Pterostilbene accelerates wound healing response in diabetic mice through Nrf2 regulation

Pterostilbene (PTS), known for its diverse beneficial effects via Nuclear factor erythroid-2 related factor (Nrf2) activation, holds potential for Diabetic Foot Ulcer (DFU) treatment. However, PTS-mediated Nrf2 regulation in diabetic wounds has yet to be elucidated. We used IC21 macrophage-conditioned media to simulate complex events that can influence the fibroblast phenotype using L929 cells during the wound healing process under a hyperglycemic microenvironment. We found that PTS attenuated fibroblast migration and alpha-smooth muscle actin (α-SMA) levels and hypoxia-inducible factor- 1 alpha (HIF1α). Furthermore, we demonstrated that wounds in diabetic mice characterized by impaired wound closure in a heightened inflammatory milieu, such as the NOD-like receptor P3 (NLRP3) and intercellular adhesion molecule 1 (ICAM1), and deficient Nrf2 response accompanying lowered Akt signaling and heme oxygenase1 (HO1) expression along with the impaired macrophage M2 marker CD206 expression, was rescued by administration of PTS. Such an elicited response was also compared favorably with the standard treatment using Regranex, a commercially available topical formulation for treating DFUs. Our findings suggest that PTS regulates Nrf2 in diabetic wounds, triggering a pro-wound healing response mediated by macrophages. This insight holds the potential for developing targeted therapies to heal chronic wounds, including DFUs.

Determinants of healing of diabetic foot ulcer comparing two offloading modalities: A randomized prospective study

INTRODUCTION

Healing of Diabetic Foot ulcer is crucial to prevent amputation. Offloading is key treatment of diabetic foot ulcers, but choosing which offloading modality is still not clear. Besides, other factors that control ulcer healing, is a question that needs to be determined.

OBJECTIVE

to assess factors that affect ulcer healing, comparing two commonly used offloading devices, removable walker and cast-shoe.

METHODS

This is a Randomized Clinical Trial which recruited 87 patients with active diabetic foot ulcers randomly assigned to either a removable walker (W-arm) or a cast-shoe (C-arm) at 3:2 ratio. Both groups received the routine ulcer care, and were followed-up for 24 weeks. Different possible factors related to healing were assessed, and a regression model was built for the most predictive factors.

RESULTS

The 24-week healing rate was 81% for the walker group and 62 % for the cast-shoe group. The mean adherence was 55 % ± 26 % and 46 % ± 29 for the walker and cast shoe groups respectively. Ulcer healing was significantly positively associated with: better adherence, device type (walker), less SINBAD score (2 or less), absence of ischemia, absence of infection, smaller ulcer area, superficial ulcer, better 4-week area reduction, and better blood glucose control. The most important predictors were adherence, total SINBAD score and 4-week area reduction.

CONCLUSION

SINBAD score at initial presentation and the degree of adherence to offloading device, are two major determinants of ulcer healing. Ulcer area reduction at 4 weeks represents an important clinical parameter to predict and guide the success of ulcer management.

Bone marrow-derived mesenchymal stem cell-conditioned medium ameliorates diabetic foot ulcers in rats

OBJECTIVES

This study aimed to explore the effects of bone marrow-derived Mesenchymal Stem Cell-Conditioned Medium (MSC-CM) treating diabetic foot ulcers in rats.

METHODS

Models of T2DM rats were induced by a high-fat diet and intraperitoneal injection of STZ in SD rats. Models of Diabetic Foot Ulcers (DFUs) were made by operation on hind limbs in diabetic rats. Rats were divided into four groups (n = 6 for each group), i.e., Normal Control group (NC), Diabetes Control group (DM-C), MSC-CM group and Mesenchymal Stem Cells group (MSCs). MSC-CM group was treated with an injection of conditioned medium derived from preconditioned rats' bone marrow MSCs around ulcers. MSCs group were treated with an injection of rats' bone marrow MSCs. The other two groups were treated with an injection of PBS. After the treatment, wound closure, re-epithelialization (thickness of the stratum granulosums of the skin, by H&E staining), cell proliferation (Ki67, by IHC), angiogenesis (CD31, by IFC), autophagy (LC3B, by IFC and WB; autolysosome, by EM) and pyroptosis (IL-1β, NLRP3, Caspase-1, GSDMD and GSDMD-N, by WB) in ulcers were evaluated.

RESULTS

After the treatment wound area rate, IL-1β by ELISA, and IL-1β, Caspase-1, GSDMD and GSDMD-N by WB of MSC-CM group were less than those of DM group. The thickness of the stratum granulosums of the skin, proliferation index of Ki67, mean optic density of CD31 and LC3B by IFC, and LC3B by WB of MSC-CM group were more than those of DM group. The present analysis demonstrated that the injection of MSC-CM into rats with DFUs enhanced the wound-healing process by accelerating wound closure, promoting cell proliferation and angiogenesis, enhancing cell autophagy, and reducing cell pyroptosis in ulcers.

CONCLUSIONS

Studies conducted indicate that MSC-CM administration could be a novel cell-free therapeutic approach to treat DFUs accelerating the wound healing process and avoiding the risk of living cells therapy.

Diabetic Foot Ulcer Ischemia and Infection Classification Using EfficientNet Deep Learning Models

Motivation: Infection (bacteria in the wound) and ischemia (insufficient blood supply) in Diabetic Foot Ulcers (DFUs) increase the risk of limb amputation. Goal: To develop an image-based DFU infection and ischemia detection system that uses deep learning. Methods: The DFU dataset was augmented using geometric and color image operations, after which binary infection and ischemia classification was done using the EfficientNet deep learning model and a comprehensive set of baselines. Results: The EfficientNets model achieved 99% accuracy in ischemia classification and 98% in infection classification, outperforming ResNet and Inception (87% accuracy) and Ensemble CNN, the prior state of the art (Classification accuracy of 90% for ischemia 73% for infection). EfficientNets also classified test images in a fraction (10% to 50%) of the time taken by baseline models. Conclusions: This work demonstrates that EfficientNets is a viable deep learning model for infection and ischemia classification.

Personalized Scaffolds for Diabetic Foot Ulcer Healing Using Extracellular Matrix from Induced Pluripotent Stem-Reprogrammed Patient Cells

Diabetic foot ulcers (DFU) are chronic wounds sustained by pathological fibroblasts and aberrant extracellular matrix (ECM). Porous collagen-based scaffolds (CS) have shown clinical promise for treating DFUs but may benefit from functional enhancements. Our previous work showed fibroblasts differentiated from induced pluripotent stem cells are an effective source of new ECM mimicking fetal matrix, which notably promotes scar-free healing. Likewise, functionalizing CS with this rejuvenated ECM showed potential for DFU healing. Here, we demonstrate for the first time an approach to DFU healing using biopsied cells from DFU patients, reprogramming those cells, and functionalizing CS with patient-specific ECM as a personalized acellular tissue engineered scaffold. We took a two-pronged approach: 1) direct ECM blending into scaffold fabrication; and 2) seeding scaffolds with reprogrammed fibroblasts for ECM deposition followed by decellularization. The decellularization approach reduced cell number requirements and maintained naturally deposited ECM proteins. Both approaches showed enhanced ECM deposition from DFU fibroblasts. Decellularized scaffolds additionally enhanced glycosaminoglycan deposition and subsequent vascularization. Finally, reprogrammed ECM scaffolds from patient-matched DFU fibroblasts outperformed those from healthy fibroblasts in several metrics, suggesting ECM is in fact able to redirect resident pathological fibroblasts in DFUs towards healing, and a patient-specific ECM signature may be beneficial.