DNA microarray analysis of gene expression in alveolar epithelial cells in response to TNFα, LPS, and cyclic stretch
Bibliographic record
Abstract
Recent evidence suggests that alveolar epithelial cells (AECs) may contribute to the development, propagation, and resolution of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS). Proinflammatory cytokines, pathogen products, and injurious mechanical ventilation are important contributors of excessive inflammatory responses in the lung. In the present study, we used cDNA microarrays to define the gene expression patterns of A549 cells (an AEC line) in the early stages of three models of pulmonary parenchymal cell activation: cells treated with tumor necrosis factor-alpha (TNFalpha) (20 ng/ml), lipopolysaccharide (LPS, 1 microg/ml), or cyclic stretch (20% elongation) for either 1 h or 4 h. Differential gene expression profiles were determined by gene array analysis. TNFalpha induced an inflammatory response pattern, including induction of genes for chemokines, inflammatory mediators, and cell surface membrane proteins. TNFalpha also increased genes related to pro- and anti-apoptotic proteins, signal transduction proteins, and transcriptional factors. TNFalpha further induced a group of genes that may form a negative feedback loop to silence the NFkappaB pathway. Stimulation of AECs with mechanical stretch changed cell morphology and activated Src protein tyrosine kinase. The combination of TNFalpha plus stretch enhanced or attenuated expression of multiple genes. LPS decreased microfilament polymerization but had less impact on NFkappaB translocation and gene expression. Results from this study indicate that AECs can tailor their response to different stimuli or/and combination of stimuli and subsequently play an important role in acute inflammatory responses in the lung.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".