The Role of Leukemia Inhibitory Factor in Cardiovascular Disease: Signaling in Inflammation, Coagulation, and Angiogenesis
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) is one of the principal causes of mortality in the world. Various factors have been identified in the pathogenesis of CVD. Leukemia inhibitory factor (LIF) as a secretory cytokine is one of these factors. The LIF receptor is located on endothelial cells and plays a role in the expression of specific genes in these cells. Endothelial cells are the innermost cells of blood vessels, and defects in these cells cause endothelial dysfunction and eventually CVD. Methods: The present study is based on PubMed database information (1982-2022) using the following words: "cardiovascular disease," "endothelial cells," "leukemia inhibitory factor," and "angiogenesis." Results: LIF can cause arteriosclerotic plaques by activating inflammatory mechanisms in monocytes through the induction of intercellular adhesion molecule-1 and vascular cell adhesion molecule-1 expression. LIF can also induce vascular endothelial growth factor expression by activating signaling pathways, eventually leading to angiogenesis. Additionally, it can activate the coagulation cascade by factor VII production promotion within endothelial cells. Conclusion: Understanding the interplay between LIF and the inflammation pathways, coagulation, and angiogenesis as key factors in CVD occurrence raises the possibility of targeting this factor as a potential strategy to mitigate CVD risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".