MétaCan
Menu
Back to cohort
Record W3127477639 · doi:10.3390/microorganisms9020287

A Murine Skin Infection Model Capable of Differentiating the Dermatopathology of Community-Associated MRSA Strain USA300 from Other MRSA Strains

2021· article· en· W3127477639 on OpenAlexafffund
Jack Zhang, John Conly, Jo‐Ann McClure, Kaiyu Wu, Björn Petri, Duane Barber, Sameer Elsayed, Glen D. Armstrong, Kunyan Zhang

Bibliographic record

VenueMicroorganisms · 2021
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsWestern UniversityAlberta Health ServicesUniversity of Calgary
FundersAlberta InnovatesAlberta Health Services
KeywordsStaphylococcus aureusChemokineMicrobiologyStaphylococcal Skin InfectionsSkin infectionMethicillin-resistant Staphylococcus aureusDermatopathologyVirulenceBiologyImmunologyStrain (injury)MedicineInflammationPathologyBacteriaGene

Abstract

fetched live from OpenAlex

(CA-MRSA) strain that is a leading cause of skin and soft tissue infections. We established a murine intradermal infection model capable of demonstrating dermatopathological differences between USA300 and other MRSA strains. In this model, USA300 induced dermonecrosis, uniformly presenting as extensive open lesions with a histologically documented profound inflammatory cell infiltrate extending below the subcutis. In contrast, USA400 and a colonizing control strain M92 caused only localized non-ulcerated skin infections associated with a mild focal inflammatory infiltrate. It was also determined that the dermonecrosis induced by USA300 was associated with significantly increased neutrophil recruitment, inhibition of an antibacterial response, and increased production of cytokines/chemokines associated with disease severity. These results suggest that induction of severe skin lesions by USA300 is related to over-activation of neutrophils, inhibition of host antibacterial responses, and selective alteration of host cytokine/chemokine profiles.

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 score0.103
Threshold uncertainty score0.734

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 studies0.0000.000
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 head0.024
GPT teacher head0.268
Teacher spread0.244 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueMicroorganismsSame topicAntimicrobial Resistance in StaphylococcusFrench-language works237,207