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Record W2932582301 · doi:10.1155/2019/2015978

Quorum Sensing: A Prospective Therapeutic Target for Bacterial Diseases

2019· review· en· W2932582301 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioMed Research International · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsUniversity of Manitoba
FundersDistinguished Young Scholar Foundation of Hunan ProvinceChina Scholarship CouncilNational Natural Science Foundation of ChinaHunan Agricultural UniversityNational Science Fund for Distinguished Young ScholarsChinese Academy of SciencesNational Science Foundation
KeywordsQuorum sensingAutoinducerBiofilmMicrobiologyBacteriaAntimicrobialHomoserineVirulenceAntibiotic resistanceAntibioticsBiologyPathogenic bacteriaBiochemistryGeneGenetics

Abstract

fetched live from OpenAlex

Bacterial quorum sensing (QS) is a cell-to-cell communication in which specific signals are activated to coordinate pathogenic behaviors and help bacteria acclimatize to the disadvantages. The QS signals in the bacteria mainly consist of acyl-homoserine lactone, autoinducing peptide, and autoinducer-2. QS signaling activation and biofilm formation lead to the antimicrobial resistance of the pathogens, thus increasing the therapy difficulty of bacterial diseases. Anti-QS agents can abolish the QS signaling and prevent the biofilm formation, therefore reducing bacterial virulence without causing drug-resistant to the pathogens, suggesting that anti-QS agents are potential alternatives for antibiotics. This review focuses on the anti-QS agents and their mediated signals in the pathogens and conveys the potential of QS targeted therapy for bacterial diseases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.107
GPT teacher head0.422
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it