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Record W1804348587 · doi:10.1111/mmi.12773

Identification of molecular mechanisms used by <scp><i>F</i></scp><i>inegoldia magna</i> to penetrate and colonize human skin

2014· article· en· W1804348587 on OpenAlexafffund
Elizabeth C. Murphy, Matthias Mörgelin, Dieter P. Reinhardt, Anders I. Olin, Lars Björck, Inga-Maria Frick

Bibliographic record

VenueMolecular Microbiology · 2014
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchLunds UniversitetKungliga Fysiografiska Sällskapet i LundVetenskapsrådetSvenska Forskningsrådet FormasRoyal Society
KeywordsBiologyMicrobiologyBasement membraneDermisSerine proteaseHuman skinBacteriaProteasesEpidermis (zoology)Cell biologyImmunologyProteaseBiochemistryAnatomyGenetics

Abstract

fetched live from OpenAlex

Finegoldia magna is a Gram-positive anaerobic commensal of the human skin microbiota, but also known to act as an opportunistic pathogen. Two primary virulence factors of F. magna are the subtilisin-like extracellular serine protease SufA and the adhesive protein FAF. This study examines the molecular mechanisms F. magna uses when colonizing or establishing an infection in the skin. FAF was found to be essential in the initial adherence of F. magna to human skin biopsies. In the upper layers of the epidermis FAF mediates adhesion through binding to galectin-7 - a keratinocyte cell marker. Once the bacteria moved deeper into the skin to the basement membrane layer, SufA was found to degrade collagen IV which forms the backbone structure of the basement membrane. It also degraded collagen V, whereby F. magna could reach deeper dermal tissue sites. In the dermis, FAF interacts with collagen V and fibrillin, which presumably helps the bacteria to establish infection in this area. The findings of this study paint a clear picture of how F. magna interacts with human skin and explain how it is such a successful opportunistic pathogen in chronic wounds and ulcers.

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 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 score0.009
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.006
GPT teacher head0.245
Teacher spread0.240 · 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

Citations57
Published2014
Admission routes2
Has abstractyes

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