Exploration of the Shared Gene Signatures and Molecular Mechanisms Between Diabetic Foot Ulcer and Diabetic Microvascular Disease

Background: Diabetic foot ulcer (DFU) is a serious complication of diabetes caused by multiple factors. Diabetic microvascular disease has a close linkage with DFU. However, the inter-relational mechanisms between them are still unclear. This article aimed to explore the shared gene signatures and potential molecular mechanisms in DFU and diabetic microvascular disease. Methods: In the GEO database, DFU microarray datasets (GSE80178, GSE68183) and diabetic microvascular disease microarray datasets (GSE43950) were downloaded. After data standardization processing, we used R software to analyze the transcriptome sequencing data of each data set to find the differentially expressed genes (DEGs) of DFU and diabetic microvascular disease. Then obtained the overlapped DEGs in DFU and diabetic microvascular disease database by Jvenn. Finally, the shared DEGs were enriched by pathway enrichment and protein-protein interaction (PPI) analysis, and the hub gene was found by node analysis. Results: Totally, 1007 DEGs were identified in the GSE80178 dataset, 338 DEGs were identified in the GSE68183 dataset, 1154 were identified in the GSE43950 dataset, Venn diagram analyses showed that there were 14 shared DEGs in these datasets. Enrichment analysis shows that the shared DEGs were mainly associated with chronic inflammatory response, leukocyte migration, cellular transition metal ion homeostasis, vascular wound healing, collagen-containing extracellular matrix and Toll-like receptor binding. Involved pathways were mainly enriched in IL-17 signaling, glycosaminoglycan degradation, and calcium signaling. PPI analysis of these shared DEGs shows that S100A9, S100A8, CSTA, ADAP2, CD34 and FGL2 were the hub gene whose plays a pivotal role in DFU.Conclusion: Our work has identified several new DFU candidate genes that can be used as biomarkers or potential therapeutic targets

Comparative Study on the Costs of Treating Diabetic Foot, Necrotizing Fasciitis, and Stage IV Pressure Ulcers

Objective: To compare the total treatment costs for patients with diabetic foot with osteomyelitis, necrotizing fasciitis, and stage IV pressure ulcers in Dalian city, to explore the economic burden of diferent types of chronic wounds. Methods: Aretrospective analysis design was used, collecting data from 18 patients (6 of each wound type) from the electronic medical record system of a large hospital in Dalian. One-way ANOVAand Tukey’s HSD post-hoc tests were applied to compare the treatment costs of diferent wound types. Results: The treat-ment costs for the necrotizing fasciitis group were signifcantly higher than those for the stage IV pressure ulcer group, while the costs for the diabetic foot group did not show signifcant diferences from the other two groups. This fnding highlights the impact of chronic wound types on treatment costs and the importance of early intervention. Conclusion: The results provide data support for the optimized allocation of med-ical resources, especially in a context of limited resources. They emphasize the necessity of early identifcation and management of chronic wounds and provide a basis for establishing cost-efective treatment strategies

Identification of ferroptosis-related molecular clusters and genes for diabetic osteoporosis based on the machine learning

BackgroundDiabetic osteoporosis exhibits heterogeneity at the molecular level. Ferroptosis, a controlled form of cell death brought on by a buildup of lipid peroxidation, contributes to the onset and development of several illnesses. The aim was to explore the molecular subtypes associated with ferroptosis in diabetic osteoporosis at the molecular level and to further elucidate the potential molecular mechanisms.MethodsIntegrating the CTD, GeneCards, FerrDb databases, and the microarray data of GSE35958, we identified ferroptosis-related genes (FRGs) associated with diabetic osteoporosis. We applied unsupervised cluster analysis to divide the 42 osteoporosis samples from the GSE56814 microarray data into different subclusters based on FRGs. Subsequently, FRGs associated with two ferroptosis subclusters were obtained by combining database genes, module-related genes of WGCNA, and differentially expressed genes (DEGs). Eventually, the key genes from FRGs associated with diabetic osteoporosis were identified using the least absolute shrinkage and selection operator (LASSO), Boruta, support vector machine recursive feature elimination (SVM ­ RFE), and extreme gradient boosting (XGBoost) machine learning algorithms. Based on ROC curves of external datasets (GSE56815), the model’s efficiency was examined.ResultsWe identified 15 differentially expressed FRGs associated with diabetic osteoporosis. In osteoporosis, two distinct molecular clusters related to ferroptosis were found. The expression results and GSVA analysis indicated that 15 FRGs exhibited significantly different biological functions and pathway activities in the two ferroptosis subclusters. Therefore, we further identified 17 FRGs associated with diabetic osteoporosis between the two subclusters. The results of the comprehensive analysis of 17 FRGs demonstrated that these genes were heterogeneous and had a specific interaction between the two subclusters. Ultimately, the prediction model had a strong foundation and excellent AUC values (0.84 for LASSO, 0.84 for SVM ­ RFE, 0.82 for Boruta, and 0.81 for XGBoost). IDH1 is a common gene to all four algorithms thus being identified as a key gene with a high AUC value (AUC = 0.698).ConclusionsAs a ferroptosis regulator, IDH1 is able to distinguish between distinct molecular subtypes of diabetic osteoporosis, which may offer fresh perspectives on the pathogenesis of the disease’s clinical symptoms and prognostic heterogeneity

