Lentivirus-mediated <i>angiopoietin-2</i> gene silencing decreases TNF-α induced apoptosis of alveolar epithelium cells
Post-publication record
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Bibliographic record
Abstract
OBJECTIVE: To investigate the role of angiopoietin-2 (Ang-2) in tumor necrosis factor-α (TNF-α) induced apoptosis of alveolar epithelium cells (AECs). METHODS: TNF-α was used to induce human alveolar epithelial HPAEpiC cells, and Ang-2 siRNA vector was transfected to the HPAEpiC cells. RT-PCR and Western blot were used. TUNEL staining was applied to observe apoptosis, and annexin V-FITC-PI staining was used to calculate apoptosis rate. RESULTS: mRNA and protein expressions of Ang-2, activated Bax, and cleaved caspase-3 in HPAEpiC cells were up-regulated, but the expression level of Bcl-2 decreased (P < 0.05). After transfection of Ang-2 siRNA, mRNA and protein expressions of Ang-2, activated Bax, and cleaved caspase-3 in HPAEpiC cells were down-regulated, but the expression level of Bcl-2 increased (P < 0.05). The number of apoptotic cells increased after TNF-α treatment; however, the number decreased after Ang-2 siRNA transfection. Annexin V-FITC-PI staining verified that the total number of apoptotic cells was elevated with TNF-α treatment, but declined after transfection of Ang-2 siRNA. CONCLUSIONS: The expression level of Ang-2 increased during TNF-α-induced apoptosis. Inhibiting Ang-2 expression may suppress the early stages of cell apoptosis and the degree of TNF-α-induced apoptosis.
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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 | 0.001 |
| 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".