Evidence map›Paper›PMID 24496794›Full record

ArticlemBio2014

Laser optical sensor, a label-free on-plate Salmonella enterica colony detection tool.

Atul K Singh, Amanda M Bettasso, Euiwon Bae, Bartek Rajwa, Murat M Dundar, Mark D Forster, Lixia Liu, Brent Barrett, Judith Lovchik, J Paul Robinson and 2 more

Abstract readEvaluation Study
In one paragraph

Article in mBio, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Biosensors · 2024
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Petri-plate, bacteria, and laser optical scattering sensor.Frontiers in cellular and infection microbiology · 2022
    Review
  7. Advancement inBiosensors · 2021
    Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

12 authors.

Atul K Singh
Amanda M Bettasso
Euiwon Bae
Bartek Rajwa
Murat M Dundar
Mark D Forster
Lixia Liu
Brent Barrett
Judith Lovchik
J Paul Robinson
E Daniel Hirleman
Arun K Bhunia

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 1R21AI085531NIAID NIH HHS R21 AI085531
6 · The paper itself

Abstract

unlabelledWe investigated the application capabilities of a laser optical sensor, BARDOT (bacterial rapid detection using optical scatter technology) to generate differentiating scatter patterns for the 20 most frequently reported serovars of Salmonella enterica. Initially, the study tested the classification ability of BARDOT by using six Salmonella serovars grown on brain heart infusion, brilliant green, xylose lysine deoxycholate, and xylose lysine tergitol 4 (XLT4) agar plates. Highly accurate discrimination (95.9%) was obtained by using scatter signatures collected from colonies grown on XLT4. Further verification used a total of 36 serovars (the top 20 plus 16) comprising 123 strains with classification precision levels of 88 to 100%. The similarities between the optical phenotypes of strains analyzed by BARDOT were in general agreement with the genotypes analyzed by pulsed-field gel electrophoresis (PFGE). BARDOT was evaluated for the real-time detection and identification of Salmonella colonies grown from inoculated (1.2 × 10(2) CFU/30 g) peanut butter, chicken breast, and spinach or from naturally contaminated meat. After a sequential enrichment in buffered peptone water and modified Rappaport Vassiliadis broth for 4 h each, followed by growth on XLT4 (~16 h), BARDOT detected S. Typhimurium with 84% accuracy in 24 h, returning results comparable to those of the USDA Food Safety and Inspection Service method, which requires ~72 h. BARDOT also detected Salmonella (90 to 100% accuracy) in the presence of background microbiota from naturally contaminated meat, verified by 16S rRNA sequencing and PFGE. Prolonged residence (28 days) of Salmonella in peanut butter did not affect the bacterial ability to form colonies with consistent optical phenotypes. This study shows BARDOT's potential for nondestructive and high-throughput detection of Salmonella in food samples. IMPORTANCE: High-throughput screening of food products for pathogens would have a significant impact on the reduction of food-borne hazards. A laser optical sensor was developed to screen pathogen colonies on an agar plate instantly without damaging the colonies; this method aids in early pathogen detection by the classical microbiological culture-based method. Here we demonstrate that this sensor was able to detect the 36 Salmonella serovars tested, including the top 20 serovars, and to identify isolates of the top 8 Salmonella serovars. Furthermore, it can detect Salmonella in food samples in the presence of background microbiota in 24 h, whereas the standard USDA Food Safety and Inspection Service method requires about 72 h.

Indexed as

Optical DevicesAnimalsArachisBacteriological TechniquesChemical PhenomenaChickensCulture MediaDNA, BacterialDNA FingerprintingElectrophoresis, Gel, Pulsed-FieldFood MicrobiologyMolecular Sequence DataSalmonella entericaSequence Analysis, DNASpinacia oleraceaCulture MediaDNA, Bacterial

Identifiers

PMID24496794
PMCPMC3950520

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.