Innovative Functional Biomaterials as Therapeutic Wound Dressings for Chronic Diabetic Foot Ulcers

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(12), P. 9900 - 9900

Published: June 8, 2023

The imbalance of local and systemic factors in individuals with diabetes mellitus (DM) delays, or even interrupts, the highly complex dynamic process wound healing, leading to diabetic foot ulceration (DFU) 15 25% cases. DFU is cause non-traumatic amputations worldwide, posing a huge threat well-being DM healthcare system. Moreover, despite all latest efforts, efficient management DFUs still remains clinical challenge, limited success rates treating severe infections. Biomaterial-based dressings have emerged as therapeutic strategy rising potential handle tricky macro micro environments DM. Indeed, biomaterials long been related unique versatility, biocompatibility, biodegradability, hydrophilicity, healing properties, features that make them ideal candidates for applications. Furthermore, may be used depot biomolecules anti-inflammatory, pro-angiogenic, antimicrobial further promoting adequate healing. Accordingly, this review aims unravel multiple functional properties promising chronic examine how these are currently being evaluated research settings cutting-edge management.

Language: Английский

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The role of machine learning in advancing diabetic foot: a review

Frontiers in Endocrinology, Journal Year: 2024, Volume and Issue: 15

Published: April 29, 2024

Background Diabetic foot complications impose a significant strain on healthcare systems worldwide, acting as principal cause of morbidity and mortality in individuals with diabetes mellitus. While traditional methods diagnosing treating these conditions have faced limitations, the emergence Machine Learning (ML) technologies heralds new era, offering promise revolutionizing diabetic care through enhanced precision tailored treatment strategies. Objective This review aims to explore transformative impact ML managing complications, highlighting its potential advance diagnostic accuracy therapeutic approaches by leveraging developments medical imaging, biomarker detection, clinical biomechanics. Methods A meticulous literature search was executed across PubMed, Scopus, Google Scholar databases identify pertinent articles published up March 2024. The strategy carefully crafted, employing combination keywords such “Machine Learning,” “Diabetic Foot,” Foot Ulcers,” Care,” “Artificial Intelligence,” “Predictive Modeling.” offers an in-depth analysis foundational principles algorithms that constitute ML, placing special emphasis their relevance sciences, particularly within specialized domain pathology. Through incorporation illustrative case studies schematic diagrams, endeavors elucidate intricate computational methodologies involved. Results has proven be invaluable deriving critical insights from complex datasets, enhancing both planning for management. highlights efficacy decision-making, underscored comparative analyses prognostic assessments applications care. Conclusion culminates prospective assessment trajectory realm We believe despite challenges limitations ethical considerations, remains at forefront paradigms management are globally applicable precision-oriented. technological evolution unprecedented possibilities opportunities patient

Language: Английский

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Sensors and Devices Based on Electrochemical Skin Conductance and Bioimpedance Measurements for the Screening of Diabetic Foot Syndrome: Review and Meta-Analysis

Biosensors, Journal Year: 2025, Volume and Issue: 15(2), P. 73 - 73

Published: Jan. 26, 2025

Diabetic foot syndrome is a multifactorial disease involving different etiological factors. This also insidious, due to frequent lack of early symptoms, and its prevalence has increased in recent years. justifies the remarkable attention being paid syndrome, although problem effective screening for this possibly at patient's home, still unsolved. However, some options appear available context. First, it was demonstrated that temperature measurement skin an interesting approach, but limitations, hence more approach should combine data from other sensors. For purpose, conductance or bioimpedance may be good option. Therefore, aim study review those studies where conductance/bioimpedance used detection diabetic syndrome. In addition, we performed meta-analysis studies, widely device exploited (SUDOSCAN®) measurement, found levels can clearly distinguish between groups patients with without neuropathy, latter one most relevant factors

Language: Английский

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Integrating bioinformatics and multiple machine learning to identify mitophagy-related targets for the diagnosis and treatment of diabetic foot ulcers: evidence from transcriptome analysis and drug docking

Frontiers in Molecular Biosciences, Journal Year: 2024, Volume and Issue: 11

Published: July 9, 2024

Background Diabetic foot ulcers are the most common and serious complication of diabetes mellitus, high morbidity, mortality, disability which greatly diminish quality life patients impose a heavy socioeconomic burden. Thus, it is urgent to identify potential biomarkers targeted drugs for diabetic ulcers. Methods In this study, we downloaded datasets related from gene expression omnibus. Dysregulation mitophagy-related genes was identified by differential analysis weighted co-expression network analysis. Multiple machine algorithms were utilized hub genes, novel artificial neural model assisting in diagnosis constructed based on their transcriptome patterns. Finally, that can target using Enrichr platform molecular docking methods. Results 702 differentially expressed ulcers, enrichment showed these associated with mitochondria energy metabolism. Subsequently, hexokinase-2, small ribosomal subunit protein us3, l-lactate dehydrogenase A chain as multiple learning validated diagnostic performance validation cohort independent present study (The areas under roc curve 0.671, 0.870, 0.739, respectively). Next, training good, 0.924 0.840, respectively. retinoic acid estradiol promising anti-diabetic targeting hexokinase-2 (−6.6 −7.2 kcal/mol), us3 (−7.5 −8.3 (−7.6 −8.5 kcal/mol). Conclusion The chain, emphasized critical roles treatment through dimensions, providing

