Un-JAMming atherosclerotic arteries: JAM-L as a target to attenuate plaque development
Bibliographic record
Abstract
Atherosclerosis is a chronic inflammatory disease and a major driver of heart attack and stroke. Atherosclerosis development is driven by the infiltration of leukocytes, including monocytes and neutrophils, among other inflammatory cells into the artery wall, monocyte differentiation to macrophages and uptake of oxidized low density lipoprotein. Macrophage activation and inflammatory cytokine production are major factors which drive ongoing inflammation and plaque development. Identification of novel pathways driving this on-going inflammatory process may provide new opportunities for therapeutic intervention. In their article published in Clinical Science (2019) (vol 133, 1215-1228), Sun and colleagues demonstrate a novel role for the junction adhesion molecule-like (JAML) protein in driving on-going atherosclerotic plaque inflammation and plaque development. They report that JAML is expressed in macrophages and other cells in atherosclerotic plaques in both humans and mice, and that silencing JAML expression attenuates atherosclerotic plaque progression in mouse models of early and late stage plaque development. They demonstrate that JAML is required for oxidized-low density lipoprotein (OxLDL)-induced up-regulation of inflammatory cytokine production by macrophages, pointing to it as a potential therapeutic target for reducing ongoing plaque inflammation.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".