Objective:To analyze risk factors for prognosis of adult patients with traumatic brain injury (TBI), construct the prognostic model of TBI and evaluate its predictive value.Methods:A case-control study was used to analyze the clinical data of 522 patients with TBI admitted to Xijing Hospital of Air Force Medical University from March 2011 to September 2019, including 438 males and 84 females; aged 18-75 years [(44.9±15.0)years]. According to the Glasgow outcome score (GOS) at discharge, the patients were divided into good prognosis group (GOS 4-5 points, n=165) and poor prognosis group (GOS 1-3 points, n=357). The two groups were compared with regards to qualitative data such as sex, underlying diseases, causes of injury, multiple injuries, open injuries, intracranial foreign bodies, cerebral herniation, consciousness status on admission and at discharge, surgery, lung infection on admission, tracheostomy, ventilator-assisted ventilation, hospital-acquired pneumonia/pathogenic bacteria and intracranial infection, and quantitative data such as Glasgow coma score (GCS) on admission and at discharge, age, measurements on admission [systolic blood pressure, diastolic blood pressure, mean arterial pressure, temperature, heart rate, creatinine, urea nitrogen, blood sodium, blood potassium, blood glucose, prothrombin time (PT), activated partial thromboplastin time (APTT), platelets, international normalized ratio (INR), pupil size of both eyes] and length of hospital stay. Univariate analysis and Lasso regression analysis were used to screen the risk factors affecting the prognosis of TBI patients, and the selected influencing factors were included in multivariate Logistic regression analysis to identify independent risk factors and construct regression equations. R was used to draw a visual nomogram based on regression equation for predicting the prognosis of TBI patients. The prognostic predictive value of the nomogram was evaluated by using the receiver operating characteristic (ROC) curve, and the area under the curve (AUC), Youden index, sensitivity, specificity and consistency index (C index) were calculated. Results:Univariate analysis showed that there were significant differences between the two groups in underlying diseases, open injuries, cerebral herniation, consciousness status on admission and at discharge, lung infection on admission, tracheostomy, ventilator-assisted ventilation, hospital-acquired pneumonia/pathogenic bacteria, GCS on admission and at discharge, age, and measurements on admission (systolic blood pressure, mean arterial pressure, body temperature, heart rate, creatinine, urea nitrogen, blood potassium, blood glucose, PT, INR, pupil size of right eye) (all P<0.05 or 0.01). There were no significant differences between the two groups in gender, causes of injury, multiple injuries, intracranial foreign bodies, surgery, intracranial infection, measurements on admission (diastolic blood pressure, blood sodium, APTT, platelets, pupil size of left eye) and length of hospital stay (all P>0.05). After screening by Lasso regression model, the results of multivariate Logistic regression analysis showed that GCS on admission ( OR=0.67, 95% CI 0.62, 0.73, P<0.01), age ( OR=1.03, 95% CI 1.01, 1.04, P<0.01), blood glucose on admission ( OR=1.17, 95% CI 1.06, 1.30, P<0.01) and INR on admission ( OR=17.08, 95% CI 2.12, 137.89, P<0.01) could be used as the main risk factors to construct the prediction model, and the regression equation was constructed: Logit [ P/(1- P)]=-0.398× "GCS on admission"+0.024× "age"+0.158×"blood glucose on admission"+2.838×"INR on admission"-1.693. The AUC for the prognosis prediction in adult patients with TBI using R based on a visual nomogram model was 0.87 (95% CI 0.83, 0.89, P<0.01). The Youden index for the predicted probability was 0.60 (sensitivity of 85.2% and specificity of 75.2%), with the C index of 0.87. Conclusion:Age, GCS on admission, blood glucose on admission and INR on admission are the main risk factors affecting the prognosis of TBI in adults, and the nomogram drawn by these parameters can better predict their clinical outcome.

