Evidence map›Paper›PMID 37780592›Full record

ReviewFood science and biotechnology2023

Comparison of the efficacy of physical and chemical strategies for the inactivation of biofilm cells of foodborne pathogens.

Ga-Hee Ban, Soo-Hwan Kim, Dong-Hyun Kang, Sang-Hyun Park

Open access · greenAbstract readReview
In one paragraph

Review in Food science and biotechnology, 2023. 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
1.1field-weighted citation impact, top 23% 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, 7 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

4 authors at 3 institutions in 2 countries.

Ga-Hee Ban *Department of Food Science and Biotechnology, Ewha Womans University, Seoul, 03760 Republic of Korea.
Soo-Hwan Kim *Department of Food and Animal Biotechnology, Department of Agricultural Biotechnology, Center for Food and Bioconvergence, Research Institute of Agricultural and Life Sciences, Seoul National University, Seoul, 08826 Republic of Korea.
Dong-Hyun KangDepartment of Food and Animal Biotechnology, Department of Agricultural Biotechnology, Center for Food and Bioconvergence, Research Institute of Agricultural and Life Sciences, Seoul National University, Seoul, 08826 Republic of Korea.
Sang-Hyun ParkDepartment of Food Science and Technology, Kongju National University, Yesan, Chungnam 32439 Republic of Korea.ORCID 0000-0003-2119-7845
Seoul National University · KREwha Womans University · KRKongju National University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biofilm formation is a strategy in which microorganisms generate a matrix of extracellular polymeric substances to increase survival under harsh conditions. The efficacy of sanitization processes is lowered when biofilms form, in particular on industrial devices. While various traditional and emerging technologies have been explored for the eradication of biofilms, cell resistance under a range of environmental conditions renders evaluation of the efficacy of control challenging. This review aimed to: (1) classify biofilm control measures into chemical, physical, and combination methods, (2) discuss mechanisms underlying inactivation by each method, and (3) summarize the reduction of biofilm cells after each treatment. The review is expected to be useful for future experimental studies and help to guide the establishment of biofilm control strategies in the food industry.

Indexed as

BiofilmChemical methodsFoodborne pathogenPhysical methods

Identifiers

PMID37780592
PMCPMC10533464
OpenAlexW4367596874

What OpenQuestion holds

Textmetadata
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.