Development and validation of a prediction model for hypoproteinemia after traumatic spinal cord injury: A multicenter retrospective clinical study.

A multicenter retrospective analysis of conventionally collected data. To identify the potential causes of hypoproteinemia after traumatic spinal cord injury (TSCI) and provide a diagnostic model for predicting an individual likelihood of developing hypoproteinemia. Hypoproteinemia is a complication of spinal cord injury (SCI), an independent risk factor for respiratory failure in elderly patients with SCI, and a predictor of outcomes in patients with cervical SCI. Few nomogram-based studies have used clinical indicators to predict the likelihood of hypoproteinemia following TSCI. This multicenter retrospective clinical analysis included patients with TSCI admitted to the First Affiliated Hospital of Guangxi Medical University, Wuzhou GongRen Hospital, and Dahua Yao Autonomous County People Hospital between 2016 and 2020. The data of patients from the First Affiliated Hospital of Guangxi Medical University were used as the training set, and those from the other 2 hospitals were used as the validation set. All patient histories, diagnostic procedures, and imaging findings were recorded. To predict whether patients with TSCI may develop hypoproteinemia, a least absolute shrinkage and selection operator regression analysis was conducted to create a nomogram. The model was validated by analyzing the consequences using decision curve analysis, calibration curves, the C-index, and receiver operating characteristic curves. After excluding patients with missing data, 534 patients were included in this study. Male/female sex, age ≥ 60 years, cervical SCI, pneumonia, pleural effusion, urinary tract infection (UTI), hyponatremia, fever, hypotension, and tracheostomy were identified as independent risk factors of hypoalbuminemia. A simple and easy-to-replicate clinical prediction nomogram was constructed using these factors. The area under the curve was 0.728 in the training set and 0.881 in the validation set. The predictive power of the nomogram was satisfactory. Hypoalbuminemia after TSCI may be predicted using the risk factors of male/female sex, age ≥ 60 years, cervical SCI, pneumonia, pleural effusion, UTI, hyponatremia, fever, hypotension, and tracheostomy.

Development and validation of a risk nomogram to estimate risk of hyponatremia after spinal cord injury: A retrospective single-center study.

OBJECTIVE: This study aimed to establish a nomogram-based assessment for predicting the risk of hyponatremia after spinal cord injury (SCI). DESIGN: The study is a retrospective single-center study. PARTICIPANTS: SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University. SETTING: The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China. METHODS: We performed a retrospective clinical study to collect SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University from 2016 to 2020. Based on their clinical scores, the SCI patients were grouped as either hyponatremic or non-hyponatremic, SCI patients in 2016-2019 were identified as the training set, and patients in 2020 were identified as the test set. A nomogram was generated, the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to validate the model. RESULTS: A total of 895 SCI patients were retrieved. After excluding patients with incomplete data, 883 patients were finally included in this study and used to construct the nomograms. The indicators used in the nomogram included sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, white blood cell (WBC), albumin and serum Ca2+. These indices were determined by the least absolute shrinkage and selection operator (LASSO) regression analysis. The C-index of the model was 0.81, the area under the curve (AUC) of the training set was 0.82(Cl:0.79-0.85), and the validation set was 0.79(Cl:0.73-0.85). CONCLUSIONS: Nomogram has good predictive ability, sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, WBC, albumin and serum Ca2+ were predictors of hyponatremia after SCI.

Co-culture of apoptotic breast cancer cells with immature dendritic cells: a novel approach for DC-based vaccination in breast cancer

A dendritic cell (DC)-based vaccine strategy could reduce the risk of recurrence and improve the survival of breast cancer patients. However, while therapy-induced apoptosis of hepatocellular and colorectal carcinoma cells can enhance maturation and antigen presentation of DCs, whether this effect occurs in breast cancer is currently unknown. In the present study, we investigated the effect of doxorubicin (ADM)-induced apoptotic MCF-7 breast cancer cells on the activation of DCs. ADM-induced apoptotic MCF-7 cells could effectively induce immature DC (iDC) maturation. The mean fluorescence intensity (MFI) of DC maturity marker CD83 was 23.3 in the ADM-induced apoptotic MCF-7 cell group compared with 8.5 in the MCF-7 cell group. The MFI of DC co-stimulatory marker CD86 and HLA-DR were also increased after iDCs were treated with ADM-induced apoptotic MCF-7 cells. Furthermore, the proliferating autologous T-lymphocytes increased from 14.2 to 40.3% after incubated with DCs induced by apoptotic MCF-7 cells. The secretion of interferon-γ by these T-lymphocytes was also increased. In addition, cell-cell interaction between apoptotic MCF-7 cells and iDCs, but not soluble factors released by apoptotic MCF-7 cells, was crucial for the maturation of iDCs. These findings constitute a novel in vitro DC-based vaccine strategy for the treatment of breast cancer by ADM-induced apoptotic MCF-7 cells.