Search Results (570)

Background: Diabetic foot ulcers (DFUs) are a serious complication of diabetes and are often difficult to treat. Hyperbaric oxygen therapy (HBOT) has been proposed as an adjunctive treatment to promote healing, but its long-term clinical and biological effects remain insufficiently characterized. This study aimed to evaluate the impact of HBOT on systemic biomarkers, local microvasculature, and clinical outcomes in patients with DFUs. Methods: In this non-randomized prospective study, 20 patients with ischemic DFUs were followed over a 36-month period. Fourteen received HBOT in addition to standard care, while six received standard care alone. Clinical outcomes—including DFU resolution, recurrence, lower extremity amputation (LEA), and mortality—were assessed alongside systemic inflammatory and angiogenic biomarkers and wound characteristics at baseline and at 3, 6, 12, and 36 months. CD31 immunostaining was performed on available tissue samples. Results: The two groups were comparable at baseline (mean age 62 ± 12 years; diabetes duration 18 ± 9 years). At 3 months, the HBOT group showed significant reductions in erythrocyte sedimentation rate and DFU size (p < 0.05), with downward trends observed in C-reactive protein (CRP), vascular endothelial growth factor (VEGF), and placental growth factor (PlGF), and an increase in stromal-derived factor-1 alpha (SDF1-α). No significant changes were observed in the control group. CD31+ microvessel density appeared to increase in HBOT-treated DFU tissue after one month, although the sample size was limited. Patients receiving HBOT had lower rates of LEA and mortality, improved wound healing, and sustained outcomes over three years. DFU recurrence rates were similar between groups. Conclusions: HBOT was associated with improved wound healing and favorable biomarker profiles in patients with treatment-resistant ischemic DFUs. While these findings are encouraging, the small sample size and non-randomized design limit their generalizability, highlighting the need for larger, controlled studies. Full article

Objective: To develop and validate a ResNet-50-based deep learning model for automatic detection of osteomyelitis (DFO) in plain radiographs of patients with diabetic foot ulcers (DFUs). Research Design and Methods: This retrospective study included 168 patients with type one or type two diabetes and clinical suspicion of DFO confirmed via a surgical bone biopsy. An experienced clinician and a pretrained ResNet-50 model independently interpreted the radiographs. The model was developed using Python-based frameworks with ChatGPT assistance for coding. The diagnostic performance was assessed against the histopathological findings, calculating sensitivity, specificity, the positive predictive value (PPV), the negative predictive value (NPV), and the likelihood ratios. Agreement between the AI model and the clinician was evaluated using Cohen’s kappa coefficient. Results: The AI model demonstrated high sensitivity (92.8%) and PPV (0.97), but low-level specificity (4.4%). The clinician showed 90.2% sensitivity and 37.8% specificity. The Cohen’s kappa coefficient between the AI model and the clinician was −0.105 (p = 0.117), indicating weak agreement. Both the methods tended to classify many cases as DFO-positive, with 81.5% agreement in the positive cases. Conclusions: This study demonstrates the potential of IA to support the radiographic diagnosis of DFO using a ResNet-50-based deep learning model. AI-assisted radiographic interpretation could enhance early DFO detection, particularly in high-prevalence settings. However, further validation is necessary to improve its specificity and assess its utility in primary care. Full article

The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical images of diabetic feet, thereby aiding in the prevention and effective treatment of foot ulcers. A comprehensive study was conducted using an annotated dataset of medical images, evaluating the performance of the models in terms of accuracy, precision, recall and F1-score. VGG19 achieved the highest accuracy at 97%, demonstrating superior ability to focus activations on relevant lesion areas in complex images. MobileNetV2, while slightly less accurate, excelled in computational efficiency, making it a suitable choice for mobile devices and environments with hardware constraints. The study also highlights the limitations of each architecture, such as increased risk of overfitting in deeper models and the lower capability of MobileNetV2 to capture fine clinical details. These findings suggest that CNNs hold significant potential in computer-aided clinical diagnosis, particularly in the early and precise detection of diabetic foot ulcers, where timely intervention is crucial to prevent amputations. Full article

