Neutrophil Extracellular Traps (NETs) Are Associated with Type 2 Diabetes and Diabetic Foot Ulcer Related Amputation: A Prospective Cohort Study.

INTRODUCTION: The prevalence of diabetes mellitus and its sequelae has been on the rise, and diabetic foot ulcer (DFU) is the leading cause of non-traumatic lower limb amputation globally. The rising occurrence and financial burden associated with DFU necessitate improved clinical assessment and treatment. Diabetes has been found to enhance the formation of neutrophil extracellular traps (NETs) by neutrophils, and excessive NETs have been implicated in tissue damage and impaired wound healing. However, there is as yet insufficient evidence to clarify the value of NETs in assessing and predicting outcomes of DFU. METHODS: We designed this prospective study with three cohorts formed from type 2 diabetes mellitus (T2DM) patients with DFU (n = 200), newly diagnosed T2DM patients (n = 42), and healthy donors (n = 38). Serum levels of NETs were detected for all groups, and the prognostic value for DFU-related amputation was analyzed. RESULTS: The results showed that serum NET levels of the DFU group were significantly higher than in the T2DM group (P < 0.05), which also had significantly elevated serum NET levels compared to healthy donors (P < 0.05). Multivariate Cox regression showed that serum NET levels, diabetic foot surgical history, and Wagner grade were the risk factors for amputation (P < 0.05), and these three variables also exhibited the highest coefficient values in additional Lasso Cox regression. For patients with DFU, Kaplan-Meier curves showed that high serum NET levels associated with higher amputation probability (HR = 0.19, P < 0.01) and ROC curve based on NET value showed good validity for amputation (AUC: 0.727, CI 0.651-0.803). CONCLUSION: Elevated serum NET levels serve as an easily accessible serological prognostic marker for assessing the risk of DFU-related amputation, thereby offering evaluation metrics for healthcare providers. Further investigations are necessary to understand the mechanisms driving this relationship.

Comparative proteomics analysis of samples from hepatic cystic echinococcosis patients using data-independent acquisition approach.

Cystic echinococcosis is a zoonotic disease resulting from infection caused by the larval stage of Echinococcus granulosus. This study aimed to assess the specific proteins that are potential candidates for the development of a vaccine against E. granulosus. The data-independent acquisition approach was employed to identify differentially expressed proteins (DEPs) in E. granulosus samples. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was employed to identify several noteworthy proteins. Results: The DEPs in E. granulosus samples were identified (245 pericystic wall vs. parasite-free yellowish granuloma (PYG, 1725 PY vs. PYG, 2274 PN vs. PYG). Further examination of these distinct proteins revealed their predominant enrichment in metabolic pathways, amyotrophic lateral sclerosis, and neurodegeneration-associated pathways. Notably, among these DEPs, SH3BGRL, MST1, TAGLN2, FABP5, UBE2V2, and RARRES2 exhibited significantly higher expression levels in the PYG group compared with the PY group (P < 0.05). The findings may contribute to the understanding of the pathological mechanisms underlying echinococcosis, providing valuable insights into the development of more effective diagnostic tools, treatment modalities, and preventive strategies. SIGNIFICANCE: CE is a major public health hazard in the western regions of China, Central Asia, South America, the Mediterranean countries, and eastern Africa. Echinococcus granulosus is responsible for zoonotic disease through infection Our analysis focuses on the proteins in various samples by data-dependent acquisition (DIA) for proteomic analysis. The importance of this research is to develop new strategies and targets to protect against E. granulosus infections in humans.

Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study.

RATIONALE AND OBJECTIVES: Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to explore the effectiveness of using radiomics and machine learning on multiparametric magnetic resonance imaging (MRI) to differentiate between MB and EM and validate its diagnostic ability with an external set. MATERIALS AND METHODS: Axial T2 weighted image (T2WI) and contrast-enhanced T1weighted image (CE-T1WI) MRI sequences of 135 patients from two centers were collected as train/test sets. Volume of interest (VOI) was manually delineated by an experienced neuroradiologist, supervised by a senior. Feature selection analysis and the least absolute shrinkage and selection operator (LASSO) algorithm identified valuable features, and Shapley additive explanations (SHAP) evaluated their significance. Five machine-learning classifiers-extreme gradient boosting (XGBoost), Bernoulli naive Bayes (Bernoulli NB), Logistic Regression (LR), support vector machine (SVM), linear support vector machine (Linear SVC) classifiers were built based on T2WI (T2 model), CE-T1WI (T1 model), and T1 + T2WI (T1 + T2 model). A human expert diagnosis was developed and corrected by senior radiologists. External validation was performed at Sun Yat-Sen University Cancer Center. RESULTS: 31 valuable features were extracted from T2WI and CE-T1WI. XGBoost demonstrated the highest performance with an area under the curve (AUC) of 0.92 on the test set and maintained an AUC of 0.80 during external validation. For the T1 model, XGBoost achieved the highest AUC of 0.85 on the test set and the highest accuracy of 0.71 on the external validation set. In the T2 model, XGBoost achieved the highest AUC of 0.86 on the test set and the highest accuracy of 0.82 on the external validation set. The human expert diagnosis had an AUC of 0.66 on the test set and 0.69 on the external validation set. The integrated T1 + T2 model achieved an AUC of 0.92 on the test set, 0.80 on the external validation set, achieved the best performance. Overall, XGBoost consistently outperformed in different classification models. CONCLUSION: The combination of radiomics and machine learning on multiparametric MRI effectively distinguishes between MB and EM in childhood, surpassing human expert diagnosis in training and testing sets.