Evidence map›Paper›PMID 21458868›Full record

ArticleTrends in pharmacological sciences2011

Biosensor diagnosis of urinary tract infections: a path to better treatment?

Kathleen E Mach, Pak Kin Wong, Joseph C Liao

Abstract read
In one paragraph

Article in Trends in pharmacological sciences, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
4.1field-weighted citation impact, top 6% of its field
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

38 citing papers in PubMed, 106 citations in OpenAlex.

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  19. Adaptable microfluidic system for single-cell pathogen classification and antimicrobial susceptibility testing.Proceedings of the National Academy of Sciences of the United States of America · 2019
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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

3 authors at 2 institutions in 1 country.

Kathleen E MachStanford University, Department of Urology and Bio-X Program, 300 Pasteur Drive, S-287, Stanford, CA 94305, USA.
Pak Kin Wong
Joseph C Liao
Stanford University · USUniversity of Arizona · US

Funding

An Integrated Diagnostic Biochip for Point of Care Pathogen IdentificationU01AI082457 · NIAID · PALO ALTO VETERANS INSTIT FOR RESEARCH · PI LIAO, JOSEPH C · 2009 to 2013
$3.7M
Mechanoregulation of Tissue MorphogenesisDP2OD007161 · OD · UNIVERSITY OF ARIZONA · PI WONG, PAK KIN · 2010 to 2010
$2.3M
NIAID NIH HHS U01 AI082457NIH HHS DP2 OD007161
6 · The paper itself

Abstract

Urinary tract infection (UTI) is among the most common bacterial infections and poses a significant healthcare burden. The standard culture-based diagnosis of UTI has a typical delay of two to three days. In the absence of definitive microbiological diagnosis at the point of care, physicians frequently initiate empirical broad-spectrum antibiotic treatment, and this has contributed to the emergence of resistant pathogens. Biosensors are emerging as a powerful diagnostic platform for infectious diseases. Paralleling how blood glucose sensors revolutionized the management of diabetes, and how pregnancy tests are now conducted in the home, biosensors are poised to improve UTI diagnosis significantly. Biosensors are amenable to integration with microfluidic technology for point-of-care (POC) applications. This review focuses on promising biosensor technology for UTI diagnosis, including pathogen identification and antimicrobial susceptibility testing, and hurdles to be surpassed in the translation of biosensor technology from bench to bedside.

Indexed as

Anti-Bacterial AgentsBiosensing TechniquesHumansMicrobial Sensitivity TestsMicrofluidic Analytical TechniquesTime FactorsUrinary Tract InfectionsAnti-Bacterial Agents

Identifiers

PMID21458868
PMCPMC3106133
OpenAlexW2103898812

What OpenQuestion holds

Textmetadata
LicenceTDM
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.