Evidence map›Paper›PMID 21108360›Full record

ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2010

Discovering the unknown: detection of emerging pathogens using a label-free light-scattering system.

Bartek Rajwa, M Murat Dundar, Ferit Akova, Amanda Bettasso, Valery Patsekin, E Dan Hirleman, Arun K Bhunia, J Paul Robinson

Abstract read
In one paragraph

Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Petri-plate, bacteria, and laser optical scattering sensor.Frontiers in cellular and infection microbiology · 2022
    Review
  7. Review
  8. Development of dual reporter imaging system forClinical and experimental vaccine research · 2018
    Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Bartek RajwaBindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA. brajwa@purdue.edu
M Murat Dundar
Ferit Akova
Amanda Bettasso
Valery Patsekin
E Dan Hirleman
Arun K Bhunia
J Paul Robinson

Funding

Machine-Learning Approach to Label-free Detection of new Bacterial PathogensR21AI085531 · NIAID · INDIANA UNIVERSITY INDIANAPOLIS · PI DUNDAR, MURAT, RAJWA, BARTLOMIEJ · 2010 to 2011
$383k
NIAID NIH HHS 5R21AI085531-02NIAID NIH HHS R21 AI085531
6 · The paper itself

Abstract

A recently introduced technique for pathogen recognition called BARDOT (BActeria Rapid Detection using Optical scattering Technology) belongs to the broad class of optical sensors and relies on forward-scatter phenotyping (FSP). The specificity of FSP derives from the morphological information that bacterial material encodes on a coherent optical wavefront passing through the colony. The system collects elastically scattered light patterns that, given a constant environment, are unique to each bacterial species and serovar. The notable similarity between FSP technology and spectroscopies is their reliance on statistical machine learning to perform recognition. Currently used methods utilize traditional supervised techniques which assume completeness of training libraries. However, this restrictive assumption is known to be false for most experimental conditions, resulting in unsatisfactory levels of accuracy, poor specificity, and consequently limited overall performance for biodetection and classification tasks. The presented work demonstrates application of the BARDOT system to classify bacteria belonging to the Salmonella class in a nonexhaustive framework, that is, without full knowledge about all the possible classes that can be encountered. Our study uses a Bayesian approach to learning with a nonexhaustive training dataset to allow for the automated detection of unknown bacterial classes.

Indexed as

LightScattering, RadiationBacterial Typing TechniquesBayes TheoremBiosensing TechniquesFood MicrobiologyPattern Recognition, AutomatedSalmonellaSensitivity and Specificity

Identifiers

PMID21108360
PMCPMC3224816

What OpenQuestion holds

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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.