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Related Concept Videos

Urine Studies II: Urine Culture and Sensitivity Test01:26

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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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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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Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...
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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Urinalysis is a widely used diagnostic test that analyzes urine's physical, chemical, and microscopic characteristics. Healthcare providers use it to detect and monitor various health conditions, including renal disease, urinary tract infections (UTIs), diabetes, and metabolic or systemic disorders.Components of UrinalysisUrinalysis consists of three primary components: physical, chemical, and microscopic examination. Each provides unique insights into the urine sample and, by extension, the...
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Updating Urinary Microbiome Analyses to Enhance Biologic Interpretation.

Nazema Y Siddiqui1, Li Ma2,3, Linda Brubaker4

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Frontiers in Cellular and Infection Microbiology
|July 28, 2022
PubMed
Summary

Updated 16S rRNA gene sequencing analysis reveals bacterial communities, not overall composition, are linked to mixed urinary incontinence (MUI). Incorporating covariates refines these associations for better clinical insights.

Keywords:
bioinformatic analysisbladder dysfunctionlactobacillimicrobiotamixed urinary incontinenceurinary microbiomeurobiome

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Urology

Background:

  • 16S rRNA gene sequencing is a key method for studying the urinary microbiome.
  • Analysis techniques for 16S rRNA data are rapidly evolving, potentially impacting clinical interpretations.
  • Previous studies have investigated the urinary microbiome in mixed urinary incontinence (MUI).

Purpose of the Study:

  • To re-analyze existing urinary microbiome data using updated bioinformatic and statistical methods.
  • To determine if advanced techniques alter clinical inferences regarding the urinary microbiome and MUI.
  • To compare taxa identified by new versus old analysis pipelines.

Main Methods:

  • Re-processing of raw 16S rRNA sequencing data using DADA2 for amplicon sequence variant (ASV) generation.
  • Comparison of taxa identified by ASV and original operational taxonomic unit (OTU) methods.
  • Application of Bayesian graphical compositional regression (BGCR) and multivariable regression to assess associations between microbial communities and MUI, adjusting for covariates.

Main Results:

  • Updated processing identified different taxa, primarily low-abundance ones, compared to original methods.
  • While overall microbial composition showed low association with MUI, clustered bacterial communities were significantly associated.
  • Associations between bacterial communities and MUI were confirmed, with improved inference after covariate adjustment.

Conclusions:

  • Advanced bioinformatic processing yields different taxonomic profiles but confirms key findings on bacterial communities in MUI.
  • Bacterial communities, rather than overall composition, are associated with MUI.
  • Adjusting for relevant covariates enhances the accuracy of associations between urinary bacterial communities and MUI.