Evidence map›Paper›PMID 27834850›Full record

ArticleSensors (Basel, Switzerland)2016

Fast and Inexpensive Detection of Bacterial Viability and Drug Effectiveness through Metabolic Monitoring.

Sondos Ayyash, Wen-I Wu, Ponnambalam Ravi Selvaganapathy

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2016. 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
0.2field-weighted citation impact, top 41% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
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 1 institution in 1 country.

Sondos AyyashSchool of Biomedical Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada. ayyashs@mcmaster.ca.
Wen-I WuDepartment of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada. wuwing@gmail.com.
Ponnambalam Ravi SelvaganapathySchool of Biomedical Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada. selvaga@mcmaster.ca.
McMaster University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conventional methods for the detection of bacterial infection such as DNA or immunoassays are expensive, time consuming, or not definitive and thus may not provide all the information sought by medical professionals. In particular, it is difficult to obtain information about viability or drug effectiveness, which is crucial to formulate a treatment. Bacterial culture tests are the "gold standard" because they are inexpensive and do not require extensive sample preparation, and most importantly, provide all the necessary information sought by healthcare professionals, such as bacterial presence, viability and drug effectiveness. These conventional culture methods, however, have a long turnaround time, anywhere between 1 day and 4 weeks. Here, we solve this problem by monitoring the growth of bacteria in thousands of nanowells simultaneously to more quickly identify their presence in the sample and their viability. The segmentation of a sample with low bacterial concentration into thousands of nanoliter wells digitizes the samples and increases the effective concentration in those wells that contain bacteria. We monitor the metabolism of aerobic bacteria by using an oxygen-sensitive fluorophore, ruthenium tris (2,2'-diprydl) dichloride hexahydrate (RTDP), which allows us to monitor the dissolved oxygen concentration in the nanowells. Using

Indexed as

Microbial ViabilityBacteria, AerobicEscherichia coliMicrofluidicsTuberculosisbacterial culturemetabolic monitoringmicrofluidicsrapid analysissample segmentation

Identifiers

PMID27834850
PMCPMC5134538
OpenAlexW2556354280

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.