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Record W2163311612 · doi:10.1183/09031936.00213211

Effects of cigarette smoke condensate on pneumococcal biofilm formation and pneumolysin

2012· article· en· W2163311612 on OpenAlexaff
N. Daphney Mutepe, Riana Cockeran, Helen C. Steel, Annette J. Theron, Timothy J. Mitchell, Charles Feldman, Ronald Anderson

Bibliographic record

VenueEuropean Respiratory Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsPneumolysinBiofilmMicrobiologyStreptococcus pneumoniaeExtracellularVirulenceChemistryToxinBacteriaStreptococcaceaeBiologyAntibioticsBiochemistryGene

Abstract

fetched live from OpenAlex

Although the well-recognised predisposition of cigarette smokers to the development of severe pneumococcal disease may be attributable to impairment of local host defences, less is known about the direct effects of smoke exposure on airway pathogens, or their virulence factors. In the current study, we have investigated the effects of cigarette smoke condensate (CSC) on biofilm formation by Streptococcus pneumoniae, and on the pore-forming activity of its major toxin, pneumolysin. Biofilm formation following exposure of the pneumococcus to CSC (20-160 μg·mL(-1)) was measured using a crystal violet-based spectrophotometric procedure, while the pore-forming activity of recombinant pneumolysin was determined by a fura-2/acetoxymethyl ester-based spectrofluorimetric procedure to monitor the uptake of extracellular Ca(2+) by isolated human neutrophils. Exposure of the pneumococcus or pneumolysin to CSC resulted in significant dose-related augmentation of biofilm formation (p≤0.05 at 80 and 160 μg·mL(-1)) and substantial attenuation of the pore-forming interactions of pneumolysin, respectively. Augmentation of biofilm formation and inactivation of pneumolysin as a consequence of smoking are likely to favour microbial colonisation and persistence, both being essential precursors of pneumococcal disease.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.026
GPT teacher head0.273
Teacher spread0.247 · 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 designBench or experimental
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

Citations75
Published2012
Admission routes1
Has abstractyes

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