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Record W2326433963 · doi:10.1093/femspd/ftw029

Lactobacilli require physical contact to reduce staphylococcal TSST-1 secretion and vaginal epithelial inflammatory response

2016· article· en· W2326433963 on OpenAlexaff
Jessica A. Younes, Gregor Reid, Henny C. van der Mei, Henk J. Busscher

Bibliographic record

VenuePathogens and Disease · 2016
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsStaphylococcus aureusMicrobiologyBiofilmSecretionToxic shock syndromeLactobacillusProbioticBiologyVirulenceInflammationBacteriaChemistryImmunologyBiochemistry

Abstract

fetched live from OpenAlex

ITALIC! Staphylococcus aureusbiofilms can be found on vaginal epithelia, secreting toxins and causing inflammation. The co-vaginal species ITALIC! Lactobacilluscan alter staphylococcal-induced epithelial secretion of inflammatory cytokines and quench staphylococcal toxic shock syndrome toxin-1 secretion. It is hypothesized that these effects of lactobacilli require direct physical contact between lactobacilli, staphylococci and the epithelium. Indeed, lactobacilli only reduced ITALIC! S. aureus-induced inflammatory cytokine expression when allowed physical contact with vaginal epithelial cells. Furthermore, a reduction in toxic shock syndrome toxin-1 secretion only occurred when a probiotic ITALIC! Lactobacillusstrain was allowed contact, but not when being physically separated from ITALIC! S. aureus Bacterial-probe atomic force microscopy demonstrated that lactobacilli and staphylococci strongly adhere to epithelial cells, while lactobacilli adhere stronger to staphylococci than staphylococci to each other, giving lactobacilli opportunity to penetrate and reside in staphylococcal biofilms, as visualized using confocal laser scanning microscopy with fluorescence ITALIC! in situhybridization probes. These results identify that physical contact and biochemical signaling by lactobacilli are intrinsically linked mechanisms that reduce virulence of ITALIC! S. aureusbiofilm.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.929
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.272
Teacher spread0.260 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2016
Admission routes1
Has abstractyes

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