The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Quorum Sensing Inhibitors as Anti-Biofilm Agents
Abstract
Biofilms are microbial sessile communities characterized by cells that are attached to a substratum or interface or to each other, are embedded in a self-produced matrix of extracellular polymeric substances and exhibit an altered phenotype compared to planktonic cells. Biofilms are estimated to be associated with 80% of microbial infections and it is currently common knowledge that growth of micro-organisms in biofilms can enhance their resistance to antimicrobial agents. As a consequence antimicrobial therapy often fails to eradicate biofilms from the site of infection. For this reason, innovative anti-biofilm agents with novel targets and modes of action are needed. One alternative approach is targeting the bacterial communication system (quorum sensing, QS). QS is a process by which bacteria produce and detect signal molecules and thereby coordinate their behavior in a cell-density dependent manner. Three main QS systems can be distinguished: the acylhomoserine lactone (AHL) QS system in Gram-negative bacteria, the autoinducing peptide (AIP) QS system in Gram-positive bacteria and the autoinducer-2 (AI-2) QS system in both Gram-negative and -positive bacteria. Although much remains to be learned about the involvement of QS in biofilm formation, maintenance, and dispersal, QS inhibitors (QSI) have been proposed as promising antibiofilm agents. In this article we will give an overview of QS inhibitors which have been shown to play a role in biofilm formation and/or maturation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Biofilms: Survival Mechanisms of Clinically Relevant Microorganisms
Donlan, R. M., & Costerton, J. W. (2002) · Clinical Microbiology Reviews · doi:10.1128/cmr.15.2.167-193.2002 · Cited by 6,644
Brackman, G., Defoirdt, T., Miyamoto, C., Bossier, P., Van Calenbergh, S., Nelis, H., & Coenye, T. (2008) · BMC Microbiology · doi:10.1186/1471-2180-8-149 · Cited by 317
Self-citation
Cited by
Biofilms in the Food Industry: Health Aspects and Control Methods
Galié, S., García-Gutiérrez, C., Miguélez, E. M., Villar, C. J., & Lombó, F. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.00898 · Cited by 964
Antimicrobial peptides (AMPs): A promising class of antimicrobial compounds
Erdem Büyükkiraz, M., & Kesmen, Z. (2022) · Journal of Applied Microbiology · doi:10.1111/jam.15314 · Cited by 454
Prevention of biofilm formation by quorum quenching
Paluch, E., Rewak-Soroczyńska, J., Jędrusik, I., Mazurkiewicz, E., & Jermakow, K. (2020) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-020-10349-w · Cited by 445
The Role of Bacterial Biofilm in Antibiotic Resistance and Food Contamination
Abebe, G. M. (2020) · International Journal of Microbiology · doi:10.1155/2020/1705814 · Cited by 406
Approaches to Dispersing Medical Biofilms
Fleming, D., & Rumbaugh, K. (2017) · Microorganisms · doi:10.3390/microorganisms5020015 · Cited by 329
Metadata
- Authors
- Brackman, G., & Coenye, T.
- Venue
- Current Pharmaceutical Design
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2014-11-18
- Volume
- 21
- Issue
- 1
- Pages
- 5-11
- ISSN
- 1381-6128 (ISSN-L)
- OpenAlex
- W1998428482
- Cited by
- 420
- References
- 2
- FWCI
- 9.86
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Quorum Sensing Inhibitors as Anti-Biofilm Agents. https://app.cochat.ai/papers/10.2174/1381612820666140905114627
- The paper
- Brackman, G., & Coenye, T. (2014). Quorum Sensing Inhibitors as Anti-Biofilm Agents. Current Pharmaceutical Design. https://doi.org/10.2174/1381612820666140905114627
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.