Deletion of Macrophage-Specific Glycogen Synthase Kinase (GSK)-3α Promotes Atherosclerotic Regression in Ldlr−/− Mice
Bibliographic record
Abstract
Recent evidence from our laboratory suggests that impeding ER stress–GSK3α/β signaling attenuates the progression and development of atherosclerosis in mouse model systems. The objective of this study was to determine if the tissue-specific genetic ablation of GSK3α/β could promote the regression of established atherosclerotic plaques. Five-week-old low-density lipoprotein receptor knockout (Ldlr−/−) mice were fed a high-fat diet for 16 weeks to promote atherosclerotic lesion formation. Mice were then injected with tamoxifen to induce macrophage-specific GSK3α/β deletion, and switched to standard diet for 12 weeks. All mice were sacrificed at 33 weeks of age and atherosclerosis was quantified and characterized. Female mice with induced macrophage-specific GSK3α deficiency, but not GSK3β deficiency, had reduced plaque volume (~25%) and necrosis (~40%) in the aortic sinus, compared to baseline mice. Atherosclerosis was also significantly reduced (~60%) in the descending aorta. Macrophage-specific GSK3α-deficient mice showed indications of increased plaque stability and reduced inflammation in plaques, as well as increased CCR7 and ABCA1 expression in lesional macrophages, consistent with regressive plaques. These results suggest that GSK3α ablation promotes atherosclerotic plaque regression and identify GSK3α as a potential target for the development of new therapies to treat existing atherosclerotic lesions in patients with cardiovascular disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".