Protein Tyrosine Phosphatase 1B Impairs Diabetic Wound Healing Through Vascular Endothelial Growth Factor Receptor 2 Dephosphorylation
Bibliographic record
Abstract
OBJECTIVE: Impaired wound healing is a major complication of diabetes mellitus. The mechanisms that govern wound healing, however, are complex and incompletely understood. In the present study, we determined the inhibitory role of protein tyrosine phosphatase 1B (PTP1B) in the process of diabetic wound healing. APPROACH AND RESULTS: First, by comparing the wound healing process in PTP1B knockout (PTP1B(-/-)) mice, ob/ob mice and their wild-type littermates in the presence or absence of streptozotocin treatment, we showed that the inhibition of mouse wound healing in streptozotocin-induced diabetic conditions is because of the upregulation and activation of PTP1B. Second, the impaired wound healing in ob/ob mice and streptozotocin-treated wild-type mice was rescued by a PTP1B inhibitor. Third, PTP1B, which is upregulated under hyperglycemic condition, inhibited the tube formation, proliferation, and migration of human microvascular endothelial cells induced by vascular endothelial growth factor, whereas this inhibition was largely abolished by the PTP1B inhibitor. Finally, mechanism study further indicated that PTP1B likely suppressed the proliferation, migration, and tube formation of vascular endothelial cells through dephosphorylation of vascular endothelial growth factor receptor 2. CONCLUSIONS: Our study demonstrated that PTP1B negatively modulated the diabetic wound healing process by dephosphorylating the endothelial cell vascular endothelial growth factor receptor 2 and that the specific inhibitor of PTP1B might serve as a potential novel therapeutic tool for diabetic wound healing.
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 | 0.000 |
| 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) | 0.002 | 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".