Evidence map›Paper›PMID 39575510›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Unsupervised Learning-Assisted Acoustic-Driven Nano-Lens Holography for the Ultrasensitive and Amplification-Free Detection of Viable Bacteria.

Yang Zhou, Junpeng Zhao, Junping Wen, Ziyan Wu, Yongzhen Dong, Yiping Chen

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
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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.

Yang ZhouState Key Laboratory of Marine Food Processing and Safety Control, Dalian Polytechnic University, Dalian, Liaoning, 116034, China.
Junpeng ZhaoCollege of Food Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, 430070, China.
Junping WenCollege of Food Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, 430070, China.
Ziyan WuCollege of Food Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, 430070, China.
Yongzhen DongState Key Laboratory of Marine Food Processing and Safety Control, Dalian Polytechnic University, Dalian, Liaoning, 116034, China.
Yiping ChenState Key Laboratory of Marine Food Processing and Safety Control, Dalian Polytechnic University, Dalian, Liaoning, 116034, China.ORCID 0000-0001-9309-2730

Funding

Basic Scientific Research Operation Expenses Special Foundation of Liaoning Provincial Undergraduate Colleges LJBKY2024043Key R&D Program of Hubei Province 2022BBA0057National Key Research and Development Program of China 2022YFF0607900National Natural Science Foundation of China 32172293
6 · The paper itself

Abstract

Bacterial infection is a crucial factor resulting in public health issues worldwide, often triggering epidemics and even fatalities. The accurate, rapid, and convenient detection of viable bacteria is an effective method for reducing infections and illness outbreaks. Here, an unsupervised learning-assisted and surface acoustic wave-interdigital transducer-driven nano-lens holography biosensing platform is developed for the ultrasensitive and amplification-free detection of viable bacteria. The monitoring device integrated with the nano-lens effect can achieve the holographic imaging of polystyrene microsphere probes in an ultra-wide field of view (∽28.28 mm

Indexed as

BacteriaBiosensing TechniquesHolographyUnsupervised Machine LearningAcousticsHumansLimit of DetectionSalmonellabacterial detectionCRISPR–Cas12a systemlens‐free holographyunsupervised learning

Identifiers

PMID39575510
PMCPMC11727406

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.