Evidence map›Paper›PMID 31448654›Full record

ArticleSLAS technology2019

Rapid Single-Cell Microbiological Analysis: Toward Precision Management of Infections and Dysbiosis.

Hui Li, Michael Morowitz, Neal Thomas, Pak Kin Wong

Abstract read
PubMed Publisher
In one paragraph

Article in SLAS technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Rapid Phenotypic Antibiotics Susceptibility Analysis by a 3D Printed Prototype.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Article
  2. Article
  3. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Hui LiDepartment of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA.
Michael MorowitzDepartment of Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Neal ThomasDepartments of Pediatrics and Public Health Sciences, Penn State University College of Medicine, Hershey, PA, USA.
Pak Kin WongDepartment of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA.

Funding

A Droplet-based single cell platform for pathogen identification and ASTR01AI117032 · NIAID · JOHNS HOPKINS UNIVERSITY · PI LIAO, JOSEPH C, WANG, TZA-HUEI JEFF · 2015 to 2019
$5.7M
NIAID NIH HHS R01 AI117032
6 · The paper itself

Abstract

Bacterial infection is a leading cause of morbidity and mortality (from infants to the elderly) and accounts for more than $20 billion in healthcare costs in the United States each year. The pathogens responsible for many of the common infectious diseases, such as urinary tract infection (UTI) and ventilator-associated infections (VAIs), have proven to be highly adept in acquiring mechanisms of antimicrobial resistance. The use of broad-spectrum antibiotics by healthcare providers and the infiltration of antibiotics in the environment have accelerated the selection and growth of resistant pathogens. To further exacerbate the problem, the need for new antibiotics has far outpaced the development of new classes of antibiotics by the pharmaceutical industry (only two new classes of antibiotics have reached the market in the last 20 years), in large part due to prohibitive cost and historically poor return on investment to develop new antibiotics. Consequently, clinicians have limited treatment options, particularly in the neediest patients. To tackle this major global health issue, we are developing novel technological approaches for rapid definitive clinical microbiological analysis. These technologies will improve the clinical management of bacterial infections and reduce the improper use of antibiotics in current practice, hopefully limiting the spread of drug-resistant organisms.

Indexed as

Automation, LaboratoryBacteriaBacterial InfectionsDysbiosisHumansLab-On-A-Chip DevicesMicrofluidicsMiniaturizationPoint-of-Care TestingSingle-Cell Analysisclinical automationlab-on-a-chipmicrofluidicsmicrotechnologypoint-of-care testing (POCT)

Identifiers

What OpenQuestion holds

Textmetadata
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.