Evidence map›Paper›PMID 29619271›Full record

ArticleJournal of analysis and testing2017

Optical Biosensing of Bacteria and Bacterial Communities.

Jiayun Hu, Paul W Bohn

Abstract read
In one paragraph

Article in Journal of analysis and testing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Microscale and Nanoscale Electrophotonic Diagnostic Devices.Cold Spring Harbor perspectives in medicine · 2019
    Review
  6. Spatiotemporal Dynamics of Molecular Messaging in Bacterial Co-Cultures Studied by Multimodal Chemical Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2019
    Article
  7. Article
  8. 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

2 authors.

Jiayun HuDepartment of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
Paul W BohnDepartment of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.

Funding

Characterization of Biofilms by Correlated Mass Spectrometric and Raman ImagingR01AI113219 · NIAID · UNIVERSITY OF NOTRE DAME · PI SHROUT, JOSHUA · 2014 to 2023
$4.7M
NIAID NIH HHS R01 AI113219
6 · The paper itself

Abstract

Bacterial sensing is important for understanding the numerous roles bacteria play in nature and in technology, understanding and managing bacterial populations, detecting pathogenic bacterial infections, and preventing the outbreak of illness. Current analytical challenges in bacterial sensing center on the dilemma of rapidly acquiring quantitative information about bacteria with high detection efficiency, sensitivity, and specificity, while operating within a reasonable budget and optimizing the use of ancillary tools, such as multivariate statistics. This review starts from a general description of bacterial sensing methods and challenges, and then focuses on bacterial characterization using optical methods including Raman spectroscopy and imaging, infrared spectroscopy, fluorescence spectroscopy and imaging, and plasmonics, including both extended and localized surface plasmon resonance spectroscopy. The advantages and drawbacks of each method in relation to the others are discussed, as are their applications. A particularly promising direction in bacterial sensing lies in combining multiple approaches to achieve multiplex analysis, and examples where this has been achieved are highlighted.

Indexed as

Bacterial sensingBiosensorsFluorescenceMicroscopyPlasmonicsRaman

Identifiers

PMID29619271
PMCPMC5880287

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.