Neddylation suppression by a macrophage membrane-coated nanoparticle promotes dual immunomodulatory repair of diabetic wounds.

Oxidative stress, infection, and vasculopathy caused by hyperglycemia are the main barriers for the rapid repair of foot ulcers in patients with diabetes mellitus (DM). In recent times, the discovery of neddylation, a new type of post-translational modification, has been found to regulate various crucial biological processes including cell metabolism and the cell cycle. Nevertheless, its capacity to control the healing of wounds in diabetic patients remains unknown. This study shows that MLN49224, a compound that inhibits neddylation at low concentrations, enhances the healing of diabetic wounds by inhibiting the polarization of M1 macrophages and reducing the secretion of inflammatory factors. Moreover, it concurrently stimulates the growth, movement, and formation of blood vessel endothelial cells, leading to expedited healing of wounds in individuals with diabetes. The drug is loaded into biomimetic macrophage-membrane-coated PLGA nanoparticles (M-NPs/MLN4924). The membrane of macrophages shields nanoparticles from being eliminated in the reticuloendothelial system and counteracts the proinflammatory cytokines to alleviate inflammation in the surrounding area. The extended discharge of MLN4924 from M-NPs/MLN4924 stimulates the growth of endothelial cells and the formation of tubes, along with the polarization of macrophages towards the anti-inflammatory M2 phenotype. By loading M-NPs/MLN4924 into a hydrogel, the final formulation is able to meaningfully repair a diabetic wound, suggesting that M-NPs/MLN4924 is a promising engineered nanoplatform for tissue engineering.

Glucose-responsive, antioxidative HA-PBA-FA/EN106 hydrogel enhanced diabetic wound healing through modulation of FEM1b-FNIP1 axis and promoting angiogenesis.

The diabetic wounds remain to be unsettled clinically, with chronic wounds characterized by drug-resistant bacterial infections, compromised angiogenesis and oxidative damage to the microenvironment. To ameliorate oxidative stress and applying antioxidant treatment in the wound site, we explore the function of folliculin-interacting protein 1 (FNIP1), a mitochondrial gatekeeper protein works to alter mitochondrial morphology, reduce oxidative phosphorylation and protect cells from unwarranted ROS accumulation. And our in vitro experiments showed the effects of FNIP1 in ameliorating oxidative stress and rescued impaired angiogenesis of HUVECs in high glucose environment. To realize the drug delivery and local regulation of FNIP1 in diabetic wound sites, a novel designed glucose-responsive HA-PBA-FA/EN106 hydrogel is introduced for improving diabetic wound healing. Due to the dynamic phenylboronate ester structure with a phenylboronic acid group between hyaluronic acid (HA) and phenylboronic acid (PBA), the hydrogel is able to realize a glucose-responsive release of drugs. Fulvic acid (FA) is added in the hydrogel, which not only severs as crosslinking agent but also provides antibacterial and anti-inflammatory abilities. Moreover, the release of FEM1b-FNIP1 axis inhibitor EN106 ameliorated oxidative stress and stimulated angiogenesis through FEM1b-FNIP1 axis regulation. These in vivo and in vitro results demonstrated that accelerated diabetic wounds repair with the use of the HA-PBA-FA/EN106 hydrogel, which may provide a promising strategy for chronic diabetic wound repair.

Predicting the Recurrence of Common Bile Duct Stones After ERCP Treatment with Automated Machine Learning Algorithms.

