Feature Ranking by Variational Dropout for Classification Using Thermograms from Diabetic Foot Ulcers

LncRNA SNHG16 Knockdown Promotes Diabetic Foot Ulcer Wound Healing via Sponging MiR-31-5p

Diabetic foot ulcers are caused by nerve abnormalities and vascular lesions in the distal lower limbs of diabetic patients. However, the causes of diabetic foot ulcers are diverse and the treatment process is complex. Therefore, understanding the pathogenesis of diabetic foot ulcers through lncRNA and formulating effective means are the key to the cure of patients. Tissues were collected from 76 diabetic foot ulcer patients and 50 non-diabetic patients undergoing traumatic amputation. Human dermal fibroblasts (HDFs) were induced by high glucose to obtain diabetic foot ulcer cell model. The lncRNA SNHG16 (SNHG16) and miR-31-5p expression in tissues and cells was detected by real-time quantitative reverse transcription PCR (RT-qPCR). Cell Counting Kit-8 (CCK-8) and Transwell assays were used to evaluate the biological behavior of the cells, and the association between SNHG16 and miR-31-5p was explored by luciferase reporting assay. SNHG16 was distinctly expressed in diabetic foot ulcer tissue samples, while miR-31-5p was decreased. In vitro cell function assays confirmed that the proliferation level was inhibited in the constructed diabetic foot ulcer cell model (HG group), as was the migration and invasion ability. After transfection with silencing SNHG16, the biological behavior of the cells was promoted. Mechanistically, SNHG16 sponge miR-31-5p regulated disease progression. Recovery experiments revealed that miR-31-5p inhibitor counteracted the effect of silencing SNHG16 on cell viability. SNHG16 knockdown may regulate the biological function of cells by targeting miR-31-5p to promote wound healing and ameliorate the condition of diabetic foot ulcer patients.

State-of-the-Art Features for Early-Stage Detection of Diabetic Foot Ulcers Based on Thermograms

Diabetic foot ulcers represent the most frequently recognized and highest risk factor among patients affected by diabetes mellitus. The associated recurrent rate is high, and amputation of the foot or lower limb is often required due to infection. Analysis of infrared thermograms covering the entire plantar aspect of both feet is considered an emerging area of research focused on identifying at an early stage the underlying conditions that sustain skin and tissue damage prior to the onset of superficial wounds. The identification of foot disorders at an early stage using thermography requires establishing a subset of relevant features to reduce decision variability and data misinterpretation and provide a better overall cost-performance for classification. The lack of standardization among thermograms as well as the unbalanced datasets towards diabetic cases hinder the establishment of this suitable subset of features. To date, most studies published are mainly based on the exploitation of the publicly available INAOE dataset, which is composed of thermogram images of healthy and diabetic subjects. However, a recently released dataset, STANDUP, provided data for extending the current state of the art. In this work, an extended and more generalized dataset was employed. A comparison was performed between the more relevant and robust features, previously extracted from the INAOE dataset, with the features extracted from the extended dataset. These features were obtained through state-of-the-art methodologies, including two classical approaches, lasso and random forest, and two variational deep learning-based methods. The extracted features were used as an input to a support vector machine classifier to distinguish between diabetic and healthy subjects. The performance metrics employed confirmed the effectiveness of both the methodology and the state-of-the-art features subsequently extracted. Most importantly, their performance was also demonstrated when considering the generalization achieved through the integration of input datasets. Notably, features associated with the MCA and LPA angiosomes seemed the most relevant.