Evidence map›Paper›PMID 28139531›Full record

ReviewThe Indian journal of medical research2016

A review on detection methods used for foodborne pathogens.

B Priyanka, Rajashekhar K Patil, Sulatha Dwarakanath

Abstract readReview
In one paragraph

Review in The Indian journal of medical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.

0numbers the graph read from it
0cells of the map it votes in
62citing 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

62 citing papers in PubMed.

  1. Article
  2. Article
  3. Foods (Basel, Switzerland) · 2026
    Review
  4. Recent Developments onFoods (Basel, Switzerland) · 2025
    Review
  5. Review
  6. Review
  7. Review
  8. Article
  9. Molecular Detection ofFoods (Basel, Switzerland) · 2025
    Article
  10. Foreign Contaminants Target Brain Health.CNS & neurological disorders drug targets · 2025
    Review
  11. Review
  12. Advancements in Detection Methods forPathogens (Basel, Switzerland) · 2024
    Review
  13. Innovative Multiplex PCR Assay for Detection ofPathogens (Basel, Switzerland) · 2024
    Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. The Rapid Detection ofFoods (Basel, Switzerland) · 2023
    Article
  20. Article

2 more citing papers are in PubMed but not listed here.

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.

B PriyankaDepartment of Applied Zoology, Mangalore University, Mangaluru, India.
Rajashekhar K PatilDepartment of Applied Zoology, Mangalore University, Mangaluru, India.
Sulatha DwarakanathNanoscience Diagnostics, Austin, Texas, 78726, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Foodborne pathogens have been a cause of a large number of diseases worldwide and more so in developing countries. This has a major economic impact. It is important to contain them, and to do so, early detection is very crucial. Detection and diagnostics relied on culture-based methods to begin with and have developed in the recent past parallel to the developments towards immunological methods such as enzyme-linked immunosorbent assays (ELISA) and molecular biology-based methods such as polymerase chain reaction (PCR). The aim has always been to find a rapid, sensitive, specific and cost-effective method. Ranging from culturing of microbes to the futuristic biosensor technology, the methods have had this common goal. This review summarizes the recent trends and brings together methods that have been developed over the years.

Indexed as

Biosensing TechniquesFood MicrobiologyBacteriaEnzyme-Linked Immunosorbent AssayFoodborne DiseasesHumansPolymerase Chain Reaction

Identifiers

PMID28139531
PMCPMC5320838

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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.