BACKGROUND: Recurrence of common bile duct stones (CBDs) commonly happens after endoscopic retrograde cholangiopancreatography (ERCP). The clinical prediction models for the recurrence of CBDs after ERCP are lacking. AIMS: We aim to develop high-performance prediction models for the recurrence of CBDS after ERCP treatment using automated machine learning (AutoML) and to assess the AutoML models versus the traditional regression models. METHODS: 473 patients with CBDs undergoing ERCP were recruited in the single-center retrospective cohort study. Samples were divided into Training Set (65%) and Validation Set (35%) randomly. Three modeling approaches, including fully automated machine learning (Fully automated), semi-automated machine learning (Semi-automated), and traditional regression were applied to fit prediction models. Models' discrimination, calibration, and clinical benefits were examined. The Shapley additive explanations (SHAP), partial dependence plot (PDP), and SHAP local explanation (SHAPLE) were proposed for the interpretation of the best model. RESULTS: The area under roc curve (AUROC) of semi-automated gradient boost machine (GBM) model was 0.749 in Validation Set, better than the other fully/semi-automated models and the traditional regression models (highest AUROC = 0.736). The calibration and clinical application of AutoML models were adequate. Through the SHAP-PDP-SHAPLE pipeline, the roles of key variables of the semi-automated GBM model were visualized. Lastly, the best model was deployed online for clinical practitioners. CONCLUSION: The GBM model based on semi-AutoML is an optimal model to predict the recurrence of CBDs after ERCP treatment. In comparison with traditional regressions, AutoML algorithms present significant strengths in modeling, which show promise in future clinical practices.

Multifunctional hydrogel enhances bone regeneration through sustained release of Stromal Cell-Derived Factor-1α and exosomes.

Fracture nonunion remains a great challenge for orthopedic surgeons. Fracture repair comprises of three phases, the inflammatory, repair and remodeling stage. Extensive advancements have been made in the field of bone repair, including development of strategies to balance the M1/M2 macrophage populations, and to improve osteogenesis and angiogenesis. However, such developments focused on only one or the latter two phases, while ignoring the inflammatory phase during which cell recruitment occurs. In this study, we combined Stromal Cell-Derived Factor-1α (SDF-1α) and M2 macrophage derived exosomes (M2D-Exos) with a hyaluronic acid (HA)-based hydrogel precursor solution to synthesize an injectable, self-healing, adhesive HA@SDF-1α/M2D-Exos hydrogel. The HA hydrogel demonstrated good biocompatibility and hemostatic ability, with the 4% HA hydrogels displaying great antibacterial activity against gram-negative E. coli and gram-positive S. aureus and Methicillin-resistant Staphylococcus aureus (MRSA). Synchronously and sustainably released SDF-1α and M2D-Exos from the HA@SDF-1α/M2D-Exos hydrogel enhanced proliferation and migration of human bone marrow mesenchymal stem cell (HMSCs) and Human Umbilical Vein Endothelial Cells (HUVECs), promoting osteogenesis and angiogenesis both in vivo and in vitro. Overall, the developed HA@ SDF-1α/M2D-Exos hydrogel was compatible with the natural healing process of fractures and provides a new modality for accelerating bone repair by coupling osteogenesis, angiogenesis, and resisting infection at all stages.

Endoscopic full­thickness resection with clip­ and snare­assisted traction for gastric submucosal tumours in the fundus: A single­centre case series.

Exposed endoscopic full-thickness resection (Eo-EFTR) has been recognized as a feasible therapy for gastrointestinal submucosal tumours (SMTs) originating deep in the muscularis propria layer; however, Eo-EFTR is difficult to perform in a retroflexed fashion in the gastric fundus. As a supportive technique, clip- and snare-assisted traction may help expose the surgical field and shorten the operation time in endoscopic resection of difficult regions. However, the application of clip- and snare-assisted traction in Eo-EFTR of SMTs in the gastric fundus is limited. Between April 2018 and December 2021, Eo-EFTR with clip- and snare-assisted traction was performed in 20 patients with SMTs in the gastric fundus at The First Affiliated Hospital of Soochow University. The relevant clinical data were collected retrospectively for all of the patients and analysed. All 20 patients underwent Eo-EFTR successfully without conversion to open surgery or severe adverse events. The en bloc resection rate and R0 resection rate were both 100%. Two patients had abdominal pain and fever after the operation, and five patients had fever, which recovered with medical therapy. No complications, such as delayed bleeding or delayed perforation, were observed. The postoperative pathology indicated that 19 cases were gastrointestinal stromal tumours and one case was leiomyoma. During the follow-up, no residual tumour, local recurrence or distant metastasis was detected by endoscopy or abdominal computed tomography. In conclusion, Eo-EFTR with clip- and snare-assisted traction appears to be a relatively safe and effective treatment for gastric SMTs in the fundus. However, prospective studies on a larger sample size are required to verify the effect of the clip- and snare-assisted traction in Eo-EFTR.