Diabetic foot ulcers (DFUs) are a serious complication of diabetes, associated with high recurrence and amputation rates. Adherence to offloading devices is critical for wound healing but remains inadequately monitored in real-world settings. This study evaluates the SmartBoot edge-computing system—a wearable, real-time remote monitoring solution integrating an inertial measurement unit (Sensoria Core) and smartwatch—for its validity in quantifying cadence and step count as digital biomarkers of frailty, and for detecting adherence. Twelve healthy adults wore two types of removable offloading boots (Össur and Foot Defender) during walking tasks at varied speeds; system outputs were validated against a gold-standard wearable and compared with staff-recorded adherence logs. Additionally, user experience was assessed using the Technology Acceptance Model (TAM) in healthy participants (n = 12) and patients with DFU (n = 81). The SmartBoot demonstrated high accuracy in cadence and step count across conditions (bias < 5.5%), with an adherence detection accuracy of 96% (Össur) and 97% (Foot Defender). TAM results indicated strong user acceptance and perceived ease of use across both cohorts. These findings support the SmartBoot system’s potential as a valid, scalable solution for real-time remote monitoring of adherence and mobility in DFU management. Further clinical validation in ongoing studies involving DFU patients is underway. Full article

Background/Objectives: We evaluated the diabetic foot ulcer (DFU) microbiome in clinical situations identified as risk factors for a worse outcome and explored the roles of the most abundant microorganisms. Methods: A prospective multicenter cohort of diabetic patients with DFU were followed up for 6 months. We obtained a DFU tissue biopsy for microbiome analysis at the baseline visit. Genomic DNA was extracted (QIAamp DNA Mini Kit, Qiagen, Hilden, Germany) and quantified (QuantiFluor dsDNA System, Promega, Madison, WI, USA), with analysis of bacterial communities focusing on relative abundances (RA) and on alpha and beta diversity. Results: Overall, 59 DFUs were analyzed. DFUs of long duration (≥4 weeks) presented a higher RA of Gammaproteobacteria compared with ulcers of short duration (p = 0.02). Non-infected DFUs had a higher proportion of Actinobacteriota phyla than infected DFUs and, particularly, a higher RA of Corynebacterium genera (means ± SD: 0.063 ± 0.14 vs. 0.028 ± 0.13, respectively; p = 0.03). Regarding the pathogenic role of Staphylococcus aureus, DFUs with low S. aureus bacterial loads (<10⁶ CFU/mL) compared with those with high loads (≥10⁶ CFU/mL) showed a higher Corynebacterium RA (0.045 ± 0.08 vs. 0.003 ± 0.01, respectively; p = 0.01). Conclusions: In clinical situations associated with poor DFU outcomes, we observed a predominance of Gammaproteobacteria in the microbiome of long-duration ulcers and a higher RA of Corynebacterium in non-infected DFUs. An inverse relationship between the predominance of Corynebacterium and the S. aureus bacterial load in DFUs was also noted, which may suggest these commensals have a modulatory role. Further studies should explore the clinical utility of microbiome analysis for DFUs. Full article

