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Human macrophage polarization determines bacterial persistence of Staphylococcus aureus in a liver-on-chip-based infection model

2022· article· en· W4282968054 on OpenAlexaff
Fatina Siwczak, Zoltán Cseresnyés, Mohamed I. Abdelwahab Hassan, Kehinde Aina, Swen Carlstedt, Anke Sigmund, Marko Gröger, Bas G. J. Surewaard, Oliver Werz, Marc Thilo Figge, Lorena Tuchscherr, Bettina Löffler, Alexander S. Mosig

Bibliographic record

VenueBiomaterials · 2022
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStaphylococcus aureusMicrobiologyMacrophage polarizationBiologyMacrophageIntracellularExtracellularSepsisPhenotypePathogenPersistence (discontinuity)Intracellular parasiteImmunologyAsymptomaticIn vitroBacteriaCell biologyMedicineGeneGeneticsPathology

Abstract

fetched live from OpenAlex

Infections with Staphylococcus aureus (S. aureus) have been reported from various organs ranging from asymptomatic colonization to severe infections and sepsis. Although considered an extracellular pathogen, S. aureus can invade and persist in professional phagocytes such as monocytes and macrophages. Its capability to persist and manipulate macrophages is considered a critical step to evade host antimicrobial reactions. We leveraged a recently established human liver-on-chip model to demonstrate that S. aureus specifically targets macrophages as essential niche facilitating bacterial persistence and phenotype switching to small colony variants (SCVs). In vitro, M2 polarization was found to favor SCV-formation and was associated with increased intracellular bacterial loads in macrophages, increased cell death, and impaired recruitment of circulating monocytes to sites of infection. These findings expand the knowledge about macrophage activation in the liver and its impact on bacterial persistence and dissemination in the course of 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 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 score0.058
Threshold uncertainty score0.942

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)0.0010.000

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 head0.033
GPT teacher head0.280
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

Citations32
Published2022
Admission routes1
Has abstractyes

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