Comparison of Processing and Sectioning Methodologies for Arteries Containing Metallic Stents
Bibliographic record
Abstract
The histological study of arteries with implanted metallic scaffolding devices, known as stents, remains a technical challenge. Given that the arterial response to stent implantation can sometimes lead to adverse outcomes, including the re-accumulation of tissue mass within the stent (or in-stent restenosis), overcoming these technical challenges is a priority for the advancement of research and development in this important clinical field. Essentially, the task is to section the stent-tissue interface with the least amount of disruption of tissue and cellular morphology. Although many methacrylate resin methodologies are successfully applied toward the study of endovascular stents by a variety of research laboratories, the exact formulations, as well as subsequent processing and sectioning methodology, remain largely coveted. In this paper, we describe in detail a methyl methacrylate resin-embedding methodology that can successfully be applied to tungsten carbide blade, as well as saw and grinding sectioning methods and transmission electron microscopy. In addition, we present a comparison of the two sectioning methodologies in terms of their effectiveness with regard to morphological, histochemical, and immunohistochemical analyses. This manuscript contains online supplemental material at http://www.jhc.org. Please visit this article online to view these materials.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".