Objective:To investigate the influence and mechanism of micro RNA (miR)-373-3p in autophagy and sunitinib sensitivity of glioblastoma cells.Methods:U251 cells were routinely cultured in vitro; and some U251 cells were subjected to 50 μmol/L sunitinib treatment for 72 h to construct sunitinib-resistant U251 cell line. (1) Real-time reverse transcription quantitative PCR (RT-qPCR) was used to detect the miR-373-3p expression in U251 and sunitinib-resistant U251 cells. Sunitinib-resistant U251 cells were divided into blank control group, nonsense sequence group and miR-373-3p mimic group; cells in the latter 2 groups were transfected with nonsense sequence and miRNA-337-3p mimic, respectively; miR-373-3p expression was detected by RT-qPCR. Cells were divided into U251 group, sunitinib-resistant U251 group, sunitinib-resistant U251+nonsense sequence group, and sunitinib-resistant U251+miR-373-3p mimic group; after each transfection, CCK-8 assay was used to evaluate the cell viability; TUNEL was used to detect the apoptotic rate; immunofluorescent assay was used to detect the expression of microtubule-associated protein light chain 3 (LC3); Western blotting was used to detect the expressions of apoptosis- and autophagy-associated proteins. (2) The pGL3-autophagy-related gene 7 (ATG7) wild-type (WT) and pGL3-ATG7 mutant type (MUT) plasmids were established; dual-luciferase reporter system was used to detect the cell luciferase activity in the miR-373-3p mimic group and nonsense sequence group. Cells were divided into U251 group, sunitinib-resistant U251 group, sunitinib-resistant U251+nonsense sequence group, and sunitinib-resistant U251+miR-373-3p mimic group; after each transfection, RT-qPCR and Western blotting were used to detect the mRNA and protein expressions of ATG7 in the cells. (3) The sunitinib-resistant U251 cells were divided into blank control group, ATG7 negative control group, and ATG7 overexpression group; after each transfection, RT-qPCR and Western blotting were used to detect the ATG7 mRNA and protein expressions. U251 and sunitinib-resistant U251 cells were divided into U251 group, sunitinib-resistant U251 group, sunitinib-resistant U251+nonsense sequence group, sunitinib-resistant U251+miR-373-3p mimic group, sunitinib-resistant U251+miR-373-3p mimic+ATG7 negative control group, and sunitinib-resistant U251+miR-373-3p mimic+ATG7 overexpression group; after each transfection, CCK-8 assay was used to evaluate the cell apoptosis, TUNEL was used to examine the apoptotic rate, and Western blotting was employed to detect the expressions of apoptosis- and autophagy-associated proteins. Results:(1) As compared with that in the U251 cells, miR-373-3p was lowly expressed in sunitinib-resistant U251 cells, with statistic difference ( P<0.05). As compared with that in the blank control group and nonsense sequence group, miR-373-3p expression was significantly elevated in the miR-373-3p mimic group ( P<0.05). As compared with the U251 group, the sunitinib-resistant U251 group had significantly increased cell viability, significantly decreased cell apoptotic rate, statistically increased B lymphocytoma-2 (Bcl-2) and Beclin 1 protein expressions, and significantly increased LC3II/LC3I values, significantly decreased Bcl-2 associated X protein (Bax) and p62 protein expressions and cleaved Caspase3/Caspase 3 values ( P<0.05). As compared with the sunitinib-resistant U251+nonsense sequence group, the sunitinib-resistant U251+miR-373-3p mimic group had significantly decreased cell viability, significantly increased cell apoptotic rate, statistically decreased Bcl-2 and Beclin 1 protein expressions, and significantly decreased LC3II/LC3I values, significantly increased Bax and p62 protein expressions and cleaved Caspase3/Caspase 3 values ( P<0.05). As compared with the U251 group, the sunitinib-resistant U251 group had increased number of fluorescent particles labeled with LC3 and enhanced fluorescent intensity; as compared with the sunitinib-resistant U251+nonsense sequence group, the sunitinib-resistant U251+miR-373-3p mimic group had decreased number of fluorescent particles labeled with LC3 and reduced fluorescent intensity. (2) The luciferase activity of pGL3-ATG7 WT plasmids in the miR-373-3p mimic group was signficantly lower than that in nonsense sequence group ( P<0.05). As compared with those in the U251 group, ATG7 mRNA and protein expressions were both significantly increased in the sunitinib-resistant U251 group ( P<0.05); as compared with those in the sunitinib-resistant U251+nonsense sequence group, ATG7 mRNA and protein expressions were both significantly decreased in the sunitinib-resistant U251+miR-373-3p mimic group ( P<0.05). (3) As compared with the blank control group and ATG7 negative control group, the ATG7 overexpression group had significantly increased ATG7 mRNA and protein expressions ( P<0.05). As compared with the sunitinib-resistant U251+nonsense sequence group, the sunitinib-resistant U251+miR-373-3p mimic group had significantly decreased cell viability, significantly increased cell apoptotic rate, statistically decreased Bcl-2 and Beclin 1 protein expressions, significantly decreased LC3II/LC3I values, significantly increased Bax and p62 protein expressions, and significantly increased cleaved Caspase3/Caspase 3 values ( P<0.05). As compared with the sunitinib-resistant U251+miR-373-3p mimic+ATG7 negative control group, the sunitinib-resistant U251+miR-373-3p mimic+ATG7 overexpression group had significantly increased cell viability, significantly decreased apoptotic rate, statistically increased Bcl-2 and Beclin 1 protein expressions, significantly increased LC3II/LC3I values, significantly decreased Bax and p62 protein expressions, and significantly decreased cleaved Caspase3/Caspase 3 values ( P<0.05) Conclusion:MiR-373-3p can enhance sunitinib sensitivity by regulating autophagy in glioblastoma cells, whose mechanism might be related to targeting ATG7.