Diabetic foot ulcers (DFUs), which affect approximately 15% of individuals with diabetes mellitus (DM), result from complex molecular disturbances involving chronic hyperglycemia, immune dysfunction, and infection. At the molecular level, chronic hyperglycemia promotes the formation of advanced glycation end products (AGEs), activates the AGE-RAGE-NF-κB axis, increases oxidative stress, and impairs macrophage polarization from the pro-inflammatory M1 to the reparative M2 phenotype, collectively disrupting normal wound healing processes. The local wound environment is further worsened by antibiotic-resistant polymicrobial infections, which sustain inflammatory signaling and promote extracellular matrix degradation. The rising threat of antimicrobial resistance complicates infection management even further. Recent studies emphasize that optimal glycemic control using antihyperglycemic agents such as metformin, Glucagon-like Peptide 1 receptor agonists (GLP-1 receptor agonists), and Dipeptidyl Peptidase 4 enzyme inhibitors (DPP-4 inhibitors) improves overall metabolic balance. These agents also influence angiogenesis, inflammation, and tissue regeneration through pathways including AMP-activated protein kinase (AMPK), mechanistic target of rapamycin (mTOR), and vascular endothelial growth factor (VEGF) signaling. Evidence indicates that maintaining glycemic stability through continuous glucose monitoring (CGM) and adherence to antihyperglycemic treatment enhances antibiotic effectiveness by improving immune cell function and reducing bacterial virulence. This review consolidates current molecular evidence on the combined effects of glycemic and antibiotic therapies in DFUs. It advocates for an integrated approach that addresses both metabolic and microbial factors to restore wound homeostasis and minimize the risk of severe outcomes such as amputation. Full article

Background/Objectives: Diabetic foot (DF) is among the leading causes of diabetes-related disability. It is important to maintain regular follow-up and patient education in the prevention and treatment of DF ulcers. In extraordinary situations such as a pandemic, there are disruptions in regular clinical follow-up and patient education, and the effects of this disruption need to be investigated. The aim of this study was to investigate the impact of the pandemic on the clinical condition of patients hospitalised for DF. Methods: Patients were divided into two groups according to the date of admission to the clinic: the pre-pandemic (1 January 2019–11 March 2020) and the pandemic period (12 March 2020–1 June 2021). Comparisons were made between the two groups in terms of DF data and clinical parameters. Data were analysed with SPSS using chi-square, Student’s t-test and Mann–Whitney U analysis. Results: As a result of the screening, data from 125 DF patients (45 pre-pandemic and 80 pandemic) were collected. The DF stage, according to the Wagner classification, was significantly more advanced in patients during the pandemic period (p = 0.015). However, the time between the onset of symptoms and hospitalisation was longer for patients during the pandemic period (p = 0.035). When analysing treatment outcomes, the rate of wound healing was found to be lower (62.2% vs. 30%), and the rate of transtibial amputation was higher (11.2% vs. 20%) during the pandemic period (p = 0.002). Conclusions: This study found that the number of patients hospitalised for DF increased during the pandemic period, as did the severity of the wound, length of admission and radical treatment interventions. Full article

The global healthcare system faces significant challenges posed by diabetes and its complications, highlighting the need for innovative strategies to improve early diagnosis and treatment. Machine learning models help in the early detection of diseases and recommendations for taking safety measures and treating the disease. A comparative analysis of existing machine learning (ML) models is necessary to identify the most suitable model while uniformly fixing the model parameters. Assessing risk based on biomarker measurement and computing overall risk is important for accurate prediction. Early prediction of complications that may arise, based on the risk of diabetes and biomarkers, using machine learning models, is key to helping patients. In this paper, a comparative model is presented to evaluate ML models based on common model characteristics. Additionally, a risk assessment model and a prediction model are presented to help predict the occurrence of complications. Random Forest (RF) is the best model for predicting the occurrence of Type 2 Diabetes (T2D) based on biomarker input. It has also been shown that the prediction of diabetes complications using neural networks is highly accurate, reaching a level of 98%. Full article

Background: Diabetic foot disease is a public health problem. The challenges of its management lie in the complexity of wound healing and, in particular, the high rate of lesion recurrence. Objectives: The primary objective of the study was to evaluate whether optimized post-healing follow-up by a multidisciplinary team can reduce the recurrence rate of foot ulcers in people living with diabetes. The secondary objectives were to assess patient needs in terms of hospitalization for recurrence, the number of amputations, pedicure care, and the use of adapted footwear. Participants: The study included 129 patients with diabetes presenting a healed foot ulcer. A total of 38 patients underwent an annual post-healing follow-up visit with a multidisciplinary team (optimized follow-up), while 91 had a visit every 2 years (minimum follow-up). Results: Of the 38 patients with optimal follow-up, 8 presented a wound recurrence (21.1%) compared with 38 out of 91 patients (41.8%) receiving minimum follow-up. The recurrence rate decreased significantly between the two groups (p < 0.05). The use of adapted shoes was also significantly better in the group with optimized follow-up (p = 0.02). Conclusions: Regular post-healing follow-up with a multidisciplinary team seems to be a contributing factor to reducing the recurrence of diabetic foot ulcers among people living with diabetes. Full article

