Traumatic brain injury (TBI) is one of the diseases with high morbidity,mortality,and disability,which seriously endangers human health.Primary and secondary injuries caused by TBI are cascade reaction of various pathophysiological interactions.Because of its many injury factors and complex mechanisms,the treatment and therapeutic effect of TBI are limited at present.In recent years,animal experiments and clinical studies have shown that stem cell therapy could alleviate TBI-mediated neurological damage to a certain extent.Therefore,activation of endogenous neural cells response and transplantation of exogenous stem cells may be new strategies for TBI treatment.This article reviews the research progress of activation of endogenous neural cells response and transplantation of exogenous stem cells after TBI,and focuses on the therapeutic effects and possible mechanisms of stem cell transplantation in TBI treatment.

Objective: To explore the risk factors of predicting white matter hyperintensities progression based on radiomics of MRI of whole-brain white matter. Methods: The imaging and clinical data of 152 patients with white matter hyperintensities admitted to Zhejiang People′s Hospital from March 2014 to October 2018 were retrospectively analyzed. The whole brain white matter on baseline T₁WI images of each patient were segmented by SPM12 software package, and images of white matter were imported into AK software for texture feature extraction and dimensionality reduction. At last, least absolute shrinkage and selection operator(LASSO) was used to calculate the score of radiomics signature of each patient. According to the improved Fazekas scale, patients with WMH progression were divided into three groups: any white matter hyperintensities (AWMH), periventricular white matter hyperintensities (PWMH) and deep white matter hyperintensities (DWMH). Statistical differences of clinical factors and radiomics signature between WMH progression subgroups and non-progression subgroups were compared with independent sample t test or Mann-Whitney U test, and Univariate logistic regression was used to select independent clinical risk factors and multivariate logistic regression along with imaging omics tags were used to construct predictive models, which was evaluated by ROC curve. And the correlation between the clinical indicators of independent risk factors and the texture feature of radiomics signature was analyzed. Results: The efficacy of the model for the detection for AWMH progression, PWMH progression and DWMH progression was 0.818,0.778 and 0.824, respectively. Age is an independent risk factor for AWMH progression and DWMH progression[OR(95%CI), 4.776(2.152-10.601) vs. 3.851(1.101-8.245); P<0.05]. BMI is an independent risk factor for DWMH[OR(95%CI), 3.004(1.204-7.370); P<0.05], and hyperlipidemia is an independent risk factor for AWMH and PWMH[OR(95%CI), 4.062(1.834-8.998) vs. 3.549(1.666-7.563); P<0.05]. In AWMH subgroup, Surface Area was negatively correlated with age and low density lipoprotein(LDL) (r=-0.401, -0.312), and Inverse Difference Moment_ALLDirection_offset 1_SD was negatively correlated with age(r=-0.412). In PWMH subgroup, Compactness 1 was negatively correlated with LDL(r=-0.198), and Inverse Difference Moment_angle 0_offset 7 was positively correlated with LDL(r=0.252). In DWMH subgroup, LongRunEmphasis_ALLDirection_offset 7 was negatively correlated with age(r=-0.322), and GLCMEntropy_angle0_offset 4 was negatively correlated with age(r=-0.278). GLCMEntropy_AllDirection_offset4 was negatively correlated with body mass index(r=-0.514). Conclusion Radiomics based on whole-brain white matter MR imaging can predict WMH progression and identify the risk factors in high-risk populations, thus providing early additional information to conventional magnetic resonance imaging to predict WMH progression.

Objective To investigate the expression of centromere protein U(CENPU)in the intestinal tissues of patients with colon cancer,and to analyze the effect of CENPU expression level on the prognosis of patients with colon cancer combined with bioinformatics.Methods Firstly,the expression of CENPU in cancer tissues and normal tissues of colon cancer patients was analyzed by the expression of CENPU in tissues was further verified by real-time quantitative real time polymerase chain reaction(qRT-PCR),Western blot(WB)and immunohistochemistry(IHC).Combined with clinical data,univariate and multivariate Cox regression are used to analyze the correlation between CENPU expression and clinical case parameters of colon cancer patients.Then,the predictive effect of CENPU expression on the prognosis of colon cancer patients are explored by drawing receiver operating characteristic(ROC)curve and Kaplan-Meier survival curve.Finally,the possible molecular mechanism of the effect of CENPU expression on the progression of colon cancer are analyzed by bioinformatics.Results By qRT-PCR,WB and IHC experiments,we find that compared with normal tissues,the expression of CENPU in cancer tissues of colon cancer patients is significantly increased.Cox regression analysis show that the expression of CENPU is significantly correlated with the age and TNM stage of patients,and is a risk factor affecting the prognosis of patients.Kaplan-Meier survival curve analysis show that colon cancer patients with high CENPU expression has significantly lower survival rates.ROC curve show that the model based on CENPU expression has a high predictive power for the prognosis of colon cancer patients area under the curve(AUC=0.832).Bioinformatics analysis show that CENPI,CENPN,CENPD,CENPK,CENPP,CENPM,CENPQ,CENPH,NDC80 and ITGB3BP have significant interaction with CENPU gene.CENPU is involved in DNA repair,MYC/TARGETS/V1 and PI3K/AKT/MTOR signaling pathways.Conclusion High expression of CENPU in cancer tissues of patients with colon cancer is significantly associated with poor prognosis of patients,suggesting that CENPU is expected to be a potential target for early diagnosis and prognosis prediction of patients with colon cancer.