Vascular Smooth Muscle Cells: The Muscle behind Vascular Biology
Bibliographic record
Abstract
Vascular smooth muscle cells (VSMCs) may be considered end-effector organs of the vasculature. They mediate the vasomotor responses orchestrated by the endothelium and are the principal pathogenic agents in diseases such as atherosclerosis, restenosis, and hypertension. Indeed, VSMCs represent an ideal target for the therapy of these conditions. In atherosclerosis, for example, endothelial cells (ECs) and macrophages are primarily involved in the initiation of this disease, whereas VSMCs are typically the last to manifest pathological change (Table 61–1). However, once they do, the proliferative, synthetic, and matrix-modulating capacities of VSMCs underlie obstructive lesion formation and play a critical role in determining plaque stability (1). VSMCs are highly responsive to their environment and are able to switch their phenotype from quiescent and contractile to migratory, synthetic, and proliferative (2). Various cues from the surrounding milieu, such as hormonal signals from overlying ECs, cytokines from invading macrophages, and mechanical stresses on the vessel wall, can both initiate and sustain the modulation of VSMC form and function. Under normal conditions, ECs lining the arterial lumen act as sentinels and gatekeepers. By virtue of their location, ECs are the first to respond to circulating factors and hemodynamic stresses, rapidly relaying these signals to VSMCs for transduction (3). ECs also prevent circulating cells and macromolecules, such as lipids or plasma proteins, from penetrating the underlying intima and media indiscriminately. This insulates the underlying VSMCs from stimuli that might otherwise activate them or initiate their phenotypic conversion. However, when ECs become diseased or damaged this barrier function may fail (4). Under these circumstances, ECs elaborate molecular “alarm bells” that initiate inflammatory cell adhesion (5), thrombosis, vasoconstriction, and eventually VSMC proliferation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".