Bioabsorbable metal zinc differentially affects mitochondria in vascular endothelial and smooth muscle cells
Bibliographic record
Abstract
Zinc is an essential trace element having various structural, catalytic and regulatory interactions with an estimated 3000 proteins. Zinc has drawn recent attention for its use, both as pure metal and alloyed, in arterial stents due to its biodegradability, biocompatibility, and low corrosion rates. Previous studies have demonstrated that zinc metal implants prevent the development of neointimal hyperplasia, which is a common cause of restenosis following coronary intervention. This suppression appears to be smooth muscle cell-specific, as reendothelization of the neointima is not inhibited. To better understand the basis of zinc's differential effects on rat aortic smooth muscle (RASMC) versus endothelial (RAENDO) cells, we conducted a transcriptomic analysis of both cell types following one-week continuous treatment with 5 µM or 50 µM zinc. This analysis indicated that genes whose protein products regulate mitochondrial functions, including oxidative phosphorylation and fusion/fission, are differentially affected by zinc in the two cell types. To better understand this, we performed Seahorse metabolic flux assays and quantitative imaging of mitochondrial networks in both cell types. Zinc treatment differently affected energy metabolism and mitochondrial structure/function in the two cell types. For example, both basal and maximal oxygen consumption rates were increased by zinc in RASMC but not in RAENDO. Zinc treatment increased apparent mitochondrial fusion in RASMC cells but increased mitochondrial fission in RAENDO cells. These results provide some insight into the mechanisms by which zinc treatment differently affects the two cell types and this information is important for understanding the role of zinc treatment in vascular cells and improving its use in biodegradable metal implants.
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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 | 0.000 |
| 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".