Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation

Pathophysiological Roles of the CX3CL1-CX3CR1 Axis in Renal Disease, Cardiovascular Disease, and Cancer

CX3CL1 and its unique receptor, CX3CR1, are leukocyte migration factors and are involved in the pathogenesis and progression of many inflammatory diseases and malignancies. The CX3CL1-CX3CR1 axis induces a variety of responses, including cell proliferation, migration, invasion, and apoptosis resistance. CX3CL1 is a transmembrane protein, and proteolysis generates a soluble form. The membrane and soluble forms of CX3CL1 exhibit different functions, but both bind to the chemokine receptor CX3CR1. The CX3CL1-CX3CR1 axis is a chemokine system that has attracted attention not only as a therapeutic target but also as a potentially useful diagnostic and prognostic marker for disease. Many studies have reported that the CX3CL1-CX3CR1 axis is involved in disease progression, but more recently there are scattered reports suggesting that it is involved in disease suppression. In this article, we summarize the latest findings on the pathophysiological role of the CX3CL1-CX3CR1 axis, with a particular focus on renal disease, cardiovascular disease, and cancer.

KDELR3 and YOD1 proteins as critical endoplasmic reticulum stress mediators and potential therapeutic targets in diabetic foot ulcers: An integrated bioinformatics analysis

BACKGROUND

Diabetic foot ulcers (DFU) represent one of the most severe complications of diabetes mellitus and are closely associated with persistent hyperglycemia. Endoplasmic reticulum stress response proteins play critical roles in the development and progression of DFU, highlighting the urgent need for further research to identify novel biomarkers and therapeutic strategies.

METHOD

This study utilized DFU datasets from the GEO database and employed bioinformatics approaches to identify differentially expressed genes encoding endoplasmic reticulum stress (ERS) response proteins. Key regulatory proteins NCCRP1, KDELR3, BOK, and YOD1 were screened using WGCNA, machine learning algorithms, and molecular docking techniques, followed by an evaluation of their correlation with the immune microenvironment. Additionally, single-cell RNA sequencing and Mendelian randomization analysis were applied to investigate the structural and functional characteristics of these proteins in DFU pathogenesis. The expression levels of key protein biomarkers were validated using qRT-PCR.

RESULT

A total of 32 differentially expressed endoplasmic reticulum stress-related proteins associated with DFU were identified. Machine learning algorithms confirmed that NCCRP1, KDELR3, BOK, and YOD1 proteins demonstrated significant diagnostic potential as biomarkers. Immune analysis revealed associations between these stress-response proteins and immune cell infiltration, while molecular docking identified metronidazole as a promising therapeutic candidate targeting the KDELR3 and YOD1 protein structures. Experimental validation confirmed the differential expression of KDELR3 and YOD1 proteins in DFU tissues, and Mendelian randomization analysis suggested that BOK protein may be a potential causal factor in DFU development due to its structural interactions with ERS pathways.

CONCLUSION

Our study characterized specific ERS response proteins with significant diagnostic potential for DFU. These findings enhance the understanding of protein-mediated DFU pathogenesis and lay the foundation for improving diagnostic and therapeutic strategies targeting these biological macromolecules.

Follistatin-like protein 1: Implications for renal disease progression

Renal diseases, including glomerulonephritis, acute kidney injury, chronic kidney failure, and kidney tumors are all current global health challenges. Lesions in other systems can cause renal diseases and can affect other systems or even the whole body. Despite ongoing advancements in pharmaceutical and technological innovations, the prognosis for end-stage renal disease, encompassing renal failure and tumors, continues to be bleak. Follistatin-like protein 1 (FSTL1) is a secreted glycoprotein produced mainly by mesenchymal cells. FSTL1 is a glycoprotein that belongs to the family of secreted, cysteine-rich acidic proteins (SPARC). It plays a pivotal role in cell survival, proliferation, differentiation, and migration, as well as in modulating inflammation and immune responses. Research has shown that FSTL1 plays a crucial role in the onset and progression of renal diseases. This review explores the functions and underlying mechanisms of FSTL1 in kidney pathology. SIGNIFICANCE STATEMENT: This review highlights the pivotal role of FSTL1 in renal diseases, particularly its involvement in renal fibrosis, inflammation, and ischemia-reperfusion injury. By elucidating its dual roles across different pathologies, this work underscores FSTL1's potential as both a biomarker and a therapeutic target, offering novel insights for managing chronic kidney disease and associated complications.

Construction and evaluation of prediction model for postoperative re-fractures in elderly patients with hip fractures

OBJECTIVE

The aim of study was to construct a postoperative re-fracture prediction model for elderly hip fracture patients using an automated machine learning algorithm to provide a basis for early identification of patients with high risk of re-fracture occurrence.

METHODS

Clinical data were collected and subjected to univariate and multivariate analyses to determine the independent risk factors affecting postoperative re-fracture of hip fracture in the elderly. The collected data were divided into training and validation sets in a ratio of 7:3, AutoGluon was applied to construct LightGBMXT, LightGBM, RandomForestGini, RandomForestEntr, CatBoost, NeuralNetFastAI, XGBoost, NeuralNetTorch, LightGBMLarge and WeightedEnsemble_L2 prediction models, and the constructed models were evaluated using evaluation indicators. The models were externally validated and the model with the best prediction performance was selected.

RESULTS

The incidence of postoperative re-fracture was about 11.7%, and age, comorbid diabetes mellitus, comorbid osteoporosis, rehabilitation exercise status, and preoperative total protein level were considered as independent risk factors. The top three models in terms of AUC values in the training set were WeightedEnsemble_L2 (0.9671), XGBoost (0.9636), and LightGBM (0.9626), the WeightedEnsemble_L2 (0.9759) was best in the external validation. Based on the AUC and other evaluation indicators, WeightedEnsemble_L2 was considered the model with the best prediction performance.

CONCLUSION

The constructed model is highly generalizable and applicable, and can be used as an effective tool for healthcare professionals to assess and manage patients' risk of re-fracture.