Differential post-transcriptional activation of human phagocytes by different Pseudomonas aeruginosa isolates
Bibliographic record
Abstract
Pseudomonas aeruginosa is a pulmonary pathogen in individuals with impaired mucociliary clearance such as cystic fibrosis or mechanical ventilation. Non-opsonic phagocytosis of P. aeruginosa can be mediated by either CR3 or CD14 and different strains appear to have a bias towards one or the other receptor. Strain Fc808 is ingested through CD14 whereas P1 (Fc194) uses CR3. In an in vitro culture system, the inflammatory response of macrophages to these two different strains of P. aeruginosa was divergent at the protein level, with higher IL-6 and tumour necrosis factor (TNF)-alpha production generated in response to strain P1 and higher IL-1 beta production in response to strain Fc808. Interaction of macrophages with these two bacterial strains induced distinct gene expression patterns as detected by gene array analysis, with prominence of genes encoding pro-inflammatory cytokines, surface receptors, transcription factors and proteins involved in phagocytosis. However, comparison of gene expression data and cytokine response data with the two bacterial strains indicated that production of IL-1 beta, IL-6 and TNF-alpha was under differential post-transcriptional control. Interestingly, this effect did not correlate with receptor bias but instead was related to the different LPSs of the two strains. The use of specific mitogen-activated protein kinase (MAPK) inhibitors suggested a role for extracellular signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK) in the differential cytokine production by strains P1 and Fc808. These results indicate that strains of the same species of bacteria may induce differential macrophage phagocytic and inflammatory responses with likely consequence for bacterial clearance and host injury.
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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".