Intestinal Barrier Function in the Pathogenesis of Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide. The mechanisms involved in NAFLD onset are complicated and multifactorial. Recent literature has indicated that altered intestinal barrier function is related to the occurrence and progression of liver disease. The intestinal barrier is important for absorbing nutrients and electrolytes and for defending against toxins and antigens in the enteric environment. Major mechanisms by which the intestinal barrier influences the development of NAFLD involve the altered epithelial layer, decreased intracellular junction integrity, and increased intestinal barrier permeability. Increased intestinal permeability leads to luminal dysbiosis and allows the translocation of pathogenic bacteria and metabolites into the liver, inducing inflammation, immune response, and hepatocyte injury in NAFLD. Although research has been directed to NAFLD in recent decades, the pathophysiological changes in NAFLD initiation and progression are still not completely understood, and the therapeutic targets remain limited. A deeper understanding on the correlation between NAFLD pathogenesis and intestinal barrier regulation must be attained. Therefore, in this review, the components of the intestinal barrier and their respective functions and disruptions during the progression of NAFLD are discussed.

Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network-Based Deep Learning Models.

Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images. In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs) or transformer architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou were randomly split into a training set (n = 2140) and an internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Model performance was assessed by the metrics accuracy, recall, and specificity and was compared with the assessments of two radiologists. A total of 2700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by the EfficientNet model (0.954). The recall and the precision of the Swin model were 0.989 and 1.000, respectively. In the test dataset, the Swin model was still the best and achieved the highest accuracy (0.980). All the DL models performed remarkably better than the two experts. Last, the time on the test set diagnosis spent by two experts-42 min, 17 s (junior); and 29 min, 43 s (senior)-was significantly higher than those of the DL models (all below 2 min). This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found that a transformer-based model, the Swin model, performed best.

Dual-Targeted Nanodiscs Revealing the Cross-Talk between Osteogenic Differentiation of Mesenchymal Stem Cells and Macrophages.

Ongoing research has highlighted the significance of the cross-play of macrophages and mesenchymal stem cells (MSCs). Lysine-specific demethylase 6B (KDM6B) has been shown to control osteogenic differentiation of MSCs by depleting trimethylated histone 3 lysine 27 (H3K27me3). However, to date, the role of KDM6B in bone marrow-derived macrophages (BMDMs) remains controversial. Here, a chromatin immunoprecipitation assay (ChIP) proved that KDM6B derived from osteogenic-induced BMSCs could bind to the promoter region of BMDMs' brain and muscle aryl hydrocarbon receptor nuclear translocator-like protein-1 (BMAL1) gene in a coculture system and activate BMAL1. Transcriptome sequencing and experiments in vitro showed that the overexpression of BMAL1 in BMDM could inhibit the TLR2/NF-κB signaling pathway, reduce pyroptosis, and decrease the M1/M2 ratio, thereby promoting osteogenic differentiation of BMSCs. Furthermore, bone and macrophage dual-targeted GSK-J4 (KDM6B inhibitor)-loaded nanodiscs were synthesized via binding SDSSD-apoA-1 peptide analogs (APA) peptide, which indirectly proved the critical role of KDM6B in osteogenesis in vivo. Overall, we demonstrated that KDM6B serves as a positive circulation trigger during osteogenic differentiation by decreasing the ratio of M1/M2 both in vitro and in vivo. Collectively, these results provide insight into basic research in the field of osteoporosis and bone repair.

Automated Multimodal Machine Learning for Esophageal Variceal Bleeding Prediction Based on Endoscopy and Structured Data.