Although emerging evidence suggests that epigenetic mechanisms contribute to the pathogenesis and progression of type 2 diabetes mellitus (T2DM), data remain limited for patients with suboptimal metabolic control. The aim of this study was to assess global DNA methylation in patients with poorly controlled T2DM and to identify diabetes-related factors associated with DNA methylation levels. The study included 107 patients and 50 healthy controls. Global DNA methylation (5mC) was measured by UHPLC-DAD method. Pro-oxidant and antioxidant biomarkers, advanced glycation end-products, high-sensitivity C-reactive protein (hsCRP) and complete blood count were determined and leukocyte indices calculated. Patients had a significantly lower 5mC than controls (3.56 ± 0.31% vs. 4.00 ± 0.68%; p < 0.001), with further reductions observed in those with longer disease duration and diabetic foot ulcers. Oxidative stress and inflammatory biomarkers were higher in the patient group. DNA hypomethylation was associated with a higher monocyte-to-lymphocyte ratio and hsCRP, pro-oxidant–antioxidant balance, ischemia-modified albumin, and advanced oxidation protein products levels. Conversely, 5mC levels showed positive correlations with total antioxidant status and total sulfhydryl groups. Principal component analysis identified five key factors: proinflammatory, pro-oxidant, aging, hyperglycemic, and antioxidant. The pro-oxidant factor emerged as the sole independent predictor of global DNA hypomethylation in T2DM (OR = 2.294; p = 0.027). Our results indicate that global DNA hypomethylation could be a biomarker of T2DM progression, reflecting the complex interactions between oxidative stress, inflammation, and epigenetic modifications in T2DM. Full article

Diabetic foot ulcers (DFUs) represent a critical global health issue, necessitating the development of advanced smart, flexible, and wearable sensors for continuous monitoring that are reimbursable within foot orthotics. This study presents the design and characterization of a pressure sensor implemented into a shoe insole to monitor diabetic wound pressures, emphasizing the need for a high sensitivity, durability under cyclic mechanical loading, and a rapid response time. This investigation focuses on the electrical and mechanical properties of carbon nanotube (CNT) composites utilizing Ecoflex and polydimethylsiloxane (PDMS). Morphological characterization was conducted using Transmission Electron Microscopy (TEM), Laser Confocal Microscopy, and Scanning Electron Microscopy (SEM). The electrical and mechanical properties of the CNT/Ecoflex- and the CNT/PDMS-based sensor composites were then investigated. CNT/Ecoflex was then further evaluated due to its lower variability performance between cycles at the same pressure, as well as its consistently higher capacitance values across all trials in comparison to CNT/PDMS. The CNT/Ecoflex composite sensor showed a high sensitivity (2.38 to 3.40 kPa⁻¹) over a pressure sensing range of 0 to 68.95 kPa. The sensor’s stability was further assessed under applied pressures simulating human weight. A custom insole prototype, incorporating 12 CNT/Ecoflex elastomeric matrix-based sensors (as an example) distributed across the metatarsal heads, midfoot, and heel regions, was developed and characterized. Capacitance measurements, ranging from 0.25 pF to 60 pF, were obtained across N = 3 feasibility trials, demonstrating the sensor’s response to varying pressure conditions linked to different body weights. These results highlight the potential of this flexible insole prototype for precise and real-time plantar surface monitoring, offering an approachable avenue for a challenging diabetic orthotics application. Full article

