Glycogen Synthase Kinase-3β Inhibition Augments Diabetic Endothelial Progenitor Cell Abundance and Functionality via Cathepsin B: A Novel Therapeutic Opportunity for Arterial Repair
Bibliographic record
Abstract
Progenitor cell therapy is hindered in patients with diabetes mellitus (DM) due to cellular senescence. Glycogen synthase kinase-3β (GSK3β) activity is increased in DM, potentially exacerbating impaired cell-based therapies. Thus, we aimed to determine if and how GSK3β inhibitors (GSKi) can improve therapeutic efficacy of endothelial progenitor cells (EPC) from patients with DM. Patients with DM had fewer EPCs and increased rates of apoptosis. DM EPCs also exhibited higher levels of GSK3β activity resulting in increased levels of phosphorylated β-catenin. Proteomic profiling of DM EPCs treated with GSKi identified 37 nonredundant, differentially regulated proteins. Cathepsin B (cathB) was subsequently confirmed to be differentially regulated and showed 40% less baseline activity in DM EPCs, an effect reversed by GSKi treatment. Finally, in vivo efficacy of cell-based therapy was assessed in a xenotransplant femoral wire injury mouse model. Administration of DM EPCs reduced the intima-to-media ratio, an effect that was further augmented when DM EPCs were pretreated with GSKi yet absent when cathB was antagonized. In DM, increased basal GSK3β activity contributes to accelerated EPC cellular senescence, an effect reversed by small molecule antagonism of GSK3β, which enhanced cell-based therapy after vascular injury.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".