Evidence map›Paper›PMID 41314282›Full record

ArticleJournal of advanced research2026

Urinary Cell-Free DNA detection platform for bacterial cystitis diagnosis via spot-based machine learning.

Yan Liu, Kai Xiao, Ziping Wu, Qiang Wu, Xiaochun Xie, Minghao He, Hui Zhang, Lei Cao, Baiyang Bao, Weihang Guo and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of advanced research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. More than an infection: the ecological puzzle of recurrent urinary tract infections.Frontiers in cellular and infection microbiology · 2026
    Review
  2. PANoptosis: potential new targets and therapeutic prospects in digestive diseases.Apoptosis : an international journal on programmed cell death · 2025
    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

14 authors.

Yan LiuDepartment of Urology, Hohhot First Hospital, Hohhot 010030, China; School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, Guangdong 511442, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangdong 510006, China.
Kai XiaoNational Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangdong 510006, China; School of Medicine, South China University of Technology, Guangzhou, Guangdong 510006, China.
Ziping WuNational Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangdong 510006, China; School of Medicine, South China University of Technology, Guangzhou, Guangdong 510006, China.
Qiang WuDepartment of Urology, Hohhot First Hospital, Hohhot 010030, China.
Xiaochun XieNational Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangdong 510006, China; School of Medicine, South China University of Technology, Guangzhou, Guangdong 510006, China.
Minghao HeSchool of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, Guangdong 511442, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangdong 510006, China.
Hui ZhangDepartment of Urology, Inner Mongolia Region People's Hospital, Inner Mongolia, Hohhot 010017, China; Baotou Medical College, Baotou, Hohhot 010017, China.
Lei CaoInstitute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518132, China.
Baiyang BaoDepartment of Urology, Inner Mongolia Region People's Hospital, Inner Mongolia, Hohhot 010017, China; Baotou Medical College, Baotou, Hohhot 010017, China.
Weihang GuoDepartment of Urology, Inner Mongolia Region People's Hospital, Inner Mongolia, Hohhot 010017, China.
Fangman ChenState Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, China. Electronic address: fangmanchen@umac.mo.
Ren MoDepartment of Urology, Inner Mongolia Region People's Hospital, Inner Mongolia, Hohhot 010017, China. Electronic address: moren325@163.com.
Kewei MaDepartment of Urology, Hohhot First Hospital, Hohhot 010030, China. Electronic address: mkwsr@163.com.
Dan ShaoSchool of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, Guangdong 511442, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangdong 510006, China; School of Medicine, South China University of Technology, Guangzhou, Guangdong 510006, China. Electronic address: shaodan@scut.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionBacterial cystitis is a prevalent infectious condition that is primarily caused by uropathogenic Escherichia coli (E. coli). The rapid and precise identification of this pathogen is crucial for guiding timely and effective clinical intervention. Urinary cell-free DNA (cfDNA) has emerged as a promising biomarker for cystitis, yet its complex origins from both inflammatory and infectious processes pose challenges in distinguishing bacterial from non-bacterial etiologies.

objectivesHere, we developed an innovative image-based, machine learning-assisted fluorescent detection system for simultaneous quantitative and qualitative analysis of urinary cfDNA to diagnose bacterial cystitis.

methodsThe detection system utilizes polyethyleneimine (PEI)-functionalized glass slides for efficient cfDNA enrichment, followed by fluorescence imaging with propidium iodide labeling. The performance of the detection system was rigorously validated using a bacterial cystitis mouse model and human urine specimens.

resultsThis platform enables rapid quantification of cfDNA within 20 min, with a calibration range of 31.25-4000 ng mL

conclusionRequiring no urine pretreatment, this rapid, on-site platform represents a transformative advance in point-of-care testing (POCT) for bacterial cystitis, with potential applications for digital biomarker detection in diverse inflammatory and infectious diseases.

Indexed as

Cell-Free Nucleic AcidsCystitisEscherichia coli InfectionsMachine LearningAnimalsBiomarkersFemaleHumansMiceBiomarkersCell-Free Nucleic AcidsBacterial cystitisCell-free DNAImageMachine learningUrinary

Identifiers

PMID41314282
PMCPMC13453861

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LicenceCC BY-NC-ND
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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.