Relationship between admission blood urea nitrogen levels and postoperative length of stay in patients with hip fracture: A retrospective study.

To investigate the relationship between admission blood urea nitrogen (BUN) levels and postoperative length of stay (LOS) in hip fracture (HF) patients. This retrospective study retrieved related data from the MIMIC-IV database, of which the laboratory variables were taken preoperatively. The patients were divided into 4 groups according to the BUN quartile levels. After exploring the nonlinear relationship between BUN and LOS by generalized additive model, their connection was further analyzed using the generalized linear models, quantile regression models, and interaction analysis. Receiver operating characteristic curve analysis and decision curve analysis were performed to evaluate its value in predicting first intensive care unit admission and in-hospital mortality. Totally 1274 patients with HF were enrolled in the study. There was a nonlinear relationship between BUN and LOS (P < .05). Besides, BUN was an independent predictor for LOS after adjusting different covariates in 3 models (P < .05). Age served as a significant interactor in this relationship (P < .05). Moreover, receiver operating characteristic curve and decision curve analysis revealed the predictive value of BUN for intensive care unit admission and in-hospital mortality in HF. Admission BUN level as a cost-effective and easy-to-collect biomarker is significantly related to LOS in patients with HF. It helps clinicians to identify potential high-risk populations and take effective preventions before surgery to reduce postoperative LOS.

Nomogram Models Based on the Gene Expression in Prediction of Breast Cancer Bone Metastasis.

Objective: The aim of this study is to design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients. Methods: Dataset GSE124647 was used as a training set, while GSE16446, GSE45255, and GSE14020 were taken as validation sets. In the training cohort, the limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC nonbone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan-Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed and evaluated. The prognostic value of the GESBN models was investigated in the GSE124647 dataset, which was validated in GSE16446 and GSE45255 datasets. Further, the expression levels of genes in the models were explored in the training set, which was validated in GSE14020. Finally, the expression and prognostic value of hub genes in BC were explored. Results: A total of 1858 DEGs were obtained. The WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival (OS). While GJA1, IGFBP6, MDFI, TGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival (PFS). Kaplan-Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Cox regression analysis further revealed that GESBN models were independent prognostic predictors for OS and PFS in BC patients. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis. Conclusion: Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.

[Establishment of finite element model of anterior cervical transpedicular system for reconstruction of cervical stability after subtotal resection of two segments of lower cervical spine].

OBJECTIVE: To establish the fixation model of anterior cervical transpedicular system (ACTPS) after subtotal resection of two segments of lower cervical spine(C3-C7) in order to provide a finite element modeling method for anterior cervical reconstruction. METHODS: The CT data of the cervical segment (C1-T1) of a 30-year-old adult healthy male volunteer was collected. Used Mimics 10.0, Rapidform XOR3, HyperMesh 10.0, CATIA5V19 and ANSYS 14.0 to establish the three-dimensional nonlinear complete model of lower cervical spine(C3-C7) as the intact group. The number of units and nodes of the complete model were recorded. After the effectiveness of the complete model was verified, the C5 and C6 vertebral subtotal resection was performed, and the ACTPS model was established as the ACTPS group. The axial force of 75 N and moment couple of 1N·m was loaded on the upper surface of C3 in intact group and ACTPS group, the range of motion(ROM)and stress distribution in states of flexion extension, lateral flexion, rotation was compared between two groups. RESULTS: There were 85 832 elements and 23 612 nodes in the complete model of lower cervical spine(C3-C7) which was established in this experiment. The stress distribution of ACTPS internal fixation model was relatively uniform. Comparing with the intact group, the overall range of motion in ACTPS group was decreased in flexion extension, lateral flexion and rotation directions, and the corresponding compensation of adjacent C3,4 segment was increased slightly. CONCLUSION: The stress distribution of ACTPS fixation system is uniform, there is no stress concentration area at the joint of screw and titanium plate, and the fracture risk of internal fixation is low. It is suitable for stability reconstruction after anterior decompression of two or more cervical segments.