‘Blow my mind(in)’ – mindin neutralization for the prevention of atherosclerosis?
Bibliographic record
Abstract
The hallmark features of atherosclerosis include accumulation of low-density lipoprotein (LDL) carrying cholesterol in the vessel wall, formation of lipid-laden foam cells, and the creation of a pro-inflammatory microenvironment. To date, no effective treatments are clinically available for increasing cholesterol efflux from vascular macrophages and inducing reverse cholesterol transport (RCT). In an article published recently in Clinical Science (vol 132, issue 6, 1199-1213), Zhang and colleagues identified the extracellular matrix protein mindin/spondin 2 as a positive regulator of atherosclerosis. Genetic knockout of mindin in apolipoprotein-E (apoE)−/− mice attenuated atherosclerosis, foam cell formation, and inflammation within the vessel wall. Conversely, selective overexpression of mindin in macrophages in apoE−/− mice was sufficient to promote the greater severity of atherosclerosis. Interestingly, foam cell formation was closely associated with the expression of cholesterol transporters (ABCA1 and ACBG1) that facilitate cholesterol efflux. Liver X receptor (LXR)-β is a key modulator of cholesterol transporter expression and formed direct interactions with mindin. Furthermore, the protective effects of mindin deficiency on foam cell formation were blocked by inhibition of LXR-β. This article highlights a novel role of mindin in modulating foam cell formation and atherosclerosis development in mice through direct regulation of LXR-β. Thus far, direct targetting of LXR-β via pharmacological agonists has proven to be problematic due to the lack of subtype selective inhibitors and associated adverse effects. Indirect targetting of LXR-β, therefore, via mindin inhibition offers a new therapeutic strategy for increasing LXR-β induced cholesterol efflux, reducing foam cell formation, and preventing or treating atherosclerosis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".