Esophageal variceal (EV) bleeding is a severe medical emergency related to cirrhosis. Early identification of cirrhotic patients with at a high risk of EV bleeding is key to improving outcomes and optimizing medical resources. This study aimed to evaluate the feasibility of automated multimodal machine learning (MMML) for predicting EV bleeding by integrating endoscopic images and clinical structured data. This study mainly includes three steps: step 1, developing deep learning (DL) models using EV images by 12-month bleeding on TensorFlow (backbones include ResNet, Xception, EfficientNet, ViT and ConvMixer); step 2, training and internally validating MMML models integrating clinical structured data and DL model outputs to predict 12-month EV bleeding on an H2O-automated machine learning platform (algorithms include DL, XGBoost, GLM, GBM, RF, and stacking); and step 3, externally testing MMML models. Furthermore, existing clinical indices, e.g., the MELD score, Child‒Pugh score, APRI, and FIB-4, were also examined. Five DL models were transfer learning to the binary classification of EV endoscopic images at admission based on the occurrence or absence of bleeding events during the 12-month follow-up. An EfficientNet model achieved the highest accuracy of 0.868 in the validation set. Then, a series of MMML models, integrating clinical structured data and the output of the EfficientNet model, were automatedly trained to predict 12-month EV bleeding. A stacking model showed the highest accuracy (0.932), sensitivity (0.952), and F1-score (0.879) in the test dataset, which was also better than the existing indices. This study is the first to evaluate the feasibility of automated MMML in predicting 12-month EV bleeding based on endoscopic images and clinical variables.

Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis.

OBJECTIVE: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. METHODS: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow University between 2014 and 2020. Participants were divided into training and validation datasets at a ratio of 8.5:1.5. Models were developed on the H2O AutoML platform in the training dataset, and then were evaluated in the validation dataset by area under receiver operating characteristic curves (AUC). The best AutoML model was interpreted by SHapley Additive exPlanation (SHAP) Plot, Partial Dependence Plots (PDP), and Local Interpretable Model Agnostic Explanation (LIME). RESULTS: The model, based on the extreme gradient boosting (XGBoost) algorithm, performed better (AUC 0.888) than the other AutoML models (logistic regression 0.673, gradient boost machine 0.886, random forest 0.866, deep learning 0.830, stacking 0.850), as well as the existing scorings (the model of end-stage liver disease [MELD] score 0.778, MELD-Na score 0.782, and albumin-bilirubin [ALBI] score 0.662). The most key variable in the XGBoost model was high-density lipoprotein cholesterol, followed by creatinine, white blood cell count, international normalized ratio, etc. Conclusion: The AutoML model based on the XGBoost algorithm presented better performance than the existing scoring systems for predicting 30-day mortality in patients with non-cholestatic cirrhosis. It shows the promise of AutoML in its future medical application.

The Development of a Prediction Model Based on Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based Study.

Accurate prediction for the prognosis of patients with pancreatic cancer (PC) is a emerge task nowadays. We aimed to develop survival models for postoperative PC patients, based on a novel algorithm, random survival forest (RSF), traditional Cox regression and neural networks (Deepsurv), using the Surveillance, Epidemiology, and End Results Program (SEER) database. A total of 3988 patients were included in this study. Eight clinicopathological features were selected using least absolute shrinkage and selection operator (LASSO) regression analysis and were utilized to develop the RSF model. The model was evaluated based on three dimensions: discrimination, calibration, and clinical benefit. It found that the RSF model predicted the cancer-specific survival (CSS) of the postoperative PC patients with a c-index of 0.723, which was higher than the models built by Cox regression (0.670) and Deepsurv (0.700). The Brier scores at 1, 3, and 5 years (0.188, 0.177, and 0.131) of the RSF model demonstrated the model's favorable calibration and the decision curve analysis illustrated the model's value of clinical implement. Moreover, the roles of the key variables were visualized in the Shapley Additive Explanations plotting. Lastly, the prediction model demonstrates value in risk stratification and individual prognosis. In this study, a high-performance prediction model for PC postoperative prognosis was developed, based on RSF The model presented significant strengths in the risk stratification and individual prognosis prediction.

HDL-C levels added to the MELD score improves 30-day mortality prediction in Asian patients with cirrhosis.

