Neointimal hyperplasia: are fatty acid transport proteins a new therapeutic target?
Bibliographic record
Abstract
PURPOSE OF REVIEW: High-fat diets contribute to hyperlipidemia and dysregulated metabolism underlying insulin resistant states and cardiovascular diseases. Neointimal hyperplasia is a significant resulting morbidity. Increased fatty acid (FA) levels lead to dysfunctional endothelium, defined as activated, proinflammatory and prothrombotic. The purpose of this review is to assess the recent literature on the emerging concept that uptake of FA into many tissues is regulated at the endothelial level, and this in turn contributes to endothelial dysfunction, an initiating factor in insulin resistant states, atherosclerosis and neointimal hyperplasia. RECENT FINDINGS: Studies support the role of endothelial FA uptake proteins as an additional level of regulation in tissue FA uptake. These proteins include CD36, FA transport proteins, FA-binding proteins and caveolin-1. In many cases, inappropriate expression of these proteins can result in a change in FA and glucose uptake, storage and utilization. Accumulation of plasma FA is one mechanism by which alterations in expression of FA uptake proteins can lead to endothelial dysfunction; changes in tissue substrate metabolism leading to inflammation are also implicated. SUMMARY: Identification of the critical players and regulators can lead to therapeutic targeting to reduce endothelial dysfunction and sequela such as insulin resistance and neointimal hyperplasia.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".