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Related Concept Videos

Urine Studies II: Urine Culture and Sensitivity Test01:26

Urine Studies II: Urine Culture and Sensitivity Test

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A urine culture and sensitivity test is a diagnostic procedure used to identify urinary tract bacterial infections and determine the most effective antibiotics for treatment. This test is generally preferred when a patient shows manifestations of a urinary tract infection, such as frequent or painful urination, cloudy or foul-smelling urine, or lower abdominal pain.Purpose of the TestThe primary goals of a urine culture and sensitivity test are to:Determine the specific bacteria causing the...
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Urinary Tract Infection III: Diagnostic Studies and Interprofessional Care01:30

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A healthcare provider can diagnose a urinary tract infection (UTI) through several methods:Medical History and Symptoms: The provider will take a detailed medical history and ask about symptoms such as frequent urination, burning sensation during urination, and lower abdominal pain.Urinalysis: A clean-catch urine sample is collected in a sterile container and tested for the presence of bacteria, white blood cells (leukocytes), nitrites, blood, and protein. The presence of leukocytes and...
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Related Experiment Video

Updated: Jan 10, 2026

Cell-Free DNA Integrity Analysis in Urine Samples
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Urinary Cell-Free DNA detection platform for bacterial cystitis diagnosis via spot-based machine learning.

Yan Liu1, Kai Xiao2, Ziping Wu2

  • 1Department 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.

Journal of Advanced Research
|November 28, 2025
PubMed
Summary

A new machine learning system rapidly detects bacterial cystitis using urinary cell-free DNA (cfDNA) in just 20 minutes. This innovative approach offers high accuracy for diagnosing bacterial infections, improving upon traditional methods.

Keywords:
Bacterial cystitisCell-free DNAImageMachine learningUrinary

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Area of Science:

  • Biomedical Engineering
  • Infectious Disease Diagnostics
  • Molecular Diagnostics

Background:

  • Bacterial cystitis, often caused by E. coli, requires rapid identification for effective treatment.
  • Urinary cell-free DNA (cfDNA) shows promise as a biomarker but distinguishing bacterial from non-bacterial causes is challenging.

Purpose of the Study:

  • To develop an image-based, machine learning-assisted fluorescent detection system for simultaneous quantitative and qualitative analysis of urinary cfDNA.
  • To enable rapid and accurate diagnosis of bacterial cystitis.

Main Methods:

  • Utilized polyethyleneimine (PEI)-functionalized glass slides for cfDNA enrichment.
  • Employed fluorescence imaging with propidium iodide labeling and YOLOv5 object detection algorithm for automated analysis.
  • Validated the system using a bacterial cystitis mouse model and 148 human urine specimens.

Main Results:

  • The platform quantifies cfDNA within 20 minutes, with a detection limit of 20.29 ng/mL.
  • Automated analysis of cfDNA spot patterns differentiated bacterial (77.12% smaller spots) from non-bacterial cystitis (13.87% smaller spots).
  • Achieved 94.43% diagnostic accuracy, 94.45% sensitivity, and 94.35% specificity for bacterial cystitis in clinical samples, significantly outperforming conventional methods.

Conclusions:

  • This rapid, on-site platform requires no urine pretreatment, advancing point-of-care testing (POCT) for bacterial cystitis.
  • The system has potential applications for digital biomarker detection in various inflammatory and infectious diseases.