Tumor necrosis factor inhibitors as novel therapeutic tools for vascular remodeling diseases
Bibliographic record
Abstract
Vascular remodeling diseases (VRDs) are characterized by enhanced inflammation and proliferative and apoptosis-resistant vascular smooth muscle cells (VSMCs). The sustainability of this phenotype has been attributed in part to the activation of the transcription factor hypoxia-inducible factor-1 (HIF-1). There is evidence that circulating cytokines can act as HIF-1 activators in a variety of tissues, including VSMCs. Increased circulating tumor necrosis factor (TNF) levels have been associated with vascular diseases, but the mechanisms involved remain unknown. We hypothesized that increased circulating levels of TNF promotes VRDs by the activation of HIF-1, resulting in VSMC proliferation and resistance to apoptosis. Circulating TNF levels were significantly increased in patients with vascular diseases (n = 19) compared with healthy donors (n = 15). Using human carotid artery smooth muscle cells (CASMCs), we demonstrated that TNF (100 ng/ml) activates HIF-1 (HIF-1α expression), leading to increased CASMC proliferation (Ki-67 and PCNA staining) and resistance to mitochondrial-dependent apoptosis [tetramethylrhodamine methyl ester perchlorate (TMRM), terminal deoxynucleotide transferase-mediated dUTP nick end labeling (TUNEL), annexin-V staining]. In vivo, TNF inhibition using polyethylene glycol coupled with TNF membrane receptor 1 (PEGsTNFR1), a soluble TNF receptor inhibiting circulating TNF, prevented carotid artery postinjury media remodeling and neointima development in rats. This effect was associated with lowered HIF-1 activation and decreased CASMC proliferation. In conclusion, we demonstrate for the first time that the inhibition of the TNF/Akt/HIF-1 axis prevents vascular remodeling. TNF inhibitors may therefore represent new and interesting therapeutic tools against VRDs.
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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".