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Record W1978085714 · doi:10.1086/596656

A Fibrinogen-Binding Lipoprotein Contributes to the Virulence of<i>Haemophilus ducreyi</i>in Humans

2009· article· en· W1978085714 on OpenAlexaff
Margaret E. Bauer, Carisa A. Townsend, Ryan S. Doster, Kate R. Fortney, Beth Zwickl, Barry P. Katz, Stanley M. Spinola, Diane M. Janowicz

Bibliographic record

VenueThe Journal of Infectious Diseases · 2009
Typearticle
Languageen
FieldMedicine
TopicSyphilis Diagnosis and Treatment
Canadian institutionsImmunovaccine (Canada)
FundersNational Center for Research ResourcesNational Institute of Allergy and Infectious Diseases
KeywordsHaemophilus ducreyiVirulenceMutantFibrinogenMicrobiologyBiologyLipoproteinGeneIn vitroVirulence factorIn vivoPasteurellaceaeGeneticsBiochemistryHaemophilus influenzaeCholesterol

Abstract

fetched live from OpenAlex

A gene expression study of Haemophilus ducreyi identified the hypothetical lipoprotein HD0192, renamed here "fibrinogen binder A" (FgbA), as being preferentially expressed in vivo. To test the role played by fgbA in virulence, an isogenic fgbA mutant (35000HPfgbA) was constructed using H. ducreyi 35000HP, and 6 volunteers were experimentally infected with 35000HP or 35000HPfgbA. The overall pustule-formation rate was 61.1% at parent sites and 22.2% at mutant sites (P = .019). Papules were significantly smaller at mutant sites than at parent sites (13.3 vs. 37.9 mm(2); P = .002) 24 h after inoculation. Thus, fgbA contributed significantly to the virulence of H. ducreyi in humans. In vitro experiments demonstrated that fgbA encodes a fibrinogen-binding protein; no other fibrinogen-binding proteins were identified in 35000HP. fgbA was conserved among clinical isolates of both class I and II H. ducreyi strains, supporting the finding that fgbA is important for H. ducreyi infection.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.274
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2009
Admission routes1
Has abstractyes

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