Spatial Gene Expression of Human Coronary Arteries Revealed the Molecular Features of Diffuse Intimal Thickening in Explanted Hearts
Bibliographic record
Abstract
Diffuse intimal thickening (DIT) is a pre-clinical stage of atherosclerosis characterized by thickened intima. The molecular basis of its susceptibility to atherogenesis is unknown, and mechanistic investigations cannot be performed in commonly used mouse models, in which DIT does not exist. Vascular smooth muscle cells (SMCs) are the predominant cell type that occupies the intima and media of DIT. The molecular differences between these two layers may reveal the earliest phenotypic changes in SMCs to promote atherosclerosis. We benchmarked the RNA quality of human coronary arteries from autopsies (n = 7) and explanted hearts (n = 7) and performed Visium spatial gene expression on tissue sections with DIT. Although autopsy samples met the RNA quality standard for Visium (DV200 ≥ 30%), only arteries from explanted hearts exhibited reliable sequencing performance. Genes enriched in TGF-β-mediated remodeling of the extracellular matrix were overrepresented in the intima. SMCs enriched in the intima are dedifferentiated, but unlike those in the atherosclerotic lesions, they are not pro-inflammatory. Our findings indicate that autopsy samples are not ideal to distinguish subtle differences among cell phenotypes. SMCs in thickened intima may lead to lipid retention but not necessarily the onset of atherosclerosis.
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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".