Clinical comprehensive treatment protocol for managing diabetic foot ulcers: A retrospective cohort study.

BACKGROUND: Diabetic foot ulcers (DFUs) are a common complication of diabetes, often leading to severe infections, amputations, and reduced quality of life. The current standard treatment protocols for DFUs have limitations in promoting efficient wound healing and preventing complications. A comprehensive treatment approach targeting multiple aspects of wound care may offer improved outcomes for patients with DFUs. The hypothesis of this study is that a comprehensive treatment protocol for DFUs will result in faster wound healing, reduced amputation rates, and improved overall patient outcomes compared to standard treatment protocols. AIM: To compare the efficacy and safety of a comprehensive treatment protocol for DFUs with those of the standard treatment protocol. METHODS: This retrospective study included 62 patients with DFUs, enrolled between January 2022 and January 2024, randomly assigned to the experimental (n = 32) or control (n = 30) group. The experimental group received a comprehensive treatment comprising blood circulation improvement, debridement, vacuum sealing drainage, recombinant human epidermal growth factor and anti-inflammatory dressing, and skin grafting. The control group received standard treatment, which included wound cleaning and dressing, antibiotics administration, and surgical debridement or amputation, if necessary. Time taken to reduce the white blood cell count, number of dressing changes, wound healing rate and time, and amputation rate were assessed. RESULTS: The experimental group exhibited significantly better outcomes than those of the control group in terms of the wound healing rate, wound healing time, and amputation rate. Additionally, the comprehensive treatment protocol was safe and well tolerated by the patients. CONCLUSION: Comprehensive treatment for DFUs is more effective than standard treatment, promoting granulation tissue growth, shortening hospitalization time, reducing pain and amputation rate, improving wound healing, and enhancing quality of life.

Segmentation of retinal detachment and retinoschisis in OCT images based on complementary multi-class segmentation networks.

Retinal detachment (RD) and retinoschisis (RS) are the main complications leading to vision loss in high myopia. Accurate segmentation of RD and RS, including its subcategories (outer, middle, and inner retinoschisis) in optical coherence tomography images is of great clinical significance in the diagnosis and management of high myopia. For this multi-class segmentation task, we propose a novel framework named complementary multi-class segmentation networks. Based on domain knowledge, a three-class segmentation path (TSP) and a five-class segmentation path (FSP) are designed, and their outputs are integrated through additional decision fusion layers to achieve improved segmentation in a complementary manner. In TSP, a cross-fusion global feature module is adopted to achieve global receptive field. In FSP, a novel three-dimensional contextual information perception module is proposed to capture long-range contexts, and a classification branch is designed to provide useful features for segmentation. A new category loss is also proposed in FSP to help better identify the lesion categories. Experiment results show that the proposed method achieves superior performance for joint segmentation of RD and the three subcategories of RS, with an average Dice coefficient of 84.83%.

Interactive Skin Wound Segmentation Based on Feature Augment Networks.

Skin wound segmentation in photographs allows non-invasive analysis of wounds that supports dermatological diagnosis and treatment. In this paper, we propose a novel feature augment network (FANet) to achieve automatic segmentation of skin wounds, and design an interactive feature augment network (IFANet) to provide interactive adjustment on the automatic segmentation results. The FANet contains the edge feature augment (EFA) module and the spatial relationship feature augment (SFA) module, which can make full use of the notable edge information and the spatial relationship information be-tween the wound and the skin. The IFANet, with FANet as the backbone, takes the user interactions and the initial result as inputs, and outputs the refined segmentation result. The pro-posed networks were tested on a dataset composed of miscellaneous skin wound images, and a public foot ulcer segmentation challenge dataset. The results indicate that the FANet gives good segmentation results while the IFANet can effectively improve them based on simple marking. Comprehensive comparative experiments show that our proposed networks outperform some other existing automatic or interactive segmentation methods, respectively.

Machine Learning-Based Model for the Prognosis of Postoperative Gastric Cancer.

BACKGROUND: The use of machine learning (ML) in predicting disease prognosis has increased, and scientists have adopted different methods for cancer classification to optimize the early screening of cancer to determine its prognosis in advance. In this study, we aimed at improving the prediction accuracy of gastric cancer in postoperation patients by constructing a highly effective prognostic model. METHODS: The study used postoperative gastric cancer patient data from the SEER database. The LASSO regression method was used to construct a clinical prognostic model, and four machine learning methods (Boruta algorithm, neural network, support vector machine, and random forest) were used to screen and recombine the features to construct an ML prognostic model. Clinical information on 955 postoperative gastric cancer patients collected from the Affiliated Tumor Hospital of Harbin Medical University was used for external verification. RESULTS: Experimental results showed that the AUC values of 1, 3 and 5 years in the training set, validation set and external validation set of clinical prognosis model and ML prognosis model directly established by LASSO regression are all around 0.8. CONCLUSION: Both models can accurately evaluate the prognosis of postoperative patients with gastric cancer, which may be helpful for accurate and personalized treatment of postoperative patients with gastric cancer.

Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer.

Background: The management of gastric cancer (GC) still lacks tumor markers with high specificity and sensitivity. The goal of current research is to find effective diagnostic and prognostic markers and to clarify their related mechanisms. Methods: In this study, we integrated GC DNA methylation data from publicly available datasets obtained from TCGA and GEO databases, and applied random forest and LASSO analysis methods to screen reliable differential methylation sites (DMSs) for GC diagnosis. We constructed a diagnostic model of GC by logistic analysis and conducted verification and clinical correlation analysis. We screened credible prognostic DMSs through univariate Cox and LASSO analyses and verified a prognostic model of GC by multivariate Cox analysis. Independent prognostic and biological function analyses were performed for the prognostic risk score. We performed TP53 correlation analysis, mutation and prognosis analysis on eleven-DNA methylation driver gene (DMG), and constructed a multifactor regulatory network of key genes. Results: The five-DMS diagnostic model distinguished GC from normal samples, and diagnostic risk value was significantly correlated with grade and tumor location. The prediction accuracy of the eleven-DMS prognostic model was verified in both the training and validation datasets, indicating its certain potential for GC survival prediction. The survival rate of the high-risk group was significantly lower than that of the low-risk group. The prognostic risk score was an independent risk factor for the prognosis of GC, which was significantly correlated with N stage and tumor location, positively correlated with the VIM gene, and negatively correlated with the CDH1 gene. The expression of CHRNB2 decreased significantly in the TP53 mutation group of gastric cancer patients, and there were significant differences in CCDC69, RASSF2, CHRNB2, ARMC9, and RPN1 between the TP53 mutation group and the TP53 non-mutation group of gastric cancer patients. In addition, CEP290, UBXN8, KDM4A, RPN1 had high frequency mutations and the function of eleven-DMG mutation related genes in GC patients is widely enriched in multiple pathways. Conclusion: Combined, the five-DMS diagnostic and eleven-DMS prognostic GC models are important tools for accurate and individualized treatment. The study provides direction for exploring potential markers of GC.

Comprehensive Analysis of the Value of SMYD Family Members in the Prognosis and Immune Infiltration of Malignant Digestive System Tumors.

BACKGROUND: The SET and MYND domain-containing (SMYD) gene family comprises a set of genes encoding lysine methyltransferases. This study aimed to clarify the relationship between the expression levels of SMYD family members and the prognosis and immune infiltration of malignant tumors of the digestive system. METHODS: The Oncomine, Ualcan, Kaplan-Meier Plotter, cBioPortal, Metascape, and TIMER databases and tools were used to analyze the correlation of SMYD family mRNA expression, clinical stage, TP53 mutation status, prognostic value, gene mutation, and immune infiltration in patients with esophageal carcinoma (ESCA), liver hepatocellular carcinoma (LIHC), and stomach adenocarcinoma (STAD). RESULTS: In ESCA, the mRNA expression of SMYD2/3/4/5 was significantly correlated with the incidence rate, that of SMYD2/3 with the clinical stage, that of SMYD2/3/4/5 with TP53 mutation status, that of SMYD2/4/5 with overall survival (OS), and that of SMYD1/2/3/4 with relapse-free survival (RFS). In LIHC, the mRNA expression of SMYD1/2/3/4/5 was significantly correlated with the incidence rate, that of SMYD2/4/5 with the clinical stage, that of SMYD3/5 with TP53 mutation status, that of SMYD2/3/4/5 with OS, and that of SMYD3/5 with RFS. In STAD, the mRNA expression of SMYD2/3/4/5 was significantly correlated with the incidence rate, that of SMYD1/4 with the clinical stage, that of SMYD1/2/3/5 with TP53 mutation status, that of SMYD1/3/4 with OS, and that of SMYD1/3 with RFS. Furthermore, the function of SMYD family mutation-related genes in ESCA, LIHC, and STAD patients was mainly related to pathways, such as mitochondrial gene expression, mitochondrial matrix, and mitochondrial translation. The expression of SMYD family genes was significantly correlated with the infiltration of six immune cell types and eight types of immune check sites. CONCLUSION: SMYD family genes are differentially expressed and frequently mutated in malignant tumors of the digestive system (ESCA, LIHC, and gastric cancer). They are potential markers for prognostic prediction and have important significance in immunity and targeted therapy.