Human macrophage polarization determines bacterial persistence of <i>Staphylococcus aureus</i> in a liver-on-chip-based infection model
Bibliographic record
Abstract
Abstract Infections with Staphylococcus aureus ( S. aureus ) have been reported from various organs ranging from asymptomatic colonization to severe infections and sepsis associated with multiple organ dysfunction. 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 phagocytes is considered a critical step to evade host antimicrobial reactions. For the first time we leveraged a human liver-on-chip model and tailored image analysis algorithms to demonstrate that S. aureus (USA300) specifically targets macrophages in the liver models 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 the role of liver macrophages in the course of systemic infection. Further, the results might be relevant for understanding infection mechanisms in patients with chronic liver disease such as fibrosis that display increased frequencies of M2 polarized liver macrophages and have a higher risk for developing chronic infections and relapsing bacteremia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".