Brachial-Ankle Pulse Wave Velocity as a Novel Modality for Detecting Early Diabetic Nephropathy in Type 2 Diabetes Patients

Brachial-ankle pulse wave velocity (baPWV) has been shown to correlate with a host of disorders associated with arterial stiffness. Type 2 diabetes is associated with the involvement of both small vessels and large vessels. Studies on the relevance of baPWV to early diabetic nephropathy are scarce. This retrospective observational case-control study enrolled 120 patients with type 2 diabetes from our medical records. We classified patients into two groups depending on the magnitude of albuminuria: 60 patients with microalbuminuria were classified as the early diabetic nephropathy group (EDN group) and 60 patients without albuminuria were classified as the diabetes without nephropathy group (DWN group). An additional 30 nondiabetic age- and sex-matched controls were also enrolled. Data regarding the lipid profile, blood pressure, baPWV, high-sensitivity C reactive protein (hs-CRP) level, anthropometric measurements, urine albumin/creatinine ratio (UACR), serum creatinine level, and glycemic control indices (i.e., fasting plasma glucose (FPG), postprandial glucose (PPG), and glycosylated hemoglobin (hemoglobin A1c, HbA1c)) were recorded for all enrolled participants. baPWV was significantly higher in the EDN group than in the DWN group. Moreover, baPWV was positively correlated with age, duration of diabetes, obesity, poor glycemic control, and high serum levels of triglycerides (TG), hs-CRP, creatinine, and uric acid as well as a high UACR (all P<0.01). A significant negative correlation was found between baPWV and high-density lipoprotein levels (P<0.05). Multivariate regression analysis showed that the hs-CRP level and duration of diabetes most strongly influenced baPWV. baPWV may be a convenient, noninvasive, and reproducible method for detecting early diabetic nephropathy

Pharmacological Properties of the Medical Maggot: A Novel Therapy Overview

In the last decade, maggot has been hailed as the miraculous “medicinal maggot” for its diverse properties, including antimicrobial, antibiofilm, anti-inflammatory, and wound healing activities. The fact that maggots show so many beneficial properties has increased the interest in these tiny larvae dramatically. Whilst there is relatively abundant clinical evidence to demonstrate the success of maggots as debridement agents, not so much emphasis has been placed on the basic science evidence, which was a combination of physical and biochemical actions. This review differs from those earlier works in that it is undertaken to provide an update of the latest scientific basis published on maggot, particularly active ingredients within maggot excretions/secretions (ES). Further investigations should focus on the isolation, identification, recombination, transgenosis, and mass production of the beneficial molecules within maggots

A generalized dual-domain generative framework with hierarchical consistency for medical image reconstruction and synthesis

Acknowledgements: This work was supported in part by the National Natural Science Foundation of China (grant number 62131015), the Science and Technology Commission of Shanghai Municipality (STCSM) (grant number 21010502600), and The Key R&D Program of Guangdong Province, China (grant number 2021B0101420006).Funder: The Key R&D Program of Guangdong Province, China (grant number 2021B0101420006)AbstractMedical image reconstruction and synthesis are critical for imaging quality, disease diagnosis and treatment. Most of the existing generative models ignore the fact that medical imaging usually occurs in the acquisition domain, which is different from, but associated with, the image domain. Such methods exploit either single-domain or dual-domain information and suffer from inefficient information coupling across domains. Moreover, these models are usually designed specifically and not general enough for different tasks. Here we present a generalized dual-domain generative framework to facilitate the connections within and across domains by elaborately-designed hierarchical consistency constraints. A multi-stage learning strategy is proposed to construct hierarchical constraints effectively and stably. We conducted experiments for representative generative tasks including low-dose PET/CT reconstruction, CT metal artifact reduction, fast MRI reconstruction, and PET/CT synthesis. All these tasks share the same framework and achieve better performance, which validates the effectiveness of our framework. This technology is expected to be applied in clinical imaging to increase diagnosis efficiency and accuracy.</jats:p

A generalized dual-domain generative framework with hierarchical consistency for medical image reconstruction and synthesis

Acknowledgements: This work was supported in part by the National Natural Science Foundation of China (grant number 62131015), the Science and Technology Commission of Shanghai Municipality (STCSM) (grant number 21010502600), and The Key R&D Program of Guangdong Province, China (grant number 2021B0101420006).Funder: The Key R&D Program of Guangdong Province, China (grant number 2021B0101420006)Medical image reconstruction and synthesis are critical for imaging quality, disease diagnosis and treatment. Most of the existing generative models ignore the fact that medical imaging usually occurs in the acquisition domain, which is different from, but associated with, the image domain. Such methods exploit either single-domain or dual-domain information and suffer from inefficient information coupling across domains. Moreover, these models are usually designed specifically and not general enough for different tasks. Here we present a generalized dual-domain generative framework to facilitate the connections within and across domains by elaborately-designed hierarchical consistency constraints. A multi-stage learning strategy is proposed to construct hierarchical constraints effectively and stably. We conducted experiments for representative generative tasks including low-dose PET/CT reconstruction, CT metal artifact reduction, fast MRI reconstruction, and PET/CT synthesis. All these tasks share the same framework and achieve better performance, which validates the effectiveness of our framework. This technology is expected to be applied in clinical imaging to increase diagnosis efficiency and accuracy