Language: Английский

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Artificial Intelligence for Diabetic Foot Screening Based on Digital Image Analysis: A Systematic Review

Journal of Diabetes Science and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Introduction: Early detection of diabetic foot complications is essential for effective management and prevention complications. Artificial intelligence (AI) technology based on digital image analysis offers a promising noninvasive method screening. This systematic review aims to identify study the development an AI model screening using analysis. Methodology: The scrutinized articles published between 2018 2023, sourced from PubMed, ProQuest, ScienceDirect. keyword-based search resulted in 2214 relevant nine that met inclusion criteria. article quality assessment was done through Quality Assessment Diagnostic Accuracy Studies (QUADAS). Data were extracted analyzed NVivo. Results: Thermal imagery or thermogram main data source, with plantar temperature distribution patterns as important indicator. Deep learning methods, specifically artificial neural networks (ANNs) convolutional (CNNs), are most commonly used methods. highest performance demonstrated by ANN MATLAB’s Image Processing Toolbox able classify each type macula 97.5% accuracy. findings show great potential improving accuracy efficiency Conclusion: research provides insights into image–based Future studies need focus evaluating clinical applicability, including ethical aspects patient security, well developing more comprehensive sets.

Language: Английский

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Classify Chronic Wounds

Published: March 3, 2025

Language: Английский

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Application of Artificial Intelligence in Tree Care in Sub-Saharan Africa

LatIA, Journal Year: 2025, Volume and Issue: 3, P. 325 - 325

Published: April 16, 2025

Artificial intelligence (AI) has emerged as a transformative tool in various industries, including environmental conservation and tree care. In Sub-Saharan Africa, where deforestation, climate change, inadequate management pose significant challenges, AI presents opportunities for improving care practices. This study explores the application of technologies monitoring, disease detection, sustainable strategies within region. Utilizing combination literature review case analysis, research evaluates AI-driven approaches such remote sensing, machine learning models, automated data collection assessing forest dynamicos. The findings indicate that enhances early optimizes resource allocation, supports decision-making efforts. However, challenges limited technological infrastructure, high implementation costs, need specialized expertise hinder widespread adoption. concludes while holds potential revolutionizing strategic investments digital policy support, capacity building are essential its successful integration into forestry

Language: Английский

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Prediction model for lower limb amputation in hospitalized diabetic foot patients using classification and regression trees

Foot and Ankle Surgery, Journal Year: 2024, Volume and Issue: 30(6), P. 471 - 479

Published: March 21, 2024

The decision to perform amputation of a limb in patient with diabetic foot ulcer (DFU) is not an easy task. Prediction models aim help the surgeon making scenarios. Currently there are no prediction model determine lower during first 30 days hospitalization for patients DFU.

Language: Английский

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Diabetic Foot Ulcers in Geriatric Patients

Clinics in Geriatric Medicine, Journal Year: 2024, Volume and Issue: 40(3), P. 437 - 447

Published: April 23, 2024

Language: Английский

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In-shoe plantar shear stress sensor design, calibration and evaluation for the diabetic foot

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(9), P. e0309514 - e0309514

Published: Sept. 4, 2024

Plantar shear stress may have an important role in the formation of a Diabetic Foot Ulcer, but its measurement is regarded as challenging and has limited research. This paper highlights importance anatomical specific sensor calibration presents feasibility study novel sensing system which measured in-shoe from gait activity on both healthy diabetic subjects. The insole was based strain gauge array embedded silicone backed with commercial normal pressure sensor. Sensor factors were investigated using custom mechanical test rig indenter to exert forces. Indenter size location varied investigate loading area position accuracy. insole, coupled procedure, tested one participant diabetes during two sessions 15 minutes treadmill walking. Calibration different areas (from 78.5 mm 2 707 ) positions (up 40 centre) showed variation measurements up 80% 90% respectively. Shear results demonstrated high repeatability (>97%) good accuracy (mean absolute error < ±18 kPa) bench top tests less than 21% variability within walking 15-minutes duration. indicate coupling between sensors materials. It also appropriate method ensure accurate measurement. presented this paper, demonstrates viable measure repeatable procedure described. validation methods outlined could be utilised standardised approach for research community develop validate similar technologies.

Language: Английский

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