Background: Diabetic foot ulcers (DFUs) are a severe complication of diabetes and are characterized by impaired wound healing and a high amputation risk. Exosomes—which are nanovesicles carrying proteins, RNAs, and lipids—mediate intercellular communication in wound microenvironments, yet their biomarker potential in DFUs remains underexplored. Methods: We analyzed transcriptomic data from GSE134431 (13 DFU vs. 8 controls) as a training set and validated findings in GSE80178 (6 DFU vs. 3 controls). A sum of 7901 differentially expressed genes (DEGs) of DFUs were detected and intersected with 125 literature-curated exosome-related genes (ERGs) to yield 51 candidates. This was followed by GO/KEGG analyses and a PPI network construction. Support vector machine–recursive feature elimination (SVM-RFE) and the Boruta random forest algorithm distilled five biomarkers (DIS3L, EXOSC7, SDC1, STX11, SYT17). Expression trends were confirmed in both datasets. Analyses included nomogram construction, functional and correlation analyses, immune infiltration, GSEA, gene co-expression and regulatory network construction, drug prediction, molecular docking, and RT-qPCR validation in clinical samples. Results: A nomogram combining these markers achieved an acceptable calibration (Hosmer–Lemeshow p = 0.0718, MAE = 0.044). Immune cell infiltration (CIBERSORT) revealed associations between biomarker levels and NK cell and neutrophil subsets. Gene set enrichment analysis (GSEA) implicated IL-17 signaling, proteasome function, and microbial infection pathways. A GeneMANIA network highlighted RNA processing and vesicle trafficking. Transcription factor and miRNA predictions uncovered regulatory circuits, and DGIdb-driven drug repurposing followed by molecular docking identified Indatuximab ravtansine and heparin as high-affinity SDC1 binders. Finally, RT-qPCR validation in clinical DFU tissues (n = 5) recapitulated the bioinformatic expression patterns. Conclusions: We present five exosome-associated genes as novel DFU biomarkers with diagnostic potential and mechanistic links to immune modulation and vesicular transport. These findings lay the groundwork for exosome-based diagnostics and therapeutic targeting in DFU management. Full article

Background/Objectives: Advanced wound healing biologics for diabetic foot ulcer (DFU) are typically withheld from persons who are at high risk for amputation. However, a prospective, single-center cohort study evaluated the use of an advanced biologic, dehydrated amniotic (DAMA) tissue as early treatment for DFUs in patients with a high risk for amputation, demonstrating benefit for a small sample. This is the report of the five-year follow-up of those high-risk participants. Methods: This chart review provides a 5-year follow-up of 18 of 20 participants in the original study. The data were collected by medical record review. Specific data points included mortality, re-ulceration and additional ulceration, amputation (minor and major), end-stage renal disease with dialysis dependence, hospitalization, and limb-threatening ischemia. Results: The 5-year mortality rate from the time of wound healing was 50% (9/18 deceased). Four of the eighteen participants (22.2%) underwent major amputation within 5 years of study completion. Two had amputations of the study limb and two had amputations of the contralateral limb. Fifty percent (2/4) of those who had amputations died within 5 years after the major amputation. Over fifty percent (55.5% or 10 out of 18) of the participants experienced the re-ulceration of the original study ulcer and 94% (17 out of 18) developed a new site ulceration. A total of 25% of the hospitalizations over the 5 years were related to DFU (infection, osteomyelitis, and sepsis). Conclusions: This small-sample 5-year follow-up shows that early treatment with dehydrated amniotic (DAMA) tissue in patients with diabetic foot ulcers of moderate-to-high amputation risk results in similar outcomes as noted in the current research on patients with low risk for amputation. In fact, this paper may suggest that advanced biologics can safely be used for early treatment in moderate-to-high amputation risk without increasing mortality and amputation over 5 years. Full article

