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Cell surface characteristics of nontypeable isolates of Streptococcus suis

2010· article· fr· W2150588161 on OpenAlexafffund
Laetitia Bonifait, Marcelo Gottschalk, Daniel Grenier

Bibliographic record

VenueFEMS Microbiology Letters · 2010
Typearticle
Languagefr
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsUniversité de MontréalFonds de Recherche du Québec - SantéUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesJohns Hopkins University
KeywordsSerotypeStreptococcus suisMicrobiologyBiologyBiofilmVirologyStreptococcaceaeBacteriaVirulenceGeneAntibiotics

Abstract

fetched live from OpenAlex

Streptococcus suis is a worldwide cause of various swine infections and is also an important agent of zoonosis. Strains of S. suis are classified according to their serotype, and currently, 35 serotypes are recognized. The aim of this study was to characterize nontypeable isolates of S. suis with regard to their cell surface properties and compare them with serotype 2 strains, the most frequently associated with infections. The seven nontypeable strains of S. suis isolated from infected animals demonstrated a stronger capacity to adhere to a fibronectin-coated polystyrene surface than the serotype 2 isolates. Three nontypeable strains were also tested for their ability to adhere to endothelial cells and were found to attach in higher amounts compared with the serotype 2 isolates. Electron microscopy analysis revealed the absence of a capsule in the seven nontypeable isolates, which correlated with a much higher cell surface hydrophobicity than that of serotype 2 isolates. All nontypeable isolates of S. suis also showed the capacity to form a biofilm while serotype 2 isolates were unable to do so. In conclusion, the nontypeable isolates of S. suis examined in this study possess surface properties different from those of serotype 2 isolates.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.236
Teacher spread0.229 · 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.

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

Citations48
Published2010
Admission routes2
Has abstractyes

Explore more

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