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2025. 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.

Shikun ZhangState Key Laboratory of Food Science and Resources, School of Food Science and Technology, International Joint Laboratory on Food Safety, Jiangnan University, Wuxi 214122, China.
Xianfeng LinState Key Laboratory of Food Science and Resources, School of Food Science and Technology, International Joint Laboratory on Food Safety, Jiangnan University, Wuxi 214122, China.
Zepeng GuState Key Laboratory of Food Science and Resources, School of Food Science and Technology, International Joint Laboratory on Food Safety, Jiangnan University, Wuxi 214122, China.
Lixin KangState Key Laboratory of Food Science and Resources, School of Food Science and Technology, International Joint Laboratory on Food Safety, Jiangnan University, Wuxi 214122, China.
Yingming ZhangState Key Laboratory of Food Science and Resources, School of Food Science and Technology, International Joint Laboratory on Food Safety, Jiangnan University, Wuxi 214122, China.
Jun SunSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
Nuo DuanState Key Laboratory of Food Science and Resources, School of Food Science and Technology, International Joint Laboratory on Food Safety, Jiangnan University, Wuxi 214122, China.
Zhouping WangState Key Laboratory of Food Science and Resources, School of Food Science and Technology, International Joint Laboratory on Food Safety, Jiangnan University, Wuxi 214122, China.
Ruijie DengCollege of Biomass Science and Engineering, Sichuan University, Chengdu 610065, China.
Shijia WuState Key Laboratory of Food Science and Resources, School of Food Science and Technology, International Joint Laboratory on Food Safety, Jiangnan University, Wuxi 214122, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acrylamide and advanced glycation end products are two typical hazards produced simultaneously through the Maillard reaction of food. Traditional instrumental detection methods require specialized instrument operation, resulting in low detection efficiency. Here, a red-green dual fluorescence detection system combined with Nt.BbvCI-assisted cyclic rolling circle amplification (CRCA) technology was designed to reduce the limit of detection to the pg/mL level. Furthermore, CRCA is encapsulated in emulsion droplets for the reaction. Especially when the target concentration is low, the fluorescence is concentrated in a small number of droplets, making the fluorescence signal easier to detect and greatly improving the detection sensitivity. The fluorescence imaging pictures were further analyzed by machine learning, and the analysis time was reduced from 3 min/picture to 1.4 s/picture, improving the analysis efficiency and accuracy, and the detection limit was lowered to 10 fg/mL, which ultimately achieved the simultaneous detection of Maillard hazards in food with high sensitivity.

Indexed as

Food safetyFood toxicologyMachine learning

Identifiers

PMID40894894
PMCPMC12392310

What OpenQuestion holds

Textmetadata
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.