<p>Modulation of Human Neutrophil Peptides on <em>P. aeruginosa</em> Killing, Epithelial Cell Inflammation and Mesenchymal Stromal Cell Secretome Profiles</p>
Bibliographic record
Abstract
OBJECTIVE: Neutrophil infiltration and release of the abundant human neutrophil peptides (HNP) are a common clinical feature in critically ill patients. We tested a hypothesis that different cell types respond to HNP differently in lung microenvironment that may influence the host responses. METHODS: , human lung epithelial cells and mesenchymal stromal cells (MSCs) were exposed to various concentrations of HNP. Bacterial killing, epithelial cell inflammation, MSC adhesion and behaviours were examined after HNP stimulation. RESULTS: or stimulation of human lung epithelial cells with HNP resulted in bacterial killing or IL-8 production at a dose of 50 μg/mL, while MSC adhesion and alternations of secretome profiles took place after HNP stimulation at a dose of 10 μg/mL. The secretome profile changes were characterized by increased release of the IL-6 family members such as C-reactive protein (CRP), leukemia inhibitory factor (LIF) and interleukin (IL-11), and first apoptosis signal (FAS) and platelet-derived growth factor-AA as compared to a vehicle control group. CONCLUSION: Stimulation of MSCs with HNP resulted in changes of secretome profiles at 5-fold lower concentration than that required for bacterial killing and lung epithelial inflammation. This undisclosed risk factor of HNP in lung environment should be taken into consideration when MSCs are applied as cell therapy in inflammatory lung diseases.
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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) | 0.002 | 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".