OBJECTIVES: Lower high-density lipoprotein cholesterol (HDL-C) levels have been observed in chronic liver disease patients. The aim of this study was to develop a model that incorporates HDL-C levels and the Model for End-stage Liver Disease (MELD) score to predict 30-day mortality in Asian cirrhosis patients. METHODS: Cirrhosis patients were recruited from two hospitals in this retrospective observational study. Propensity score matching was used. The model's performance was evaluated, including its ability to predict 30-day mortality, accuracy, and clinical utility. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated. RESULTS: The HDL-C + MELD model showed good ability to predict 30-day mortality (area under the curve, 0.784; sensitivity, 0.797; specificity, 0.632), which was better than that of the MELD score alone. It also showed good calibration and a net benefit for all patients, which was better than that of the MELD score, except at the threshold probability. NRI and IDI results confirmed that adding HDL-C levels to the MELD score improved the model's performance in predicting 30-day mortality. CONCLUSION: We added HDL-C levels to the MELD score to predict 30-day mortality in Asian patients with cirrhosis. The HDLC + MELD model shows good ability to predict 30-day mortality and clinical utility.

Injectable Bacteria-Sensitive Hydrogel Promotes Repair of Infected Fractures via Sustained Release of miRNA Antagonist.

Fracture nonunion can result in considerable physical harm and limitation of quality of life in patients, exerting an extensive economic burden to the society. Nonunion largely results from unresolved inflammation and impaired osteogenesis. Despite advancements in surgical techniques, the indispensable treatment for nonunion is robust anti-inflammation therapy and the promotion of osteogenic differentiation. Herein, we report that plasma exosomes derived from infected fracture nonunion patients (Non-Exos) delayed fracture repair in mice by inhibiting the osteogenic differentiation of bone marrow stromal cells in vivo and in vitro. Unique molecular identifier microRNA-sequencing (UID miRNA-seq) suggested that microRNA-708-5p (miR-708-5p) was overexpressed in Non-Exos. Mechanistically, miR-708-5p targeted structure-specific recognition protein 1, thereby suppressing the Wnt/ß-catenin signaling pathway, which, in turn, impaired osteogenic differentiation. AntagomicroRNA-708-5p (antagomiR-708-5p) could partly reverse the above process. A bacteria-sensitive natural polymer hyaluronic-acid-based hydrogel (HA hydrogel) loaded with antagomiR-708-5p exhibited promising effects in an in vivo study through antibacterial and pro-osteogenic differentiation functions in infected fractures. Overall, the effectiveness and reliability of an injectable bacteria-sensitive hydrogel with sustained release of agents represent a promising approach for infected fractures.

MiR-1224-5p modulates osteogenesis by coordinating osteoblast/osteoclast differentiation via the Rap1 signaling target ADCY2.

MicroRNAs (miRNAs) broadly regulate normal biological functions of bone and the progression of fracture healing and osteoporosis. Recently, it has been reported that miR-1224-5p in fracture plasma is a potential therapy for osteogenesis. To investigate the roles of miR-1224-5p and the Rap1 signaling pathway in fracture healing and osteoporosis development and progression, we used BMMs, BMSCs, and skull osteoblast precursor cells for in vitro osteogenesis and osteoclastogenesis studies. Osteoblastogenesis and osteoclastogenesis were detected by ALP, ARS, and TRAP staining and bone slice resorption pit assays. The miR-1224-5p target gene was assessed by siRNA-mediated target gene knockdown and luciferase reporter assays. To explore the Rap1 pathway, we performed high-throughput sequencing, western blotting, RT-PCR, chromatin immunoprecipitation assays and immunohistochemical staining. In vivo, bone healing was judged by the cortical femoral defect, cranial bone defect and femoral fracture models. Progression of osteoporosis was evaluated by an ovariectomy model and an aged osteoporosis model. We discovered that the expression of miR-1224-5p was positively correlated with fracture healing progression. Moreover, in vitro, overexpression of miR-1224-5p slowed Rankl-induced osteoclast differentiation and promoted osteoblast differentiation via the Rap1-signaling pathway by targeting ADCY2. In addition, in vivo overexpression of miR-1224-5p significantly promoted fracture healing and ameliorated the progression of osteoporosis caused by estrogen deficiency or aging. Furthermore, knockdown of miRNA-1224-5p inhibited bone regeneration in mice and accelerated the progression of osteoporosis in elderly mice. Taken together, these results identify miR-1224-5p as a key bone osteogenic regulator, which may be a potential therapeutic target for osteoporosis and fracture nonunion.

