The TG-interacting Factor TGIF1 Regulates Stress-induced Proinflammatory Phenotype of Endothelial Cells
Bibliographic record
Abstract
The endothelium contributes to the control of the tissue inflammatory response following stress and in particular after exposure to ionizing radiation. We previously showed that the TG-interacting factor 1 (TGIF1) plays a role in radiation-induced normal tissue injury. In this study we hypothesized that this protein could play a role in inflammation. The role of TGIF1 in the stress-induced proinflammatory phenotype was investigated in human endothelial cells. In HUVECs ionizing radiation induces TGIF1 expression as well as a proinflammatory phenotype associated with up-regulation of IL-6, IL-8, CXCL1, MIP-2, and MCP-1. TGIF1 overexpression enhances the radiation-induced proinflammatory phenotype whereas TGIF1 silencing limits both the TNF-α- and radiation-induced overexpression of proinflammatory cytokines. Interestingly, in vivo, in radiation-induced intestinal inflammation in mice, TGIF1 genetic deficiency is associated with a reduced radiation-induced overexpression of proinflammatory molecules. In HUVECs, TNF-α- and radiation-induced NF-κB pathway activation is not influenced by TGIF1 expression, whereas TGIF1 knockdown inhibits both TNF-α- and radiation-induced p38 MAPK pathway activation. This study demonstrates that TGIF1 plays a role in TNF-α- and radiation-induced inflammation and suggests that it could be a target in limiting this event in the vascular compartment.
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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".