Objective: This study aims to elucidate the potential mechanisms by which Fespixon cream promotes diabetic foot ulcer (DFU) healing using network pharmacology, molecular docking, and RT-qPCR validation in clinical tissue samples. Methods: Active components of Fespixon cream were screened from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and relevant literature, and their corresponding targets were standardized using the Universal Protein Resource (UniProt) database. Diabetic foot ulcer (DFU)-related targets were retrieved and filtered from the GeneCards database and the Online Mendelian Inheritance in Man (OMIM) database. The intersection of drug and disease targets was identified, and a protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The interaction network was visualized using Cytoscape version 3.7.2 software. The potential mechanisms of the shared targets were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using R software packages, and results were visualized through Bioinformatics online tools. Molecular docking was performed to validate the binding between key active compounds of Fespixon cream and core DFU targets using AutoDock Vina version 1.1.2 and PyMOL software. Furthermore, RT-qPCR analysis was performed on wound edge tissue samples from DFU patients treated with Fespixon cream to experimentally verify the mRNA expression levels of predicted hub genes. Results: Network pharmacology analysis identified eight active compounds in Fespixon cream, along with 153 potential therapeutic targets related to diabetic foot ulcer (DFU). Among these, 21 were determined as core targets, with the top five ranked by degree value being RAC-αserine/threonine-protein kinase (AKT1), Cellular tumor antigen p53 (TP53), Tumor necrosis factor (TNF), Interleukin-6 (IL6), and Mitogen-activated protein kinase 1 (MAPK1). GO enrichment analysis indicated that the targets of Fespixon cream were primarily involved in various biological processes related to cellular stress responses. KEGG pathway enrichment revealed that these targets were significantly enriched in pathways associated with diabetic complications, atherosclerosis, inflammation, and cancer. Molecular docking confirmed stable binding interactions between the five major active compounds—quercetin, apigenin, rosmarinic acid, salvigenin, and cirsimaritin—and the five core targets (AKT1, TP53, TNF, IL6, MAPK1). Among them, quercetin exhibited the strongest binding affinity with AKT1. RT-qPCR validation in clinical DFU tissue samples demonstrated consistent expression trends with computational predictions: AKT1 was significantly upregulated, while TP53, TNF, IL6, and MAPK1 were markedly downregulated in the Fespixon-treated group compared to controls (p < 0.001), supporting the proposed multi-target therapeutic mechanism. Conclusions: Our study reveals the potential mechanisms by which Fespixon cream exerts therapeutic effects on DFUs. The efficacy of Fespixon cream in treating DFUs is attributed to the synergistic actions of its bioactive components through multiple targets and multiple signaling pathways. Full article

Chronic wounds, including diabetic foot ulcers and pressure sores, are becoming more prevalent due to aging populations and increased metabolic problems. These wounds often persist due to impaired healing, chronic inflammation, oxidative stress, and infections caused by multidrug-resistant pathogens, making conventional treatments—including antibiotics and antiseptics—largely inadequate. This creates an urgent need for advanced, biologically responsive therapies that can both combat infection and promote tissue regeneration. Probiotics have surfaced as a viable option owing to their capacity to regulate immune responses, impede pathogenic biofilms, and generate antibacterial and antioxidant metabolites. However, their clinical application is limited by poor viability, sensitivity to environmental conditions, and short retention at wound sites. Nanotechnology-based delivery systems address these limitations by protecting probiotics from degradation, enhancing site-specific delivery, and enabling controlled, stimuli-responsive release. Encapsulation techniques using materials like chitosan, PLGA, liposomes, nanogels, nanofibers, and microneedles have shown significant success in improving wound healing outcomes in preclinical and clinical models. This review summarizes the current landscape of chronic wound challenges and presents recent advances in probiotic-loaded nanotechnologies. It explores various nano-delivery systems, their mechanisms of action, biological effects, and therapeutic outcomes, highlighting the synergy between probiotics and nanocarriers as a novel, multifaceted strategy for managing chronic wounds. Full article