Automated Machine Learning for the Early Prediction of the Severity of Acute Pancreatitis in Hospitals.

Background: Machine learning (ML) algorithms are widely applied in building models of medicine due to their powerful studying and generalizing ability. This study aims to explore different ML models for early identification of severe acute pancreatitis (SAP) among patients hospitalized for acute pancreatitis. Methods: This retrospective study enrolled patients with acute pancreatitis (AP) from multiple centers. Data from the First Affiliated Hospital and Changshu No. 1 Hospital of Soochow University were adopted for training and internal validation, and data from the Second Affiliated Hospital of Soochow University were adopted for external validation from January 2017 to December 2021. The diagnosis of AP and SAP was based on the 2012 revised Atlanta classification of acute pancreatitis. Models were built using traditional logistic regression (LR) and automated machine learning (AutoML) analysis with five types of algorithms. The performance of models was evaluated by the receiver operating characteristic (ROC) curve, the calibration curve, and the decision curve analysis (DCA) based on LR and feature importance, SHapley Additive exPlanation (SHAP) Plot, and Local Interpretable Model Agnostic Explanation (LIME) based on AutoML. Results: A total of 1,012 patients were included in this study to develop the AutoML models in the training/validation dataset. An independent dataset of 212 patients was used to test the models. The model developed by the gradient boost machine (GBM) outperformed other models with an area under the ROC curve (AUC) of 0.937 in the validation set and an AUC of 0.945 in the test set. Furthermore, the GBM model achieved the highest sensitivity value of 0.583 among these AutoML models. The model developed by eXtreme Gradient Boosting (XGBoost) achieved the highest specificity value of 0.980 and the highest accuracy of 0.958 in the test set. Conclusions: The AutoML model based on the GBM algorithm for early prediction of SAP showed evident clinical practicability.

Development of a Deep Learning Model for Malignant Small Bowel Tumors Survival: A SEER-Based Study.

Background This study aims to explore a deep learning (DL) algorithm for developing a prognostic model and perform survival analyses in SBT patients. Methods The demographic and clinical features of patients with SBTs were extracted from the Surveillance, Epidemiology and End Results (SEER) database. We randomly split the samples into the training set and the validation set at 7:3. Cox proportional hazards (Cox-PH) analysis and the DeepSurv algorithm were used to develop models. The performance of the Cox-PH and DeepSurv models was evaluated using receiver operating characteristic curves, calibration curves, C-statistics and decision-curve analysis (DCA). A Kaplan−Meier (K−M) survival analysis was performed for further explanation on prognostic effect of the Cox-PH model. Results The multivariate analysis demonstrated that seven variables were associated with cancer-specific survival (CSS) (all p < 0.05). The DeepSurv model showed better performance than the Cox-PH model (C-index: 0.871 vs. 0.866). The calibration curves and DCA revealed that the two models had good discrimination and calibration. Moreover, patients with ileac malignancy and N2 stage disease were not responding to surgery according to the K−M analysis. Conclusions This study reported a DeepSurv model that performed well in CSS in SBT patients. It might offer insights into future research to explore more DL algorithms in cohort studies.

Antioxidant Therapy and Antioxidant-Related Bionanomaterials in Diabetic Wound Healing.

Ulcers are a lower-extremity complication of diabetes with high recurrence rates. Oxidative stress has been identified as a key factor in impaired diabetic wound healing. Hyperglycemia induces an accumulation of intracellular reactive oxygen species (ROS) and advanced glycation end products, activation of intracellular metabolic pathways, such as the polyol pathway, and PKC signaling leading to suppression of antioxidant enzymes and compounds. Excessive and uncontrolled oxidative stress impairs the function of cells involved in the wound healing process, resulting in chronic non-healing wounds. Given the central role of oxidative stress in the pathology of diabetic ulcers, we performed a comprehensive review on the mechanism of oxidative stress in diabetic wound healing, focusing on the progress of antioxidant therapeutics. We summarize the antioxidant therapies proposed in the past 5 years for use in diabetic wound healing, including Nrf2- and NFκB-pathway-related antioxidant therapy, vitamins, enzymes, hormones, medicinal plants, and biological materials.